为了提高纯电动汽车电驱总成的故障诊断准确率,提出了一种基于粒子群优化(Particle Swarm Optimizing,PSO)算法的改进BP(Improved Back Propagation,IBP)神经网络(PSO-IBP)故障诊断方法.应用线性整流单元(Rectified Linear Unit,ReLU)作为BP神经网络的激活函数,通过粒子群优化算法对BP神经网络权值和阈值进行动态寻优,构建PSO-IBP模型.通过采集纯电动汽车电驱总成故障数据,分别对PSO-IBP神经网络模型、BP神经网络模型和概率神经网络(Probabilistic Neural Network,PNN)模型进行训练与仿真,结果表明,相比于BP神经网络方法及概率神经网络方法,基于PSO-IBP神经网络模型的纯电动汽车电驱总成故障诊断方法具有更高的准确率.
Fault diagnosis of electric drive assembly of electric vehicle based on PSO-IBP neural network
In order to improve the accuracy of fault diagnosis for the electric drive assembly of pure electric vehicles,a fault diag-nosis 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 elec-tric drive assembly based on PSO-IBP neural network model has higher accuracy.
pure electric vehicleParticle Swarm Optimizing algorithmBP neural networkfault diagnosis
万方数据
维普
