基于综合型卡尔曼滤波的锂离子电池荷电状态估算

doi:10.19595/j.cnki.1000-6753.tces.171560

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (15818 KB)
输出: BibTeX | EndNote (RIS)

摘要

针对锂离子电池的荷电状态（SOC）估算问题,给出一种综合型卡尔曼滤波算法。该算法采用递推最小二乘算法（RLS）对锂离子电池模型参数进行实时在线辨识和参数更改;采用综合型卡尔曼滤波器估计电池SOC,即针对模型状态空间方程中的线性部分和非线性部分,分别使用线性卡尔曼滤波器（KF）和平方根高阶容积卡尔曼滤波器（SHCKF）计算。两种卡尔曼滤波器结合的综合型策略能够有效减小计算复杂度。其中,SHCKF结合了五阶球面-径向容积法则和平方根滤波技术,比扩展卡尔曼滤波器（EKF）、无迹卡尔曼滤波器（UKF）和容积卡尔曼滤波器（CKF）等传统非线性滤波器的估计精度更高,数值稳定性更强。实验结果证明了该综合型算法的可行性和有效性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	谷苗
	夏超英
	田聪颖

关键词 ：电池, 荷电状态, 平方根高阶容积卡尔曼滤波, 综合型卡尔曼滤波

Abstract：

A comprehensive Kalman filtering algorithm is given to estimate the state of charge (SOC) of Li-ion battery. The battery model parameters are identified and modified by recursive least squares algorithm (RLS) in real time, and a comprehensive Kalman filter is applied to estimate the battery SOC, namely a linear Kalman filter (KF) and a square-root high-degree cubature Kalman filter (SHCKF) are used to process the linear part and nonlinear part of the model respectively. The combination of two filters efficiently reduces the computation complexity. By adopting the 5th-degree spherical-radial cubature quadrature rule and square root filtering technology, SHCKF achieves higher estimation accuracy and strong numerical stability than traditional nonlinear filters, such as extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF). The experimental results prove the feasibility and effectiveness of the proposed algorithm.

Key words： Battery state of charge (SOC) square-root high-degree cubature Kalman filter (SHCKF) comprehensive Kalman filter

收稿日期: 2017-11-16 出版日期: 2019-01-29

PACS:

TM912

通讯作者: 谷苗女,1993年生,硕士研究生,研究方向为电动汽车电池管理系统设计。E-mail: 2016203010@tju.edu.cn

作者简介: 夏超英男,1958年生,教授,博士生导师,研究方向为控制理论与应用、自适应控制系统、电力电子技术及装置、电动汽车、混合动力汽车等。E-mail: xiachaoying@126.com

引用本文:

谷苗, 夏超英, 田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2019, 34(2): 419-426. Gu Miao, Xia Chaoying, Tian Congying. Li-ion Battery State of Charge Estimation Based on Comprehensive Kalman Filter. Transactions of China Electrotechnical Society, 2019, 34(2): 419-426.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.171560 https://dgjsxb.ces-transaction.com/CN/Y2019/V34/I2/419

