基于自适应H∞观测器的锂电池SOC与容量联合估计

doi:10.3969/j.issn.1000-1158.2024.08.17

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Abstract In order to improve the estimation accuracy of lithium battery SOC, based on adaptive H∞ observer a joint estimation method of lithium battery SOC and capacity is proposed. Based on the second-order RC equivalent circuit model of lithium battery, and the coupling relationship between SOC and capacity is considered, the capacity of lithium battery is also observed as the state variable of the system. An adaptive H∞ observer is designed. The parameters of the observer can be adjusted adaptively with the state change of lithium battery. Since the influence of capacity is considered in SOC estimation, the adaptive H∞ observer can realize the simultaneous accurate estimation of SOC and capacity. The experimental results show that the adaptive H∞ observer has high estimation accuracy and strong robustness. The average SOC estimation error of the battery is always within 0.43%. Compared with EKF and H∞ observers, the adaptive H∞ observer has higher estimation accuracy and stability.

Key words： electrical measurement;lithium battery state of charge;capacity estimation;adaptive law;adaptive H&infin observer;joint estimation

Received: 16 October 2023 Published: 05 September 2024

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TB971

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	WU Zhongqiang
	CHEN Haijia

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WU Zhongqiang,CHEN Haijia. Joint SOC and Capacity Estimation of Lithium Battery Based on Adaptive H∞ Observer[J]. Acta Metrologica Sinica, 2024, 45(8): 1209-1215.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.08.17 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I8/1209

