Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

Abstract

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

1. Introduction

Electric vehicles (EVs) have been used since the 1900s [1]. Despite the early emergence of internal combustion engines, their growth and efficiency have led to a significant decline in the use of EVs in road transportation. Issues such as environmental pollution, scarcity, the rising cost of petroleum products, and the pursuit of energy independence have recently reignited interest in EVs as an alternative mode of transportation. Considering that the transportation sector is expected to account for 50% of the global energy-related greenhouse gas (GHG) emissions by 2050, there is an urgent need to transition to cleaner, low-carbon alternatives, such as EVs. This need has been recognized by both governments and businesses [2]. EVs are environmentally friendly, emit almost no harmful gases, such as carbon dioxide, and can reduce noise and vibration, providing a more comfortable travel experience. Policymakers in countries and regions such as the United States, China, Europe, and South Korea are encouraging technological innovation in EVs and implementing policies to promote advancements in EV technology [3,4,5,6,7]. Technological innovations in the field of EVs are a topic of widespread concern.

Driven by technological innovation and a commitment to environmental protection, the transportation sector is transitioning significantly towards sustainability [8]. Although EVs have made progress toward achieving greener and more sustainable transportation [9], several challenges persist. One of the most frequently mentioned challenges is the limited driving range of EVs [10,11]. Compared to traditional vehicles, most EVs have a shorter range, leading to concerns about flexibility and the need to plan trips carefully [12]. Another challenge relates to the inadequacy of the charging infrastructure, which is crucial for the widespread adoption of EVs [10,13,14]. The availability and accessibility of charging stations are essential for facilitating convenient and efficient charging for EV owners, and the insufficient number of charging stations further reduces user flexibility and comfort [15,16,17,18]. The prolonged battery charging time is also often cited as a barrier to large-scale adoption, as the charging process for EVs is more time-consuming than refueling traditional vehicles [19,20,21]. Additionally, EVs face the challenge of battery capacity degradation [22]. The battery’s capacity gradually diminishes with each charge and discharge cycle, eventually necessitating replacement [23]. A 2023 survey indicated that 79% of participants expressed concerns about range anxiety and the availability of EV charging [24]. Moreover, another key challenge faced by EVs is the potential strain on existing public power grids [15,25,26]. Large-scale EV charging requires a substantial and stable power supply from the grid, which may overload distribution systems [25,26].

EVs face additional challenges on the environmental front. Although the adoption of EV technology is generally viewed as a favorable option for reducing environmental impacts [27], some scholars have argued that EVs may not significantly reduce GHG emissions [28,29,30]. While EVs do not produce any local pollutants or GHG during operation, the electricity needed to meet their energy demands may result in high emissions if the energy mix is heavily carbon-intensive [27,31]. In some regions, the electricity used to charge EVs comes from sources that generate significant GHG emissions, making the widespread adoption of EVs seem less environmentally friendly [30,32,33,34]. From a life-cycle analysis perspective, the electricity used to charge EV batteries must come from renewable or clean sources for these vehicles to achieve zero emissions [35]. On the other hand, when EVs are charged using electricity generated by traditional power plants (e.g., oil or coal-fired plants), they may produce as much, if not more, GHG emissions as conventional gasoline vehicles [35,36]. Another environmental concern involves the mining demands for producing batteries and other related components. There are questions about the emissions generated by mining operations for battery materials such as lithium [14,31,36]. Additional concerns have been raised about whether batteries and their components can be properly recycled at the end of their life cycle [37,38,39].

Table 1. Challenges facing electric vehicles (source: own elaboration based on [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]).

Type	Potential Challenges
Technological	Limited driving range
	Inadequate charging infrastructure
	long charging times
	Battery capacity fading
	Overloaded power grids
Environmental	Greenhouse gas emissions
	Environmental impact of mining raw materials for batteries
	Battery recycling issues

lists the challenges faced by EVs in the pursuit of sustainability from both technological and environmental perspectives.

Technological innovation is a crucial tool for achieving sustainability and is also an important means of addressing barriers to product adoption [40,41,42]. Some scholars have demonstrated the significance of technological innovation in advancing sustainability. These studies span various disciplines, including engineering, energy, economics, entrepreneurship, and policy. Areas of focus include the social impact of technology, information and communication technology and sustainability, technology-driven sustainability, education and e-learning, globalization and predictive technologies, social entrepreneurship and technology, and green energy technology and sustainability [40,43,44]. Patents serve as valuable indicators of technological innovation across various research domains, allowing for tracking technological advancements over time [45]. The growth trajectory of patents in a specific technology provides insightful indications of its development status, including its life cycle stages and time to maturity [46]. A technology life cycle forecasts the progression or growth of technologies and industries over time [47]. It maps the development stages of a technology, starting from the initial research in the emergence phase to its eventual decline or obsolescence [48]. Represented as an S-curve, it predicts the trajectory and pace of technological development, helping identify current trends and patterns and enhancing understanding of the different life cycle stages [47,49]. It is crucial to comprehend the current stage of its life cycle to predict the future development of technology and determine investment decisions [50].

SNA explores the connections and relationships among individuals or groups within a social network. The primary goals of SNA are to understand the structure and characteristics of these networks, identify significant actors and influential figures, and uncover interaction patterns [51]. Unlike focusing on individual actors and their attributes, SNA emphasizes the relationships between them [52]. It helps identify key players, collaboration patterns, and the central technologies driving innovation [53]. This method has been extensively applied to comprehend the complex interactions in technological evolution [54,55], as the network structure of patents and their International Patent Classifications (IPCs) can reveal intricate interdependencies and trends in technological development and fusion [56,57,58].

In previous literature, Chen et al. [59] traced the technology life cycles of hydrogen and fuel cell technologies using patent data and the logistic growth curve model. Yuan and Cai [60] predicted future trends in transmission technologies using patent data. Fang and Li [48,61] analyzed the technology life cycle of EVs and pure EVs in China using patent data and the logistic growth curve model. Tiago et al. [62] tracked the technology life cycle of fuel cell EVs in the United States and Europe through patent data and the logistic growth curve model. Wu and Leu [63] suggested using patent co-word map analysis to examine technological development trends in hydrogen energy. Koo et al. [64] analyzed the core IPCs of EVs in South Korea through annual growth rates. However, in previous studies, analyses have either focused on a single technological area of EVs (e.g., fuel cell technology, transmission technology) or the technology life cycle of EVs in a specific country or region. To the best of our knowledge, no existing literature analyzes the technology life cycle of EVs in both global and South Korean markets.

