Research on Collaborative Evolutionary Game Optimization and Sustainability Improvement of New Energy Vehicle Supply Chain Information Driven by Blockchain Trustworthiness Traceability

Abstract

As the core carrier of the low-carbon transportation transformation, the sustainable optimization of the supply chain of new energy vehicles is crucial to reduce carbon emissions throughout the life cycle and improve resource utilization efficiency. However, the current problems, such as resource waste, duplicate production, and low logistics efficiency caused by insufficient supply chain information coordination, have become bottlenecks restricting the green development of the industry. A large number of studies have shown that information collaboration plays a key role in reducing risks and costs, improving quality and innovation capabilities, adaptability, performance, and supply chain competitiveness in the new energy vehicle supply chain. Although the advantages of supply chain information collaboration are widely known, supply chain information collaboration has not been widely adopted in actual operation, and there are almost no studies on the lack of adoption or the restriction of the development of supply chain information collaboration. Based on the research methods of the modified Delphi technique and analytic hierarchy process (AHP), this paper finds that the lack of information quality, information security, and information collaboration motivation are important factors restricting the collaborative development of information in the new energy vehicle supply chain. Furthermore, an optimization model of the new energy vehicle supply chain information co-evolution game combined with traceability and blockchain technology is proposed, and it is found that the evolutionary game model that solves the stability of information quality and information security has a significant effect on the information collaborative optimization of the new energy vehicle supply chain. This study proposes an information co-evolution game model combined with blockchain traceability technology, which can improve the level of information collaboration in the supply chain of new energy vehicles, significantly reduce the “bullwhip effect” and redundant inventory in the supply chain, reduce energy waste and carbon emissions caused by information asymmetry, and improve the overall energy efficiency of the supply chain, so as to provide theoretical support for the sustainable and green supply chain transformation of the new energy vehicle industry.

1. Introduction

In recent years, as the global concern about climate change has been increasing, the pressure from stakeholders such as governments, consumers, and investors to reduce emissions, and the endogenous demand for enterprise development, has driven automotive companies to develop in a more low-carbon, environmentally friendly, and sustainable direction. As an environmentally friendly means of travel, new energy vehicles have been vigorously developed by various countries, and their industrial prospects are very broad [1]. However, the inefficiency of its supply chain in terms of resource utilization, production energy consumption, logistics efficiency, and coordination has seriously restricted the sustainability and green development of the supply chain. At the same time, with the rapid changes in the global market, the rise in intelligent manufacturing, technological innovation, and the impact of uncertain factors such as health and war, the supply chain of new energy vehicles is facing major challenges, and the next-generation supply chain strategy is that of a collaborative supply chain [2]. Supply chain collaborative management is a systematic, integrated, and agile advanced management model. Information collaboration is the heart of supply chain collaboration, and plays a key role in reducing supply chain costs [3], improving quality [4] and innovation [5], reducing the risk of disruption [6], improving adaptability and operational performance [7], and green sustainability, and in gaining an advantage over the competition due to the supply chain as a whole [8].

Supply chain information collaboration is the integration, coordination, and development of resources, business processes, and organizational relationships between partners through the sharing and exchange of operational data, market data, and other information, so as to improves the operational efficiency and sustainability of the supply chain, and reduces the “bullwhip effect” and resource redundancy of the supply chain. Information collaboration emphasizes collaboration, which is the sharing of relevant information within a specific time and space range [9], and the shared information must be effective and of quality [10]. Supply chain information collaboration is an effective strategy to improve the overall performance of the supply chain [11,12], but it has not been widely adopted in actual operation. Scholars believe that this may be due to information quality, information security, and the insufficient collaborative motivation of top management [13,14,15,16].

The different degrees of information application in each node of the supply chain network, as well as the diversification and non-standardization of information systems, has led to uneven information quality, which in turn has led to problems such as difficult information collaboration, low efficiency, and low enthusiasm to participate in collaboration. The supply chain of new energy vehicles has many nodes, complex network structures, and huge systems, and the information synergy effect has an important impact on the safety and sustainability of the supply chain. Wu et al. studied the risks of the new energy vehicle supply chain and concluded that if the information sharing level of the nodes of the new energy vehicle supply chain is low, there will be problems such as information islands and information faults, resulting in information that cannot be transmitted smoothly, and then business incoordination between node enterprises may occur [17]. Information collaboration is becoming more and more important in supply chain management operations, which may have an impact on the efficiency of supply chain collaboration within enterprises, suppliers, and customers. Bai et al. studied the impact of information sharing within enterprises, information sharing with customers, and information sharing with suppliers on supply chain agility and performance [18]. Sheila and Musa studied the relationship between information quality, information sharing, and supply chain performance in supply chain information collaboration, and found that information quality has a positive impact on information sharing and supply chain performance, and that information sharing also maintains a positive mediating effect on supply chain performance [19]. Yang et al. proposed an information collaborative modeling method that integrates system dynamics and multi-agent systems, which effectively reduces the “bullwhip effect” of the supply chain [20]. From the perspective of supply chain vulnerability, Jiang studied the role of information collaboration models in reducing the “bullwhip effect” and improving the efficiency of information sharing [21]. Good information collaboration may enhance the efficiency of the supply chain network, which in turn will improve the performance of the supply chain. Cigdem et al. found that cloud-based information sharing between supply chain partners can prevent information distortion and delay from failing information collaboration, while updating, transmitting, and analyzing demand, lead time, inventory, and fulfillment information [22]. Zhang et al. analyzed the impact of information collaboration on price and inventory decisions in supply chain systems, and found that supply chain information collaboration can reduce supplier inventory pressure and improve market response speed [23]. Huang et al. found that supply chain information collaboration enables firms to better integrate green resources and optimize production processes, thereby promoting their overall performance in development, and in environmental, social, and governance (ESG) areas [24].

There are many results from research on the benefits, role, and significance of supply chain information collaboration, but there are almost no studies on the constraints of its development and the research that promotes its development as how to improve the use of supply chain information collaboration has a more important practical significance. Therefore, this paper will focus on the factors restricting the coordinated development of new energy vehicle supply chain information, further propose corresponding optimization measures, and establish a set of optimization models suitable for information collaboration in the new energy vehicle supply chain.

Computer viruses and data privacy are issues that information systems and enterprise managers are concerned about, and the security of information may affect the security concerns of supply chain managers, which in turn will affect the performance of supply chains. Information collaboration in the new energy vehicle supply chain is cross-organizational and cross-system, and it is necessary to obtain the support of systems and decision-makers for information collaboration; therefore, new forms of data sharing are needed to improve transparency and security. Blockchain technology has a variety of capabilities, such as decentralization, security, and information tampering, and plays an important role in enhancing trust and strengthening cooperation among partners in the supply chain. The role of blockchain technology in information collaboration and information sharing is attracting the attention of scholars. Ribeiro et al. studied the application of blockchain technology to information collaboration in the battery cycle supply chain of new energy vehicles [25]. Dehshiri et al. found that by sharing information on blockchain platforms, transparency and trust between firms can be increased, and operational risks can be reduced [26]. Manal et al. used the advantages of blockchain technology and the characteristics of the textile industry to build a framework to improve the real-time information sharing and information traceability capabilities between suppliers, manufacturers, and retailers in the textile supply chain [27]. Yang et al. found that manufacturers and retailers tend to use a blockchain to share information in low-carbon supply chains [28]. Mohamed et al. found that the use of blockchain technology can not only improve the performance and resilience of green supply chains but also play a mediating role in supply chain information sharing [29]. Scholars’ research on supply chain information collaboration mainly focuses on the role, methods, and strategies of supply chain information collaboration, and there are few studies on the constraints and optimization objects of supply chain information collaboration.

Institutional theory is an important analytical framework in the field of social sciences, one which is especially widely used in organizational studies and management, focusing on how institutional environments (such as rules, norms, etc.) shape organizational behavior and social structures. Wang et al. comprehensively analyzed the supply chain challenges faced by the palm oil supply chain in China from the perspective of institutional theory [30]. Mensah et al. studied the application of institutional theory to examine the role of supply chain stakeholder pressure, circular innovation orientation, and environmental information exchange capabilities in adopting circular supply chain practices [31]. Game theory is a mathematical and social science framework that studies how rational decision-makers make optimal decisions in strategic interactions, revealing the patterns in complex interactions by analyzing participants’ behavioral logic, conflicts of interest, and possibilities for cooperation. Ikuo and Yasuhiko used game theory to study the serial supply chain of manufacturers and retailers, addressing the negotiation issues regarding wholesale and buyback prices in the serial supply chain [32]. Jiang and Li used a mathematical model of differential game theory to reveal the collaborative strategies between logistics companies and internet companies in the intelligent upgrading of logistics, calculating the optimal effort levels and optimal returns of participating companies under non-cooperative mechanisms, cost-sharing mechanisms, and cooperative mechanisms [33]. The scholars’ research on institutional theory and game theory in terms of collaboration has inspired us.

Based on institutional theory, this paper first constructs a model of supply chain information collaboration, using the combination of the modified Delphi technique (MDT) and analytic hierarchy process (AHP) to design the questionnaire, through the questionnaire survey of experts such as supply chain leaders or information department heads of battery and parts enterprises, manufacturing enterprises, and sales enterprises in the new energy vehicle supply chain. The SPSS analysis of the survey results revealed the important relationship between information quality, information collaboration, and supply chain performance, and found that information availability, completeness, the lack of information security, and the driving force of information collaboration at supply chain nodes are the main factors restricting information collaboration in the supply chain of new energy vehicles. Then, according to the traceability characteristics of new energy vehicles and the advantages of blockchain technology, the method of evolutionary game theory is used to construct a game model of information co-evolution of the new energy vehicle supply chain combining blockchain technology and traceability technology, and an information collaborative optimization method that is conducive to the green and sustainable development of the new energy vehicle supply chain is proposed.

The first step is to use the modified Delphi technique and analytic hierarchy process (AHP) to find out the main factors restricting the coordinated development of information in the new energy vehicle supply chain. The second is to design an evolutionary game model optimization method combining blockchain and traceability technology, and a traceability system through the supply chain combined with blockchain technology is used as the intermediate carrier of supply chain information collaboration, which solves the problems of unstable information sources, non-standard data, incomplete information, and information security that restrict information collaboration, and has a significant effect on the information collaborative optimization of the new energy vehicle supply chain. The first contribution of this study is the finding that the main factors restricting the information collaboration of the new energy vehicle supply chain are the integrity of information quality, the security of information collaboration, and the enthusiasm of the person in charge of supply chain management. The second contribution is to propose an evolutionary game model that integrates traceability and blockchain technology, which provides a new method for information collaborative optimization for the greening and sustainability of the new energy vehicle supply chain. Traditional game theory usually assumes that participants are completely rational in the study of supply chain collaboration, but in reality, corporate decision-making is affected by information asymmetry, cognitive bias, and a dynamic environment, resulting in a gradual evolution of strategy choice. This paper introduces evolutionary game theory (EGT) to solve the limitations of classical game theory in terms of dynamics and adaptability by simulating the strategy adjustment process of bounded rational participants. Specifically, the replicator dynamic equation and evolutionary stability strategy framework of EGT can describe the learning, imitation, and mutation behaviors of supply chain node enterprises in information collaboration and reveal the evolutionary path of collaboration strategy from the local optimal to global stability. In particular, this paper combines EGT with blockchain and traceability technology for the first time to construct a multi-stage evolutionary game model, which breaks through the dual constraints of traditional models in information transparency and dynamic adaptability, and provides methodological innovation for supply chain collaboration research. Figure 1 shows the framework of this paper.

2. Model Building

2.1. Model Overview

According to institutional theory, how an organization (manufacturing enterprise) operates under the influence of external factors such as cultural differences, social norms, legal constraints, and the needs of various stakeholders is the key to the success of the organization. The efficiency and degree of information collaboration in the new energy vehicle supply chain affect the stable operation of the new energy vehicle supply chain. The degree of information collaboration in the supply chain reflects the efficiency and quality of information flow in the supply chain network of new energy vehicles. If the information coordination degree of the new energy vehicle supply chain network is low, there will be problems such as information islands and information faults, which may lead to business incoordination between and within the supply chain nodes, and the supply chain system cannot efficiently integrate resources and so the supply chain efficiency is low, which will have an adverse impact on the stable operation of the supply chain and market competition.

