A systematic literature review exploring the application of deep learning in electric vehicles from 2015 to 2025

Engineering notes

Crucially, the scope of this synthesis extends into state-of-the-art frameworks spanning 2025 and 2026, evaluating deep learning’s dual footprint in vehicle-level mechanical safety systems, such as machine learning-driven brake-blending policies optimizing regenerative energy capture and fleet-level performance logistics via neural network-driven predictive maintenance optimization.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Introduction Electric vehicles (EVs) are rapidly gaining popularity and global recognition, driven by their reliability, flexibility, simplicity, and scalability. This paper provides a systematic literature review of research at the intersection of electric vehicles and deep learning, aiming to identify current advancements and explore their potential for future scalability. Methods A total of 92 publications from 2015 to 2025 were included in the final synthesis phase of the review. These works were categorized into five key themes: data-driven research on electric vehicles and deep learning, societal integration of electric vehicles, implications of electric vehicle adoption, software considerations, and challenges and solutions enabled by deep learning. Crucially, the scope of this synthesis extends into state-of-the-art frameworks spanning 2025 and 2026, evaluating deep learning’s dual footprint in vehicle-level mechanical safety systems, such as machine learning-driven brake-blending policies optimizing regenerative energy capture and fleet-level performance logistics via neural network-driven predictive maintenance optimization. Results The findings for each theme and their implications for research and practice are thoroughly discussed. Additionally, a descriptive analysis of research trends shows: (1) a steady increase in publications each year; (2) a majority of contributions originating from China; (3) diverse deep learning approaches being applied to tackle various challenges within the electric vehicle industry; and (4) significant opportunities for the development, testing, and deployment of deep learning technologies and algorithms in the electric vehicle domain. Discussion The findings highlight the growing applicwation of deep learning across the electric vehicle domain and demonstrate significant opportunities for the continued development, testing, and deployment of deep learning technologies and algorithms to support future advancements and scalability in electric vehicles.

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