This study is the first to conduct a comparative analysis of the global and South Korean markets, offering a perspective that has not been previously discussed in the literature. To determine future research directions for EVs, Koo et al. [64] analyzed the core IPCs for EVs using annual growth rates, but their study was limited to South Korea. Nevertheless, the EV market is a globally competitive landscape, and focusing solely on South Korea does not fully capture the technological developments in the EV sector. In contrast, Ma et al. [65] utilized text mining to analyze global patents related to EVs, but their data extend only through 2016 and so may not fully reflect the most recent technological advancements. Considering the rapid development of the EV market and the continuous emergence of new technologies, revisiting this research area is vital. Therefore, this study utilizes the most recent data available (up to 2023) to analyze the global electric vehicle industry. Unlike previous studies [63,66] that used only SNA for patent analysis, this study combines SNA with bubble charts for time series and core applicant layouts. This approach allows for a more sustainable identification of future research directions and is, therefore, an innovative methodological advancement. Additionally, this study will further investigate the identified research directions in greater depth based on the challenges faced by EVs in the pursuit of sustainability (Table 1). This analysis will be conducted from both technological and environmental perspectives.

Table 2. Research directions and limitations of previous studies (source: own elaboration based on [48,61,62,64,65]).

Study	Findings	Limitations
Koo et al. (2023) [64]	Identified H01M (processes for converting chemical energy to electrical energy), B60L (vehicles in general), and G06Q (related to real-time and sensing keywords) as future research directions.	The study is limited to South Korea, and the analysis only extends to the subclass level of IPC codes.
Ma et al. (2022) [65]	Highlighted battery-related technologies, fast charging, and charging infrastructure as key development areas.	Study data only extend through 2016.
Fang and Li (2020) [48,61]	China’s pure electric vehicle technology reached a saturation point in 2023, and electric vehicle technology is expected to reach saturation in 2026.
Tiago et al. (2022) [62]	Fuel cell electric vehicles (FCEVs) in Europe reached technological saturation in 2012, while those in the United States reached technological saturation in 2016.

summarizes the findings and limitations of previous research.

This study aims to address several key research questions:

• At what stage of the technology life cycle are EV technologies globally and in South Korea?
• What is South Korea’s position in the global competition for EV technology?
• What are the prevailing technological trends and future directions in the EV sector?
• Can these future research directions address the sustainability challenges faced by EVs?

2. Materials and Methods

2.1. Data Collection and Organization

The patent dataset was obtained from Lens, a free online database provided by the Australian nonprofit Cambia. Previously called the Patent Lens, it offers access to global patent documents and integrates them with scholarly literature and regulatory and business data. The database contains over 225 million scholarly works, 127 million global patent records, and 370 million biological sequences [67]. The search in the database was made using specific keywords and a thesaurus to locate patents related to EVs (

Table 3. The search strategy for patent collection (source: own elaboration).

Scope of inquiry: Title, Abstract, or Claims
Step 1. Scope of investigation Filing Date = (1 January 2004 – 31 December 2023) (Resulted in 899,426 documents)
Step 2. Grouped by Simple Families (Resulted in 659,975 documents)
Step 3. Document Type = (Patent_application, Granted_patent) (Resulted in 450,054 documents)
Step 4. Legal Status = (Active, Inactive, Expired) (Resulted in 210,056 documents)
Step 5. Manually reviewing (Resulted in 187,700 documents)
Search strategy	title:(‘Electric vehicle*’) OR abstract:(‘Electric vehicle*’) OR claim:(‘Electric vehicle*’) OR title:(‘Electric automobile*’) OR abstract:(‘Electric automobile*’) OR claim:(‘Electric automobile*’) OR title:(‘new energy car* ‘) OR abstract:(‘new energy car*’) OR claim:(‘new energy car* ‘) OR title:(‘new energy automobile* ‘) OR abstract:(‘new energy automobile* ‘) OR claim:(‘new energy automobile* ‘) OR title:(‘new energy vehicle* ‘) OR abstract:(‘new energy vehicle* ‘) OR claim:(‘new energy vehicle* ‘) OR title:(‘alternative fuel vehicle* ‘) OR abstract:(‘alternative fuel vehicle* ‘) OR claim:(‘alternative fuel vehicle* ‘) OR title:(‘alternative fuel automobile* ‘) OR abstract:(‘alternative fuel automobile* ‘) OR claim:(‘alternative fuel automobile* ‘) OR title:(‘alternative fuel car* ‘) OR abstract:(‘alternative fuel car* ‘) OR claim:(‘alternative fuel car* ‘) OR title:(‘Eco-Friendly car* ‘) OR abstract:(‘Eco-Friendly car* ‘) OR claim:(‘Eco-Friendly car* ‘) OR title:(‘Eco-Friendly automobile* ‘) OR abstract:(‘Eco-Friendly automobile* ‘) OR claim:(‘Eco-Friendly automobile* ‘) OR title:(‘Eco-Friendly vehicle* ‘) OR abstract:(‘Eco-Friendly vehicle* ‘) OR claim:(‘Eco-Friendly vehicle* ‘) OR title:(‘Green vehicle* ‘) OR abstract:(‘Green vehicle* ‘) OR claim:(‘Green vehicle* ‘) OR title:(‘Green automobile* ‘) OR abstract:(‘Green automobile* ‘) OR claim:(‘Green automobile* ‘) OR title:(‘Green vehicle* ‘) OR abstract:(‘Green vehicle* ‘) OR claim:(‘Green vehicle* ‘) OR title:(‘fuel cell vehicle* ‘) OR abstract:(‘fuel cell vehicle* ‘) OR claim:(‘fuel cell vehicle* ‘) OR title:(‘fuel cell automobile* ‘) OR abstract:(‘fuel cell automobile* ‘) OR claim:(‘fuel cell automobile* ‘) OR title:(‘fuel cell car* ‘) OR abstract:(‘fuel cell car* ‘) OR claim:(‘fuel cell car* ‘) OR title:(‘hybrid car* ‘) OR abstract:(‘hybrid car* ‘) OR claim:(‘hybrid car* ‘) OR title:(‘hybrid automobile* ‘) OR abstract:(‘hybrid automobile* ‘) OR claim:(‘hybrid automobile* ‘) OR title:(‘hybrid vehicle* ‘) OR abstract:(‘hybrid vehicle* ‘) OR claim:(‘hybrid vehicle* ‘) OR title:(‘battery vehicle* ‘) OR abstract:(‘battery vehicle* ‘) OR claim:(‘battery vehicle* ‘) OR title:(‘battery car* ‘) OR abstract:(‘battery car* ‘) OR claim:(‘battery car* ‘) OR title:(‘battery automobile* ‘) OR abstract:(‘battery automobile* ‘) OR claim:(‘battery automobile* ‘)

Note * for wildcard searches, e.g., valve* can match valve, valves, valvelet, etc. [67].