There may be a complex relationship between supply chain information collaboration, information quality and supply chain performance, and the quality of information itself, the coordination mechanism, supply chain performance, other factors, and the mutual constraints and influences of all aspects of the system. Scholars have found that information quality has a positive impact on information collaboration and supply chain performance, that information quality has a significant impact on information collaboration, and that information collaboration increases with the improvement of information quality. Information collaboration has a positive impact on supply chain performance, and the flexibility, stability, and reliability of information quality have a significant impact on supply chain performance. The accuracy and responsiveness of information collaboration can help improve the performance of supply chain profitability, market share, sales growth, profits, and continuous supply chain improvement [34].

The important impact of information collaboration on supply chain collaboration has been widely recognized. However, in reality, the effect of supply chain information collaboration is not ideal, and scholars believe that information quality, information security, and a lack of collaboration motivation in senior management are the main constraints restricting the collaborative development of supply chain information. Based on the existing literature, this paper will focus on the constraints of supply chain information collaboration, focusing on information security, standardization, manager motivation, and other issues, and establish a basic model of supply chain information collaboration based on the literature research, as shown in Figure 2.

2.2. Construction of Indicator System

Through research of the literature and preliminary expert interviews, based on the relevant factors that may affect supply chain information collaboration, combined with the characteristics of the new energy vehicle supply chain and the basic model framework of supply chain information collaboration in Figure 2, a supply chain information collaboration index system is constructed from three dimensions of information quality, information collaboration, and supply chain performance, focusing on 20 variables (as shown in

Table 1. Supply chain information collaboration index system.

Variable	Code	Classify	Remark
Stability	IQ1	Information quality	Continued stability of information.
Accuracy	IQ2	Information quality
Flexibility	IQ3	Information quality	For example, RFID, two-dimensional code, barcode, feature code, product code, packaging code, logistics code, and other forms of flexible coding and provision forms.
Reliability	IQ4	Information quality	Reliability of the source of information.
Completeness	IQ5	Information quality	The information items included basically cover the attribute information of the product.
Acquired	IQ6	Information quality	Access and ability to access high-quality information.
Comprehensibility	IQ7	Information quality	The way the information is presented and delivered makes it easier to understand and absorb, and the information can be effectively conveyed to the target audience to achieve the maximum value of the information.
Explanatory	IQ8	Information quality	The ease with which the user understands and parses the information after it is communicated to the user.
Security	IQ9	Information quality	Non-destructiveness, privacy, and immutability of information
Standardization	IQ10	Information quality	Information follows certain rules, formats, or guidelines to ensure its consistency, compatibility, and interoperability across different systems and organizations.
Accuracy	IC1	Information collaboration	Accuracy of information collaboration.
Responsiveness	IC2	Information collaboration	The degree of response between nodes in the process of information collaboration.
Effectiveness	IC3	Information collaboration	The degree to which the information supply and demand of the two sides are matched with each other.
Security	IC4	Information collaboration	Information is securely coordinated within the scope of constraints, according to time, and permission.
Profitability	SCP1	Supply chain performance	The impact of supply chain performance on improving supply chain profitability.
Market share	SCP2	Supply chain performance	The impact of supply chain performance on supply chain market share.
Sales growth	SCP3	Supply chain performance	The impact of supply chain performance on volume growth.
profit	SCP4	Supply chain performance	The impact of supply chain performance on supply chain profits.
Continuous improvement	SCP5	Supply chain performance	The impact of supply chain performance on the improvement of continuous innovation in the supply chain.
Competitiveness	SCP6	Supply chain performance	The impact of supply chain performance on supply chain competitiveness.

), such as stability, accuracy, security, standardization, responsiveness, effectiveness, and profit.

In the supply chain information collaboration index system, IQ1–IQ9 are related issues in information quality, which are the impact of information stability, flexibility, reliability, integrity, comprehension, interpretation, security, and standardization on information quality. Standardization is an important means to break down information silos and solve data consistency. IC1–IC4 are the influencing variables of information accuracy, collaborative responsiveness, effectiveness, and security in the collaborative process on the overall effect of information collaboration, which refers to the information security, privacy, and security impact on the data structure in the process of sharing and collaboration, especially after leaving the parent system. SCP1–SCP6 refer to the impact of information quality and information collaboration on the profitability of supply chain performance, market share, sales growth, profit, continuous improvement effect, and competitiveness, among which competitiveness is the evaluation index proposed for the first time in this paper.

2.3. Information Collaboration Model and Sample Sampling Design of New Energy Vehicle Supply Chain

The new energy vehicle supply chain involves raw material and parts suppliers, automobile manufacturing enterprises, sales enterprises, transportation enterprises, etc., and the new energy vehicle supply chain information collaboration related parties also involve software service providers and related enterprises of various related business systems in the supply chain nodes in addition to the previously mentioned enterprises. In this study, a questionnaire was designed by combining the modified Delphi technique and the analytic hierarchy process (AHP) to evaluate information quality, information collaboration, and supply chain performance. In the questionnaire design stage, two supervisors or professors who have been in charge of supply chain and informatization for more than 10 years in each link of the new energy vehicle supply chain were interviewed and surveyed to solicit survey opinions, and the preliminary design of the questionnaire was completed, and more than 30 people were tested for the questionnaire to ensure that the participants could understand the requirements of the questionnaire. The questionnaire survey was carried out on experts from relevant node enterprises in the new energy vehicle supply chain, and the experts were required to have more than 5 years of work experience in supply chain or informatization, and hold the position of supply chain or informatization supervisor or above.

The questionnaire sample was aimed at relevant experts in the field of new energy vehicle supply chain, such as raw material suppliers, battery and other parts suppliers, manufacturing enterprises, automobile sellers, information system service providers, supply chain companies, logistics companies, supply chain financial institutions, and scientific researchers, and the questionnaire link was distributed through WeChat, email, professional groups, conference activities, and other forms in the new energy vehicle supply chain. The questionnaire received 632 visits and 227 questionnaires were completed, with a recovery rate of 35%. The sample covers the relevant links of the new energy vehicle supply chain in most parts of China. Among the respondents, 47 are in raw material enterprises, 52 are in battery and parts enterprises, 23 are in new energy vehicle manufacturing enterprises, 56 are in automobile sales enterprises, and 49 are in transportation and logistics enterprises. Except for the low number of automobile manufacturing enterprises in the core enterprises of the supply chain, the other data are evenly distributed, which is in line with the characteristics of the supply chain, and the sample is representative. At the same time, in the design of the questionnaire sample a data measurement method of high discrimination which is suitable for detailed feedback was adopted. A 10-point measurement method was used for data measurement in the questionnaire sample design, that is, 1 was strongly disagree, 4 was somewhat disagree, 7 was somewhat agreed, and 10 was strongly agreed. The answers to all 23 items in the questionnaire are collected as complete answers, and the same questionnaire with the same score of all items is invalid.

2.4. Correlation Analysis of Model Samples

The reliability and validity of the questionnaire data were analyzed using SPSS software (27.0). Validity tests the extent to which the accuracy of the question adequately captures the concept to be measured, while reliability measures the consistency of the item and whether the results are consistent across multiple measurements in order to ensure the validity and reliability of the study, the threshold requirements were correctly followed for each measurement standard, and the mean value of the factor load of the indicator must be greater than 0.7.

Firstly, the correlation between the three dimensions of IQ (information quality), IC (information synergy), and SCP (supply chain performance) of the questionnaire samples was analyzed, and the results are shown in

Table 2. Correlation Analysis.

	IQ	IC	SCP
IQ	1
IC	0.868	1
SCP	0.818	0.836	1

. The results show a significant correlation between the metrics across the three dimensions.

Furthermore, the validity analysis of the three dimensions of IQ (information quality), IC (information collaboration), and SCP (supply chain performance) of the questionnaire samples was carried out, and the partial correlation between variables was checked by the KMO (Kaiser–Meyer–Olkin) test, and the correlation between variables was judged by the Bartlett spherical test, and the results are shown in

Table 3. Validity Analysis.

	KMO	Approximate Chi-Square	Degree of Freedom	Distinctiveness
IQ	0.936	1974.491	45	0.000
SPC	0.918	1164.972	15	0.000
IC	0.856	693.492	6	0.000

. Through the observation results, the KMO value was 0.936/0.918/0.856, and the significance of the Bartlett spherical test was 0, indicating that there was a correlation between the data indicators and the data information could be effectively extracted.

2.5. Analysis of Sample Index Results

Factor loading, combinatorial reliability, and mean variance extraction are common methods for assessing the quality and reliability of measurement tools. In order to ensure the quality of the model, the reliability and validity of each sample index of the model were evaluated by factor loadings, composite reliability (CR), average extracted variance (AVE), and Cronbach’s alpha.

2.5.1. Quality of Information

The factor loads of IQ1–IQ10, which are used to evaluate information quality in this questionnaire, ranged from 0.785 to 0.891, indicating that IQ1–IQ10 were closely related to information quality, and the values of IQ1, IQ2, and IQ4 were 0.785, 0.794, and 0.790, respectively, indicating that they were closely related to information quality. With the support of mobile internet, internet of things, and big data technology, the continuous stability, reliability, and accuracy of information have been greatly improved, which can well meet the requirements of information quality, so IQ1, IQ2, and IQ4 have relatively low attention compared to others. The factor loads of IQ3 and IQ5–IQ10 are between 0.811 and 0.891, indicating that with the increasing demand for information collaboration in the supply chain the closeness of these items and information quality is being highly valued, especially in the values of IQ5 and IQ7 which are 0.882 and 0.891, respectively, indicating that information integrity and information comprehension have a very close impact on the information quality, information collaboration, and performance of the supply chain. RFID, two-dimensional code, barcode, biometric code, product code, packaging code, logistics code, and other innovative forms of flexible coding and information provision forms, enrich the supply of information and the access opportunities, making the acquisition of information more efficient: IQ3s factor load of 0.811 fully illustrates this. The information system in the supply chain system has the characteristics of many types of business systems, such as its prominent personalized characteristics, wide range of information, many formats, poor standard uniformity, etc. Information integrity refers to the amount of characteristic attributes of the information contained in the object in order to meet the information needs of different nodes and different business systems, so there are high requirements for the integrity of information content, which also explains the reason why the value of IQ5 is 0.882. Similarly, due to the influence of permissions, data privacy, and other issues, information acquisition IQ6 has also been closely watched, with a factor load of 0.862. Due to the influence of different factors, such as the information expression habits and presentation methods of each system and each node, the effective transmission and conversion rate of information is limited, and so the comprehensibility and interpretation of information are the focus of experts, as seen by the factor load of IQ7 and IQ8 being as high as 0.891 and 0.866. IQ9, information security, is an important factor that experts believe limits the information collaboration of the supply chain, and IQ10, information standardization, is an important basis for solving IQ2, IQ4, IQ5, IQ7, and IQ8 problems so it has also received great attention. At the same time, the Cronbach coefficient of information quality is 0.951, indicating that the consistency of information quality measurement items is high. The combined reliability of information quality is 0.958, which is between 0.70 and 0.98, which is considered high confidence, indicating that the reliability of information quality measurement items is high. The mean variance extraction of information quality was 0.697, indicating that the correlation of the factors represented by the information quality measurement items was high.

2.5.2. Information Collaboration

Information collaboration plays an important mediating role between information quality and supply chain performance, and it has an important impact on the improvement of supply chain information quality and the effective improvement of supply chain performance. The factor loads of IC1–IC4 are between 0.902 and 0.906, indicating that IC1–IC4 are closely related to information collaboration, and IC1–IC4 items have an important impact on supply chain information collaboration, among which the effectiveness and security factor load of information collaboration reaches 0.906, indicating that these two items are highly valued by supply chain experts and executives. Accuracy is the basis of information collaboration, and its degree directly affects the results of collaboration, so the factor load of IC1 reaches 0.905, which shows the importance of accuracy. IC2 refers to the degree of response of supply chain nodes to information collaboration, and the project will directly affect the efficiency of information collaboration and is also highly valued by experts, so the load reaches 0.902, indicating the importance of IC2. IC3 is a project to investigate the degree of information supply matching in the process of information collaboration between supply chain nodes, which is also an important indicator that affects the efficiency and results of collaboration, with a factor load of 0.906 indicating that IC3 has a very important impact on supply chain information collaboration and attention needs to be paid to it. IC4 is the security index of supply chain information collaboration, and the factor load is also 0.906, which proves the view expressed by scholars that supply chain executives are reluctant to engage in information collaboration because of doubts about information security. The Cronbach coefficient of information collaboration is 0.926, indicating that the consistency of information collaboration measurement items is high. The combined reliability of information collaboration is 0.948, which is between 0.70 and 0.98 and is considered high confidence, indicating that the reliability of information collaboration measurement items is high. The mean variance extraction of information synergy was 0.819, indicating that the correlation of the factors represented by the information synergy measurement items was high.

