中圖分類號：TP305 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223057 中文引用格式： 杜廣波,，蔡茂，張鑫,，等. 基于ESN的鋰電池SOC評估方法與仿真研究[J]. 電子技術(shù)應(yīng)用,，2023,，49(1)：45-51. 英文引用格式： Du Guangbo,，Cai Mao，Zhang Xin,，et al. Research on SOC evaluation method and simulation of lithiumbattery based on echo state network[J]. Application of Electronic Technique,，2023，49(1)：45-51.
Research on SOC evaluation method and simulation of lithiumbattery based on echo state network
Du Guangbo1,，Cai Mao2,，Zhang Xin2，F(xiàn)an Xingming2,，Cheng Jianghua1
1.China United Engineering Corporation Limited,， Hangzhou 310052， China,； 2.Dep.of Electrical Engineering & Automation,， Guilin University of Electronic and Technology ，Guilin 541004,， China
Abstract： Taking lithium battery of new energy vehicles as the research object,an echo state network (ESN) model is established to predict the state of charge (SOC) of the vehicle's lithium battery. The cross-validation method is used to optimize the parameters of the ESN to solve difficulty to select arameters of the model. The echo state network is trained by recursive least squares method with forgetting factors to calculate the output weight matrix so as to improve the adaptability and accuracy of the network.The feasibility of the prediction algorithm is further analyzed and verified by the model simulation. The research further analyzes and compares the predicted SOC of the established ESN model, the BP neural network algorithm and radial basis function (RBF) network algorithm under UDDS, US06 and NYCC. The research results show that the established echo state network model is superior to the BP algorithm and RBF algorithm in estimating the performance and effect of lithium-ion battery SOC evaluation. Using ESN model to predict SOC has a good application prospect and can provide a reference for long-term and effective SOC prediction of the lithium battery.
Key words : lithium battery,；state of charge；echo state network,；parameters optimization and selection,；cross validation
