The Technology Innovation of Hybrid Electric Vehicles: A Patent-Based Study

Abstract

A hybrid electric vehicle (HEV) is a relatively practical technology that has emerged as electric vehicle technology has gradually matured. The analysis of the HEV patent lifecycle is crucial for understanding its impact on the development of this technology. This lifecycle tracks the progress of HEV technologies from their inception and patenting, through their market adoption, and to the expiration of their patent protection. In this study, we aimed to evaluate the technology lifecycle of the HEV industry using the growth S-curve method. The purpose of this study is to describe the technological lifecycle trajectory and current stage of the HEV industry, as well as the technical stages of each sub-technology, to facilitate better decision making. As part of this study, we used patent family data collected from the Derwent Innovation Index database from 1975 to 2022 and established an S-curve model for HEVs and their sub-technologies using logistic regression. In 2022, the technological maturity of HEVs reached 44%. The sub-technologies with the most substantial diffusion capabilities are energy management, propulsion systems, and cooling circuits. According to predictions, the saturation period for the patent family quantity related to HEVs is estimated to be around 53 years.

1. Introduction

Over the past thirty years, or possibly even longer, the transportation sector has been considered the main culprit behind the negative impact that human environmental activities have on the planet [1]. Ecological damage caused by the transportation sector is often considered one of the critical issues in the fight against global warming. If those in the transportation sector cannot implement effective emission control, it seems unlikely that the goal of alleviating global carbon dioxide emissions will be achieved [2]. Worldwide, the transportation sector generates about 29% of the annual greenhouse gas emissions and contributes to 25% of global CO₂ emissions [3].

Efforts have been made to reduce the impact of such emissions on the environment. For example, LEVs (low-emission vehicles) with other powertrain systems, such as BEVs (battery electric vehicles), FCEVs (fuel cell electric vehicles), and HEVs (hybrid electric vehicles), have already appeared in the automotive market [4]. Some countries have promoted hybrid and all-electric vehicles while also increasing public awareness. In the past few decades, the automotive manufacturing industry has gradually shifted towards the production of hybrid electric vehicles in response to changes in consumer preferences for renewable energy and support for environmental policies [5,6]. Taking urban vehicles as an example, compared to ICEVs (internal combustion engine vehicles), the use of HEVs can aid in reducing carbon dioxide emissions by 23–43% [7].

Many countries support technological research on and the development of HEVs while simultaneously encouraging the public to use EVs [8]. At the legislative level, due to specific automobile emission reduction legislation, automobile manufacturers are increasingly using hybrid vehicles in their product portfolios. Considering the fuel economy of vehicles in general, hybrid vehicles emit fewer pollutants than traditional fuel vehicles. They are considered a bridge between conventional fuel vehicles and pure electric vehicles, which are widely recognized on the market [9,10]. By comparing the technological trends in LEVs, researchers believe that HEVs have the most favorable development prospects, followed by BEVs and ICEVs; as a promising technology, HEVs integrate an internal combustion engine and electric motor technologies, representing the trend in technological convergence in the near future [11]. HEVs represent a pivotal bridge in the automotive industry’s pursuit of environmental sustainability. By integrating both traditional internal combustion engines and electric propulsion systems, HEVs offer reduced fuel consumption and emissions compared to conventional vehicles. This dual-power approach allows HEVs to operate more efficiently, particularly in urban settings where frequent stops and starts are common. With advancements in battery technology and regenerative braking systems, the environmental footprint of HEVs continues to improve through reduced reliance on fossil fuels and the mitigation of greenhouse gas emissions. The lifecycle emissions of HEVs include the manufacturing, use, and disposal stages, with battery production being the main source of environmental burden. Although HEVs demonstrate their environmental advantages by reducing fuel consumption and exhaust emissions during use, the high energy consumption and potential pollution issues in battery manufacturing and disposal remain challenges. Improving the environmental friendliness and efficiency of battery production, as well as optimizing the recycling and disposal processes of batteries and vehicles, are key directions for reducing the overall environmental impact of HEVs in the future. As the automotive sector evolves towards greener alternatives, HEVs serve as a crucial stepping stone toward a future of more environmentally friendly transportation.

Studying the technological lifecycle of hybrid vehicles is significant to all stakeholders. In strategic decision making, the initial stage may require increased investment to drive innovation, while the mature stage may focus more on improving efficiency and reducing costs [12]. The risk preferences and long-term goals of investors at different stages have different impacts, which can be used to evaluate the risk of and return on investments [13]. Governments and regulatory agencies require more incentive measures to promote research and development and market promotion in the initial stage of technological development. In contrast, more regulatory support is required in the mature stage [14]. Technology prediction can help enterprises and R&D institutions better understand technological bottlenecks and opportunities, promote innovation, continuously improve technical levels, and promote the sustainable development of industries.

