The global transition toward sustainable energy and environmental protection has positioned the electric vehicle (EV) industry as a pivotal sector for innovation and growth. In recent years, the electric vehicle market has experienced rapid expansion in terms of technological advancements, market penetration, and industrial scale. For instance, China’s electric vehicle production and sales have consistently led the world, with 2024 figures reaching 12.888 million and 12.866 million units, respectively, representing year-on-year growth of 34.4% and 35.5%, and accounting for 40.9% of total new car sales. This underscores the industry’s robust development and its critical role in the global automotive landscape. Scholars worldwide have extensively studied technological innovations, market dynamics, and policy support in the electric vehicle domain, yielding significant insights. However, existing research often focuses on either literature or patent analysis in isolation, with limited integration of both perspectives. Patents serve as key indicators of technological innovation, and their combined analysis with academic literature can provide a more comprehensive view of industry trends. This study employs CiteSpace and Gephi tools to analyze academic literature and patent data, respectively, systematically summarizing the current state of electric vehicle research and identifying emerging trends. By integrating these analyses, we aim to uncover technological evolution pathways, pinpoint key areas, and forecast future directions, thereby offering scientific support for policy-making and corporate strategy.
The data for this study were sourced from the Web of Science Core Collection for international literature and the China National Knowledge Infrastructure (CNKI) for domestic literature, covering the period from 2015 to 2024. For patent analysis, the IncoPat database was utilized, with a focus on invention patents granted in China and the United States. CiteSpace was employed for keyword clustering and burst detection in literature, while Gephi facilitated network analysis of patent co-occurrence matrices. The clustering results for domestic and international literature revealed distinct research priorities. In China, clusters centered on power batteries, charging piles, electric vehicles, and industrial chains, highlighting policy-driven concerns such as cost control and infrastructure. Internationally, clusters like cathode materials, electric vehicle charging, hybrid electric vehicles, and edge computing emphasized foundational research and system integration. Burst keyword analysis further indicated that China’s research frontiers include subsidy policies, evolutionary game theory, carbon neutrality, and thermal runaway, whereas international trends focus on high-energy-density materials, solid electrolyte interphases, and fault diagnosis. These differences reflect China’s macro-level policy orientation versus the international community’s emphasis on material innovation and intelligent systems.
Patent analysis using Gephi examined the technological networks in China and the U.S. across three phases: 2005–2009 (exploration), 2010–2018 (rapid growth), and 2019–2024 (maturation). Key network metrics, such as density, average degree, and clustering coefficient, were calculated to assess structural evolution. In China, network density decreased from 0.262 to 0.100, and average degree fluctuated, indicating a shift toward diversified and niche technologies. The U.S. showed higher density and average degree in the middle phase, reflecting intense innovation activity. Core patent technologies were categorized, with China excelling in electrode materials, battery manufacturing, and fuel cells, while the U.S. led in battery management, control systems, and communication technologies. Burst analysis of patents highlighted China’s focus on H01M10/40 (battery technology) and B23K37/00 (welding materials), whereas the U.S. emphasized G05D1/00 (vehicle control) and H01M10/48 (battery construction). These findings illustrate China’s strength in application-driven engineering and the U.S.’s prowess in systemic intelligence and control.
To quantify the research and patent trends, we introduce mathematical representations. For literature analysis, the burst strength of keywords can be modeled using a probability distribution function. Let \( B(t) \) represent the burst strength of a keyword at time \( t \), which can be expressed as:
$$ B(t) = \alpha \cdot e^{-\beta (t – t_0)^2} $$
where \( \alpha \) is the maximum burst intensity, \( \beta \) controls the width of the burst period, and \( t_0 \) is the peak time. This Gaussian-like function captures the temporal dynamics of research hotspots. Similarly, for patent network analysis, the clustering coefficient \( C \) of a node in the Gephi network is defined as:
$$ C_i = \frac{2e_i}{k_i(k_i – 1)} $$
where \( e_i \) is the number of edges between the neighbors of node \( i \), and \( k_i \) is the degree of node \( i \). The average clustering coefficient for the entire network is then:
$$ \bar{C} = \frac{1}{N} \sum_{i=1}^{N} C_i $$
where \( N \) is the total number of nodes. This metric helps assess the tendency of nodes to form clusters, indicating technological convergence.
