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)