). The search scope was limited to titles, abstracts, or claims to capture patents specifically related to core EV technologies, including battery systems, propulsion technologies, charging infrastructure, and control systems. The initial search included patents filed between 1 January 2004, and 31 December 2023.

• Step 1: Initial search scope: The initial search yielded 899,426 patent documents based on the specified time frame and keywords.
• Step 2: Grouping by simple families [68]: To eliminate duplicate entries across different jurisdictions, patents were grouped into simple families, reducing the dataset to 659,975 unique patent families.
• Step 3: Document type filtering: The dataset was further refined by filtering for patent applications and granted patents, resulting in 450,054 documents.
• Step 4: Legal status filtering: Patents were then filtered based on their legal status (active, inactive, expired), reducing the dataset to 210,056 documents.
• Step 5: Manual review: A manual review was conducted to ensure the relevance of the remaining patents, culminating in a final dataset of 187,700 documents. IPC co-occurrence data were processed into a co-linearity matrix and saved as a CSV file for visualization using Gephi 0.10.1. Figure 1 illustrates the research framework of this study.

2.2. Research Methods

2.2.1. S-Curve Model

Soviet economist Nikolai Kondratiev was the first to observe the technology life cycle in his book The Major Economic Cycles [69]. A technological life cycle may be depicted by a maturity curve that illustrates a technology’s or a product’s growth or evolution over time [48]. The concept assumes that all technologies expand until they reach a plateau, followed by a saturation phase or leap to a new cycle [70]. Prior studies have demonstrated that the technology life cycle follows an S-curve pattern, where growth accelerates, slows after a certain period, and eventually reaches a limit or saturation point [71]. The technological S-curve comprises four stages—emergence, growth, maturity, and saturation. This S-curve model helps identify the current phase of technology development, estimates the time required to achieve the saturation point, and is widely employed in technology development research as an effective tool for analyzing growth trajectories and forecasting emerging technologies [72].

The S-curve model has two main variations—a symmetric logistic curve and an asymmetric Gompertz curve. A logistic curve is typically utilized when the growth of the research subject is affected by both the growth rate and the waiting growth rate [61]. This study, therefore, used a logistic model to describe EV patent behavior of EVs. The logistic model can be mathematically expressed with the following equation:

$$Y_t=f\left(t\right)={\displaystyle \frac{k}{1+\alpha e^{-\beta t}}}$$

(1)

Here, $f\left(t\right)$ represents the cumulative technological development in the industry over time, expressed by the annual cumulative number of patent applications. The constant $k$ denotes the saturation point of the growth of the technology, which also estimates the maximum cumulative number of patent applications achievable, $\alpha$ represents the growth rate of the S-curve, and $\beta$ indicates the slope of the technology’s growth curve, which is related to the growth time from $t_{10}-t_{90}$ (Figure 2), where $t$ indicates time. Fitting the annual cumulative patent application data for a specific technology to a logistics curve enables the identification of different phases within its life cycle [73]. As presented in Figure 2, $k$ is the maximum value of $f\left(t\right)$, $f\left(t\right)=0.1k$, $f\left(t\right)=0.5k$, and $f\left(t\right)=0.9k$. Typically, the period before $t_{10}$ is considered the incubation phase, the period between $t_{10}$ and $t_{50}$ is the growth phase, the period between $t_{50}$ and $t_{90}$ is the maturity phase, and the period after $t_{90}$ is the saturation phase.

This study, leveraging the logistic analysis model and Loglet Lab 4 software developed by Rockefeller University, facilitates S-curve fitting for the EV industry in both the global and Korean contexts [74]. The data from the lens were used to input the logistic fits. To evaluate the precision of these fits, parametric bootstrapping was applied at a 95% confidence level, utilizing Monte Carlo iteration methods to recreate and resample the data [75]. Loglet Lab automatically performed 200 bootstrap iterations, generating parameter estimates, confidence intervals, and regions, covering all curves and values for a given parameter.

2.2.2. IP Strategies According to the Technology Life Cycle

The earlier section describes the four stages of the technological S-curve. During the emerging phase, technological progress is slow despite heavy investments in research and development. The growth phase accounts for a high ratio of technological progress compared to R and D spending, which declines as technology matures. In the saturation phase, further technological improvements require substantial R and D efforts for minimal gains [21]. Different technological development stages, therefore, require different patent strategies (

Table 4. Characteristics and patent strategies at different stages of the S-curve model (source: own elaboration based on [76]).

Stage of the S-Curve	Characteristics	Patent Strategy
Emerging	Slow technological progress despite heavy R and D investments	Focus on creating and securing foundational patents, monitoring competitors, and acquiring core technologies
Growth	High ratio of technological progress compared to R and D spending	Secure improvement and application patents to strengthen market position, differentiate with design and trademarks
Maturity	R and D spending increases while technological progress slows	Actively protect existing patents, consider strategic alliances, and license out patents to generate revenue
Saturation	Further improvements require substantial R and D for minimal gains	Actively explore future promising technologies as market demand for current technologies decreases

) [76].

2.2.3. IPC Code Co-Occurrence

Core technologies, in essence, refer to technologies that are both operationally and theoretically essential for producing specific products or services [77] and contribute significantly to the developmental process in that technological domain [78]. To rephrase, core technologies are crucial for establishing core competitiveness in the production of specific products or services. While the technology life cycle can indicate the current stage of technological development within the EV industry, it does not pinpoint specific future research directions. Patent analysis is an effective method for identifying core technologies [79]. Patents are particularly valuable for technological analysis due to their structured and classifiable nature. As illustrated in Figure 3, the IPC code is structured to include details on sections, classes, subclasses, main groups, and technology subgroups. The depth of analysis dictates the level of the IPC codes applied. The network analysis was performed at the main group level for this study, as this level has been broadly endorsed by previous research [58,80,81]. In addition, most existing studies on IPC for EVs have focused primarily on the subclass level. This study determined the main group level as the analysis target to further investigate the specific technological fields of current EV research on EVs [64].