Patent data are essential components of enterprise strategy, providing a detailed technical knowledge base. By analyzing these data, people can understand the evolution process, trends, and future development directions of technology, thereby guiding the formulation of scientific research, innovation, and business strategies. In the recent past, researchers have carried out investigations to analyze HEV development in different technical fields. López et al. [15] studied the technical status of HEV propulsion systems through a review to evaluate whether current technological developments can meet the technological goals of the next generation of HEV vehicles. Feng et al. [16] used a patent dataset to analyze the performance improvement rate of the key sub-areas of electric vehicles. Such studies have guided the selection of research topics and companies’ business strategies. Xu et al. [17] used the FP (frequency pattern) growth algorithm along with input–output analysis to identify the core and cutting-edge technologies of the HEV patent family dataset in different periods and analyzed China’s technological layout and evolution in the HEV field. Aaldering et al. [18] established a framework for analyzing the technical knowledge flow (TKF) of HEVs and used link prediction technology to analyze the competition and cooperation between different technical knowledge fields.

Most research based on patent datasets involves the analysis of performance goals, improvement efficiency, and competition and cooperation between different fields of HEV technology. However, few studies have proposed predicting the technological trends in HEVs and identifying key sub-technologies and their development in the development process of HEVs. Through this article, we engage in answering the following questions: What is the technical maturity level of HEVs? Which sub-technologies will promote the development of HEVs? What is the technology diffusion state of these sub-technologies? To aid in answering these questions, an evolution map of the current HEV technologies was drawn using a patent dataset, indicating the main technological trends. The patent dataset was collected on the Derwent Innovation Index platform, using the International Patent Classification (IPC) code to ensure the correct patent scope of the search results. An analysis was carried out using S-curves and technology diffusion (TD), and specific trends were identified.

The difference between this study and previous studies is that, in addition to studying the overall technology of HEVs, our study further subdivides the field of HEV technology into multiple sub-technologies (electric vehicles, engine clutch, driving mode, cooling circuits, engine torque, propulsion systems, electric generators, energy management, high-voltage batteries, and maximum power), which allows us to better focus on the development of HEV sub-technologies, understand the degree of the globalization of sub-technologies, and pay attention to their impact.

Predicting the development trend in hybrid vehicles is integral to fostering sustainability in the automotive industry. Anticipating advancements in hybrid technology allows manufacturers to invest in research and development aimed at enhancing fuel efficiency, thereby reducing emissions and improving overall performance. By staying ahead of emerging trends, such as battery technology innovations, improved powertrain designs, and enhanced energy management systems, stakeholders can strategically align their efforts with environmental goals. This proactive approach not only promotes the widespread adoption of hybrid vehicles but also accelerates the transition towards a more environmentally friendly transportation ecosystem, ultimately contributing to a green future.

2. Literature Review

2.1. HEV Technology Development Trends

In the study of HEV technology development, different theoretical perspectives have provided valuable insights. From an economic standpoint, market dynamics emphasize the interplay of supply and demand, the influence of government policies, and how technological innovation drives competition and economic growth [19]. Sociologically, theories such as the Diffusion of Innovations and the Technology Acceptance Model (TAM) explore how social norms, values, and consumer behaviors impact the adoption of HEVs [20]. Environmental science contributes through Life Cycle Assessment (LCA), which focuses on the entire lifecycle of HEVs from manufacturing to disposal, assessing their performance in terms of resource efficiency and reducing environmental footprints [21].

Theories of the technology lifecycle, such as the Technology Maturity Curve and innovation diffusion models like Agent-based modeling [22] and the Bass model [23], elucidate the progression of technology from innovation to market saturation. The Technology Acceptance Model (TAM) and System Dynamics provide tools to analyze the behavior of users adopting new technologies and their dynamic diffusion in the market [24]. Social Network Theory [25] and Innovation Systems [26], including Regional Innovation Systems and National Innovation Systems, further reveal the influence of social structures and macro policies on the adoption and diffusion of technology.

Schumpeter’s innovation theory emphasizes that innovation is the core driver of economic development, particularly through technological innovation and entrepreneurial spirit, which drive industry transformation [27]. Rogers’ theory of innovation diffusion explores the process of innovation spread within social systems, highlighting the five stages of adoption: knowledge, persuasion, decision, implementation, and confirmation, as well as the roles and impacts of different types of adopters (innovators, early adopters, early majority, late majority, and laggards) [28]. Christensen’s theory of disruptive innovation points out that disruptive innovations, by initially meeting unmet market needs, eventually challenge and replace existing technologies in the mainstream market [29]. These theories provide a crucial theoretical foundation for understanding the lifecycle and diffusion of HEV technology, assisting in the analysis of technological progress, market acceptance, and competitive dynamics, thereby enabling the formulation of more effective strategies and policies.

