【摘要】 为了解决纯电动汽车电池剩余电量估算难题,采用粒子群优化神经网络方法,用于BP(BackPropagation,BP)神经网络权值和阈值优化,并把优化后的神经网络用于荷电状态(SOC)离散估算。以100AhLiFePO4电池作为实验对象采集实验数据,将温度、充放电倍率和充放电电压作为PSO-BP(ParticleSwarmOptimization,PSO)神经网络输入特征向量,将电池SOC作为输出向量进行网络学习和训练,用训练好的网络对不同充放电倍率下SOC进行离散点预测,采用插值估算实现实时预测。实验结果表明,PSO-BP算法对SOC值为20%～80%区间估算准确,能够满足电动汽车正常运行的SOC估算要求。更多还原

【Abstract】 To solve the problem of estimating SOC(state of charge) of pure EV, a neural network method based on quantumbehaved particle swarm optimization was used to optimize BP(back propagation, BP) network values and thresholds, and then the optimized neural network was used for SOC discrete estimate. 100 Ah LiFePO4 battery was studied as the subject, taking the temperature, charge-discharge rate and charge-discharge voltage as PSO-BP (Particle swarm optimization) neural network input eigenvectors, with battery SOC as output vector to perform net learning and training. The trained net and interpolation method was used to real-time predict SOC discrete points in different charge-discharge rates. The results show that PSO-BP algorithm can accurately estimate SOC value between 20% and 80%, and meet the requirements to estimate SOC of pure EV.更多还原