In recent years, SNA has frequently been used to identify core technologies [53]; this is primarily because various technologies exhibit interdependencies, making it challenging to delineate boundaries between different technological domains [82]. Typically, patents contain at least one IPC code. However, when a patent includes multiple IPC codes, it suggests integrating various technological areas within the patent [83]. Therefore, analyzing the co-occurrence structure of IPC codes can uncover the connections and relationships among different technological fields. An IPC code with high centrality in a network is considered a core and primary technological area [84,85,86].

In SNA, centrality measures include degree, closeness, and betweenness centrality. Betweenness centrality [87] is a well-known metric for ranking the importance of nodes. It is defined as the ratio of shortest paths that pass through a node to the network’s total number of shortest paths. In this context, a node with a higher betweenness centrality indicates greater importance within the network. A node with higher betweenness centrality in a technology network signifies that the technology occupies a key position in the network [88].

Within patent analysis, betweenness centrality has gained widespread adoption for various purposes, including identifying the innovation potential of alliance networks [89], tracing knowledge flows among industrial sectors [90], and delineating the characteristics of the technological properties and functions of inventive concept networks [91]. This study employs the betweenness centrality to identify core technologies within the EV domain.

Betweenness centrality measures the extent to which a node acts as a mediator in a network. Suppose a node is situated on the only path other nodes must traverse for communication, connection, transportation, or transactions. In that case, it is considered important and likely to have high betweenness centrality [87]. The fundamental formula for the betweenness centrality, $C_b,$ is provided in Equation (2). Wasserman et al. (1994) standardized $C_b$ by dividing by ${\displaystyle \frac{\left(n-1\right)\left(n-2\right)}{2}},$ resulting in the standardized formula ${C^{\prime }}_d$, as presented in Equation (3). In these equations, ${\displaystyle \sum }_{j<k}{\displaystyle \frac{Gjk\left(Ni\right)}{Gjk}}$ represents the number of times node $N$ is located between any other two nodes in the network [92]. The higher the betweenness centrality, the more frequently the node $N$ falls between pairs of other nodes on the shortest path of all connections in the network. As presented in Figure 4, node A is positioned between nodes E and C, E and D, B and D, and B and C. Therefore, according to Equation (2), for point A, $C_b=4$ and ${C^{\prime }}_d=0.53$ [93].

$$C_b\left(Ni\right)={{\displaystyle \sum }}_{j<k}{\displaystyle \frac{Gjk\left(Ni\right)}{Gjk}}$$

(2)

$${C^{\prime }}_b\left(Ni\right)={\displaystyle \frac{2{{\displaystyle \sum }}_{j<k}\frac{Gjk\left(Ni\right)}{Gjk}}{\left(n-1\right)\left(n-2\right)}}$$

(3)

Figure 5 presents the derivation process for the IPC co-occurrence networks, depicting nodes as IPC codes and links as the co-occurrence of IPC codes within a patent. Multiple and repeated co-occurrences between two or more IPC codes may be identified by increasing the weights of the links. This approach assumes that if a particular IPC code frequently co-occurs with another, there is a strong relationship between these technological areas, indicating that they are interconnected [94].

This study utilized Gephi 0.10.1 software to construct an IPC code co-occurrence map, employing normalized betweenness centrality to evaluate key classifications in the IPC co-occurrence network and filtering the data based on node occurrence frequency. By calculating the ranking of the importance of the IPC classification, a co-occurrence threshold (edge weight) of 200 was set to filter out nodes with weak associations with other nodes. A normalized betweenness centrality threshold of 0.01 and a node occurrence frequency threshold (degree) of 10 were then established. The IPC classifications obtained through this filtering process were identified as core patent technology areas in the global EV field.

2.2.4. Bubble Charts for Time Series and Core Applicant Layouts

This study utilizes bubble charts to visualize and analyze the time series data and technological layout of core applicants in the EV technology field filtered through IPC code co-occurrence. In the time series analysis, bubble charts were used to illustrate the progression of various technological domains over time. Each bubble represents a specific year, with its size corresponding to the number of patent applications filed in that year. This visual method helps identify the developmental trends of different EV technology sectors within the global and Korean markets. Simultaneously, core applicants were identified based on the volume of patent applications, selecting the top 10 applicants as core representatives. Bubble chart analyses of the technological layouts of these core applicants were presented within the EV technology field filtered through IPC code co-occurrence. Each bubble represents a core applicant, with the bubble size indicating the number of patents filed, highlighting their research focus and technological strengths. These bubble charts provide a clear and intuitive visual representation of the data, facilitating a deeper understanding of the temporal evolution in the EV technology domain and the strategic positioning of core applicants. They also enable further assessment of the IPC code co-occurrence-filtered EV technology field, identifying sustainable directions for future development.

3. Results

3.1. Electric Vehicles Technology Development Trend

3.1.1. Global Technology Application Trends

Understanding the shifts in the patent landscape over various periods was possible by analyzing the trends in patent applications for EV technology. Analyzing the trends in EV technology applications from 2004 to 2023 in Figure 6 revealed four distinct phases. There was a period of steady growth from 2004 to 2009, with the number of patent applications related to EV technology increasing by approximately 500 per year compared with the previous year. Subsequently, from 2010 to 2015, the EV technology landscape entered a phase of rapid development characterized by a significant increase in the annual number of patent applications compared with the preceding period. During this phase, the number of patent applications increased by approximately 1400 per year compared with the previous year. Starting in 2016, the number of patent applications surged rapidly and peaked in 2019. Overall, there was an increasing trend in the number of applications per year, characterized by a rapid growth pattern followed by a slowdown. There has been a downward trend in annual patent applications since 2020.

Typically, there is a delay between patent applications and their disclosure. Generally, it can take three to 18 months for invention patents to be revealed after they are applied for [95]. The data for 2022 and 2023 were, therefore, provided for reference only.

3.1.2. Technology Application Trends in Major Automobile-Producing Countries

Figure 7 presents the annual trends in EV-related patent applications from major automobile-producing countries between 2004 and 2023. It was possible to understand the technological development trends in the EV field and identify the primary contributors to EV innovation by analyzing the patent applications for EV technology from the world’s major automobile-producing countries. As depicted in the figure, Japan consistently held the highest number of patent applications from 2004 to 2012, leading other countries. However, in 2013, China surpassed Japan for the first time and continued to lead in subsequent years. Furthermore, Japan, the United States, and Germany experienced a turning point in 2019, with a subsequent annual decline in the number of patent applications. Korea experienced a turning point in 2020, and although China did not, its growth rate presented a significant slowdown.

