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作 者:顾乃朋 王亚平[2] 杨驹丰 栗欢欢[1] GU Naipeng;WANG Yaping;YANG Jufeng;LI Huanhuan(Automotive Engineering Research Institute of Jiangsu University,Zhenjiang Jiangsu 212013,China;School of Material Science&Engineering,Jiangsu University,Zhenjiang Jiangsu 212013,China)
机构地区:[1]江苏大学汽车工程研究院,江苏镇江212013 [2]江苏大学材料科学与工程学院,江苏镇江212013
出 处:《电源技术》2024年第1期134-142,共9页Chinese Journal of Power Sources
基 金:江苏省自然科学基金面上项目(BK20201426,BK20181163);泰州市科技支撑重点项目(TG202118)。
摘 要:精确的荷电状态(SOC)估算对可靠的电池管理系统来说十分关键。基于二阶等效电路模型,提出了一种加权多新息自适应扩展卡尔曼滤波(WMIAEKF)算法,该算法可以解决传统多新息算法中误差累积的问题,从而提高SOC估算精度。实验仿真结果表明,所提算法比传统的自适应扩展卡尔曼(AEKF)以及多新息自适应扩展卡尔曼(MIAEKF)精度要高,最大误差控制在1.15%以内。此外,基于该算法提出了一种改进的多时间尺度双卡尔曼滤波算法,其中,WMIAEKF用于SOC估算,AEKF用于容量估算,两者结合对电池的SOC和容量进行实时的联合估算。所提算法能够对电池SOC以及容量进行较精确的估计,在新欧洲行驶工况(NEDC)下,SOC估算误差始终控制在1.2%,并且在面对错误容量初始值时也能保持较好的鲁棒性。Accurate SOC estimation is critical for a reliable battery management system.In this paper,based on the double-order polarization(DP)model,a weighted multi-innovation adaptive extended Kalman filter(WMIAEKF)algorithm is proposed,which can solve the problem of error accumulation in the traditional multi-innovation algorithm and thus improve the accuracy of SOC estimation.The simulation results show that the proposed algorithm has higher precision than the traditional AEKF and MIAEKF,and the maximum error is limited within 1.15%.In addition,an improved multi-time scale dual Kalman filtering algorithm is proposed based on this algorithm to jointly estimate the SOC and capacity of the battery in real time,in which WMIAEKF is used for SOC estimation and adaptive expanded Kalman(AEKF)is used for capacity estimation.The results show that the proposed algorithm is able to estimate the battery SOC and capacity more accurately,and the SOC estimation error is always controlled at 1.2%under NEDC conditions;and it maintains good robustness in the face of incorrect initial capacity.
关 键 词:等效电路模型 加权多新息算法 扩展卡尔曼滤波算法 SOC估算
分 类 号:TM912[电气工程—电力电子与电力传动]
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