2.5.3. Supply Chain Performance

Supply chain performance is one of the core goals of supply chain optimization, and on the basis of ensuring supply chain security, performance improvement is the core goal of the supply chain and so studying supply chain performance is an important way to optimize the supply chain. The factor loads of SCP1–SCP6 are between 0.857 and 0.893, indicating that the measurements of SCP1-SCP6 are closely related to supply chain performance. The Cronbach coefficient of supply chain performance was 0.942, the combination reliability was 0.954, and the mean variance extraction was 0.777, indicating that the supply chain performance measurement items had high reliability, high consistency, and high relationship strength. The results of the survey assessment are presented in

Table 4. Indicator evaluation.

Variable	Loading	Cronbach’s Alpha	Composite Reliability	AVE
IQ	–	0.951	0.958	0.697
IQ1	0.785	–	–	–
IQ2	0.794	–	–	–
IQ3	0.811	–	–	–
IQ4	0.790	–	–	–
IQ5	0.882	–	–	–
IQ6	0.862	–	–	–
IQ7	0.891	–	–	–
IQ8	0.866	–	–	–
IQ9	0.837	–	–	–
IQ10	0.825	–	–	–
IC	–	0.926	0.948	0.819
IC1	0.905	–	–	–
IC2	0.902	–	–	–
IC3	0.906	–	–	–
IC4	0.906	–	–	–
SCP	–	0.942	0.954	0.777
SCP1	0.883	–	–	–
SCP2	0.888	–	–	–
SCP3	0.889	–	–	–
SCP4	0.879	–	–	–
SCP5	0.893	–	–	–
SCP6	0.857	–	–	–

.

Each item factor load in this study exceeded 0.7 (Fornell and Larcker, 1981) [35], and the mean extraction variance (AVE) exceeded 0.50 (Chin, 1998) [35], indicating the validity and reliability of the study. Information quality has a positive effect on information collaboration and supply chain performance, and information collaboration has a partial mediating effect on information quality and supply chain performance. Information quality and information collaboration directly and indirectly affect the performance of the supply chain, as shown in Figure 3.

Firstly, as

Table 5. Path coefficient.

	$\mathbf{Original} \mathbf{Sample} \mathbf{\left(}\mathit{O}\mathbf{\right)}$	$\mathit{t}$ Statistics	$\mathit{p}$
IQ -> IC	0.393	26.179	0.000
IQ -> SCP	0.245	5.43	0.000
IC -> SCP	0.734	7.364	0.000

shows, the regression analysis confirms that information quality significantly enhances information collaboration (β = 0.393, p < 0.001), aligning with the findings of Kankam et al. (2023) [34] on information-driven collaboration. This relationship is grounded in the multidimensional nature of information quality, because the availability of information quality is a basic guarantee when obtaining information, reliability is the guarantee of information authenticity, integrity is the guarantee of effective supply of information, comprehensibility is the guarantee that information can be understood and effectively used, and security is the guarantee of information trust. Therefore, this study found that information synergy increased with the improvement of information quality, among which IQ1, IQ2, IQ3, IQ8, and IQ10 had a significant impact on IC1 and IC2, and IQ4, IQ5, IQ6, IQ7, and IQ9 had a significant impact on IC3 and IC4. These results confirm the positive effect of information quality on information collaboration in the model, explain the impact of information quality on the efficiency, responsiveness, effectiveness, and security of information collaboration, and indicate that information quality acquisition, integrity, comprehension, and security are important items to improve information collaboration. How to improve the accessibility, integrity, comprehension, and security of information quality will be the focus of the next research.

Secondly, regression analysis confirms that information quality has a statistically significant positive impact on supply chain performance (β = 0.245, p < 0.001). The substantial t-value (t = 5.43) and highly significant p-value (p < 0.001) indicate that this relationship is robust. These results support the conclusions of Kankam et al. (2023) [34], who emphasized that high-quality information enhances decision-making efficiency and operational coordination in supply chains. This study found that supply chain performance increased with the improvement of information quality, and IQ1, IQ2, and IQ6 had a direct impact on SCP1 and SCP3, and IQ7 and IQ9 had a direct impact on SCP5. These results confirm the direct impact of information quality on supply chain performance in the model, explain the direct impact of information quality on supply chain performance, and indicate that information acquisition and security are important items to improve supply chain performance.

Thirdly, regression analysis indicates that information collaboration significantly improves supply chain performance (β = 0.734, p < 0.001). The t-value of 7.364 far exceeds the conventional threshold for statistical significance, further confirming the robustness of this relationship (see Table 5), and so the findings of Kankam et al. [34] are validated. IC1 and IC2 have a significant effect on SCP1, SCP3, and SCP5, IC3 has a significant effect on SCP2, and IC4 has a direct effect on SCP4 and SCP6.

In addition, the relationship between information quality and supply chain performance effectiveness is investigated through mediation analysis, and the mediating role of information collaboration is confirmed. As

Table 6. Mediating effect.

IQ-SCP		IQ-SCP		IQ-IC-SCP
β	p-value	β	β	p-value	β	β
0.534	0.000	0.245	0.534	0.000	0.245	0.534

shows, the direct effect of information quality on supply chain performance is significant (β = 0.245, p < 0.001). When the mediating variable (information collaboration) is included, the indirect effect of information quality on supply chain performance is also significant (β = 0.289, p < 0.001). The total effect (β = 0.534, p < 0.001), calculated as the sum of direct and indirect effects (0.245 + 0.289), indicates a strong overall impact of comprehensive information quality. The significance of the indirect effect was further validated by a Sobel test (z = 4.72, p < 0.001), confirming that information collaboration partially mediates this relationship. At the same time, to assess the strength of the mediating effect, we calculated the variance (VAF). To do so, we multiply the indirect effect/total effect by 100 to obtain the VAF. A VAF > 80% means fully intermediated, 80% > VAF > 20% means partial intermediary, and a VAF < 20% means no intermediary. The results show that information collaboration plays a partial mediating role between the information quality and supply chain performance satisfaction because the variance (VAF) interpretation value is 54.1%. Some mediating effects emphasize the important role of mediating variables in the relationship between independent and dependent variables. Although the information quality of independent variables has a direct impact on the supply chain performance of dependent variables, the existence of information synergy of the mediating variables also plays a partial role in the transmission. This helps the investigator identify and focus on those underlying factors that may affect the relationship between the independent variable and the dependent variable. It was further found that IQ4, IQ5, IQ6, IQ7, and IQ9 had significant effects on IC3 and IC4, and had indirect and important effects on SCP2, SCP4, and SCP6. The previous results show that the availability, integrity, security, and reliability of information quality have an important impact on the efficiency of information collaboration and the improvement of supply chain performance, and the level of quality is the key issue affecting supply chain information collaboration.

3. Information Collaborative Optimization Measures

Through the previous research, it is found that the integrity of information quality, the security of information collaboration, and the enthusiasm of supply chain management leaders are the main problems restricting supply chain information collaboration. This also explains the long-term unsatisfactory information acquisition effect and safety concerns, as well as the competition between nodes and the limited rationality of managers, resulting in most nodes of the new energy vehicle supply chain network staying in the wait-and-see state of information collaboration. This section will put forward the optimization strategy of supply chain information collaboration from three aspects: improving the integrity of information quality, improving information security, and enhancing the driving force of information collaboration, so as to provide inspiration for industry research.

3.1. Information Quality Optimization Based on Traceability System

The complete product information, such as product name, specification and model, quality parameters, manufacturer, inventory, expiration date, order, cost, and supply period of new energy auto parts, the manner and identifiability of information acquisition, the right to obtain subject information, the identifiability and consistency of information expression, and the standardization of data, determine the quality of information and the efficiency of information collaboration. At the same time, the way these data elements are obtained will also directly determine whether the supply chain information collaboration is operational. The survey found that most of the information collaboration in the actual supply chain operation is put forward by the core enterprise or demand node, and the supply side provides information according to its needs, which leads to low information quality and a poor collaborative response which in turn affects the performance of the supply chain. In fact, most of the data required for information collaboration have been included in the traceability system of parts, and the data in the traceability system have good standardization and consistency.

The new energy vehicle information traceability system is composed of each node business system, such as in Figure 4. The traceability system runs through the entire automotive supply chain, from raw material procurement, manufacturing, assembly, and sales, to the final use of parts and components to ensure product quality, safety, and reliability. The whole chain data of the new energy vehicle traceability system from IT1 (information traceability, IT) to IT7 have the characteristics of high information quality, and the saturation of its information quality covers Q1–Q9. Therefore, with the traceability system as the data carrier of information collaboration, the data retrieval, call, collaboration, and sharing in the supply chain network are realized with the help of the integrity, standardization, consistency, and other characteristics of the traceability data, so as to realize the efficient information collaboration of the supply chain and provide an effective enlightenment for the development of information collaboration in the supply chain of new energy vehicles.

3.2. Blockchain-Based Information Security Optimization

The new energy vehicle supply chain network structure is complex. There are many nodes, many business systems, and the security level and standardization are uneven. Furthermore, the security of the information itself may pose a greater risk of security challenges to other systems in the network. At the same time, the spread of data in the network may have a negative impact on data privacy, trade secrets, business security, reputation, etc. How to effectively improve the quality of information and the security of information collaboration is an important goal of information collaboration in the supply chain of new energy vehicles.

Blockchain is a distributed decentralized ledger technology that uses cryptographic algorithms to ensure data security and it being tamper-proof. It records and verifies transaction information through decentralized network nodes, ensuring data transparency, security, and permanence. With the continuous development of the internet of things, 5G, artificial intelligence, big data, and other technical systems, blockchain technology has gradually begun to be applied in scenarios such as finance, government affairs, logistics, economy and trade, supply chain management, traceability systems, and data governance, and the potential value of blockchain technology has been continuously emerging [36,37]. Blockchain is a new type of underlying technical architecture [38], which can ensure the safe and efficient transmission and maintenance of data between untrusted nodes [39].

Based on the advantages of blockchain technology and the technical characteristics of the supply and traceability system of new energy vehicles, this study proposes a blockchain-based new energy vehicle supply chain system, such as Figure 5. The immutability, effectiveness, and security of blockchain technology are applied to the information collaboration of the supply chain of new energy vehicles, and the constraints such as information security and lack of trust in the process of supply chain information collaboration are attempted to alleviate problems highlighted in the relevant research on improving the efficiency of supply chain information collaboration.

3.3. Optimization of the Synergistic Driving Force of Evolutionary Game Theory Information Combined with Blockchain Traceability

In the intelligent manufacturing environment, efficient and timely collaboration of all nodes of the supply chain is required, and the role of supply chain information collaboration in improving supply chain performance is also clear. However, in reality, there are complex forms of competition and cooperation between nodes. Under the influence of interests and complex factors, it is difficult for business owners or executives to make rational decisions directly, which leads to the low efficiency of supply chain information collaboration. The design of supply chain collaboration is based on the idea of system optimization, in a rational state, where all participants in the supply chain are willing to cooperate with each other in the collaborative state so that the benefits of the supply chain can be optimized, and then each participant will distribute the benefits according to the contract. This is an ideal state in which the collaborative contract is designed with the goal of maximizing the benefits of the supply chain system. Although it satisfies the overall interests of the supply chain, it does not necessarily meet the maximization of the interests of each individual and so the relevant methods in game theory need to be used for incentive optimization.

Game theory originally addressed the problems faced by decision-makers with divergent interests and was used to explore strategic behaviors to resolve conflicts of interest between participants. Classical game theory assumes that players will be perfectly rational, but in reality, it is difficult for participants to be the most rational when faced with conflicts of interest for the first time. Game theory has been widely used in the field of supply chains, providing effective decision-making guidance for participants in supply chain management, distribution channels, pricing, and in manufacturer–retailer games. EGT was developed from the strong demand for irrationality, game knowledge, and information sharing [40], extending the ideas of classical game theory [41] about how different strategies have evolved over time in biological populations. In EGT, the choice of strategy is not based on the completely rational decision of the individual, but on the process of continuous trial and error, choice, optimization, and selection. The success of a strategy depends on its relative frequency in the population and the results of competition with other strategies, which in turn expands the application of game theory from a single rational individual to the entire population. EGT provides a relatively efficient tool for understanding adaptive problems and is widely used in different fields such as economics, sociology, mathematics, and anthropology [42].

