A Review of the Application of Machine Learning in the Optimization of Electric Vehicle Powertrain Systems

Xinxu Li

doi:10.47297/taposatWSP2633-456925.20260708

PDF
Export
Share
Collection
Album

A Review of the Application of Machine Learning in the Optimization of Electric Vehicle Powertrain Systems

Volume 7, Issue 8

Views: 0 Downloads: 0

A Review of the Application of Machine Learning in the Optimization of Electric Vehicle Powertrain Systems
A Review of the Application of Machine Learning in the Optimization of Electric Vehicle Powertrain Systems
College of Intelligent Manufacturing, Guangdong Technology College,Zhaoqing, Guangdong, 526100, China
Vol. 7, Issue 8, Pages: 128-131(2026)
Published：30 August 2026
DOI：10.47297/taposatWSP2633-456925.20260708
Accepted：

Scan QR Code

Li Xinxu.A Review of the Application of Machine Learning in the Optimization of Electric Vehicle Powertrain Systems[J].Theory and Practice of Science and Technology,2026,07(08):128-131.
DOI：

Li Xinxu.A Review of the Application of Machine Learning in the Optimization of Electric Vehicle Powertrain Systems[J].Theory and Practice of Science and Technology,2026,07(08):128-131. DOI： 10.47297/taposatWSP2633-456925.20260708.

摘要

Abstract

Optimization of electric vehicle powertrain systems is a key technical approach to enhancing overall vehicle performance. In recent years

machine learning has been widely applied in this field and has achieved certain results. However

through a review of literature and research in the past decade

this paper discovers a problem that has not been given sufficient attention. Most studies have a tendency of "emphasizing algorithms over ontology"

which makes many research results need to be carefully evaluated for their feasibility. Based on this

this paper puts forward the academic view that "ontology is the core and algorithm is the application" - the value of machine learning depends on its deep integration with physical properties. Finally

this paper offers some suggestions in the hope of providing a reference for the development of this field.

关键词

Keywords

references

Hou Zhenyu. Simulation Analysis and Experimental Study on Transmission Efficiency of Pure Electric Vehicle Transmissions [D]. Wuhan Polytechnic University, 2025.

Chen Feng, Li Weilin, Weng Wenxiang, et al. Analysis and Experimental Study of Transmission Efficiency of Reducers for Electric Vehicles [J]. High-Tech Communications, 2023, 33(2):221-230.

Zhu Xuhui. Research on Matching and Optimization of Pure Electric Vehicle Power Systems [D]. Jiangsu University, 2023.

Niu Qinyu, Li Zhenxi, Wang Zhichao, et al. Parameter Matching and Optimization of Electric Vehicle Powertrain Systems [J]. Mechanical Transmission, 2019, 43(2):129-136.

Wu Tao, Zhao Linlin. Multi-objective Optimization of Gear Transmission in Pure Electric Vehicles [J]. Equipment Machinery, 2024, (4):39-44.

Yuan Haocheng. Research on Efficiency Optimization Strategies for Electric Vehicle Electric Drive Systems [D]. Guizhou University, 2025.

Chen Feng, Li Weilin, He Yinda, et al. Study on Factors Affecting Transmission Efficiency of Reducers for Electric Vehicles [J]. Mechanical Transmission, 2022, 46(9):11-18+83.

Wang Min. Shape Optimization Design of Multi-Stage Gear Transmission Systems Based on Machine Learning under Multiple Operating Conditions [D]. East China Jiaotong University, 2021.

Zhang Kangkang, Xu Liangfei, Hua Jianfeng, et al. Parameter Matching Method for Pure Electric Vehicle Powertrain Based on Multi-Objective Optimization [J]. Automotive Engineering, 2015, 37(7):757-765.

Zhu Yongqi, Huang Kang. Discrete Optimization of Transmission Ratio in Two-Speed AMT Pure Electric Vehicle Transmission [J]. Mechanical Transmission, 2017, 41(11):8-12+77..

He Zhicheng, Xie Zejun, Liu Kan, et al. Collaborative Design and Optimization of Motor Structural Parameters in Pure Electric Vehicle Transmission Systems [J]. Automotive Engineering, 2023, 45(11):2113-2122.

Huang Kang, Liu Zelian, Qiu Mingming, et al. Multi-Target Parameter Decoupling Optimization Method for Two-Speed Pure Electric Vehicles [J]. China Mechanical Engineering, 2020, 31(6):746-755.

Guan Congxin. Research on Energy Management and Mode Switching Strategies for Dual-Motor Multi-Speed Coupling Drive Systems [D]. Hunan University, 2019.

Alert me when the article has been cited

Submit

Optimizing Sparrow Search Algorithm for Energy Consumption Prediction in Commercial Building HVAC Systems

Finite Element Analysis of Node Stability in Prefabricated Buildings

Authorship Patterns and Citation Dynamics in Quantum Physics:A Bibliometric and Machine Learning Analysis

Research on Flat-Band Feature Recognition Model Based on Energy Band Images

A Systematic Review of Machine Learning Architectures for Subscriber Retention and Lifetime Value Estimation: A Cross-Cultural Comparative Study of Global and Chinese SVOD Platforms

Related Author

Jizhe Sun The Fourth Construction Co.,Ltd of China Construction Eighth Engineering Division

Shimeng Zhao The Fourth Construction Co.,Ltd of China Construction Eighth Engineering Division

Fengzhu lv The Fourth Construction Co.,Ltd of China Construction Eighth Engineering Division

Qingshan Wang The Fourth Construction Co.,Ltd of China Construction Eighth Engineering Division

Zhiping Huang The Fourth Construction Co.,Ltd of China Construction Eighth Engineering Division

Zechao Zhang The Fourth Construction Co.,Ltd of China Construction Eighth Engineering Division

Zhengyu Li School of Civil Engineering, Hunan City University

Huijing Huang The Experimental High School Attached to Beijing Normal University

Related Institution

The Fourth Construction Co.,Ltd of China Construction Eighth Engineering Division

School of Civil Engineering, Hunan City University

The Experimental High School Attached to Beijing Normal University

College of Science, China Jiliang University

School of Business Administration, Shandong University of Finance and Economics

⁰