[1] Aylor J H, Thieme A, Johnso B W.A battery state- of-charge indicator for electric wheelchairs[J]. IEEE Transactions on Industrial Electronics, 1992, 39(5): 398-409.
[2] Rémy Mingant, Bernard J, Moynot VS, et al. EIS measurements for determining the SOC and SOH of Li-ion batteries[J]. ECS Transactions, 2011, 33(39): 41-53.
[3] Liu R H, Sun Y K, Ji X F.Battery state of charge estimation for electric vehicle based on neural network[C]//IEEE 3rd International Conference on Information and Computer Networks, Xi'an, China, 2011:493-496.
[4] Álvarez Antón J C, García Nieto P J, de Cos Juez F J, et al. Battery state-of-charge estimator using the SVM technique[J]. IEEE Transactions on Power Electronics, 2013, 37(9): 6244-6253.
[5] Chen Xiaopeng, Shen Weixiang, Cao Zhenwei, et al.A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles[J]. Journal of Power Sources, 2014, 246(3): 667-678.
[6] Xu Jun, Mi C C, Cao Binggang, et al.The state of charge estimation of lithium-ion batteries based on a proportional-integral observer[J]. IEEE Transactions on Vehicular Technology, 2014, 63(4): 1614-1621.
[7] Plett G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 3, state and parameter estimation[J]. Journal of Power Sources, 2004, 134: 277-292.
[8] Julier S, Uhlmann J, Durrantwhyte H F.A new method for the nonlinear transformation of means and covariances in filters and estimators[J]. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482.
[9] 张金龙, 魏艳君, 李向丽, 等. 基于模型参数在线辨识的蓄电池SOC估算[J]. 电工技术学报, 2014, 29(增刊1): 23-28.
Zhang Jinlong, Wei Yanjun, Li Xiangli, et al.Battery SOC estimation based on online parameter identi- fication[J]. Transactions of China Electrotechnical Society, 2014, 29(S1): 23-28.
[10] 魏克新, 陈峭岩. 基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J]. 中国电机工程学报, 2014, 34(3): 445-452.
Wei Kexin, Chen Qiaoyan.States estimation of Li-ion power batteries based on adaptive unscented Kalman filters[J]. Proceedings of the CSEE, 2014, 34(3): 445-452.
[11] 张金龙, 佟微, 漆汉宏, 等. 平方根采样点卡尔曼滤波在磷酸铁锂电池组荷电状态估算中的应用[J]. 中国电机工程学报, 2016, 36(22): 6246-6253.
Zhang Jinlong, Tong Wei, Qi Hanhong, et al.Application of square root sigma point Kalman filter to SOC estimation of LiFePO₄ battery pack[J]. Proceedings of the CSEE, 2016, 36(22): 6246-6253.
[12] Arasaratnam I, Haykin S.Cubature Kalman filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269.
[13] Sun Dong, Chen Xikun.Adaptive parameter identification method and state of charge estimation of lithium ion battery[C]//The 17th International Conference on Electrical Machines and Systems, Hangzhou, China, 2014: 855-860.
[14] 程泽, 吕继考, 刘继光, 等. 等效滞回模型在锂离子电池SOC估计中的应用[J]. 湖南大学学报: 自然科学版, 2015, 42(4): 63-70.
Cheng Ze, Lü Jikao, Liu Jiguang, et al.Application of equivalent hysteresis model in estimation of state of charge of lithium-ion battery[J]. Journal of Hunan University: Natural Sciences, 2015, 42(4): 63-70.
[15] 于智龙, 郭艳玲, 王海英. 自放电修正的锂动力电池SCKF-STF的SOC估算策略[J]. 电机与控制学报, 2013, 17(10): 70-76.
Yu Zhilong, Guo Yanling, Wang Haiying.Research on state of charge estimation of Li-ion battery based on SCKF-STF[J]. Electric Machines and Control, 2013, 17(10): 70-76.
[16] Tulsyan A, Tsai Y, Gopaluni R B, et al.State- of-charge estimation in lithium-ion batteries: a particle filter approach[J]. Journal of Power Sources, 2016, 331: 208-223.
[17] 程泽, 孙幸勉, 程思璐. 一种锂离子电池荷电状态估计与功率预测方法[J]. 电工技术学报, 2017, 32(15): 180-189.
Cheng Ze, Sun Xingmian, Cheng Silu.Method for estimation of state of charge and power prediction of lithium-ion battery[J]. Transactions of China Electro- technical Society, 2017, 32(15): 180-189.
[18] Zhang Jinlong, Xia Chaoying.Application of statistical parameter identification in battery management system[C]//The 8th World Congress on Intelligent Control and Automation, Jinan, China, 2010: 5938-5942.
[19] Jia Bin, Xin Ming, Cheng Yang.High-degree cubature Kalman filter[J]. Automatica, 2013, 49(2): 510-518.