In order to accurately predict the remaining useful life of lithium-ion batteries and re-duce the risk of battery operations,a novel model was proposed for online remaining useful life predic-tion of lithium-ion batteries.On the basis of historical operation data of lithium-ion batteries,six types of health indicators were extracted to characterize the degradation of batteries.The random for-est(RF)algorithm was adopted to evaluate and screen the health indicators.The generalized regres-sion neural network(GA-GRNN),which was optimized by genetic algorithm,was used to estimate the residual capacity of the battery.Then,a hybrid model combining bidirectional long short-term memory(Bi-LSTM)network model and nonlinear autoregressive(NAR)neural network(hybrid Bi-LSTM-NAR model)was used to predict the remaining useful life for lithium-ion batteries.A case study was conducted with the NASA open data.The results show that by way of screening the indica-tors,the accuracy of capacity estimation and remaining useful life prediction of lithium-ion batteries are ensured.Compared with the prediction results of existing methods,the prediction accuracy of the proposed hybrid prediction model is improved effectively.
关键词
锂离子电池/健康因子/神经网络/剩余寿命预测
Key words
lithium-ion battery/health indicator/neural network/remaining useful life predition
维普
万方数据