IEA’s (International Energy Agency) annual Global Electric Vehicle Outlook indicated that over 10 million electric cars would be sold in 2022, and the share of the overall car market rose from around 4% in 2020 to 14% in 2022 [30]. From a long-term development perspective, sales are set to reach 100 million in 2050 [31]. From the perspectives of fuel economy and environmental protection, hybrid vehicles adopt a combination of electric motors and ICEs to achieve more efficient energy utilization and reduce harmful gas emissions [32]. The energy recovery system can convert the kinetic energy generated during vehicle deceleration into electrical power and store it in the battery for future use, thereby reducing the impact on the environment and improving the vehicle’s fuel economy [33]. From the perspective of driving performance and driving experience, hybrid vehicles are usually equipped with intelligent control systems that automatically select power sources and adjust power output according to driving conditions [34] to improve vehicle performance and reliability and provide a better driving experience for users [35]. According to the research of Yuan et al., HEVs will shortly represent the trend in technology convergence, which will promote the technological innovation of LEVs for their technology convergence [11].

End-users focus on the main technical aspects of HEVs, including power, speed, efficiency, reliability, and economic cost [36], which are all related to the motors, power electronics equipment, and cooling systems of HEVs. Only by making significant technological progress can we reduce the cost of popularizing HEV technology [15], making it more competitive in the market.

Energy management determines the energy utilization efficiency of HEVs, with the aim of improving the economy of vehicles, reducing emissions, and enhancing vehicle durability by optimizing the efficiency of ICEs and electric motors. Kim et al. [37] established that the high energy utilization of HEVs is influenced by the driving cycle; appropriate EMSs (energy management strategies) can be used to adjust the working mode in a timely manner in order to achieve reasonable energy utilization and allocation [38]. Xue et al. [39] reviewed the control strategies of various HEV energy management systems. They believed that the future development of HEV energy management systems should comprehensively consider energy conservation, environmental protection, and cost and that they should learn from each other to supplement and intelligently optimize different control strategies. A sound cooling system is crucial for HEV power equipment’s expected lifespan and stability. High temperatures can not only damage the mechanical components in motors but also have an impact on the performance of permanent magnets. Carriero et al. [40] reviewed motor cooling technology and compared different solutions.

The development of HEVs still faces technological challenges, which affect vehicle performance, cost, and market acceptance. Hybrid vehicles currently face technical challenges, and overcoming these challenges requires cooperation between manufacturers, research institutions, and governments to promote the progress of HEV technology and facilitate market promotion jointly.

2.2. Patent Dataset Analysis

Multiple technological characteristics and innovation trends in hybrid vehicles can be analyzed through a patent dataset. Researchers and enterprises can understand technological innovation trends, evaluate competitive trends, make strategic decisions, and promote the further development of hybrid vehicle technology. Unlike previous research, patent analysis methods are mainly divided into network-based and keyword-based morphological patent analysis [41]. From the dynamic analysis of the patent dataset, competition between different technologies was analyzed, and it was found that automobile manufacturers from different countries have different strategic positions [42]. The evolution trajectory and critical technological development of HEV patent technology can be analyzed using the patent dataset [43], as well as the characteristics and performance of the HEV patent technology cooperation network at different stages of patent quantity growth [44].

Text mining is used in patent analysis, and the keyword strategy of the patent dataset will affect the reliability and effectiveness of the research [45]. In patent dataset search, keywords are specific to a language [46], and the system overview of related technical fields is usually limited to these keywords [47]. The International Patent Classification (IPC) system can be used to avoid overlapping fields [48] and aids in finding and clustering patents based on specific technologies described therein [49].

2.3. Patent Growing Model

The patent growing model includes the Abernathy–Utterback model [50], S-curve model [51], Rogers’ innovation diffusion theory [28], product lifecycle model [52], and Gartner hype cycle [53]. The Abernathy–Utterback model focuses on industry-level innovation patterns, the technology adoption lifecycle and Rogers’ theory emphasize consumer adoption processes, the product lifecycle models track market performance, and the Gartner hype cycles showcase changes in market sentiment.

In this study, the S-curve model was chosen to study the HEV technology because it can effectively capture the key stages of technological performance improvement and market penetration, demonstrating the entire process from slow growth in the early stages to rapid development and maturity saturation. This enables the S-curve model to accurately reflect the progress speed and future development trends in HEV technology, providing a better strategic planning and forecasting basis for stakeholders.

An S-curve can often describe the change in patent growth in a certain field due to the typical lifecycle of technological innovation and development. The S-curve, or sigmoid curve, is a graphical representation of a formula that depicts a type of growth that starts slowly, accelerates, and then slows down again, eventually leveling off. The S-curve model captures this innovation lifecycle in each technological field, reflecting the slow start, rapid growth, and eventual leveling off of patent filings. It provides a framework for understanding and predicting the progression of technological advancement and saturation within specific domains.

