| Summary: | 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.
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