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Mapping innovation pathways and future potential of electric cars: Insights from patent data analysis
Journal article   Open access   Peer reviewed

Mapping innovation pathways and future potential of electric cars: Insights from patent data analysis

Mehari Beyene Teshome, M Podrecca and Guido Orzes
Transport Economics and Management, Vol.4, pp.98-115
4
2026
Handle:
https://hdl.handle.net/10863/52310

Abstract

Innovation mapping Low-carbon transport Topic modeling Patent analysis Electric cars Machine Learning
The global push to mitigate climate change and advance sustainable transport has intensified the transition away from fossil-fuel-based mobility. Electric cars have therefore become central to reducing greenhouse gas emissions and supporting low-carbon development. Yet the technological landscape underpinning electric cars is insufficiently mapped, limiting understanding of their innovation pathways. This study addresses this gap through a comprehensive patent analysis of 87,548 patents employing Latent Dirichlet Allocation (LDA) topic modeling methodology to explore the latent technological patterns. Seven core technological domains are identified: electric car structures and design, electric power control, electric car charging methods and infrastructure, battery technology and power supply, intelligent control systems, electric vacuum pumps, and electric drive systems. To assess each domain's current lifecycle stage and future trajectory, forecasting models (Logistic, Gompertz, and Richards) were applied to cumulative patent counts, revealing that foundational domains are approaching saturation while intelligent control systems and electric drive systems still exhibit substantial growth potential. These insights can support researchers, industry, and policymakers in strengthening strategic decisions that accelerate sustainable, efficient, and low-carbon transport development.
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Open Access
url
https://doi.org/10.1016/j.team.2026.05.001View

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