The logistic, Gompertz, and Richards models are three mathematical approaches used to describe the S-curve pattern observed in various growth processes, including technology adoption, population growth, and the number of patents in a certain field [54,55].

The logistic model is one of the simplest and most widely used methods for modeling S-shaped growth curves, being useful due to its mathematical simplicity and its capacity to fit a wide range of S-curved data with relatively few parameters, thus making it broadly applicable and easily interpretable [56,57,58,59,60,61,62,63].

The Gompertz model is particularly well suited for processes where the growth rate decreases exponentially over time; unlike the logistic model, the Gompertz curve is asymmetrical—it tends to have a steeper decline after the inflection point. The Gompertz curve’s flexibility in fitting data where the slowing of growth is more pronounced at later stages makes it suitable for scenarios where innovations or adoptions face increasing resistance or obstacles over time [64,65,66,67,68,69].

The Richards model generalizes the logistic model by introducing an additional parameter that allows for the adjustment of the symmetry of the S-curve. This flexibility enables it to describe a wider variety of S-shaped growth processes. By adjusting its parameters, the Richards model can emulate both the logistic and Gompertz models, providing a more versatile framework for modeling growth curves that do not fit the standard symmetrical logistic shape, which is particularly suitable for complex systems where the growth does not follow the standard logistic assumptions of constant proportional growth or where the inflection point’s symmetry is not consistent with the observed data [57,70,71,72,73,74].

In this study, after obtaining the patent data, the most suitable growth model was selected by comparing the fitting effects of the three growth models.

2.4. Technology Diffusion

Technology diffusion is crucial for the commercial success and sustained revenue growth of goods utilizing this technology [75]. The citation of patents reflects the diffusion of technology [76] and indicates the spillover effect of knowledge [77]. The technology represented by frequently cited patents is easier to commercialize and has more significant market development potential [78,79].

Technology diffusion (TD) is calculated as follows:

$$\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{n}\mathrm{o}\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{y}\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{f}\mathrm{u}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}={\displaystyle \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{w}\mathrm{a}\mathrm{r}\mathrm{d}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{p}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}}},$$

(1)

For a description of the maturity level of the technology field using the patent dataset, we selected three indicators: technological maturity (TM), technology remaining life (TR), and technology growth potential (TP). Technological maturity TM is calculated based on the growth model’s current patent dataset saturation level. According to the analysis of the S-curve theory, TM falls within the range of 0 to 0.1 for emerging stages, 0.1–0.5 for growth stages, 0.5–0.9 for maturity stages, and 0.9–1 for recession stages [57]. The TR is calculated using the time in the growth model—the time required to move from the current location to the 99% growth limit. Similarly, the TP is considered to be the number of patents needed to reach saturation from recent growth. The calculation of these three parameters is as follows:

TM = N_t/K,

(2)

TR = T_99% − t,

(3)

TP = K − N_t,

(4)

where ${\mathrm{N}}_{\mathrm{t}}$ is the cumulative number of patents at time t and ${\mathrm{t}}_{99\%}$ is the time in the growth model when ${\mathrm{N}}_{\mathrm{t}}$ reaches 99% of the upper limit.

3. Research Design

3.1. Methodology

In this study, there are five steps involved in the research methodology. Step 1 is the literature review, which shows the technology developments and trends in HEVs.

In step 2, we use the patent retrieving strategy proposed by Borgstedt et al. [80], considering that it takes an average of four years for family patents to be cited after publication [81]; the period for selecting family patent publications is 1975–2019, while the period for the relevant family patent citation dataset is 1975–2023. Hence, the search instruction used in this study was “IC = (B60K0006* OR B60W0020 OR B60L00071* OR B60L000720) AND TAB = (vehicle* OR car OR automobile*) AND TAB = (“hybrid vehicle*” OR “hybrid electric vehicle*” OR “hybrid propulsion”)) AND (DP >= (19750101) AND DP <= (20191231)”. The above search instruction was carried out on 29 October 2023, using the DII database, and through this, the patent dataset for this study was obtained.

In step 3, we analyze the patent dataset’s growing mode, technology maturity, top countries, and top assignees.

In step 4, we define the keywords used for HEV sub-technologies, which are based on previous studies by other researchers [82,83,84]. For sub-technologies, our research includes the annual number of patent family publications, technology diffusion, top cited patents, top countries, and IPC codes for the patent family.

For the final step, the conclusions of the study are presented and we summarize the research.

3.2. Data Analysis

This study used the Derwent Innovation Index (DII) platform as the search source for the dataset collection of HEV patent applications and citations worldwide. The DII combines the Derwent World Patents Index (WPI) with the Patents Citation Index to provide worldwide patent information and technology trends [59]. Unlike directly searching patent datasets, using the patent family dataset can aid in avoiding the duplicate counting of the same or substantially similar patent datasets that have been repeatedly applied for and published by the same priority document in different countries or regions and regional patent organizations [85].