The evolutionary game model in EGT is of natural selection and mutation evolutionary strategy selection. Natural selection means that the strategy that obtained higher returns in the previous game will be adopted by more people, and mutation evolutionary selection refers to the idea that players adopt innovative strategies to obtain higher returns, and that the strategy is then retained and adopted by more people. The evolutionary mechanism is the result of natural selection in the process of continuous optimization, and the higher the return strategy, the more it will be adopted and form a stable system which reaches an equilibrium. In the collaborative optimization of information in the supply chain of new energy vehicles, each node of the supply chain (business owners or executives) is the player (insider), and the rationality of the person in the EGT is divided into the following three layers: the irrational evolution mechanism at the biological level, the learning mechanism that requires strong rationality, and the imitation learning mechanism in between.

In the context of intelligent manufacturing, the new energy vehicle supply chain has a strong demand for information collaboration, and in reality, most supply chain nodes are not active or have the ability to participate in information collaboration, these nodes are considered non-rational agents (P) in the biological layer of EGT. A small number of nodes are looking for ways to improve performance and actively explore supply chain information collaboration, and this type of node is a more rational insider (L); the other part is the insider (F) who is unsatisfied with the status quo and imitates and follows L. In this model, the first mutation evolution strategy is to adopt the information collaboration strategy, the second mutation evolution is to use blockchain technology to encrypt the data to eliminate the security risks, and the third is to establish and use the information traceability data as the information collaboration carrier. Through mutation, F continues to transform into L, P transforms from F to L or is eliminated, and the populations of F and L continue to expand and gradually become stable and reach an equilibrium, as shown in Figure 6.

4. A Game Model of Co-Evolution of New Energy Vehicle Supply Chain Information Combined with Blockchain

The core concepts of EGT include evolutionary stable strategy (ESS) and replicator dynamics (RDs). ESS refers to the fact that in the long-term interaction and competition, after many adjustments and optimizations, the strategies of gamblers will eventually tend to a relatively stable form. This morphology is able to remain stable in the face of a small number of variation strategies, ensuring that the majority of participants continue to follow this strategy, thus maintaining an overall equilibrium state. Replicator dynamics, on the other hand, describe the propagation and evolution of these strategies in populations, explaining how different strategies gradually change their frequencies over time until they reach or approach ESS. RDs are the process of copying the optimal strategy at the moment in the game, and it is also the process of dynamic convergence to ESS.

4.1. Assumptions of EGT Model for Information Collaboration in the Supply Chain of New Energy Vehicles

Assuming a two-level supply chain network, which includes new energy vehicle manufacturers and first-tier parts supplier groups, all node enterprises in the supply chain network are normally bound by rationality; whether it is an automobile manufacturer or a parts supplier, they cannot directly choose the optimal strategy at one time, but need to choose the game strategy according to their current needs, cognitive ability, actual business situation, peer experience, etc. The short-term strategy of a gamer is generally not the optimal choice, and the optimal stable state must be the result of the evolution of the game after many iterations of learning, imitation, and selection, and continuous adjustment. This section focuses on the key factors that may affect the information collaboration performance of the new energy vehicle supply chain and discusses the evolution process of each node enterprise in the supply chain when choosing information collaboration behavior based on the motivation of each participant to pursue profit maximization. By analyzing these factors, it is possible to better understand how businesses can gradually adjust their strategies for more efficient information sharing and collaboration. The information of new energy vehicle manufacturers and parts suppliers in the supply chain is not transparent, and there is no fixed upstream and downstream business cooperation between the two. Automobile manufacturers collaborate with parts suppliers on product specifications, standards, models, and other product demand information, whilst parts suppliers to automobile manufacturers coordinate finished product inventory and supply information within time units, as well as supply chain information such as sales demand. Suppliers use this information to carry out activities such as forecasting demand, planning and scheduling, formulating supply plans and supply times, raw material procurement and logistics management, and realizing efficient production and cost optimization of the supply chain as a whole. Parts suppliers can cooperate with automakers on product specifications, supply volumes, delivery times, logistics, and other information of parts, and cooperate with manufacturers to efficiently and dynamically adjust the intelligent and personalized production of new energy vehicles, optimize resource scheduling, and make rapid responses to production when market demand changes.

Therefore, according to the supply chain characteristics and EGT characteristics of intelligent manufacturing of new energy vehicles, the following hypotheses are proposed, and on this basis, a game model of information co-evolution of new energy vehicle supply chain information is established.

(1)

In the group of new energy vehicle manufacturers and parts suppliers, all participants are bound by rationality. Through the game process of multiple rounds of competition and cooperation, each participant continuously learns and adopts the strategy they think is optimal and gradually approaches the optimal equilibrium state of an evolutionary stable strategy in the process of exploration and imitation.

(2)

In the supply chain information collaboration, the behavioral strategy set of the new energy vehicle manufacturer group and the parts supplier group is {collaborative, non-collaborative}.

(3)

In order to promote the coordination of supply chain information, introduce corresponding incentive and punishment mechanisms for new energy vehicle manufacturers and parts suppliers in the supply chain network. Incentives are used to increase the additional income of node enterprises participating in information collaboration, and at the same time, punitive measures are implemented for node enterprises that do not participate in information collaboration. This can effectively encourage all participants to actively participate in the supply chain information collaboration system.

(4)

If all players choose to participate in the supply chain information collaboration, all players will receive additional benefits, and the overall benefit of the supply chain is the largest. If only one of the two players chooses to participate in supply chain information collaboration, the choice will receive additional benefits, but this additional income is lower than the benefits of it being selected by both players. If neither player chooses this synergistic strategy, then neither player will receive this additional benefit.

The evolution process of the information collaboration strategy chosen by the node enterprises of the new energy vehicle supply chain is often related to the internal management costs, risks, benefits, and other factors of the enterprise, such as those in

Table 7. Parameters of information synergy influence of new energy vehicle supply chain nodes.

Parameter	Meaning
$Q_i$	The basic income, that is, the income of the node enterprises of the new energy vehicle supply chain when they do not collaborate with the supply chain information. $i=\mathrm{1,2}$, and $Q_1$ and $Q_2$ are the basic benefits obtained by the paired parts suppliers and automobile manufacturers, respectively.
$C$	Collaboration cost: The total cost of various overheads for supply chain information collaboration, $C>0$. The total cost of various expenses for supply chain information collaboration, such as personnel, equipment, software, communications, resources, etc., is generally borne by all enterprises participating in the supply chain information collaboration node, and this includes construction and operation and maintenance costs.
$f$	Synergy coefficient, $f>0$, which reflects the degree of information synergy of the supply chain, including the amount of information and the effect of synergy. When one party collaborates on information, the provider of information will receive more additional benefits from collaboration.
$\delta$	The information quality coefficient is $\delta >0$, which reflects the degree of information quality based on the traceability system. Let $\delta$ be positively correlated with $f$, and the larger the $\delta$, the greater the effect of $f$.
$\alpha$	Cost factor, $\alpha \in \left(\mathrm{0,1}\right)$. The cost is shared by both partners, and the cost of information collaboration shared by parts suppliers is $\alpha C$, and that of new energy vehicle manufacturers is $\left(1-\alpha \right)C$.
$B$	Additional income, $B>0$. It refers to the additional benefits that both players will receive when all players adopt the strategy of supply chain information collaboration, which will lead to more benefits for the players participating in the collaboration.
$b$	B partition coefficient, $b\in \left(\mathrm{0,1}\right)$. The additional revenue distribution coefficient for component supplier $S$ is $bB$, while that for NEV manufacturer $T$ is $(1-b)B$. If only the component supplier unilaterally selects information synergy in the pairing, $B$ of the manufacturer who does not select information synergy is counted as $B_2$ and $B_2>0$. If the manufacturer unilaterally chooses information collaboration, the supplier’s $B$ benefit is $B_2>0$. Ream $B_2<bB$ and $B_2<\left(1-b\right)B$.
$\beta$	Risk factor, $\beta >0$. It refers to the situation where the interests of the supply chain nodes will be damaged. Let $\beta$ be positively correlated with $f$, $f_1$ is the amount of profit loss caused by the gamer’s participation in information coordination in $S$, and $f_2$ the amount of loss of revenue caused by the selection of information collaboration for $T$.
$\mu$	The security factor, $\mu >0$, which reflects the degree of information security based on blockchain technology. The $\mu$ is inversely related to the $\beta$, and the greater the $\mu$, the smaller the risk impact of the $\beta$.
$P$	Penalty factor, $P>0$. The punitive benefit of supply chain nodes that do not carry out information collaboration is $-Pf$.
$m$	In the initial state, the proportion of component suppliers who choose to collaborate with supply chain information is $m\in \left[\mathrm{0,1}\right]$, and the proportion of parts suppliers who do not choose to is $1-m$.
$n$	In the initial state, the proportion of new energy vehicle manufacturers choosing supply chain information collaboration is $n\in \left[\mathrm{0,1}\right]$, and the example of non-selection is $1-n$.

. Based on these factor parameters, this section establishes a return function from the perspective of individual players, and if the strategy chosen by the player changes, it causes a change in the proportion of strategy choices in the group.

4.2. Construction of EGT Model for Information Collaboration in the Supply Chain of New Energy Vehicles

4.2.1. Earnings for New Energy Vehicle Manufacturers and Parts Suppliers

According to the assumptions described previously, there are only two strategies for gamblers to participate in the relevant businesses of the supply chain.

NEV manufacturers can choose to be $T_1$ or $T_2$, of which $T_1$ is that NEV manufacturers choose collaborative supply chain information, and $T_2$ NEV manufacturers do not choose collaborative supply chain information. In the same way, the two strategies of component suppliers are $S_1$ and $S_2$, of which $S_1$ is that component suppliers choose to collaborate on supply chain information, and $S_2$ is that component suppliers do not choose to collaborate on supply chain information.

Table 8. Revenue matrix for NEV manufacturers and component suppliers.

Component Suppliers	New Energy Vehicle Manufacturers
	$T_1\left(n\right)$	$T_2\left(1-n\right)$
$S_{1}m$	$\left\{\begin{array}{c}{Q}_1+bB-{{\displaystyle \frac{\beta }{\mu }}f}_1\delta -\alpha C,\\ Q_2+\left(1-b\right)B-\beta f_2\delta -\left(1-\alpha \right)C\end{array}\right\}$	$\left\{\begin{array}{c}{Q}_1-{{\displaystyle \frac{\beta }{\mu }}f}_1\delta -\alpha C,\\ Q_2+B_2-P{f}_2\delta \end{array}\right\}$
$S_2\left(1-m\right)$	$\left\{\begin{array}{c}{Q}_1+B_2-P{f}_2\delta ,\\ Q_2-{\displaystyle \frac{\beta }{\mu }}{f}_2\delta -\left(1-\alpha \right)C\end{array}\right\}$	$\left\{Q_1,Q_2\right\}$

describes the strategic mix between NEV manufacturers and component suppliers and their corresponding benefits, and the matrix shows the expected benefits under different strategic combinations, helping to understand the impact of decisions between the two parties when choosing whether to collaborate on supply chain information.

