ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Engine and Automotive Engineering
Power Parameter Allocation of electric vehicles by Integrating Optimized AFSA and System Parameter Classification
Provisionally accepted
- 1Huanghe Jiaotong University, Zhengzhou, China
- 2Henan University of Science and Technology, Luoyang, China
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Traditional methods for allocating power parameters of electric vehicles are prone to getting stuck in local optima, making it difficult to meet the increasing performance demands. To improve the accuracy of power parameter allocation for electric vehicles, a new energy vehicle power parameter allocation method that integrates optimized Artificial Fish Swarm Algorithm (AFSA) and system parameter classification is built. After constructing a parameter classification model based on sensitivity analysis, the improved AFSA is used for parameter optimization. The electric vehicles based on sensitivity analysis system power parameter classification exhibited good performance. When the main reduction ratio was 11:1, the high-speed re-acceleration time was 5.7 seconds. When the coolant flow rate was 7 L/min, the peak power duration was 30.1 seconds. Compared to other methods, the comprehensive energy consumption based on the improved AFSA for power parameter allocation was the lowest. When the battery capacity was 80kWh, the comprehensive energy consumption was 13.8 kWh/100km. The method effectively overcomes the shortcomings of traditional methods that are prone to falling into local optima, significantly improving the power performance and energy efficiency of electric vehicles.
Keywords: AFSA, parameter allocation, Electric Vehicles, Power parameters, sensitivity analysis
Received: 10 Sep 2025; Accepted: 27 Nov 2025.
Copyright: Âİ 2025 Liu, Guo and Yao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Liu
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