Considering the four-year gap, on average, between the initial publication and the first citing of a patent [81], the timespan we chose in this study was 1975–2022 in order to calculate the growth mode, TM, TR, and TP, while 1975–2019 was selected in order to calculate the TD. In total, 32,640 patent families and 120,531 citations were collected for HEV technology from the DII between 1975 and 2022. The patent dataset was downloaded on 19 December 2023. Figure 1 shows the accumulated number and annual number of patent families of HEV technology.

3.3. Growing Model

Using the collected patent family data, we used the logistic, Gompertz, and Richards models to fit the changes in patent data and compared the fitting effects to select the most suitable S-curve for this study. The fitting effect parameters of the S-curve for the three models are shown in

Table 1. S-curve growth model fitting parameters.

Parameter 1	Logistic	Gompertz	Richards
SSE	15,086,356	3,785,489	4,092,495
RMS	747	425	413
MAD	698	272	377
MAPE	0.232	0.0431	0.0937
SE	777	459	452
ln(MLE)	−217	−157	−179
AIC	439	321	367
R2	0.974	0.998	0.995
p	2.33 × 10−21	6.76 × 10−28	6.09 × 10−27

¹ SSE (sum of squared errors), RMS (root mean square), MAD (median absolute deviation), MAPE (mean absolute percentage error), SE (standard error), MLE (maximum likelihood estimation), AIC (Akaike information criterion), R² (R-squared), and p (p-value).

.

Based on the data in Table 1, the Gompertz model is the most suitable for fitting the patent data. It has the lowest SSE (3,785,489), RMS (425), MAD (272), and AIC (321), along with the highest R² (0.998) and ln(MLE) (−157). Its MAPE (0.0431) and SE (459) are also better than the other models, and it has a highly significant p-value (6.76 × 10⁻²⁸). These metrics indicate that the Gompertz model provides the best fit and accuracy. Thus, the patent lifecycle and patent growth situation is defined as follows:

$$\mathrm{N}\left(\mathrm{t}\right)=\mathrm{K}{\mathrm{e}}^{-{\mathrm{e}}^{\mathrm{a}(\mathrm{t}-\mathrm{b})}}$$

(5)

where N(t) denotes the number of publications of family patents with the year t, r denotes the growth rate of the S-curve, b denotes the inflection point/turning point of the S-curve, and K denotes the upper limit of the family patent publication growth. To determine the coefficients of the model, different methods can be used, such as the Monte Carlo method, [86] and the least-squares method [87,88]. In this study, we used the Monte Carlo method with 10,000 iterations. For more information on multivariate system life cycle estimation methods, see [89,90,91,92,93].

4. Results and Discussion

4.1. Overall Results

Table 2. The IPCs of patent families in the field of HEV technology.

IPC Code	Percentage	IPC Description
B60W 20/00	14%	Control systems specially adapted for hybrid vehicles
B60W 10/08	12%	Joint control of power units for vehicle subsystems of different types or functions
B60W 10/06	11%	Joint control of vehicle subsystems with different types or functions of internal combustion engine control
B60L 50/16	10%	Electric traction of internal power sources in vehicles with mechanical direct-drive devices
B60K 6/445	6%	The arrangement or installation of multiple different prime movers for shared or universal power devices with differential gear distribution types
B60K 6/48	6%	The arrangement or installation of multiple different prime movers in parallel for shared or universal power devices
B60W 10/26	5%	Joint control of vehicle subsystems for different types or functions of electrical energy
Other	35%	/

shows the main IPCs of patents in the HEV technology field. The share of B60W-20/00 is 14%, that of B60W-10/08 is 12%, and that of B60W-10/06 is 11%, and the other categories are B60L-50/16, B60K-6/445, B60K-6/48, and B60W-10/26, each with a share of 10%, 6%, 6%, and 5%, respectively. The code B60W-20/00 refers to control systems specially adapted for hybrid vehicles, while the code B60W-10/08 refers to controlling electric propulsion units (such as motors or generators). The code B60W-10/06 refers to the differential gearing distribution type. The code B60K-6/48 refers to parallel-type hybrid vehicles. The code B60L-50/16 refers to a separate direct mechanical propulsion device (including inner combustion engines). The code B60W-10/26 refers to the conjoint control of vehicle sub-units of different types or functions for electrical energy.

Figure 2a shows that Japan (JP) holds the highest proportion of patents, accounting for 42%, followed by China (CN) at 21%, the United States (US) at 11%, Germany (DE) at 7%, and South Korea (KR) at 6%. The patent family priority countries/regions of BEV technology are mainly concentrated in China, Japan, the United States, and Europe [94]. For ICEV technology, the patent family priority countries/regions and organizations are primarily focused on Japan, Germany, the United States, and France as well as the World Intellectual Property Organization.