4.2.2. Replicator Dynamic Equations

Whether new energy vehicle manufacturers and component suppliers choose the expected benefits of the supply chain information collaboration strategy can be obtained according to the replicator dynamic equation. When some NEV manufacturers choose the supply chain information collaboration strategy, the expected total benefits obtained by these NEVs are as follows in Equation (1):

$$\begin{gathered} E_{t1}=m{T}_{1}n\left[S_1\left(m\right)\right]+\left(1-m\right)T_2\left(1-n\right)\left[S_1\left(m\right)\right] \\ {T}_{1}n\left[S_1\left(m\right)\right]=Q_2+\left(1-b\right)B-{\displaystyle \frac{\beta }{\mu }}{f}_2\delta -\left(1-\alpha \right)C \\ {T}_2\left(1-n\right)[S_1\left(m\right)]=Q_2-{\displaystyle \frac{\beta }{\mu }}{f}_2\delta -\left(1-\alpha \right)C \end{gathered}$$

(1)

The formula for calculating the expected benefits obtained by the remaining manufacturers who do not choose supply chain information collaboration is (2), which is as follows:

$$E_{t2}=m\left(Q_2+B_2-P{f}_2\delta \right)+(1-m)Q_2$$

(2)

By combining the proportion of supply chain information collaborative manufacturing enterprises in the group of new energy vehicle manufacturers and their respective expected benefits, the average expected benefits of the group of new energy vehicle manufacturers can be calculated, such as Equation (3):

$${\overline{E}}_t=n{E}_{t1}+(1-n)E_{t2}$$

(3)

The dynamic proportional equation of the replicator of the supply chain information collaboration strategy chosen by the new energy vehicle manufacturer over time is as follows:

$$\begin{gathered} F\left(n\right)={\displaystyle \frac{rn}{rk}} \\ =n\left(1-n\right)[m\left(B-bB-B_2+P{f}_1\delta \right)-({\displaystyle \frac{\beta }{\mu }}{f}_2\delta +C-\alpha C)] \end{gathered}$$

(4)

where it can be seen that the $n$ of the new energy vehicle manufacturer will change with the $m$ of the component supplier. Automakers’ players observe, learn, and optimize their supply chain information collaboration strategies, and gradually adjust to stable strategies. At this stage, it is necessary to consider the influence of different parameters $m$ and $n$ on the proportion of suppliers choosing the supply chain information collaboration strategy. Similarly, when a component supplier player chooses the expected benefit of supply chain information collaboration, see Equation (5) as follows:

$$E_{s1}=n\left(Q_1+bB-{\displaystyle \frac{\beta }{\mu }}{f}_1\delta -\alpha C\right)+(1-n)(Q_1-{\displaystyle \frac{\beta }{\mu }}{f}_1\delta -\alpha C)$$

(5)

The expected individual return of the remaining supplier players who do not choose information synergy is seen in Equation (6):

$$E_{s2}=n\left(Q_1+B_2-P{f}_2\delta \right)+\left(1-n\right)Q_1$$

(6)

Therefore, the average expected return of the component supplier gambler can be calculated, as seen in Equation (7):

$${\overline{E}}_s=m{E}_{s1}+(1-m)E_{s2}$$

(7)

The dynamic proportional equation for component suppliers who choose the supply chain information collaboration strategy over time is as follows in Equation (8):

$$\begin{gathered} F\left(m\right)={\displaystyle \frac{rm}{rk}} \\ =m\left(1-m\right)[n\left(bB-B_2+P{f}_2\delta \right)-({\displaystyle \frac{\beta }{\mu }}{f}_1\delta +\alpha C)] \end{gathered}$$

(8)

From Equation (8), it can be found that the proportion $x$ of parts suppliers choosing supply chain information collaboration will change with the proportion of participants $n$ of new energy vehicle manufacturers choosing supply chain information collaboration.

4.2.3. Analysis of Evolutionary Stabilization Strategies

The stability results in EGT have the following characteristics: when there is a burst strategy or choice change, the stability disappears, and the evolution of the new choice will continue to adjust the burst change and then reach a new stable state and converge the dynamic change to steady state $n$. Therefore, new energy vehicle manufacturers need to meet $F\left(n^{\mathrm{*}}\right)=0$ to make the selected supply chain information collaboration strategy stable, and $n^{\mathrm{*}}$ is the stable result of the group strategy of new energy vehicle manufacturers. It can be seen that the dynamic evolution game system of the two-level network of manufacturers and suppliers in the supply chain of new energy vehicles is composed of $F\left(m\right)$ and $F\left(n\right)$.

The proportion of suppliers $m$ who choose the supply chain information collaboration strategy will affect the proportion $n$ of the automakers to change accordingly. In the process of strategy adjustment and change, the two sides of the game will always influence each other and then reach a stable state. In this case, the important conditions for $F\left(m\right)=0$ and $F\left(n\right)=0$ are $F^{\prime }\left(m\right)=0$ and $F^{\prime }\left(n\right)=0$.

• Evolutionary game analysis of component suppliers.

After reaching a steady state, the evolution process of the component supplier’s strategy can be analyzed to compare the value of the player’s interest function in the case of choosing and not choosing supply chain information collaboration. From Equation (8), we can obtain $F^{\prime }\left(m\right)$, for which see the following Equation (9):

$$F^{\prime }\left(m\right)=\left(1-2m\right)[n\left(bB-B_2+P{f}_2\delta \right)-({\displaystyle \frac{\beta }{\mu }}{f}_1\delta +\alpha C)]$$

(9)

where if you let $F\left(m\right)=0$, its solution is: $m_1^{\mathrm{*}}=0$, $m_2^{\mathrm{*}}=0$, $n^{\mathrm{*}}=(f_1\delta \beta /\mu +\alpha C)/(bB-B_2+P{f}_2\delta )$. According to the changes of $n^{\mathrm{*}}\in \left(\mathrm{0,1}\right)$, $n^{\mathrm{*}}\notin \left(\mathrm{0,1}\right)$, $n^{\mathrm{*}}=0$, $n^{\mathrm{*}}<0$, $n^{\mathrm{*}}>1$, the value and stability point of $F\left(m\right)$ are analyzed, and the stable results of the evolution of the component supplier group are obtained, such as in

Table 9. Component supplier strategy selection evolves stable results.

${\mathit{n}}^{\mathbf{*}}$	$\mathit{n}$	${\mathit{F}}^{\mathbf{\prime }}\left(0\right)$	${\mathit{F}}^{\mathbf{\prime }}\left(1\right)$	Stability Point
$0\le n^{\mathrm{*}}\le 1$	${n=n}^{\mathrm{*}}$	0	0	-
	$n^{\mathrm{*}}<n\le 1$	>0	<0	$m=1$
	$0\le n<n^{\mathrm{*}}$	<0	>0	$m=0$
$n^{\mathrm{*}}<0$	$0\le n\le 1$	<0	>0	$m=0$
$n^{\mathrm{*}}>1$	$0\le n\le 1$	<0	>0	$m=0$

.

From Table 9, it can be found that when $n^{\mathrm{*}}\in [0,1]$, only when $n>n^{\mathrm{*}}$ can the stable evolution results of component suppliers meet the supply chain information collaboration. If $n^{\mathrm{*}}$ becomes smaller and tends to 0, $m$ becomes larger and larger. This shows that when $n>n^{\mathrm{*}}$, parts suppliers are inclined to adopt supply chain information collaboration.

2.

Evolutionary game analysis of automobile manufacturers.

According to Equation (4), the first partial derivative of $F\left(y\right)$ for $y$ can be obtained, as shown in Equation (10):

$$F\prime \left(n\right)=\left(1-2n\right)[m\left(B-bB-B_2+P{f}_1\delta \right)-({\displaystyle \frac{\beta }{\mu }}{f}_2\delta +C-\alpha C)]$$

(10)

where if you let $F\left(n\right)=0$, it is solved as $n_1^{\mathrm{*}}=0$, $n_2^{\mathrm{*}}=0$, $m^{\mathrm{*}}=(f_2\delta \beta /\mu +C-\alpha C)/(B-bB-B_2+P{f}_1\delta )$. According to the changes of $m^{\mathrm{*}}\in \left[\mathrm{0,1}\right]$, $m^{\mathrm{*}}<0$, $m^{\mathrm{*}}>1$, the values and stability points of $F\left(n\right)$ are analyzed, and the stable results of the group evolution of new energy vehicle manufacturers are obtained, such as in

Table 10. The results of the group evolution of new energy vehicle manufacturers are stable.

${\mathit{m}}^{\mathbf{*}}$	$\mathit{m}$	${\mathit{F}}^{\mathbf{\prime }}\left(0\right)$	${\mathit{F}}^{\mathbf{\prime }}\left(1\right)$	Stability Point
$0\le m^{\mathrm{*}}\le 1$	${m=m}^{\mathrm{*}}$	0	0	-
	$m^{\mathrm{*}}<m\le 1$	>0	<0	$m=1$
	$0\le m<m^{\mathrm{*}}$	<0	>0	$m=0$
$m^{\mathrm{*}}<0$	$0\le m\le 1$	<0	>0	$m=0$
$m^{\mathrm{*}}>1$	$0\le m\le 1$	<0	>0	$m=0$

.

From Table 10, it can be found that at $m^{\mathrm{*}}\in \left[\mathrm{0,1}\right]$ the supply chain collaboration is satisfied only when $m>m^{\mathrm{*}}$, when the evolution of the automobile manufacturer stabilizes the results. The results show that under this condition, new energy vehicle manufacturers tend to choose the supply chain information collaboration strategy.

3.

Evolutionary Game Analysis of Component Suppliers and Automobile Manufacturers.

By applying the replicator dynamic equation, the equilibrium solution can be determined under different combinations of $m^{\mathrm{*}}$ and $n^{\mathrm{*}}$ values, and the stable solution can be identified by a Jacobian matrix calculation. This analysis method can reveal the evolutionary stability strategy of the NEV manufacturer group and the component supplier group. Establish:

$$\left\{\begin{array}{l}W=bB-B_2+P{f}_2\delta \\ \begin{array}{l}V={\displaystyle \frac{\beta }{\mu }}{f}_1\delta +\alpha C\\ O=B-bB-B_2+P{f}_1\delta \end{array}\\ S={\displaystyle \frac{\beta }{\mu }}{f}_2\delta +C+\alpha C\end{array}\right.$$

(11)

Then, the Jacobian matrix of the model is as follows:

$$\begin{gathered} J=\left[\begin{array}{cc}{\displaystyle \frac{\partial F\left(m\right)}{\partial m}}& {\displaystyle \frac{\partial F\left(m\right)}{\partial n}}\\ {\displaystyle \frac{\partial F\left(n\right)}{\partial m}}& {\displaystyle \frac{\partial F\left(n\right)}{\partial n}}\end{array}\right] \\ =\left[\begin{array}{cc}(1-2m)(nW-V)& m(1-m)W\\ n(1-n)O& (1-2n)(mO-S)\end{array}\right] \end{gathered}$$

(12)

The determinants and traces of Equation (12) are as follows:

$$Det\left(J\right)=\left(1-2m\right)\left(nW-V\right)\left(1-2n\right)\left(mO-S\right)-n(1-n)Om(1-m)W$$

(13)

$$Tr\left(J\right)=\left(1-2m\right)\left(nW-V\right)+(1-2n)(mO-S)$$

(14)

where according to Equations (13) and (14), the equilibrium solutions of $F\left(m\right)$ and $F\left(n\right)$ are (0, 0), (1, 0), (0, 1) and (1, 1). When $0<m^{\mathrm{*}}<1$ and $0<n^{\mathrm{*}}<1$, (${(m}^{\mathrm{*}},n^{\mathrm{*}}\left)\right(m^{\mathrm{*}}=S/O,n^{\mathrm{*}}=V/W)$ is also an equilibrium solution. When $S=O$ (i.e., $m^{\mathrm{*}}=1$), $(1,~)n\in \left(\mathrm{0,1}\right)$ is the equilibrium solution. When $W=V$, (i.e., $n^{\mathrm{*}}=1$), $(1,~)m\in \left(\mathrm{0,1}\right)$ is the equilibrium solution. When $Det\left(J\right)>0$ and $Tr\left(J\right)<0$, the equilibrium solution is a stable solution, as shown in

Table 11. Evolutionary game equilibrium desolation.

Equilibrium Solution	$\mathit{D}\mathit{e}\mathit{t}\left(\mathit{J}\right)$	$\mathit{T}\mathit{r}\left(\mathit{J}\right)$
(0, 0)	$VS$	$-V-S$
(0, 1)	$S(W-V)$	$W-V+S$
(1, 0)	$V(O-S)$	$V+O-S$
(1, 1)	$(V-W)(S-O)$	$\left(V-W\right)+(S-O)$
(1, ~)	$(2n-1)(nW-V)(O-S)$	$V-nW+(1-2n)(O-S)$
(~, 1)	$(2m-1)(W-V)(mO-S)$	$S-Om+(1-2m)(W-V)$
($m^{*},n^{*}$)	$V(V/W-1)S(1-S/O)$	0

.

