摘要： 以新能源车载锂电池为研究对象，建立基于回声状态网络（ESN）预测锂电池的荷电状态（SOC）评估模型。采用交叉验证方法优选回声状态网络参数，以此解决网络模型的参数选择困难。通过带遗忘因子的递归最小二乘法训练建立的回声状态网络模型，实时更新输出权值矩阵以此提高网络的适应性和精度。通过模型仿真分析验证了预测算法的可行性，进一步对比分析了所建立的ESN预测模型与BP神经网络算法、径向基（RBF）网络算法在UDDS、US06和NYCC工况条件下的锂电池SOC评估预测效果，结果表明所建立的回声状态网络模型方法用于锂电池SOC评估预测的性能和效果优于BP算法和RBF算法，具有较好的应用前景，可以为锂电池SOC长期长效预测评估提供参考。

中图分类号：TP305
文献标志码：A
DOI: 10.16157/j.issn.0258-7998.223057
中文引用格式： 杜广波，蔡茂，张鑫，等. 基于ESN的锂电池SOC评估方法与仿真研究[J]. 电子技术应用，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

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