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汽车安全与节能学报

汽车安全与节能学报 ›› 2025, Vol. 16 ›› Issue (2): 277-285.DOI: 10.3969/j.issn.1674-8484.2025.02.011

• 汽车节能与环保 • 上一篇    下一篇

新能源汽车驱动电机冷却系统劣化故障预测

柳炽伟1(), 黄韵迪2   

  1. 1.中山职业技术学院 机电工程学院，中山 528403，中国
    2.中山职业技术学院 信息工程学院，中山 528403，中国
  • 收稿日期:2024-11-16 修回日期:2024-12-05 出版日期:2025-04-30 发布日期:2025-04-22
  • 作者简介:柳炽伟（1970—），男（汉），广东，副教授。E-mail：luke1011@sohu.com。
  • 基金资助:
    广东省教育厅特色创新科研项目(2023KTSCX367);中山职业技术学院科研项目(KYB2303)

Deterioration fault prediction of the drive-motor cooling-system for new energy vehicles

LIU Chiwei1(), HUANG Yundi2   

  1. 1. Institute of Mechanical and Electrical Engineering, Zhongshan Polytechnic, Zhongshan 528404, China
    2. Institute of Information Engineering, Zhongshan Polytechnic, Zhongshan 528404, China
  • Received:2024-11-16 Revised:2024-12-05 Online:2025-04-30 Published:2025-04-22

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摘要：

提出一种主成分分析及粒子群优化支持向量机（PCA-GOA-LSSVM）的多分类器模型，用于尽早检测和预测新能源汽车驱动电机冷却系统的劣化，减少因冷却液温度过高导致的电机功率限制或停机状况的发生。其中主成分分析法（PCA）用于对故障特征进行降维重构处理，蝗虫算法（GOA）用来优化最小二乘支持向量机（LSSVM）的参数。通过实车故障试验采集样本数据，分别输入至LSSVM预测模型、PCA-PSO-SVM及PCA-GOA-LSSVM模型，进行对比测试。结果表明：基于PCA-GOA-LSSVM的多分类器预测模型准确率达91.41%、精确率达86.25%，高于对比的预测模型，可准确提醒及时维护车辆及有效判断故障类型；该模型能够用于新能源汽车驱动电机冷却系统性能劣化预测和故障诊断中。

关键词: 新能源汽车, 驱动电机冷却系统, 故障预测, 最小二乘支持向量机（LSSVM）, 蝗虫算法（GOA）, 主成分分析（PCA）

Abstract:

A multi-classifier model of Principal-Component-Analysis and the Particle-Swarm-Optimization Support-Vector-Machine (PCA-GOA-LSSVM) was proposed to detect and predict the deterioration of the cooling system of the drive motor of new energy vehicles as early as possible and reduce the occurrence of motor power limit or shutdown caused by excessive coolant temperature. The Principal Component Analysis (PCA) method was used to reduce the dimensionality and reconstruct the fault characteristics. The Grasshopper Optimization Algorithm (GOA) was used to optimize parameters of Least Square Support Vector Machine (LSSVM). The sample data collected from the real vehicle fault test, were respectively input to the LSSVM prediction model, (PCA-PSO-SVM), and the PCA-GOA-LSSVM models for comparison testing. The results show that for the multi-classification prediction model based on PCA-GOA-LSSVM, the accuracy reaches 91.41% with a precision of 86.25%, which is higher than the compared prediction model. The model can be used in the performance deterioration prediction and fault diagnosis of the cooling system of the drive motor of new energy vehicles, and can accurately remind to maintain the vehicle timely and effectively judge the fault type.

Key words: new energy vehicles, drive-motor cooling-system, fault prediction, least squares support vector machine (LSSVM), grasshopper optimization algorithm (GOA), principal component analysis (PCA)

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