3.2. Technology Life Cycle Analysis

Technology Life Cycle Using the Logistic Curve

Figure 8 presents the growth curves based on the actual data and fitted models for 2004–2021.

Table 5. The technological life cycle stages globally and in Korea (source: own elaboration).

Scope	Emerging (Years)	Growth (Years)	Maturity (Years)	Saturation (Years)	Saturation (Numbers)
Global	2001	2007	2014	2021	22,805
Korea	1999	2006	2012	2019	2080

and

Table 6. Parameters and accuracy of the logistic fits (source: own elaboration).

Scope	Logistic Fits												Congruence Analysis
-	Midpoint (t50) (Years)				Growth Time (t10 − t90) (Years)				Saturation (k) (Numbers)				R2	p Value 4
-	Value 1	Min 2	Max 2	Error 3	Value 1	Min 2	Max 2	Error 3	Value 1	Min 2	Max 2	Error 3	-	-
Global	2014	2014	2015	0.002	13	11	16	0.19	22,805	20,653	26,793	0.13	0.961	0.00
Korea	2012	2011	2013	0.005	12.5	11	14	0.12	2080	1951	2200	0.06	0.983	0.00

Notes. ¹ Estimated by logistic curve. ² Estimated by the bootstrap method with 95% confidence level. ³ Estimated by the ratio of the average distance of the parameter value from its estimated min and max to the parameter value. ⁴ Model congruence at a 5% significance level. All tests are statistically significant with p-values < 0.05.

present the analytical results of each fit.

The life cycle of EV technology is illustrated in Figure 8. Combining Figure 8 with Table 5 and Table 6 shows that the global EV technology growth time is 13 years, with a midpoint year ($t_{50}$) of 2014. The development phase of the global EV technology began in 2001 and has been declining since 2021. In contrast, the growth time for Korean EV technology was 12.5 years, with a midpoint year ($t_{50}$) in 2012, and it entered the saturation phase in 2019. Compared with the global average, Korea’s technology growth period was relatively short, and it entered the saturation phase two years earlier than the global average; this indicates that Korea engaged in earlier technological development and patent deployment in the EV sector, positioning it ahead of the global average. According to the preceding text, companies should actively explore technologies that hold promising prospects during the technological saturation phase.

Table 6 summarizes the parameters and accuracy of the logistic fit. The results of the congruence analysis demonstrate that the logical curves and bootstrap confidence intervals for EV technology in both global and South Korean contexts minimize forecasting errors and achieve high accuracy. The final two columns display the model’s goodness of fit (R²) and significance levels. All the models demonstrated statistically significant and very high R² values (p < 0.05).

3.3. Social Network Analysis

3.3.1. Social Network Analysis Using IPC Code Co-Occurrence

After IPC co-occurrence filtering, 20 core patent classifications were identified. Figure 9 provides a visual representation of the core IPC classification codes and their interrelationships within the domain of EV technology. Each node represents a unique IPC classification (IPC) code. The nodes’ sizes reflect their occurrence frequency, with larger nodes indicating higher frequencies (in degrees). Large nodes, such as B60W10, B60L53, H02J7, and B60R16, had higher occurrence frequencies. The purple nodes possess high standardized betweenness centrality, for instance, B60W10, B60L53, H02J7, B60R16, and B60L50, suggesting their pivotal roles in the network. Edges represent the co-occurrence relationships between the IPC classification codes, with thicknesses indicating high co-occurrence frequencies. For instance, the connection between B60W10 and B60W20 implies a strong association between the linked IPC codes. The core technical classification codes, technical content, and standardized betweenness centralities are listed in

Table 7. The standardized betweenness centrality of core patents (source: own elaboration).

IPC Main-Group	The Technical Content [96]	Standardized Betweenness Centrality
B60W10	Conjoint control of vehicle sub-units of different types or different functions	0.133101
H02J7	Circuit arrangements for charging or depolarizing batteries or for supplying loads from batteries	0.116794
B60R16	Electric or fluid circuits specially adapted for vehicles and not otherwise provided for	0.10219
B60L50	Electric propulsion with power supplied within the vehicle	0.096273
B60L53	Methods of charging batteries, specially adapted for EVs; charging stations or on-board charging equipment; exchange of energy storage elements in EVs	0.095048
B60K6	Arrangement or mounting of plural diverse prime movers for mutual or common propulsion	0.061831
B60W20	Control systems specially adapted for hybrid vehicles	0.04334
B60L11	Electric propulsion with power supplied within the vehicle	0.043142
B60L58	Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for EVs	0.040302
B60K1	Arrangement or mounting of electrical propulsion units	0.034509
B60L15	Methods, circuits, or devices for controlling the propulsion of electrically propelled vehicles	0.030198
G01R31	Purposes of road vehicle drive control systems not related to the control of a particular sub-unit	0.027657
B60T13	Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; brake systems incorporating such transmitting means	0.027589
B60L7	Electrodynamic brake systems for vehicles in general	0.021699
B60W30	Purposes of road vehicle drive control systems not related to the control of a particular sub-unit	0.02151
G06Q50	Information and communication technology [ICT] specially adapted for the implementation of business processes in specific business sectors	0.017998
B60L3	Electric devices on electrically propelled vehicles for safety purposes; monitoring operating variables	0.017412
H01M10	Secondary cells; manufacture thereof	0.016875
G06Q10	Administration; management	0.014035
B60K17	Arrangement or mounting of transmissions in vehicles	0.013854

.

3.3.2. Trends in Core Technology Applications

Figure 10 presents the technology trends time series graph categorized by year for the 20 core IPC codes identified in Table 5. From the chart, the number of patent applications for IPC code B60L50 (electric propulsion system) steadily increased from 2004 to 2018, followed by a sharp decline after 2019; this indicates that the primary technological developments in EV propulsion systems have matured. It may be inferred that additional technologies surrounding electric propulsion systems have been developed since 2019. Furthermore, IPC codes H01M10 (secondary battery cell) and H02J7 (battery charging circuits) present similar annual trends to B60L50. The patent applications show a year-on-year decline, but relative to other IPC codes, they still have a relatively high number of patent applications.

