Study on the State of Health Detection of Power Batteries Based on Adaptive Unscented Kalman Filters and the Battery Echelon Utilization
Yan Xiangwu1, Deng Haoran2, Guo Qi3, Qu Wei1
1. Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid North China Electric Power University Baoding 071003 China; 2. China Automotive Technology & Research Center Tianjin 300162 China; 3. State Grid Hubei Corporation Maintenance Company Wuhan 430000 China
Abstract：It is essential to estimate the state of charge (SOC) and state of health (SOH) of the cell in the electric vehicle li-ion power battery accurately for extending the power battery life and the battery echelon utilization. Based on the Thevenin equivalent circuit model of battery, the adaptive unscented Kalman filter (AUKF) is used to estimate the ohmic resistance and the state of charge in real time. According to the function between the ohmic resistance and the state of health, the state of health can be estimated in real time. The charging and discharging experiments of battery under two different conditions verify the feasibility and accuracy of this method. In addition, through the estimation of SOH of the power battery pack and cells, the unqualified cell can be located, the intact rate of battery can be quantified, and a clear solution for the electric vehicle li-ion power battery echelon utilization can be formulated, which maximizes the resource utilization of waste power batteries.
颜湘武, 邓浩然, 郭琪, 曲伟. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34(18): 3937-3948.
Yan Xiangwu, Deng Haoran, Guo Qi, Qu Wei. Study on the State of Health Detection of Power Batteries Based on Adaptive Unscented Kalman Filters and the Battery Echelon Utilization. Transactions of China Electrotechnical Society, 2019, 34(18): 3937-3948.
[1] Meissner E, Richter G.The challenge to the automotive battery industry: the battery has to become an increasingly integrated component within the vehicle electric power system[J]. Journal of Power Sources, 2005, 144(2): 438-460. [2] Kong SoonNg, Moo Chin-Sien, Chen Yi-Ping, et al. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries[J]. Applied Energy, 2009, 86(9): 1506-1511. [3] 冯飞, 宋凯, 逯仁贵, 等. 磷酸铁锂电池组均衡控制策略及荷电状态估计算法[J]. 电工技术学报, 2015, 30(1): 22-29. Feng Fei, Song Kai, Lu Rengui, et al.Equalization control strategy and SOC estimation for LiFePO4 battery pack[J]. Transactions of China Electro- technical Society, 2015, 30(1): 22-29. [4] Xia Bizhong, Lao Zizhou, Zhang Ruifeng, et al.State-of-charge estimation for lithium-ion batteries based on a nonlinear fractional model[J]. IEEE Transactions on Control Systems Technology, 2016, 25(1): 3-11. [5] 焦东升, 王海云, 朱洁等. 基于离散Fréchet距离的电动汽车电池健康状态诊断方法[J]. 电力系统保护与控制, 2016, 44(12): 68-74. Jiao Dongsheng, Wang Haiyun, Zhu Jie, et al.EV battery SOH diagnosis method based on discrete Fréchet distance[J]. Power System Protection and Control, 2016, 44(12): 68-74. [6] 朱小平, 张涛. 基于自适应理论的锂离子电池SOC估计[J]. 电气技术, 2013, 14(7): 48-51. Zhu Xiaoping, Zhang Tao.New method of SOC estimation for lithium-ion batteries based on self- adaptive system[J]. Electrical Engineering, 2013, 14(7): 48-51. [7] 李晓宇, 徐佳宁, 胡泽徽, 等. 磷酸铁锂电池梯次利用健康特征参数提取方法[J]. 电工技术学报, 2018, 33(1): 9-16. Li Xiaoyu, Xu Jianing, Hu Zehui, et al.The health parameter estimation method for lifepo4battery echelon use[J]. Transactions of China Electro- technical Society, 2018, 33(1): 9-16. [8] Pattipati B, Sankavaram C, Pattipati K R.System identification and estimation framework for pivotal automotive battery management system characteristics[J]. IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, 2011, 41(6): 869-884. [9] 孙雷, 岳云涛. 