Figure 2b shows the published dataset of global HEV patent families by assignee classification. By analyzing the global assignee patent family dataset, we can see that, again, patents mainly originate from Japan, which may be related to the high proportion of Japanese patents in the total patent priority. Among the top assignees, Toyota has a representative share of 30%, followed by Nissan at 7%, and Honda at 4%. The major automotive enterprise in South Korea, Hyundai, accounts for 7%. Ford, the major automotive enterprise in the United States, accounts for 5%.

To determine the growth model of HEV technology, we used Loglet Lab to analyze the accumulated publication dataset of patent families between the years 1975 and 2022. To determine parameters such as the K value of the Gompertz growth model, in comparison to the use of the least-squares regression method in other studies [87,88,95], we utilized the Monte Carlo method [86] with 10,000 iterations; the S-shaped growth model is shown in Figure 3.

The blue dots shown in Figure 3 represent the cumulative publication number of patent families collected and filtered from the DII database each year, while the black line represents the S-curve of the fitted cumulative patent data. Table 2 shows the parameters and statistical data of the S-curve regression model. Then, Equation (5) can be mathematically represented as follows:

$$\mathrm{N}\left(\mathrm{t}\right)=\mathrm{K}{\mathrm{e}}^{-{\mathrm{e}}^{\mathrm{a}(\mathrm{t}-\mathrm{b})}}=73438{\mathrm{e}}^{-{\mathrm{e}}^{0.0707(\mathrm{t}-2020)}}$$

(6)

From Figure 3, it can be seen that the upper limit of the HEV technology’s patent family’s logistic growth is K = 73,438. If 99%K is considered the saturation point, it will reach 72,704 by 2077. The potential number of patent families released stands at 40,798, and it will take another 53 years to reach saturation (TR). According to the S-shaped curve, it is expected to reach 90% saturation by 2048, with a technological maturity level (TM) of 44% in 2022.

Based on the TM we obtained, it can be concluded that HEV technology is in the growth stage globally and is gradually transitioning toward the maturity stage. In the growth stage, due to growth returns [96], technological innovation receives positive feedback, and growth accelerates. It enters the mature stage after reaching the maximum growth rate (TM of the S-curve). Due to the increase in marginal costs, technological growth begins to slow down and eventually reaches the saturation stage, where technological innovation no longer increases [97]. Our research is based on analyzing the dataset released by the global HEV technology patent family publication dataset to determine the technology lifecycle. Therefore, the technology lifecycle may vary due to differences in politics, geography, markets, and other aspects in specific countries.

4.2. HEV Sub-Technologies

4.2.1. Patent Family Publications by Year

By conducting a semantic analysis of the HEV patent family, extracting keywords from patent titles, abstracts, and claims, and aggregating topics of different categories based on semantic relevance, 10 sub-technologies of HEVs were obtained, namely electric vehicles (EVs), engine clutch (EC), driving mode (DM), cooling circuits (CCs), engine torque (ET), propulsion system (PS), electric generators (EGs), energy management (EM), high voltage batteries (HVBs), and maximum power (MP). The corresponding patent family dataset for the sub-technologies can be obtained by adding sub-technologies as keywords to the HEV search strategy.

Figure 4 and Figure 5 show the distribution of the number of HEV sub-technology patent families published yearly. Figure 4 shows the dataset for EV and EC, where the sub-technology EV accounts for the most significant proportion of all sub-technologies, and most sub-technologies experienced two declines between 2010 and 2015. Due to the established 18-month gap between patent applications and publication [98], we examined the impact of the hybrid vehicle market during the period from 2008 to 2015, including two significant declines in global crude oil prices between 2008 and 2015 [99], which resulted in higher usage costs for hybrid vehicles, compared to internal combustion engine vehicles [100], affecting the acceptance of the hybrid vehicle market.

4.2.2. Priority Countries

The top five countries/regions of each sub-technology’s patent family publication can be seen in Figure 6. Japan and China demonstrate significant advantages in these ten sub-technologies, with Japan ranking first in six sub-technology areas, including EC, DM, CC, ET, PS, and HVB, and China ranking first in the four other sub-technology areas, including EV, EG, EM, and MP. This indicates that these two countries have an essential influence on HEV technology. Other countries/regions and organizations include the United States (US), South Korea (KR), Germany (DE), and the European Patent Office (EP).