［6］	吴忠强, 尚梦瑶, 申丹丹, 等. 基于神经网络和MS-AUKF算法的蓄电池荷电状态估计［J］. 中国电机工程学报, 2019, 39(21): 6336-6344.
［8］	庞辉, 郭龙, 武龙星, 等. 考虑环境温度影响的锂离子电池改进双极化模型及其荷电状态估算［J］. 电工技术学报, 2021, 36(10): 2178-2189.
［14］	乌江, 张燕, 刘泽龙, 等. 考虑驾驶工况及老化程度的锂电池荷电状态估算［J/OL］. 上海交通大学学报: 1-21［2023-07-26］.
［5］	罗勇, 祁朋伟, 黄欢, 等. 基于容量修正的安时积分SOC估算方法研究［J］. 汽车工程, 2020, 42(5): 681-687.
［17］	王晓兰, 靳皓晴, 刘祥远. 基于融合模型的锂离子电池荷电状态在线估计［J］. 工程科学学报, 2020, 42(9): 1200-1208.
［23］	毕贵红, 谢旭, 蔡子龙, 等. 动态条件下基于深度学习的锂电池容量估计［J］. 汽车工程, 2022, 44(6): 868-877.
［4］	陈德海, 马原, 潘韦驰. 改进PSO-RBF模型的分阶查表法荷电状态估计［J］. 储能科学与技术, 2019, 8(6): 1190-1196.
［7］	吴忠强, 胡晓宇, 马博岩, 等. 基于RFF及GWO-PF的锂电池SOC估计［J］. 计量学报, 2022, 43(9): 1200-1207.
［11］	李昆, 赵理 , 赵博阳, 等. 基于频繁项统计的流-安时积分SOC估计方法［J］. 重庆理工大学学报(自然科学), 2022, 36(3): 19-27.
［13］	张照娓, 郭天滋, 高明裕, 等. 电动汽车锂离子电池荷电状态估算方法研究综述［J］. 电子与信息学报, 2021, 43(7): 1803-1815.
［18］	陈峥, 李磊磊, 舒星, 等. 基于改进容量增量分析法的锂电池可用容量估计［J］. 中国公路学报, 2022, 35(8): 20-30.
［2］	ZHENG Y, OUYANG M, HAN X, et al. Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles［J］. Journal of Power Sources, 2018, 377: 161-188.
	CHEN D H, MA Y, PAN W C. State of charge estimation by stepwise look-up table method based on improved PSO-RBF model ［J］. Energy Storage Science and Technology, 2019, 8(6): 1190-1196.
	LUO Y, QI P W, HUANG H, et al. Research on Ampere-hour Integrated SOC Estimation Method Based on Capacity Correction［J］. Automotive Engineering, 2019, 42(5): 681-687.
［10］	耿萌萌, 范茂松, 杨凯, 等. 基于EIS和神经网络的退役电池SOH快速估计［J］. 储能科学与技术, 2022, 11(2): 673-678.
	LI K, ZHAO L, ZHAO B Y, et al. Current-ampere-hour integral SOC estimation method based on frequent term statistics ［J］. Journal of Chongqing University of Technology (Natural Science), 2022, 36(3): 19-27.
［16］	ZHANG S, WAN Y, DING J, et al. State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H∞ Filtering［J］. Energies, 2021, 14(6): 1655.
［19］	郑涛, 张里, 侯杨成, 等. 基于自适应CKF的老化锂电池SOC估计［J］. 储能科学与技术, 2020, 9(4): 1193-1199.
［24］	吴忠强, 王国勇, 谢宗奎, 等. 基于WOA-UKF算法的锂电池容量与SOC联合估计［J］. 计量学报, 2022, 43(5): 649-656.
［3］	SUN X, CHEN Q, ZHENG L, et al. Joint estimation of state-of-health and state-of-charge for lithium-ion battery based on electrochemical model optimized by neural network［J］. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2022, 4(1): 168-177.
	PANG H, GUO L, WU L X, et al. An improved dual-polarization model for Li-ion batteries considering the influenceof ambient temperature and its state of charge estimation［J］. Transactions of China Electrotechnical Society, 2021, 36(10): 2178-2189.
	WU J, ZHANG Y, LIU Z L, et al. Consider the driving condition and aging degree of lithium battery charged state estimation ［J/OL］. Journal of Shanghai jiaotong university: 1-21 ［2023-07-26］.
	WANG X L, JIN H Q, LIU X Y. Online estimation of lithium-ion battery state of charge based on fusion model［J］. Chinese Journal of Engineering Sciences, 2019, 42(9): 1200-1208.
［20］	GUO P, CHENG Z, YANG L. A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction［J］. Journal of Power Sources, 2019, 412: 442-450.
［21］	MA Z, YANG R, WANG Z. A novel data-model fusion state-of-health estimation approach for lithium-ion batteries［J］. Applied energy, 2019, 237: 836-847.
	LAI X, QIN C, ZHENG Y J, et al. Adaptive capacity estimation method of lithium-ion battery based on voltage characteristic points of constant current charging curve ［J］. Automotive engineering, 2019, 9 (1) : 1-6 .
	BI G H, XIE X, CAI Z L, et al. Capacity estimation of lithium batteries under Dynamic conditions based on Deep Learning ［J］. Automotive Engineering, 2022, 44(6): 868-877.
［1］	TIAN H, QIN P, LI K, et al. A review of the state of health for lithium-ion batteries: Research status and suggestions［J］. Journal of Cleaner Production, 2020, 261: 120813.
	WU Z Q, HU X Y, MA B Y, et al. SOC estimation of lithium battery based on RFF and GWO-PF ［J］. Acta Metrology Sinica, 2022, 43(9): 1200-1207.
	ZHANG Z W, GUO T Z, GAO M Y, et al. A review of charging state estimation methods for lithium-ion batteries of electric vehicles ［J］. Journal of Electronics and Information Technology, 2021, 43(7): 1803-1815.
	ZHENG T, ZHANG L, HOU Y C, et al. Lithium battery SOC estimation based on adaptive CKF aging ［J］. Energy storage science and technology, 2020, 9(4): 1193-1199.
	WU Z Q, SHANG M Y, SHEN D D, et al. State estimation of battery charge based on neural network and MS-AUKF algorithm ［J］. Proceedings of the CSEE, 2019, 39(21): 6336-6344.
［12］	HOW D N T, HANNAN M A, LIPU M S H, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review［J］. IEEE Access, 2019, 7: 136116-136136.
	CHEN Z, LI L L, SHU X, et al. Incremental analysis method based on improved capacity lithium battery capacity estimation ［J］. China journal of highway and transport, 2022, 35 (8): 20-30.
	WU Z Q, WANG G Y, XIE Z K, et al. Joint estimation of lithium battery capacity and SOC based on WOA-UKF algorithm ［J］. Acta Metrologica Sinica, 2022, 43(5): 649-656.
	GENG M M, FAN M S, YANG K, et al. Neural network based on EIS and retired battery SOH rapid estimation ［J］. Energy storage science and technology, 2022, 11 (2): 673-678.
［9］	MENG J, STROE D I, RICCO M, et al. A novel multiple correction approach for fast open circuit voltage prediction of lithium-ion battery［J］. IEEE Transactions on Energy Conversion, 2018, 34(2): 1115-1123.
［15］	NATH A, GUPTA R, MEHTA R, et al. Attractive ellipsoid sliding mode observer design for state of charge estimation of lithium-ion cells［J］. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14701-14712.
［22］	来鑫, 秦超, 郑岳久, 等. 基于恒流充电曲线电压特征点的锂离子电池自适应容量估计方法［J］. 汽车工程, 2019, 41(1): 1-6.