基于DSP的模糊PD控制算法的研究[J]. 电工技术学报, 2015, 30(1): 465-468. Sun Lei, Yue Yuntao.Research of fuzzy PD control algorithm based on DSP[J]. Transactions of China Electrotechnical Society, 2015, 30(1): 465-468. [10] Chang W Y.Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA[J]. International Journal of Electrical Power & Energy Systems, 2013, 53(4): 603-611. [11] Lee Y S, Wang W Y, Kuo T Y.Soft computing for battery state-of-charge (BSOC)-estimation in battery string systems[J]. IEEE Transactions on Industrial Electronics, 2008, 55(1): 229-239. [12] Taimoor Zahid.动力电池荷电状态估计算法研究[D]. 深圳: 中国科学院大学(中国科学院深圳先进技术研究院), 2018. [13] Alvarez Anton J C, Garcia Nieto P J, Blanco Viejo C, et al. Support vector machines used to estimate the battery state of charge[J]. IEEE Transactions on Power Electronics, 2013, 28(12): 5919-5926. [14] Anton J C A, Nieto P J G, Juez F J D C, et al. Battery state-of-charge estimator using the SVM techni- que[J]. Applied Mathematical Modelling, 2013, 37(9): 6244-6253. [15] 李江, 王义伟, 魏超, 等. 卡尔曼滤波理论在电力系统中的应用综述[J]. 电力系统保护与控制, 2014, 42(6): 135-144. Li Jiang, Wang Yiwei, Wei Chao, et al.A survey on the application of Kalman filtering method in power system[J]. Power System Protection and Control, 2014, 42(6): 135-144. [16] Wang Junping, Guo Jingang, Ding Lei.An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack[J]. Energy Conversion & Management, 2009, 50(12): 3182-3186. [17] Kim J, Lee S, Cho B H.Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction[J]. IEEE Transactions on Power Elec- tronics, 2012, 27(1): 436-451. [18] Dai Haifeng, Wei Xuezhe, Sun Zechang, et al.Online cell SOC Estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV appli- cations[J]. Applied Energy, 2012, 95(2): 227-237. [19] Zhang Caiping, Jiang Jiuchun, Zhang Weige, et al.Estimation of state of charge of lithium-ion batteries used in HEV using robust extended Kalman filtering[J]. Energies, 2012, 5(4): 1098-1115. [20] Schwunk S, Armbruster N, Straub S, et al.Particle filter for state of charge and state of health estimation for lithium-ion phosphate batteries[J]. Journal of Power Sources, 2013, 239(10): 705-710. [21] Takeno K, Ichimura M, Takano K, et al.Quick testing of batteries in lithium-ion battery packs with impedance-measuring technology[J]. Journal of Power Sources, 2004, 128(1): 67-75. [22] Abrahama D P, Knuth J L, Dees D W, et al.Performance degradation of high-power lithium-ion cells-electrochemistry of harvested electrodes[J]. Journal of Power Sources, 2007, 170(2): 465-475. [23] 许巧巧. 锂离子动力电池剩余容量估计算法研究与实现[D]. 重庆: 重庆大学, 2013. [24] Sharkh S A, Doerffel D.Rapid test and non-linear model characterization of solid-state lithium-ion batteries[J]. Journal of Power Sources, 2004, 130(1): 266-274. [25] 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 Elec- trotechnical Society, 2017, 32(15): 180-189. [26] 魏克新, 陈峤岩. 基于多模型自适应卡尔曼滤波器的电动汽车电池荷电状态估计[J]. 中国电机工程学报, 2012, 32(31): 19-26. Wei Kexin, Chen Qiaoyan.Electric vehicle battery SOC estimation based on multiple-model adaptive Kalman filter[J]. Proceedings of the CSEE, 2012, 32(31): 19-26. [27] 刘毅, 谭国俊, 何晓群. 优化电池模型的自适应Sigma卡尔曼荷电状态估算[J]. 电工技术学报, 2017, 32(2): 108-118. Liu Yi, Tan Guojun, He Xiaoqun.Optimized battery model based adaptive sigma Kalman filter for state of charge estimation[J]. Transactions of China Electro- technical Society, 2017, 32(2): 108-118. [28] 陈峤岩. 电动汽车电池状态估计及均衡管理研究[D]. 天津: 天津大学, 2013. [29] Dai Haifeng, Wei Xuezhe, Sun Zechang.A new SOH prediction concept for the power lithium-ion battery used on HEVs[C]//2009 IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, USA, 2009: 1649-1653.
2019, Vol. 34 