The following table summarizes the key network metrics for China and the U.S. across the three phases, derived from Gephi analysis:
| Country | Phase (Years) | Network Density | Average Degree | Average Path Length | Average Clustering Coefficient |
|---|---|---|---|---|---|
| China | 2005–2009 | 0.262 | 26.962 | 2.057 | 0.708 |
| 2010–2018 | 0.238 | 29.226 | 2.272 | 0.731 | |
| 2019–2024 | 0.100 | 10.855 | 3.526 | 0.690 | |
| U.S. | 2005–2009 | 0.262 | 27.000 | 1.987 | 0.689 |
| 2010–2018 | 0.312 | 35.530 | 1.811 | 0.699 | |
| 2019–2024 | 0.185 | 26.127 | 2.214 | 0.704 |
Another critical aspect is the categorization of core technologies based on IPC codes. The table below outlines the core patent technologies for China and the U.S., highlighting their respective focuses:
| Core Technology Category | Core Patent Technologies (IPC Codes) – China | Core Patent Technologies (IPC Codes) – U.S. |
|---|---|---|
| Electrode Materials and Structural Innovation | H01M4/92, H01M4/58, H01M4/88, H01M4/48, H01M4/86, H01M4/02, H01M4/1397, H01M4/505, H01M4/62, H01M4/36, H01M4/526 | H01M10/36, H01M10/46, H01M4/58, H01M4/13, H01M4/02, H01M4/36, H01M10/0525, H01M10/52, H01M10/42, H01M10/48 |
| Electrolyte and Electrolyte Technology | H01M8/02, H01M8/04, H01M8/10 | N/A (Not a primary focus) |
| Battery Structure Design and Manufacturing | H01M10/36, H01M10/04, H01M10/06, H01M10/40, H01M10/052, H01M10/54 | B60K6/445, B60K1/00, F16H3/72, F16H3/62 |
| Fuel Cell Key Technologies | H01M2/02, H01M2/10 | F02B33/44, F02M25/07 |
| High-Performance Additives and Polymers | C08K13/02, C08K7/00 | N/A (Not a primary focus) |
| Battery Management and Testing | G01M17/007 | G06F19/00, G06F7/00, G06F17/00, G08G1/16, G05D1/02, G05D1/00 |
| Electric Vehicle and Charging Technology | N/A (Not a primary focus) | B60L3/00, B60L11/18, H02J7/00, H02J5/00, H02J7/02 |
| Communication and Network Technology | N/A (Not a primary focus) | H04B5/00 |
The evolution of electric vehicle technologies can also be described using growth models. For instance, the logistic growth model captures the adoption rate of innovations:
$$ N(t) = \frac{K}{1 + \left( \frac{K – N_0}{N_0} \right) e^{-rt}} $$
where \( N(t) \) is the number of patents or publications at time \( t \), \( K \) is the carrying capacity (maximum potential), \( N_0 \) is the initial value, and \( r \) is the growth rate. Applying this to China EV patent data, we observe an initial slow growth, followed by exponential expansion, and eventual saturation, aligning with the phases identified in the patent trend analysis.
In conclusion, the integration of literature and patent analyses reveals distinct trajectories for China and the U.S. in the electric vehicle industry. China’s research and patent activities are characterized by policy-driven initiatives and rapid industrialization, with strengths in battery manufacturing and materials. In contrast, the U.S. demonstrates a focus on foundational innovations, intelligent systems, and control technologies. The mathematical models and network metrics provide quantitative support for these observations. Moving forward, China should enhance its core technology R&D, particularly in areas like smart control and materials science, while fostering international collaboration to bridge gaps in systemic innovation. The electric vehicle sector, especially China EV markets, must prioritize sustainability and digital integration to maintain global competitiveness. This study underscores the importance of holistic analysis in guiding future research and policy decisions for the evolving electric vehicle landscape.
Furthermore, the burst analysis of keywords and patents indicates that emerging trends in the electric vehicle domain are increasingly interdisciplinary. For example, the integration of artificial intelligence in battery management systems can be modeled using optimization algorithms. Consider a typical problem of minimizing charging time while maximizing battery lifespan, which can be formulated as:
$$ \min_{x} f(x) = \alpha \cdot T_{\text{charge}} + \beta \cdot \frac{1}{L_{\text{lifespan}}} $$
subject to constraints such as voltage limits and temperature ranges, where \( x \) represents control variables, \( T_{\text{charge}} \) is charging time, \( L_{\text{lifespan}} \) is battery lifespan, and \( \alpha, \beta \) are weighting factors. This approach highlights the role of computational methods in advancing electric vehicle technologies.
Overall, the electric vehicle industry is poised for continued growth, driven by innovations in China EV production and global technological synergies. The insights from this study can inform stakeholders in academia, industry, and government to strategically allocate resources and foster collaborations that accelerate the transition to sustainable transportation.