The dominance of Japan and China in various HEV sub-technologies, as illustrated by patent publication data, can be largely attributed to a combination of aggressive governmental incentives, robust market dynamics, and strong societal norms towards green technology. In Japan, government incentives like tax reductions and subsidies, coupled with a cultural inclination towards environmental responsibility, foster both the adoption of HEVs and innovation within the sector. Similarly, in China, substantial government subsidies and a national strategy focused on leading in green technologies drive the development and proliferation of HEV technologies. In contrast to the challenges observed in Malaysia, where consumer skepticism and less supportive policies prevail, both Japan and China benefit from mature automotive markets and societal attitudes that not only facilitate rapid adoption but also encourage continual technological advancements, as reflected in their substantial patent outputs. These factors collectively position Japan and China as leaders in the global HEV technology landscape, underlining the critical role of integrated strategies that include policy support, market readiness, and societal acceptance [101,102,103].

4.2.3. Top Assignees

Figure A1 shows that among the ten sub-technologies, nine include the highest number of patent families published by Toyota, as well as one published by Hyundai. Toyota is a Japanese car manufacturer founded in 1937. In 1969, Toyota began to research and develop hybrid technology, and in 1997, the world’s first mass-produced hybrid car, the Prius, was launched on the market. The Hyundai Motor Company was founded in 1967 and integrated with Kia in 1998, and the Hyundai Motor Group was established in 2000. Compared to Toyota, Hyundai Motors is a relatively young enterprise. It can be observed from the patent owners ranking high in all sub-technologies that Japanese companies represent the highest proportion, which is consistent with the results found for the country/region. Although China possesses a significant number of patents in various sub-technology fields of HEVs, from the perspective of patent owners, the country does not have the same advantages as Korean and American companies.

Toyota’s early focus on in-house knowledge development and technological innovation, which established a solid foundation for its leadership in hybrid technologies, was notably marked by the successful launch of the Prius [104]. Japan’s strategic industrial policies aimed at maintaining global competitiveness in the auto industry have likely fostered an environment conducive to such extensive patenting activities [105]. Additionally, the significant role of industry-led governance in HEVs suggests that Japanese firms like Toyota are well placed to leverage these frameworks to drive innovation [106]. Toyota, as an incumbent, effectively manages dual roles in defending traditional automotive technologies and promoting new, innovative ones, enabling it to influence broader market dynamics through strategic niche-level activities [107]. In contrast, Hyundai, though a significant player, lacks Toyota’s extensive historical involvement and breadth in HEV technology, which is reflected in its relatively smaller patent portfolio. Meanwhile, despite China’s notable patent activities in HEV technologies, it does not match the scale of influence held by established Japanese companies, which benefit from early starts, sustained innovation, and strategic policy support within a well-structured industry governance environment.

4.2.4. Technology Diffusion of Sub-Technologies

Table 3. The technology diffusion of the sub-technologies.

Sub-Technology	Number of Patent Families	Number of Citations	Technology Diffusion
Electric vehicles	7948	49,040	6.17
Engine clutch	6100	29,356	4.81
Driving mode	1530	9037	5.91
Cooling circuit	1588	11,715	7.38
Engine torque	1365	6783	4.97
Propulsion system	1289	10,492	8.14
Electric generator	1292	9377	7.26
Energy management	760	6850	9.01
High-voltage battery	542	3251	6.00
Maximum power	136	961	7.07

shows the diffusion of HEV sub-technologies between 1975 and 2019, calculated using Equation (2). The calculation results indicate that the following sub-technologies have high diffusion rates: energy management (9.01), propulsion system (8.14), cooling circuit (7.38), electric generator (7.26), and maximum power (7.07). Therefore, the abovementioned technologies have more significant commercial potential than other technical aspects. Maximum power technology has steadily grown in the past two decades. Although it comprises the fewest patents, it possesses a high technology diffusion ability.

Over the years, there has been great interest in research on power transmission systems to ensure that they comply with current and future standards and to obtain approval for their use [108]. Between 2021 and 2022, the share of hybrid electric vehicles on the European market increased by 4.2%. Regarding the electrified power system, hybrid vehicles possess the largest share [109], and these trends symbolize the changes in the energy landscape since the Paris Agreement was adopted in 2015.

The energy management strategy is an essential factor in determining the fuel economy of hybrid vehicles, which includes how to allocate the required power to the engine and motor of the hybrid vehicle [110] and, based on the efficiency characteristics of the motor and engine, the use of a shifting strategy to operate them in areas with high efficiency to minimize fuel consumption as much as possible [10]. In 2020, the share of HEVs in global passenger car sales increased to 7%, and the global HEV market value is estimated to be USD 256 billion. By 2030, HEV global passenger car sales are expected to increase up to 39%; by 2026, HEV market value is estimated to increase to USD 2 trillion [111].

Table A1. Top cited patents for the sub-technologies.

