中国机械工程2016，Vol.27Issue(21)：2975-2981,7.DOI:10.3969/j.issn.1004-132X.2016.21.023
基于PSO-RBF神经网络的锂离子电池健康状态预测
Li-ion Battery SOH Prediction Based on PSO-RBF Neural Network
摘要
Abstract
For the traditional method to hardly estimate the internal parameters of Li-ion battery SOH,a PSO algorithm based on RBF neural network for SOH prediction of Li-ion batteries was pro-posed.Based on the Li-ion battery equivalent model,several key parameters which affected the SOH characteristics of the battery were determined by experimental data of the charged and discharged processes.The test data were input simulation model for network training and verification.Simulation results show that,compared to the BP neural network and the general RBF neural network,the algo-rithm may increase 20% of prediction accuracy,save more than 66.7% of the optimization time.关键词
锂离子电池/健康状况/粒子群优先/径向基函数Key words
Li-ion battery/SOH(state of health)/particle swarm optimization/radical basis func-tion(RBF)分类
信息技术与安全科学引用本文复制引用
张任,胥芳,陈教料,潘国兵..基于PSO-RBF神经网络的锂离子电池健康状态预测[J].中国机械工程,2016,27(21):2975-2981,7.基金项目
国际科技合作专项(2014DFA70980) （2014DFA70980）
浙江省自然科学基金资助项目(LY15E070004) （LY15E070004）