In contrast, IPC codes B60L53 (battery charging methods) and B60L58 (battery control and monitoring system) had 1942 and 1414 patent applications in 2021, demonstrating significant growth from 2015 to 2021; this suggests ongoing technological advancements in EV batteries and charging infrastructure, shifting the focus from earlier developments in propulsion systems and higher efficiency batteries towards refining charging infrastructure and battery control and monitoring. Additionally, the number of patent applications for IPC codes G06Q10 (EV management system) and G60Q50 (EV information communication technology) increased from 326 and 390 in 2018 to 496 and 567 in 2021, respectively. Despite lower overall numbers, they demonstrate a high growth rate, indicating that these areas are transitioning from early stages to a growth phase in technological development. Therefore, EV management systems and information communication technologies for EVs are promising areas for future technological advancements.

3.3.3. Technology Layouts of Core Applicants

A distribution analysis of their technological layouts was conducted for the 20 core technology areas identified in Table 5 (Figure 11).

The figure shows that Toyota dominates the EV technology sector, holding the most patent applications for several core technologies; this indicates Toyota’s significant investment in EV R and D and notable progress in various core technologies. Building on the previous conclusions, further analysis reveals that Toyota and other companies have extensively developed categories such as B60L50. Although categories H01M10 and H02J7 have fewer applications than B60L50, they still have several patent applications. Combining the previous conclusions, categories such as B60L53, B60L58, G06Q10, and G06Q50 present high growth rates and currently have few patent applications from core applicants, indicating potential areas for further technological research and development.

4. Discussion

4.1. Policy Initiatives Driving EV Adoption

In its 2024 report on the mid- to long-term outlook of the electric vehicle market, the International Energy Agency (IEA) predicts that EVs will account for more than 50% of global new car sales in 2035 [97]. However, in 2023, EVs accounted for only 15% of global new car sales [97], indicating that traditional internal combustion engine vehicles (ICEVs) still dominate sales; this also suggests that EVs still have significant growth potential over the medium to long term. Namely, the EV industry is still in a rapid growth phase; this upward trend is expected to continue as technological advancements and policy measures increasingly favor the adoption of EVs. As mentioned in Section 1, several countries and regions have proposed or implemented policies to promote the development of the EV industry.

In the United States, the Biden administration introduced several policies to accelerate the transition to EVs. One of the key initiatives is the Inflation Reduction Act (IRA) [3], which includes provisions for significant investment in clean energy, including EVs. California has set ambitious targets for reducing GHG emissions and increasing the number of zero-emission vehicles (ZEV) [5] on the road. The state has implemented a mandate that requires all new car sales to reach ZEV by 2035.

Furthermore, California’s ZEV program incentivizes automakers to produce more electric and hydrogen fuel cell vehicles by requiring a certain percentage of their vehicle sales to be zero-emission models. In Korea, the government has implemented various incentives and policies to promote EV adoption, including subsidies, tax benefits, and investments in charging infrastructure [7]. Major Korean automakers like Hyundai and Kia have announced ambitious plans to expand their EV offerings. These efforts align with global trends in which automakers are shifting towards electrification in response to stricter emission regulations and changing consumer preferences. However, the rapid advancement of such policies may intensify competition among EV manufacturers. Influenced by stringent emissions regulations and consumer demand for greener alternatives, several major automakers worldwide are committed to transitioning to fully electric fleets shortly [98]; this implies that more automakers will compete in the EV industry.

4.2. Technological Life Cycle and Market Dynamics in the EV Industry

Previous research [99] indicates that EVs’ current low overall market share is due to the lack of EV products in several market segments. Several segments, such as various vehicle classes, remain largely untapped, with minimal market penetration of EVs. EVs hold a high market share in several high-end price brackets; however, the number of vehicles sold is relatively small. Correspondingly, in already-developed markets, EV sales are compared with those of conventional vehicles, which are the most similar to EVs. By 2020, this ratio exceeded 1, indicating that the market share of EVs had reached over 50% in these developed markets; this aligns with the earlier conclusions that there is still significant growth potential for the EV industry and that EVs are becoming increasingly competitive compared with similar conventional vehicles. However, competition among EV manufacturers intensifies in mature or already developed EV markets. Therefore, to gain a competitive edge in this increasingly competitive environment and tap into more market segments, it is crucial to understand the current technological development stage of the EV industry and propose feasible technological development strategies and research directions for innovation.

Considering the importance of technological innovation for the further development of EVs and the existing literature [48,61,63], research on the stages of EV technological development generally focuses on single regions or countries. However, the current EV industry is characterized by global competition. Therefore, the technology life cycle method was adopted in the third part of this study to assess the technological development stages of the global and South Korean EV industries and propose technological development strategies. The results demonstrate that the global and South Korean EV industries will enter the technology saturation stage in 2021 and 2019, respectively. South Korea’s EV technology is two years ahead of the global average. Fang and Li’s [48,61] previous research indicated that Chinese EV technology and pure EV technology will enter the technology saturation stage by 2026 and 2023, respectively.

Furthermore, it is worth noting that although China’s EV technology started relatively late, the number of invention patents has grown rapidly since 2008; this reflects the significant investments by China and its startups in EV technology development. Comparatively, over half of the EVs sold in 2018 and 2019 were in China [100], indicating that transitioning from conventional vehicles to EVs presents enormous commercial opportunities. Combining these studies, it may be concluded that the existing EV industry technology has entered, or is about to enter, the technology saturation stage. It is imperative for automakers to actively explore promising technologies related to future research; this also corroborates previous discussions that competition among manufacturers in mature EV markets is intensifying, whereas the overall market share of EVs remains low.

4.3. A Comparison with Previous Research

Previous research on the IPC trend analysis by Koo et al. [64] indicated that H01M (processes for converting chemical to electrical energy) and B60L (vehicles in general) were core areas in technological development from 2014 to 2021. Recently, interest in the G06Q code has increased, suggesting its relevance to real-time and sensing keywords. These conclusions are largely consistent with this study’s findings, with the primary difference being that the research by Koo et al. (2023) [64] only extends to the subclass level of IPC codes. In the study by Ma et al. [65], battery-related technologies and fast-charging and charging infrastructure technologies are identified as key areas of development. Although the conclusions of these researchers in recognizing fast-charging and charging infrastructure as critical development areas are consistent with those of this study, there are notable differences. Specifically, research on battery-related technologies has shown further segmentation. The focus on battery performance has declined, while technologies related to battery monitoring and control methods continue to advance. Additionally, this study reveals rapid growth in AI and information and communication technology (ICT) related to EVs. Compared to those in 2016, the technology directions in the electric vehicle industry have undergone further differentiation, with new technologies continuously emerging. This study delves into the main class categories, providing better insights into specific technological development areas and offering perspectives on future trends and innovation potential.