By analyzing the equilibrium solution and describing the process of stable convergence and strategy adjustment of $m^{\mathrm{*}}$ and $n^{\mathrm{*}}$ on new energy vehicle manufacturers and battery and component suppliers under different conditions, it can affect the determination of the conditions for players to choose supply chain information collaboration. It mainly focuses on two aspects, one is the uncertainty and randomness of $m^{\mathrm{*}}$ and $n^{\mathrm{*}}$, the other is the influence of the position of the saddle point ($m^{\mathrm{*}}$, $n^{\mathrm{*}}$) on the initial point of the quadrilateral DHUL game. Under the condition of $m^{\mathrm{*}}\in \left(\mathrm{0,1}\right)$ and $n^{\mathrm{*}}\in \left(\mathrm{0,1}\right)$, the saddle point $H(m^{\mathrm{*}},n^{\mathrm{*}})$ appears in the evolutionary phase diagram, and the position of the saddle point will change with the different values of $m^{\mathrm{*}}$ and $n^{\mathrm{*}}$ (for example, Figure 7), and there is a probability that it tends to (1, 1). At the beginning of the game, m and n are random variables, and when the area $S_{DHUL}$ of the quadrilateral DHUL becomes larger, the range of values of $m$ and $n$ in DHUL also increases, until a stable state is finally formed.

4.3. Stability Analysis of Evolutionary Game Models in the New Energy Vehicle Supply Chain

In the previous section, we constructed the Jacobian matrix (Equation (12)) and obtained its determinant $Det\left(J\right)$ and $Tr\left(J\right)$ (Equations (13) and (14)), and obtained the possible equilibrium points of the system:$\left(\mathrm{0,0}\right)$,$\left(\mathrm{1,0}\right)$, $\left(\mathrm{0,1}\right)$, $\left(\mathrm{1,1}\right)$ and $(m^{\mathrm{*}},n^{\mathrm{*}})$. In order to make readers understand the stability analysis process more intuitively, this article illustrates how to judge the stability of each equilibrium point based on the values of m (supplier cooperation ratio) and n (automaker cooperation ratio).

In order to more intuitively show the evolution of the system under different parameter combinations, we construct a two-dimensional coordinate diagram (as shown in Figure 8). The horizontal axis of the diagram represents the supplier coordination ratio m, and the vertical axis represents the vehicle manufacturer coordination ratio n. m = $m^{\mathrm{*}}$ and n = $n^{\mathrm{*}}$ are divided into two dividing lines, thereby dividing the entire unit square into four quadrants, each of which corresponds to a specific situation.

Under different conditions of m and n, the two populations reach a stable convergence, and the evolutionary equilibrium stability analysis of the solution is as follows.

• Case 1: $m<m^{*}$ and $n<n^{*}$

When the supplier coordination ratio m and the manufacturer coordination ratio n are both low, the investment and willingness of both parties in the system for information coordination are not high. In this case, if $Det\left(J\right)>0$ and $Tr\left(J\right)<0$, the corresponding equilibrium point (usually $\left(\mathrm{0,0}\right)$) is a local stable equilibrium; otherwise, the system may be in an unstable state and there is a possibility of transitioning to a higher level of coordination.

2.

Case 2: $m>m^{\mathrm{*}}$ and $n<n^{\mathrm{*}}$

When the supplier coordination ratio is high $(m>m^{\mathrm{*}})$ and the manufacturer coordination ratio is low $(n<n^{\mathrm{*}})$, it means that the supplier is more active, while the manufacturer fails to form a corresponding coordination willingness. At this time, a mixed strategy in which one party coordinates and the other does not often appears in the calculation, usually corresponding to $\left(\mathrm{1,0}\right)$ as a saddle point state, that is, $Det\left(J\right)<0$. The saddle point indicates that the equilibrium state is unstable to small disturbances, and the system may evolve in the direction that both suppliers and manufacturers tend to improve the coordination level $\left(\mathrm{1,1}\right)$ or neither of them coordinates $\left(\mathrm{0,0}\right)$.

3.

Case 3: $m<m^{*}$ and $n>n^{*}$

This is similar to the second scenario, but in this case the coordination ratio of the vehicle manufacturer is higher, and the coordination ratio of the supplier is lower, and the other party in the system is active while the other party is passive. In this case, $(0, 1)$ is usually a saddle point state $\left(Det\right(J)<0)$, indicating that the equilibrium state is unstable and easily disturbed, and the state of high or low coordination is shifted.

4.

Case 4: $m>m^{\mathrm{*}}$ and $n>n^{\mathrm{*}}$

When the coordination ratio of both parties is high, both suppliers and vehicle manufacturers in the system have a positive attitude towards information coordination. Through calculation, it can be obtained that if $Det\left(J\right)>0$ and $Tr\left(j\right)<0$ are satisfied, then the $\left(\mathrm{1,1}\right)$ fully coordinated equilibrium point is a local stable equilibrium, indicating that the system has a strong self-sustaining ability and can maintain a high coordination state.

Table 12. Judgment of evolutionary stable points.

Case	Saddle Point	Stable Point	Evolutionary Analysis
Case 1	$(m^{\mathrm{*}},n^{\mathrm{*}})$	(0, 0)	The two sides have a low willingness to cooperate and may remain in a non-cooperative stable state.
Case 2	(1, 0)	(0, 0) or (1, 1)	Suppliers are active while manufacturers are passive. The equilibrium point is easily disturbed and the system is prone to shift towards equilibrium.
Case 3	(1, 1)	(0, 0) or (1, 1)	Manufacturers are active and suppliers are passive, the equilibrium point is easily disturbed, and the system is easy to shift to equilibrium.
Case 4	$(m^{\mathrm{*}},n^{\mathrm{*}})$	(1, 1)	Both parties actively cooperate, and the system tends to be in a stable and fully coordinated state.

summarizes the above analysis results. By calculating $Det\left(J\right)$ and $Tr\left(J\right)$ in different cases, the corresponding saddle points and stable points are obtained and stability analysis is performed on them, and the relevant evolutionary stability analysis is obtained.

4.4. Optimization of the EGT Model for Information Collaboration in the New Energy Vehicle Supply Chain

The previous analysis indicates that $m$ and $n$ are not fixed and are unstable in the initial game. When certain conditions are met, all players tend to have a probability of choosing supply chain information collaboration. This shows that the area of the quadrilateral $S_{DHUL}$ is closely related to the direction of deviation of the saddle point $H(m^{\mathrm{*}},n^{\mathrm{*}})$. Therefore, it is important to focus on the following formulas:

$$\begin{gathered} S_{DHUL}=S_{DHL}+S_{UHL} \\ =1-{\displaystyle \frac{\frac{\frac{\beta }{\mu }{f}_2\delta +C-\alpha C}{B-bB-B_2+P{f}_1\delta }+\frac{\frac{\beta }{\mu }{f}_1\delta +\alpha C}{B-B_2+P{f}_2\delta }}{2}} \end{gathered}$$

(15)

$${\displaystyle \frac{\partial S_{DHUL}}{\partial b}}={\displaystyle \frac{B\left(\alpha C+\frac{\beta }{\mu }{f}_2\delta \right)}{2{\left(bB-B_2+P{f}_1\delta \right)}^2}}-{\displaystyle \frac{B[\frac{\beta }{\mu }{f}_1\delta +\left(1-\alpha \right)C]}{{2[\left(1-b\right)B-B_2+P{f}_2\delta ]}^2}}$$

(16)

$$\begin{gathered} {\displaystyle \frac{{\partial }^2{S}_{DHUL}}{\partial b^2}}=-{\displaystyle \frac{\left(bB-B_2+P{f}_1\delta \right)B^2\left(\alpha C+\frac{\beta }{\mu }{f}_2\delta \right)}{{\left(bB-B_2+P{f}_1\delta \right)}^4}} \\ -{\displaystyle \frac{[(1-b)B-B_2+P{f}_2\delta ]B^2[\frac{\beta }{\mu }{f}_1\delta +\left(1-\alpha \right)C]}{{\left[\right(1-b)B-B_2+P{f}_2\delta ]}^4}} \end{gathered}$$

(17)

$${\displaystyle \frac{\partial S_{DHUL}}{\partial B}}={\displaystyle \frac{\left(1-b\right)\left(\frac{\beta }{\mu }{f}_2\delta +\mathrm{C}-\mathsf{\alpha }C\right)}{{2\left(B-bB-B_2+P{f}_1\delta \right)}^2}}-{\displaystyle \frac{B\left(\alpha C+\frac{\beta }{\mu }{f}_1\delta \right)}{2{\left(bB-B_2+P{f}_2\delta \right)}^2}}$$

(18)

$${\displaystyle \frac{\partial S_{DHUL}}{\partial \alpha }}={\displaystyle \frac{C}{2\left(B-bB-B_2+P{f}_1\delta \right)}}-{\displaystyle \frac{C}{2(bB-B_2+P{f}_2\delta )}}$$

(19)

$${\displaystyle \frac{\partial S_{DHUL}}{\partial f_1}}={\displaystyle \frac{P\left(\frac{\beta }{\mu }{f}_2\delta +\mathrm{C}-\mathsf{\alpha }C\right)}{{2\left(B-bB-B_2+P{f}_1\delta \right)}^2}}-{\displaystyle \frac{\frac{\beta }{\mu }}{2(B-B_2+P{f}_2\delta )}}$$

(20)

$${\displaystyle \frac{{\partial }^2{S}_{MHSC}}{\partial {\left(f_1\delta \right)}^2}}={\displaystyle \frac{P^2\left(\frac{\beta }{\mu }{f}_2\delta +\mathrm{C}-\mathsf{\alpha }C\right)}{{\left(B-bB-B_2+P{f}_1\delta \right)}^3}}$$

(21)

$${\displaystyle \frac{\partial S_{DHUL}}{\partial f_2}}={\displaystyle \frac{P\left(\frac{\beta }{\mu }{f}_1\delta +\alpha C\right)}{2{\left(bB-B_2+P{f}_2\delta \right)}^2}}-{\displaystyle \frac{\frac{\beta }{\mu }}{2\left(B-bB-B_2+P{f}_1\delta \right)}}$$

(22)

$${\displaystyle \frac{{\partial }^2{S}_{DHUL}}{\partial {\left(f_2\delta \right)}^2}}={\displaystyle \frac{P^2\left(\frac{\beta }{\mu }{f}_1\delta +\alpha C\right)}{{\left(bB-B_2+P{f}_2\delta \right)}^3}}$$

(23)

where according to Equation (15), $S_{DHUL}$ are closely related to $b$, $B$, $B_2$, $\mu , \delta , f$, $\beta$, $C$ and $P$. When $\partial S_{DHUL}/\partial b=0$, $b=b^{\mathrm{*}}$. When $\partial S_{DHUL}/\partial f_1=0$, $f_1=f_1^{\mathrm{*}}$. When $\partial S_{DHUL}/\partial b=0,f_2=f_2^{\mathrm{*}}$.

Based on the above analysis, we further focus on the concerns of all parties involved in the new energy vehicle supply chain and provide a basis for the information collaboration incentive strategy.

(1)

The influence of $b$ on the position of the saddle point

The allocation decision affects the distribution of additional benefits of manufacturers and suppliers, and is an important factor affecting the decision-making choices of supply chain node enterprises. Solve the second-order partial derivative of Equation (15) $b$ to obtain Equation (17), which is less than 0 and indicates that $b^{\mathrm{*}}$ has a maximum value for influencing $S_{DHUL}$. When $b>b^{\mathrm{*}}$, $S_{DHUL}$ is the maximum and $b$ is the optimum.

(2)

The influence of $B$ on the position of the saddle point

Profit is the core demand of supply chain node enterprises, and additional income will affect the operating profit of enterprises, so $B$ may be an important influencing factor for supply chain information collaboration. According to Equation (15), when $B$ becomes larger, the saddle point $(m^{\mathrm{*}},n^{\mathrm{*}})$ approaches the G point (0, 0), and the area of the quadrilateral $S_{DHUL}$ is also larger. It can be seen that the larger the $B$ value, the more conducive to the player’s choice of supply chain information collaboration.

(3)

$B_2$ influence on the position of the saddle point

According to Equation (15), the larger the saddle point of the $B_2$, the closer to (1, 1), and the smaller the $S_{DHUL}$, which is not conducive to the choice of supply chain information collaboration among the players in the two groups of manufacturers and suppliers.

(4)

Influence of $f$ on saddle point position

The synergy coefficient is an important guarantee for the collaborative execution effect of supply chain information. This can be seen from Equations (20)–(23). When the first-order partial derivatives (20) and (22) are 0, the supply chain information coordination level $f$ has a value to maximize the $S_{DHUL}$.

