Design and Optimization of an Electric Vehicle Powertrain Based on an Electromechanical Efficiency Analysis

Integrating the electric motor with a multi-speed transmission is an effective way to improve the efficiency and performance of battery electric vehicles (BEVs). This paper innovatively proposes a design method for matching a single-motor and dual-speed dual-clutch transmission (2-Speed Wet DCT) powertrain system and constructs a variable speed efficiency model (VSEM) and constant speed efficiency model (CSEM) for the inverter, motor, and transmission. Research shows that the design parameters of the motor and transmission significantly affect the optimal powertrain system. This study uses an enhanced NSGA-II multi-objective genetic algorithm to optimize the driving performance of energy efficiency and powertrain cost under two different acceleration times (10 s and 12 s), with the key parameters of the motor and transmission as optimization variables and dynamic indicators as constraints, and compares VSEM and CSEM. The optimization results indicate that VSEM have better energy-saving effects than CSEM, with the energy consumption reduced by 3.7% and 3.3% under the two driving performances, respectively. The Pareto frontier further confirms that, for multi-speed transmission systems in electric vehicles, matching a high-power, high-torque motor with a smaller transmission ratio powertrain can achieve higher energy efficiency and thus longer driving range. Additionally, this study quantifies the correlation between energy efficiency and powertrain cost using grey relational analysis (GRA), with a result of 0.77431.

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Publication Details

Journal
Processes
Published
2025-05-29
DOI
https://doi.org/10.3390/pr13061698
Citations
6
Primary Topic
Mechanical Engineering and Vibrations Research
Type
article
Field-Weighted Citation Impact
4.28

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article

Design and Optimization of an Electric Vehicle Powertrain Based on an Electromechanical Efficiency Analysis

Jie Hu, Bowen Zhou, Haichang Wang, Zhejun Li et al.
6 citations
Processes
Mechanical Engineering and Vibrations Research
4.28
article

Design and Optimization of an Electric Vehicle Powertrain Based on an Electromechanical Efficiency Analysis

Jie Hu, Bowen Zhou, Haichang Wang, Zhejun Li, Feng Jiang, Y X Cui
article en
6 citations

Abstract

Integrating the electric motor with a multi-speed transmission is an effective way to improve the efficiency and performance of battery electric vehicles (BEVs). This paper innovatively proposes a design method for matching a single-motor and dual-speed dual-clutch transmission (2-Speed Wet DCT) powertrain system and constructs a variable speed efficiency model (VSEM) and constant speed efficiency model (CSEM) for the inverter, motor, and transmission. Research shows that the design parameters of the motor and transmission significantly affect the optimal powertrain system. This study uses an enhanced NSGA-II multi-objective genetic algorithm to optimize the driving performance of energy efficiency and powertrain cost under two different acceleration times (10 s and 12 s), with the key parameters of the motor and transmission as optimization variables and dynamic indicators as constraints, and compares VSEM and CSEM. The optimization results indicate that VSEM have better energy-saving effects than CSEM, with the energy consumption reduced by 3.7% and 3.3% under the two driving performances, respectively. The Pareto frontier further confirms that, for multi-speed transmission systems in electric vehicles, matching a high-power, high-torque motor with a smaller transmission ratio powertrain can achieve higher energy efficiency and thus longer driving range. Additionally, this study quantifies the correlation between energy efficiency and powertrain cost using grey relational analysis (GRA), with a result of 0.77431.

ProcessesVol. 13(6)
Guangxi University of Science and Technology (CN)
National Science and Technology Major Project, Guangxi Key Research and Development Program
Openalex Percentile: Top 6%
Mechanical Engineering and Vibrations Research
4.28
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