4.4. EV Charging Technology and Infrastructure

The following combines previous research [98,99,100,101,102,103] to further discuss the future sustainable technology areas identified in the result section. B60L53 and B60L58 belong to the B60L category; the former primarily focuses on battery charging technology and related charging infrastructure, while the latter centers on battery monitoring or control methods. Kenneth et al.’s [39] research shows that the average density of fast-charging stations for EVs increased nearly fourfold between 2014 and 2020, explaining the significant increase in patent applications for B60L53 after 2015. Funke et al. [101] pointed out that alleviating consumers’ range anxiety for EVs may be achieved by further improving battery technology to increase the range and ensuring the availability of convenient, widely distributed charging facilities and faster charging technology. Despite the increasing installation rate of charging stations, their prevalence still lags far behind traditional gas stations. For example, in the United States, the limited availability of charging stations in some rural areas makes EV ownership more challenging in places like Washington and Oregon, where the demand for EVs may be lower [98]. As presented in previous studies [102,103], it was indicated that drivers prefer having a range of over 400 miles to handle unexpected contingencies and occasional long-distance trips. However, the latest data on EV ranges [104] show that the Chevrolet Silverado EV, developed by General Motors, has achieved a 400-mile range in testing.

In contrast, the charging time still takes 1 h and 15 min, incomparable to the refueling time of internal combustion vehicles; this means that while the current EV range meets the needs of most drivers, the charging experience still falls short compared to internal combustion vehicles. Additionally, low temperatures can significantly impact the range of EVs. A recent study in the UK found that cold weather can reduce the driving range by up to 30% [105]. This further underscores the need for advancements in fast-charging technology and charging infrastructure. Until the issue of reduced range in low temperatures is resolved, it is foreseeable that traditional internal combustion EVs will continue to have an irreplaceable advantage in cold regions and areas with insufficient charging facilities. The two types of vehicles will continue to maintain a reasonable ratio.

Furthermore, the IEA’s recent mid- to long-term outlook report on EVs mentioned that achieving the 2035 sales forecast will require a six-fold increase in charging stations, reaching 25 million locations [97]; this also explains the recent decline in patent applications for traditional hot categories like H01M10 and H02J7, which focus on battery performance research, and the rapid growth in patent applications related to charging technology and infrastructure. By combining past research and the content of IPC codes, this study concludes that developing faster charging technology and optimizing charging infrastructure and technology are core areas for future technological development.

4.5. EV Charging Technology and Infrastructure and Sustainability Challenges

Based on the discussion in the previous section, we have identified that EV charging technology and infrastructure is indeed a future research direction. However, a critical question remains: Can this direction effectively address the sustainability challenges faced by EVs? This section analyzes this by examining how advancements in this area could potentially address these challenges.

Limited driving range: Based on previous research, the driving range of EVs has already reached 400 miles, meeting the requirements of most drivers [102,103,104]. This means that, aside from cold regions, the sustainability challenge of the limited driving range has been largely overcome through technological advancements [105].

Inadequate charging infrastructure: One of the most pressing challenges is the availability and accessibility of charging stations. Expanding the charging network, particularly in underserved areas, is crucial for the widespread adoption of EVs. To address this sustainability challenge, there is a need to focus further on optimizing the layout of charging stations [106,107] and increasing their density [108,109,110,111].

Long charging times: Charging time has consistently been a critical weakness of electrified transportation technologies. EVs take longer to charge compared to the refueling time of internal combustion engine vehicles, making them less appealing to the public [19,20,21]. This is one of the significant challenges in the sustainable development of EVs. To address this challenge, several technologies have emerged, like ultra-fast charging stations [112,113], wireless charging technology [114,115], and battery-swapping technology [116,117,118].

Overloaded power grids: Overloaded power grids present a significant challenge as the adoption of EVs increases, particularly during peak charging times [15,25,26]. However, several technologies related to charging infrastructure can help mitigate this issue. Smart grids use advanced sensors, communication networks, and automation to manage electricity distribution more efficiently [119]. They can balance the load on the grid by redirecting power to where it is needed most and optimizing the timing of EV charging to avoid peaks [120,121]. Vehicle-to-grid (V2G) systems allow EVs to return power to the grid during peak demand times [122]. This bi-directional energy flow can help stabilize the grid by providing additional power during high-demand periods and absorbing excess energy during low-demand times [122,123].

Greenhouse gas emissions: One of the most significant challenges in the widespread adoption of EVs is the potential for high GHG emissions, particularly if the electricity used to charge EVs is generated from fossil fuels [27,31]. While EVs themselves produce zero emissions during operation, the overall environmental impact depends heavily on the source of the electricity [35]. Transitioning to renewable energy sources such as wind, solar, and hydroelectric power is crucial for reducing the GHG emissions associated with EV charging [120]. As more renewable energy is integrated into the grid, the carbon footprint of EVs decreases [124,125]. Additionally, V2G technology can be leveraged to optimize the use of renewable energy by allowing EVs to store excess power during periods of high renewable energy production and return it to the grid during peak demand, thereby minimizing reliance on fossil fuel power generation while further alleviating pressure on the power grid [126,127,128].

4.6. Battery Monitoring and Management and Sustainability Challenges

Battery monitoring and management play a vital role in addressing the challenge of battery capacity fading. Over time, an EV’s battery capacity decreases with each charge and discharge cycle, eventually necessitating replacement [15]. EVs typically use lithium-ion battery modules connected in parallel and series [129]. An effective battery management system (BMS), along with regular maintenance practices, energy balancing, and state of charge (SoC) balancing, is crucial for optimizing battery life and minimizing the need for replacements [130,131].

