1.重庆大学，汽车协同创新中心，重庆 400044;2.重庆大学，机械传动国家重点实验室，重庆 400044
刘长贺（1995—），男，硕士研究生，主要从事锂离子电池管理系统研究，(E-mail)Changhe_L@163.com。
胡明辉，男，教授，博士生导师，主要从事车辆动力传动及控制研究，(E-mail) minghui_h@163.com。
国家自然科学基金资助项目（52072053）；重庆市重大主题专项（cstc2019jscx-zdztzxX0047）。
1.Automotive Collaborative Innovation Center, Chongqing University, Chongqing 400044, P. R. China;2.State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, P. R. China
Supported by National Natural Science Foundation of China (52072053), and the Major Theme Program of Chongqing Municipality (cstc2019jscx-zdztzxX0047).
电池荷电状态（SOC）的准确估计对延长电池使用寿命、提高电池利用率和保障电池安全性具有重要意义。在不同环境温度下进行了锂离子电池的基本性能试验和动态工况试验，建立了温变双极化等效电路模型。基于该模型，采用H无穷滤波算法代替传统的扩展卡尔曼滤波算法，在无需假设过程噪声和测量噪声均服从高斯分布的前提下，实现了SOC的精确估计。在考虑温变和电池模型存在误差的条件下进行验证，不同温度条件下的SOC估计最大误差保持在±0.03范围内，证明了所提出的SOC估计算法具有较高的温度适应性和鲁棒性。
Accurate state of charge (SOC) estimation of a lithium-ion battery is of great significance for prolonging battery life, improving battery utilization, and ensuring battery safety. An SOC estimation algorithm based on a temperature-dependent dual-polarization equivalent circuit model was established after the basic performance test and dynamic condition test of the lithium-ion battery were performed at different ambient temperatures. The traditional extended Kalman filtering algorithm was replaced by the H-infinity filtering algorithm, and accurate SOC estimation was realized without assuming that the process noise and measurement noise obeyed Gaussian distribution. The proposed model was verified considering the temperature change and the battery model error. The results show that the maximum error of SOC estimation under different temperature conditions can be kept within ±0.03, which proves that the proposed SOC estimation algorithm has higher temperature adaptability and robustness.
刘长贺,胡明辉,李兰.基于温变双极化模型的锂离子电池荷电状态估计[J].重庆大学学报,2023,46(4):13-26.
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