Sub-Technology	Patent Number	Number of Citations
Electric vehicles	JP2010098844A	1272
	US6116363A	347
	US5892346A	327
Engine clutch	US6371878B1	322
	US7246672B2	252
	US5755303A	241
Driving mode	US5841201A	263
	US5697466A	206
	US6364807B1	164
Cooling circuit	US20080312782A1	224
	US5881559A	209
	US5251588A	151
Engine torque	US6441506B2	135
	US6203468B1	119
	US6301529B1	115
Propulsion system	US5327987A	302
	US5713425A	290
	US6083138A	230
Electric generator	US5786640A	325
	US6205379B1	180
	US5495906A	155
Energy management	US5291960A	555
	US20050228553A1	435
	US20080027639A1	223
High-voltage battery	US6476571B1	155
	JP2001327001A	88
	JP2001320807A	70
Maximum power	US5345154A	207
	US6662096B2	63
	JP2011125184A	62

presents the most cited patents among the sub-technologies with the broadest dissemination and most significant commercial potential, which is demonstrated as follows: an electric power system with a control apparatus that stops the operation of the air-conditioner temporarily until the change in the connection unit is completed; a continuously variable transmission for HEVs, which brakes and delivers electrical energy to the electric motor/generator in different speed ranges; parallel hybrid vehicles; an HEV control device with variable motor auxiliary torque based on power capability; communication from electric vehicles to the network and the monitoring of batteries; using the heat from the engine and electric motor of a hybrid vehicle to drive a generator for power generation; a power output rate control of HEV generators based on battery charging and discharging states; HEV braking energy recovery, utilization, or transfer; an HEV multipower supply system and equipment; and an electric continuously variable drive system for HEVs.

4.2.5. Growth Mode of Sub-Technologies

Considering that it usually takes four years for patents to be published and cited for the first time, we calculated the TM, TR, and TP of the ten sub-technologies of HEVs using Equations (2)–(4) based on the publication and citation patent family data of these ten sub-technologies in the Derwent Innovation Index database from 1975 to 2022, as shown in

Table 4. The growth mode of the sub-technologies.

Sub-Technology	TM	TR	TP
Electric vehicles	44.45%	48	14,635
Engine clutch	44.45%	50	10,326
Driving mode	44.28%	43	2741
Cooling circuit	44.51%	56	2611
Engine torque	44.50%	54	2326
Propulsion system	44.46%	63	2150
Electric generator	44.49%	53	2044
Energy management	44.50%	47	1578
High-voltage battery	44.48%	50	935
Maximum power	44.66%	58	233

. The TM of each sub-technology is similar, indicating that the sub-technologies are in a transitional period from the development stage to the stable phase. The TR of each sub-technology ranges from 43 to 63 years, with the TR of the sub-technology propulsion system being 63 years; as the TM of each sub-technology is similar, this indicates that this technology field can achieve relatively long-term technological progress.

5. Conclusions

In this study, we investigated leading technological sectors related to hybrid vehicles and highlighted their possible development trends by means of utilizing available patent datasets. This study may serve as guidance for technological management for policymakers and enterprises through our analysis of the dissemination potential and technological development trends in relevant sub-technologies. Based on the dataset collected and analyzed in this study, the following conclusions can be drawn.

In the overall research conducted on HEV technology, the countries with the highest priority for patent families in this field are Japan, which accounted for 42%; China, which accounted for 21%; and the United States, which accounted for 11%.

The most representative patent owners in the patent family were Toyota, accounting for 27%; Hyundai, accounting for 7%; and Nissan, accounting for 7%. According to estimates, the patent family for HEVs will reach saturation with 72704 patents by 2077.

The level of technological maturity in 2022 was 44%, with an estimated remaining time of 53 years. In this study, a patent citation analysis of sub-technologies revealed that the following technologies have high technology diffusion capabilities: energy management (9.01), propulsion system (8.14), and cooling circuit (7.38). The above technologies may have more significant commercial potential compared to the other sub-technologies examined.

This study acknowledges inherent limitations arising from the exclusive use of patent datasets to represent technological advancements in hybrid electric vehicles (HEVs). Patents, while valuable, encapsulate merely one facet of innovation, omitting significant contributions from journal articles and proprietary, confidential technologies. Additionally, the reliance solely on patent families and citation counts as indicators may not comprehensively evaluate the quality and broader impact of technological innovations. Moreover, the generalizability of our findings is constrained by these methodological choices. The predictive analysis of HEV technology growth is particularly sensitive to policy dynamics, suggesting a substantial area for further inquiry.

Future research should consider employing a longitudinal design to trace the evolution of HEV technologies over an extended period, thereby capturing long-term trends and transformations. Cross-industry comparative studies could elucidate differential technological challenges and opportunities. In-depth case studies focusing on specific HEV innovations might also offer insights into the complex interrelations between technological development, market adaptation, and regulatory influence. These methodological expansions would not only deepen the understanding of HEV technologies but also enhance the predictive accuracy of their developmental trajectories.