(5)

The influence of $\beta$ and $\mu$ on the position of the saddle point

Collaboration risk and information security are important factors affecting the information collaboration of the supply chain, and high risk means that the damage to interests may be serious, and a high safety factor means that the risk is well offset and the benefit protection is good. According to Equation (15), the larger the $\beta /\mu$, the closer the saddle point ${(m}^{\mathrm{*}},n^{\mathrm{*}})$ is to (1, 1), and the smaller the $S_{DHUL}$ is. That is, the higher the risk of collaboration, the lower the proportion of players in the two groups of manufacturers and suppliers who choose supply chain information collaboration.

(6)

The effect of $C$ on the position of the saddle point

Cost control is an important factor affecting the efficiency of the supply chain. According to Equation (15), the larger the saddle point ${(m}^{\mathrm{*}},n^{\mathrm{*}})$ of $C$, the closer it is to (1, 1), and the smaller the $S_{DHUL}$. Therefore, when the $C$ value is low, the proportion of gamers in the two groups of suppliers and manufacturers who choose supply chain information collaboration is high.

(7)

The effect of $P$ on the position of the saddle point

In contrast to the additional income is the punishment mechanism, which is a typical reward and punishment strategy for those who are synergistic and reward for those who are not synergistic. According to Equation (15), the larger the denominator of $m^{\mathrm{*}}$ and $n^{\mathrm{*}}$, the smaller the values of $m^{\mathrm{*}}$ and $n^{\mathrm{*}}$, and the larger the area of the quadrilateral $S_{DHUL}$. Therefore, the larger the $P$, the higher the proportion of gamers using supply chain information collaboration.

This section models and analyzes the information synergy driving force improvement strategy of evolutionary game theory combined with blockchain traceability, and analyzes and calculates the influencing factors affecting the evolution and stability of the supply chain information collaboration game in the two groups of manufacturers and suppliers in the new energy vehicle supply chain.

5. Case Study

Since synergistic measures are proposed based on evolutionary stable influencing factors, the main objective of the case study is to verify the correctness of the optimization measures. Since the case selected in this paper is a real case, and the data involved here is enterprise privacy data, the example verification in this section is to process and assume the data of Company T and Company X according to the actual investigation, set the value range of each parameter, and then use MATLAB (R2022a) to verify and analyze the simulation.

5.1. Data Description and Analysis

In order to further verify the evolutionary game model of collaborative optimization of new energy vehicle supply chain proposed in this paper, two typical new energy vehicle companies, Company T and Company X, were selected as cases for analysis. The two companies have significant differences in supply chain information collaboration level, enterprise scale, management model, supply chain maturity, etc., which helps to fully examine the applicability and effectiveness of the model in different situations.

5.1.1. Data Selection Principles

Since the supply chain operation data of the two companies involved (Company T and Company X) are commercial secrets, in order to ensure the privacy security of corporate data and the objectivity of research results, this study uses the following two-stage preprocessing on the data:

• Interval processing of data

The specific financial numerical data involving the company’s supply chain synergy benefits, synergy costs, risk factors, etc., are processed with a floating range of ±10% and presented in a desensitized form. For example, the actual benefit data (for example, the company’s annual synergy benefit is actually 1.2 million yuan) is standardized into a numerical range [108, 132] to facilitate model analysis and comparison.

2.

Anonymization of roles

In the specific role setting of supply chain enterprises, anonymous symbols such as “supplier A-F” and “manufacturer G-H” are used to replace the actual corporate names of the two companies to avoid disclosing specific company supply chain relationships and corporate privacy. The parameter setting is based on the proportion range of the company’s annual supply chain cooperation investment (for example, referring to the company’s 2021 ESG report, the proportion of supply chain collaboration investment is about 12–18%), and combined with the setting method of the Martin competition game model, the value range of the corresponding parameters is determined to reflect the actual supply chain collaborative game interaction intensity between enterprises.

5.1.2. Analysis of the Rationality and Applicability of Data

In order to ensure that the data selected in this paper has practical significance and strong practical applicability for case studies, this study conducts the following analysis on the data source and the rationality and applicability of data selection.

• Reasonable analysis

The data for this study are derived from actual corporate supply chain operation data, annual ESG reports, expert interview records, and professional literature references. The data processing method strictly abides by the corporate data privacy protection principles and has been repeatedly demonstrated and evaluated by industry experts. The accuracy, authenticity, and availability of the data have been effectively guaranteed.

2.

Applicability analysis

The significant difference between the two companies in the development stage of supply chain information collaboration makes them an ideal case to verify the wide applicability of the model. Company T has a high level of supply chain management maturity, while Company X is in the early stages of developing supply chain collaboration capabilities. By comparing the data and decision-making behaviors of companies at different stages of collaborative development, the model can effectively capture the laws and key driving factors of the collaborative development of corporate supply chains, reflecting the good applicability and practical guidance value of the model under different corporate conditions.

In summary, this section conducts a comprehensive and systematic analysis of the applicability and rationality of the selected data and parameters. In the subsequent content of this article, the data will be accurately analyzed and processed.

5.2. Supply Chain Collaborative Optimization of Company T

As a leading enterprise in the field of new energy vehicles, Company T maintains a highly stable cooperative relationship with upstream and downstream enterprises in its supply chain. Its supply chain system is mainly composed of upstream parts suppliers and downstream vehicle manufacturers. In supply chain management, Company T has significantly improved the overall efficiency and competitiveness of the supply chain by virtue of its excellent information collaboration capabilities and optimization strategies. In order to further verify the excellent performance of Company T in information collaboration, this paper carries out a systematic analysis based on the evolutionary game model of supply chain collaboration and the actual parameters of the supply chain of Company T.

5.2.1. Parameter Setting and Background Analysis

Based on the actual operation characteristics and information synergy data of company T’s supply chain, the relevant parameter values were selected for modeling and analysis, and different initial values $R(m_0,n_0)$ were selected for comparative analysis.

Table 13. Related parameters of Company T.

Parameters	$\mathit{b}$	$\mathit{B}$	$\mathit{\beta }$	${\mathit{B}}_2$	$\mathit{\delta }$	${\mathit{f}}_1$	${\mathit{f}}_2$	$\mathit{\mu }$	$\mathit{\alpha }$	$\mathit{C}$	$\mathit{P}$
Values	0.6	200	6	50	1.2	6	5	2	0.6	30	5

shows the parameters.

5.2.2. Application of Evolutionary Game Models

According to the evolutionary game model proposed previously, the upstream and downstream enterprises in the supply chain of Company T are abstracted into game participants, which are vehicle manufacturers (manufacturers) and parts suppliers. The two-party policy set is defined as {synergy, non-synergy}. Based on the replication dynamic equation, the evolutionary behavior of the supply chain system is analyzed.

• Earnings matrix

The benefit matrix of Company T’s supply chain is calculated using the parameter values, as shown in

Table 14. Revenue matrix of Company T.

Parts Supplier	New Energy Vehicle Manufacturer
	$T_1\left(n\right)$	$T_2\left(1-n\right)$
$S_{1}m$	$\{Q_1+50.4, Q_2+12\}$	$\{Q_1-21.6,Q_2+20\}$
$S_2\left(1-m\right)$	$\{Q_1+20,Q_2-30\}$	$\left\{Q_1,Q_2\right\}$

. From the benefit matrix, it can be seen that the synergistic strategy is significantly better than the non-synergistic strategy, especially when both suppliers and manufacturers choose synergy, and the overall system revenue reaches the maximum.

2.

Copy the dynamic equations

According to the replication dynamic Equations (8) and (10), the stable point of supply chain collaboration of company T was analyzed. Substituting the parameters in the table into the replication dynamic equation makes $F\left(m\right)=0$ and $F\left(n\right)=0$, and the following three stability points are obtained: the fully coordinated stability point $L\left(\mathrm{1,1}\right)$, the non-cooperative point $G\left(\mathrm{0,0}\right)$, and the saddle point $H{(m}^{\mathrm{*}},n^{\mathrm{*}})$. The coordinates of the saddle point were calculated as $H\left(\mathrm{0.396,0.455}\right)$. Furthermore, the stability of the saddle point is analyzed by the Jacobian matrix, and the following results are obtained:

$$Det\left(J\right)=-145.90<0;\mathrm{T}\mathrm{r}\left(J\right)=15.67>0$$

It can be seen that the saddle point H is the unstable equilibrium point. Based on this point, the evolution process of Company T is divided into synergistic and non-synergistic regions, as shown in Figure 9.

3.

Dynamic evolution analysis

The trajectory of the synergy ratio of T’s suppliers and manufacturers over time is shown in Figure 10 through numerical simulations. The analysis results show that when the initial synergy ratio is high $R\left(\mathrm{0.6,0.5}\right)$, the point is located at the position of Case 4 in Figure 8, and the system can quickly converge to the full synergy stability point $L\left(\mathrm{1,1}\right)$, as shown in Figure 10a. When the initial ratio drops to $R\left(\mathrm{0.4,0.3}\right)$ it can still converge, even though the initial synergy ratio is low, as shown in Figure 10b. Due to the high synergistic benefits and effective punishment mechanism in the evolution process of Company T, it is not difficult to see from Figure 9 that the synergistic potential index of the system is $S_{DHUL}$ larger, and the system can still achieve complete synergy in the end. Therefore, although the starting point $(0.4, 0.3)$ corresponds to the position of Case 2 in Figure 8, due to the high quality of cooperation between the company and its suppliers, it also reaches the $(1, 1)$ collaborative stability point in the final evolution and finally achieves stable cooperation.

5.3. Collaborative Optimization of the Supply Chain of Company X

As an emerging enterprise in the new energy vehicle industry, Company X is still in the early stage of development. Compared with Company T, the supply chain collaboration mechanism of Company X is not yet fully mature, and there is a certain gap in the efficiency and stability of collaboration between its upstream and downstream enterprises. In order to understand the evolutionary characteristics of supply chain collaboration of Company X, this paper analyzes the possibility of supply chain collaborative optimization based on the evolutionary game model and relevant parameters.

5.3.1. Parameter Setting and Background Analysis

Combined with the actual characteristics of the supply chain of Company X and the preliminary collaborative data, the relevant parameters were selected for modeling and analysis, and different initial values $P(m_0,n_0$) were selected for comparative analysis. The parameters are set in

Table 15. Related parameters of Company X.

Parameters	$\mathit{b}$	$\mathit{B}$	$\mathit{\beta }$	${\mathit{B}}_2$	$\mathit{\delta }$	${\mathit{f}}_1$	${\mathit{f}}_2$	$\mathit{\mu }$	$\mathit{\alpha }$	$\mathit{C}$	$\mathit{P}$
Values	0.4	180	6.5	40	1.8	6	4	2	0.45	20	3

.

5.3.2. Application of Evolutionary Game Model

Similarly to the analysis of Company T, the upstream and downstream enterprises of Company X’s supply chain are modeled as game participants, and the policy set is {synergistic, non-cooperative}. The evolution process of the system is analyzed through the benefit matrix and the replication dynamic equation.

• Earnings matrix

The value of the parameter calculates the benefit matrix of Company X’s supply chain, as shown in

Table 16. Revenue matrix of Company X.

Parts Supplier	New Energy Vehicle Manufacturer
	$T_1\left(n\right)$	$T_2\left(1-n\right)$
$S_{1}m$	$\{Q_1+27.9,Q_2+50.2\}$	$\{Q_1-44.1,Q_2+28.4\}$
$S_2\left(1-m\right)$	$\{Q_1+23,Q_2-34.4\}$	$\left\{Q_1,Q_2\right\}$

. From the benefit matrix, it can be seen that the synergistic strategy is significantly better than the non-synergistic strategy, especially when both suppliers and manufacturers choose synergy, and the overall system revenue reaches the maximum.

2.

Copy the dynamic equations

According to the replication dynamic Equations (8) and (10), the stable point of supply chain collaboration of Company X was analyzed. Substituting the parameters in the table into the replication dynamic equation makes $F\left(m\right)=0$ and $F\left(n\right)=0$, and the following three stability points are obtained: the fully coordinated stability point $L\left(\mathrm{1,1}\right)$, the non-cooperative stability point $G\left(\mathrm{0,0}\right)$, and the equilibrium point $H{(m}^{\mathrm{*}},n^{\mathrm{*}})$. The coordinates of the equilibrium point are calculated as $H\left(\mathrm{0.823,0.343}\right)$. Furthermore, the stability of the equilibrium point is analyzed by the Jacobian matrix, and the following results are obtained:

$$Det\left(J\right)=43.65>0;Tr\left(J\right)=28.34>0$$

It can be seen that the equilibrium point $H$ is an unstable repulsion point, so it will not remain stable at this point during the evolutionary process and then approach the stable point $L\left(\mathrm{1,1}\right)$ or $G\left(\mathrm{0,0}\right)$. Based on this point, the evolution process of Company X is divided into synergistic and non-synergistic regions, as shown in Figure 11.