4.7. EV Intelligence and ICT Technologies

Next, from the technical content of G06Q10 and G06Q50, belonging to the information and communication technologies (ICT) field, it may be observed that the number of ICT patent applications has significantly increased since 2018. As previously mentioned, the immaturity of charging infrastructure, long charging times, and the resulting range anxiety are considered major obstacles to the adoption of EVs [10,13,14,19,20,21]. However, some studies suggest that an overemphasis on improving EV technology while paying less attention to consumer behavior may also hinder EV adoption. Most research on EVs has focused on charging infrastructure, range, pricing, and government subsidies, while the experiential or hedonic dimensions have largely been overlooked [132]. Prior studies indicate that consumers are willing to pay a premium for vehicle automation and internet connectivity features [133]. Although users still value traditional vehicle parameters, the rapid development of EV technology, AI, and the Internet of Things (IoT) has changed the landscape. The scenario has shifted to one where information and communication technology (ICT) drives the acquisition and retention of car users [132]. Increasingly, customers demand that their vehicles feature smart connectivity functions (SCF) to enhance the user experience [133].

Additionally, achieving remote monitoring, fault prediction, and energy optimization management through big data analysis and intelligent management systems can improve vehicle reliability and user satisfaction [134]. AI can significantly address the challenges faced by EVs in sustainable development, particularly in managing battery capacity degradation. Integrating AI into battery management systems (BMS) enhances the accuracy of battery diagnostics and predictive maintenance, helping to extend battery life and optimize performance [135,136]. Additionally, AI improves the integration of EVs with smart grids (SG), further enhancing the reliability and resilience of smart grid systems [137]. This finding suggests that the future development of EVs will rely on advancements in traditional mechanical and electrical technologies and on integrating advanced information technologies to achieve comprehensive intelligent management.

4.8. Environmental Challenges and Research Limitations in EV Adoption

Despite the rapid growth in EV sales and strong government support, EVs may not be as environmentally friendly as often perceived when considering the full life cycle of their batteries [28,29,30,32,33,34]. Life cycle assessment (LCA) provides a comprehensive view of the total environmental impact, including energy consumption and GHG emissions. Research indicates that plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) show lower energy consumption compared to conventional ICEVs, with reductions of 20 and 30% [138], respectively; this demonstrates that EVs can reduce energy consumption to some extent. In terms of GHG emissions, BEVs exhibit the lowest life cycle emissions. According to the International Council on Clean Transportation (ICCT), BEVs have average emissions that are 66–69% lower than gasoline vehicles [139]. However, this relies on the use of renewable energy for EVs; otherwise, it would still result in significant pollution [27,31,35]. This underscores the importance of integrating renewable energy into the grid [124,125]. Additionally, among the sustainability challenges previously mentioned for EVs, two issues that were not addressed are the environmental impact of mining raw materials for batteries [14,31,36] and battery recycling issues [37,38,39]. The production phase, particularly battery manufacturing, entails significant energy use and environmental impact [140]. Moreover, the extraction of raw materials for batteries poses additional pollution risks [141]. The recycling phase also presents challenges; discarded EV batteries retain 70–80% of their capacity, and improper disposal can cause considerable environmental harm [142].

Therefore, it is crucial to concurrently enhance environmental management efforts while promoting the adoption of EVs. To better address the sustainability challenges faced by EVs, clean production technologies should be employed during manufacturing and raw material extraction. Additionally, research and development in secondary utilization or recycling technologies for batteries are essential.

This study is based on the analysis of patent technology, and its limitations lie in the fact that while patent data can reflect trends in technological development, they cannot fully represent the actual level of technology applied in the market. Future research could also consider analyzing non-patent literature. This study additionally focuses on the overall analysis of the EV field, which may not be sufficiently specific to individual sub-industries. Future research could focus on specific subindustries by combining market application data and user feedback to derive more detailed and in-depth conclusions.

5. Conclusions

This study presents a groundbreaking analysis of patent application data in EV technology, offering critical insights into the EV industry’s current state and future trajectory. By providing a comparative analysis of the global and South Korean markets, this research fills a notable gap in the existing literature, highlighting key differences and strategic advantages that were previously unexplored. The study reveals that the global EV industry entered the technology saturation phase in 2021. Previous research indicates that China is expected to reach technology saturation by 2026 [61]. In contrast, South Korea reached this phase two years earlier, in 2019, demonstrating its advantageous position in the global EV landscape. A distinctive feature of this study is the innovative integration of the S-curve model, SNA, time series analysis, and core applicant layouts. Compared to Ma’s study [65], fast charging and charging infrastructure remain key areas for future research. However, there has been further diversification in the technology directions of the electric vehicle industry. There is now less emphasis on battery performance, while technologies related to battery monitoring and control methods have continued to advance. Artificial intelligence technologies related to EVs have additionally seen rapid application. This aligns with the current sustainability challenges faced by EVs. This comprehensive approach identifies sustainable technological domains and offers a strategic roadmap for future research and development in the EV sector. The analysis points to future research areas that warrant attention:

• Fast-charging infrastructure technology: More efficient and quicker charging technologies and optimized layouts of charging facilities can effectively alleviate range anxiety. For the challenge of overloaded power grids, smart grid technology and V2G systems are also worth focusing on. Additionally, integrating renewable energy into the grid and using V2G technology to optimize the use of renewable energy should be prioritized.
• Battery monitoring and management technology development: The development of battery technology has shifted from a traditionally increasing range to a focus on battery monitoring and management. Moreover, battery monitoring and management technology can optimize battery lifespan, better addressing the challenge of battery capacity degradation faced by EVs.
• Application of communication technology to enhance vehicle intelligence: Further development and application of ICT can enhance the intelligence level of EVs and optimize the user experience. At the same time, optimizing battery management systems and improving the integration of EVs with smart grids (SG) will help us better address the sustainability challenges faced by EVs.

This research provides a deeper understanding of the EV technology life cycle, identifying promising research directions and key entities leading these innovations. The methodological approach of this study offers valuable insights that can inform future technological developments and strategic decisions in the fast-evolving EV sector.

However, this study is not without its limitations.

• The analysis is based on patent data, which may not fully capture all aspects of technological innovation, such as academic papers and news reports.
• The study focuses on global and South Korean markets, which may not fully represent technological trends in other significant regions.
• The use of IPC co-occurrence and betweenness centrality to identify core technologies in the current phase of EV development may be somewhat narrow.

Future research could incorporate additional data sources, such as academic papers and news reports, to provide a more comprehensive understanding of technological advancements. Expanding the analysis to include other major countries and regions could offer a broader perspective on global technological trends. Future studies may employ various evaluation methods, such as patent citations, keyword co-occurrence, and topic modeling, to comprehensively examine both patent and non-patent literature.