关键词: 纯电动汽车, 粒子群算法, BP神经网络, 故障诊断

Abstract: In order to improve the accuracy of fault diagnosis for the electric drive assembly of pure electric vehicles,a fault diagnosis method based on Particle Swarm Optimizing (PSO) algorithm was proposed to optimize the Improved Back Propagation (IBP) neural network.The Rectified Linear Unit (ReLU) was used as the activation function for the BP neural network.Through the Particle Swarm Optimizing algorithm,the weights and thresholds of the BP neural network were dynamically optimized to build the PSO-IBP model. By collecting fault data from the electric drive assembly of pure electric vehicles,PSO-IBP model,along with the BP neural network model and the Probabilistic Neural Network (PNN) model,were trained and simulated. The results showed that compared to the BP neural network methods and PNN methods,fault diagnosis method for pure electric vehicle electric drive assembly based on PSO-IBP neural network model has higher accuracy.

Key words: pure electric vehicle, Particle Swarm Optimizing algorithm, BP neural network, fault diagnosis