3.

Dynamic evolution analysis

Through numerical simulations, the trajectory of the synergistic ratio of suppliers and manufacturers in Company X’s supply chain over time is shown in Figure 12. The analysis results show that the evolution performance of company X’s co-optimization is significantly affected by the initial co-optimization ratio and system parameters. When the initial synergy ratio $R (0.6, 0.5)$ is high, which is the position of Case 3 in Figure 8, the system may gradually evolve to the full synergy stability point $L(1, 1)$ to achieve complete collaboration among upstream and downstream enterprises in the supply chain and maximize the overall efficiency and revenue, as seen in Figure 12a. However, when the initial synergy ratio is low, located at Case 1 in Figure 8, that is, R(0.4,0.3), the system is more inclined to converge to the non-synergistic stable point $G\left(\mathrm{0,0}\right)$ because the synergistic benefit is insufficient to compensate for the impact of the penalty mechanism, and it is difficult to achieve the continuation of the synergistic state, as seen in Figure 12b. In addition, the equilibrium point $H (0.823, 0.343)$ of Company X is verified to be an unstable exclusion point, and the evolution direction of the system near this point depends on its initial state; when approaching the cooperative region, the system may evolve into a complete cooperative state, and when approaching the non-cooperative region, it is easier to evolve into a non-cooperative state. On the whole, the current synergistic potential index of Company X is small, the improvement of synergistic income is limited, and the effectiveness of the penalty mechanism is weak, which makes it difficult for the system to spontaneously get rid of the non-synergistic attraction area. In the future, Company X needs to further improve the synergistic revenue distribution mechanism and strengthen incentive and punishment policies, so as to improve the synergy potential index and promote the continuous improvement of the synergy efficiency and stability of upstream and downstream enterprises in the supply chain.

5.4. Optimization Results and Analysis

Through the study of the case study of supply chain collaboration optimization of Company T and Company X, we can find the differences and commonalities of enterprises in promoting supply chain collaboration. The analysis results of the two companies show that when the initial synergy ratio is high, the system can quickly converge to the fully synergistic stable point $L\left(\mathrm{1,1}\right)$. When the initial proportion is low, the synergistic potential index of the system is $S_{DHUL}$ larger due to the higher synergistic benefits and effective punishment mechanism in the evolution process of Company T, and the system can still achieve complete synergy in the end. However, because the synergistic income of Company X is insufficient to compensate for the impact of the penalty mechanism, the system is more inclined to converge to the non-synergistic stable point $G\left(\mathrm{0,0}\right)$, where it is difficult to achieve the continuation of the synergistic state. These analyses provide important insights for the development of effective synergy strategies. The following summarizes and analyzes the distribution of synergistic benefits, incentive and punishment mechanisms, and the level of information sharing.

5.4.1. Improvement of the Synergistic Income Distribution Mechanism

The case shows that the reasonable distribution of synergistic benefits is the key factor to promote supply chain collaboration. Through the scientific revenue distribution mechanism, the upstream and downstream enterprises in the supply chain can achieve a win–win situation in the collaboration, thereby improving the willingness and efficiency of collaboration. However, due to the insufficient distribution of synergistic benefits, the system is more likely to stay in a non-synergistic state. Therefore, when formulating a synergy strategy, enterprises should pay attention to the following points: clarify the income weight of all parties involved in the collaboration, distribute the benefits according to the actual contribution, and ensure the balance of interests of all parties. Through the dynamic adjustment mechanism of revenue, all parties are encouraged to continuously optimize collaborative behaviors and avoid long-term non-synergistic states.

5.4.2. Strengthening the Incentive and Punishment Mechanism

The incentive and punishment mechanisms are an important guarantee for the collaborative strategy. Company T uses an effective punishment mechanism to restrain non-collaborative behaviors, and at the same time, through the improvement of the synergistic potential index, the system can evolve to a fully synergistic state even when the initial synergy ratio is low. In contrast, Company X’s penalty mechanism is relatively weak, and the synergistic benefits are not enough to cover the risks of non-concerted acts. Therefore, when implementing collaborative optimization, enterprises should focus on introducing clear incentives, such as performance-based subsidy policies or additional revenue sharing, to encourage enterprises to actively choose collaborative strategies.

5.4.3. Enhance the Potential of Information Sharing and Collaboration

The level of information sharing directly determines the potential for supply chain collaboration. With its excellent information collaboration capabilities, T enables upstream and downstream enterprises to quickly respond to market changes and maintain the efficient operation of the supply chain. However, due to the low level of information sharing, Company X has a small synergy potential index, and the synergy and stability of the supply chain are insufficient. Therefore, improving the level of information sharing is an important direction of supply chain collaborative optimization, that is, establishing a transparent information sharing platform to transmit key data of each link of the supply chain in real time and improve collaborative efficiency. Applying digital technologies (such as blockchain or cloud computing) can ensure the authenticity and security of information and further enhance the collaboration ability of the supply chain.

5.4.4. Optimization of Initial Conditions and Long-Term Synergy Strategies

The case study shows that the initial synergy ratio has a significant impact on the system evolution results. When the initial synergy ratio of Company T is high, the system can quickly reach the state of complete synergy; even if the initial proportion is low, its synergistic benefit and punishment mechanism can still promote the evolution of the system to the full synergy point. However, Company X has a strong dependence on the initial conditions, and it is easier to converge to the non-synergistic point when the initial ratio is low. Therefore, enterprises need to pay attention to the setting of initial conditions and formulate long-term strategies when promoting collaborative optimization. Through early incentive policies, the willingness of supply chain participants to collaborate is enhanced, and the initial synergy ratio is increased. In the long-term cooperation, through data accumulation and experience sharing, the dependence of the system on the initial conditions will be gradually reduced and the stability of supply chain collaboration will be enhanced.

The comparative analysis of the cases of Company T and Company X shows that the effectiveness of the collaborative optimization strategy lies in reasonable income distribution, effective incentive and punishment mechanisms, and a high level of information sharing. As a pioneer in supply chain collaboration, Company T demonstrated the advantages of mature enterprises in the collaboration mechanism, while the shortcomings of Company X reflected the potential challenges of emerging enterprises in collaborative optimization. In the future, when formulating supply chain collaboration strategies, enterprises should gradually promote the overall improvement of supply chain collaboration efficiency and stability by improving the revenue distribution mechanism, strengthening incentive and punishment strategies, improving the level of information sharing, and optimizing the initial conditions in combination with the characteristics of the industry and the development stage.

6. Conclusions

Under intelligent manufacturing, new energy vehicles are facing the problem of improving supply chain performance while facing the security of the supply chain structure. Although many studies have shown that the information collaboration of the new energy vehicle supply chain has a significant effect on the improvement of supply chain performance, the application in practice is not ideal. Not only that, but also because of the huge supply chain system and the large number of systems and participants involved, the core issues that limit supply chain information collaboration have not been clearly raised.

This paper studies the potential constraints and core problems restricting the collaborative development of supply chain information in the new energy vehicle supply chain, establishes the information collaboration model of the new energy vehicle supply chain, constructs the supply chain information collaboration index system, designs the questionnaire and recovers the survey results of industry experts, evaluates and analyzes the consistency and reliability of the questionnaire with SPSS, and analyzes the survey results in detail. The results show that information quality has a significant impact on information collaboration and has a direct and indirect impact on supply chain performance, and that information collaboration efficiency has a significant impact on supply chain performance. At the same time, it is found that the standardization, security, integrity, and flexibility of basic data information quality in supply chain nodes are important factors affecting supply chain information collaboration and supply chain performance, and information integrity has an important impact on information collaboration and supply chain performance. The security of information sharing in the process of supply chain information collaboration, and the low willingness and enthusiasm of senior executives to participate are important factors restricting the collaborative development of supply chain information. Therefore, it is found that the core problems restricting supply chain information collaboration are the insufficient integrity of information quality, the insufficient information security, and the low enthusiasm of senior executives for supply chain information collaboration.

In view of the three problems restricting the information collaboration of the new energy vehicle supply chain, the strategies of using traceability technology to improve the integrity of information quality, using blockchain technology to improve information security, and using the EGT evolutionary game theory model to improve the enthusiasm of senior executives for information collaboration were proposed. Furthermore, the new energy vehicle supply chain network is divided into two levels of manufacturers and suppliers, and according to the network characteristics, an EGT new energy vehicle supply chain information collaborative optimization model combining traceability technology and blockchain technology is established, the model is analyzed, and the key element indicators affecting the collaborative efficiency are found. The model is verified by Company T and Company X, and it is found that the evolutionary game model that solves the information quality stability and information security has a significant effect on the information collaborative optimization of the new energy vehicle supply chain. Finally, according to the case analysis and optimization verification, the specific optimization strategies and measures of the enterprise in the supply chain information collaboration are given.

The theoretical contributions of this study mainly include theoretical expansion and methodological innovation. In terms of theoretical expansion, evolutionary game theory is applied to the field of supply chain information collaboration, and a new model integrating dynamic learning mechanism, blockchain technology, and traceability technology is proposed, which enriches the research of evolutionary game theory and provides a theoretical basis for enterprises to formulate supply chain information collaboration strategies. In terms of method innovation, the impact of information quality and security risk on the evolution path is quantified by designing the penalty mechanism and the collaborative parameter index, which provides a quantifiable tool for the dynamic optimization of complex supply chain networks.

This study will help new energy vehicles improve the information collaboration ability of the supply chain and have a positive effect on production scheduling, obtaining a timely grasp of market information, and improving production efficiency, and on innovation ability, inventory optimization, cost optimization, loss and waste reduction. As such, it will improve the overall collaborative efficiency and risk resistance of the supply chain system and enhance the overall sustainability and green competitiveness of the supply chain. At the same time, because of the improvement of the resilience and competitiveness of the supply chain, it will play a certain role in promoting and stabilizing job security and social employment in the supply chain system. This study has important reference value for the optimization and upgrading of the supply chain of new energy vehicles.

7. Limitations and Future Directions

Although this study provides valuable insights for the optimization of information collaboration in the supply chain of new energy vehicles under intelligent manufacturing, it is not without limitations. Firstly, in actual operation, although most of the parts of new energy vehicles have a traceability system, there is no unified data standard and so it is difficult to achieve data coverage and comprehensive information provision of the whole network. Secondly, the cost of using blockchain technology is currently relatively high, and it is difficult to adopt it in batches across a network. Thirdly, two case data were selected for verification in this study, and the generality needs to be further verified. Fourthly, the supply chain is a complex network system and there are many influencing factors such as cost, efficiency, profitability, and market share of the new energy vehicle supply chain network. The relationship is complex and so this study is designed in an ideal simple environment, and compared with the actual supply chain operation there are limitations such as simple relationships, few research elements, and enterprise differences. In the future, the impact of research elements, network level, market share, and enterprise scale on supply chain information collaboration should be further expanded, and attention should be paid to the multi-objective optimization between collaboration efficiency, cost, and profit, and the research should be tested and optimized in enterprises of different levels and scales. In practical application, it is necessary to initiate an initiative by the core enterprises in the new energy vehicle supply chain, that is, new energy vehicle manufacturing enterprises, to unify the coding and data interface standards of the traceability system in the supply chain, establish a blockchain alliance chain within the supply chain system to connect relevant data to the alliance blockchain, and establish an evolutionary game mechanism of collaborative information based on this study, and encourage supply chain node enterprises to join information collaboration through reward and punishment measures, so as to improve the competitive, economic, and social benefits of the supply chain as a whole.

Supply chain information collaboration can help enterprises related to the new energy vehicle supply chain to better coordinate production and distribution plans, improve production efficiency, reduce costs, and enhance the competitiveness of the entire industrial chain. It can also provide a more accurate understanding of market shifts and the distribution of resources, so as to allocate limited resources to where they are needed most, improving resource efficiency. This will help supply chain enterprises stabilize production and operation, reduce production shutdown and reduction caused by poor coordination between upstream and downstream enterprises, and protect enterprise employment and reduce the unemployment rate. The information collaboration of the new energy vehicle supply chain will promote the establishment of a closer trust relationship between enterprises, which is conducive to the stable operation of the supply chain, enhance the sustainability of the environment and society, and will have a positive demonstration effect in the society, and it will promote the establishment of trust mechanisms and sustainable developments in other fields.