SOC estimation of lithium-ion batteries under multiple temperatures conditions based on MIARUKF algorithm

doi:10.19799/j.cnki.2095-4239.2024.0341

Abstract

Abstract:

Accurate state of charge (SOC) estimation is the key to ensure the safe and stable operation of power batteries. However, in practical applications, the environment factors such as temperature change and noise interference make the accurate estimation of SOC difficult. In order to solve this problem, this paper proposes a joint estimation method of multi-timescales of the SOC of lithium ion batteries in wide temperature range based on the multi-new interest adaptive robust untrace Kalman filter (MIARUKF) algorithm, which integrates multi-new interest theory, adaptive filtering and robust algorithm based on the UKF algorithm. The proposed algorithm uses the multi-interest vector to correct the state estimates and timely update the noise covariance, so as to improve the estimation accuracy of SOC and improve the robustness of the algorithm by introducing the H filtering algorithm. Meanwhile, in order to reduce the computational burden of BMS, the UKF algorithm was used to estimate the model parameters online on the macroscopic time scale, and the MIARUKF algorithm was used to estimate the battery SOC on the microscopic time scale. Finally, the estimation results of battery SOC were compared and analyzed under different initial SOC initial values and temperature conditions, and the maximum and average absolute errors of the proposed method were 1.05% and 0.42%, respectively, indicating the high accuracy and good robustness.

Key words: lithium-ion battery, state of charge (SOC), multiple temperatures, MIARUKF

CLC Number:

TM 912

Xuefeng HU, Xianlei CHANG, Xiaoxiao LIU, Wei XU, Wenbin ZHANG. SOC estimation of lithium-ion batteries under multiple temperatures conditions based on MIARUKF algorithm[J]. Energy Storage Science and Technology, 2024, 13(9): 2983-2994.

Figures/Tables 11

Fig. 1

Fig. 2

Fig. 3

Fig.4

Table 1

Fig. 5

Fig. 6

Table 2

Fig. 7

Fig. 8

Fig. 9

References 19

1	LIU Z, DANG X J, JING B Q, et al. A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter[J]. Electric Power Systems Research, 2019, 177: 105951. DOI: 10.1016/j.epsr.2019.105951.
2	CHEN K, ZHOU S Y, LIU K, et al. State of charge estimation for lithium-ion battery based on whale optimization algorithm and multi-kernel relevance vector machine[J]. 2023, 158(10): 104110. DOI: 10.1063/5.0139376.
3	ZHOU W L, ZHENG Y P, PAN Z J, et al. Review on the battery model and SOC estimation method[J]. Processes, 2021, 9(9): 1685. DOI: 10.3390/pr9091685.
4	LEONORI S, BALDINI L, RIZZI A, et al. A physically inspired equivalent neural network circuit model for SoC estimation of electrochemical cells[J]. Energies, 2021, 14(21): 7386. DOI: 10.3390/en14217386.
5	BUCHICCHIO E, DE ANGELIS A, SANTONI F, et al. Battery SOC estimation from EIS data based on machine learning and equivalent circuit model[J]. Energy, 2023, 283: 128461. DOI: 10.1016/j.energy.2023.128461.
6	李伟, 刘伟嵬, 邓业林. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程, 2020, 31(3): 321-327, 343. DOI: 10.3969/j.issn.1004-132X.2020.03.010.
	LI W, LIU W W, DENG Y L. SOC estimation for lithium-ion batteries based on EKF[J]. China Mechanical Engineering, 2020, 31(3): 321-327, 343. DOI: 10.3969/j.issn.1004-132X.2020.03.010.
7	HIDALGO-REYES J I, GÓMEZ-AGUILAR J F, ALVARADO-MARTÍNEZ V M, et al. Battery state-of-charge estimation using fractional extended Kalman filter with Mittag-Leffler memory[J]. Alexandria Engineering Journal, 2020, 59(4): 1919-1929. DOI: 10.1016/j.aej.2019.12.006.
8	安治国, 田茂飞, 赵琳, 等. 基于自适应无迹卡尔曼滤波的锂电池SOC估计[J]. 储能科学与技术, 2019, 8(5): 856-861. DOI: 10.12028/j.issn.2095-4239.2019.0113.
	AN Z G, TIAN M F, ZHAO L, et al. SOC estimation of lithium battery based on adaptive untracked Kalman filter[J]. Energy Storage Science and Technology, 2019, 8(5): 856-861. DOI: 10.12028/j.issn.2095-4239.2019.0113.
9	ZHENG L F, ZHU J G, WANG G X, et al. Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter[J]. Energy, 2018, 158: 1028-1037. DOI: 10.1016/j.energy.2018.06.113.
10	WANG S L, JIA X Y, TAKYI-ANINAKWA P, et al. Review—Optimized particle filtering strategies for high-accuracy state of charge estimation of LIBs[J]. Journal of the Electrochemical Society, 2023, 170(5): 050514. DOI: 10.1149/1945-7111/acd148.
11	WANG K, FENG X, PANG J B, et al. State of charge (SOC) estimation of lithium-ion battery based on adaptive square root unscented Kalman filter[J]. International Journal of Electrochemical Science, 2020, 15(9): 9499-9516. DOI: 10.20964/2020.09.84.
12	GUO F, HU G D, XIANG S, et al. A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters[J]. Energy, 2019, 178: 79-88. DOI: 10.1016/j.energy.2019.04.126.
13	MENG J H, STROE D I, RICCO M, et al. A simplified model-based state-of-charge estimation approach for lithium-ion battery with dynamic linear model[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 7717-7727. DOI: 10.1109/TIE.2018. 2880668.
14	陈德海, 王超, 朱正坤, 等. 交互多模型无迹卡尔曼滤波算法预测锂电池SOC[J]. 储能科学与技术, 2020, 9(1): 257-265. DOI: 10.19799/j.cnki.2095-4239.2019.0207.
	CHEN D H, WANG C, ZHU Z K, et al. Lithium battery state-of-charge estimation based on interactive multimodel unscented Kalman filter Algorithm[J]. Energy Storage Science and Technology, 2020, 9(1): 257-265. DOI: 10.19799/j.cnki.2095-4239.2019.0207.
15	HE H W, XIONG R, FAN J X. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach[J]. Energies, 2011, 4(4): 582-598. DOI: 10.3390/en4040582.
16	LIANG Y W, WANG S L, FAN Y C, et al. An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries[J]. Journal of Energy Storage, 2023, 68: 107629. DOI: 10.1016/j.est.2023.107629.
17	FENG S, LI X G, ZHANG S, et al. A review: State estimation based on hybrid models of Kalman filter and neural network[J]. Systems Science & Control Engineering, 2023, 11(1). DOI: 10.1080/21642583.2023.2173682.
18	CHEN Z G, ZHOU J X, ZHOU F, et al. State-of-charge estimation of lithium-ion batteries based on improved H infinity filter algorithm and its novel equalization method[J]. Journal of Cleaner Production, 2021, 290: 125180. DOI: 10.1016/j.jclepro. 2020.125180.
19	DING F, WANG X H, MAO L, et al. Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering[J]. Digital Signal Processing, 2017, 62: 211-223. DOI: 10.1016/j.dsp. 2016.11.010.

规格	ICR18650-22P
额定电压	3.62 V
额定容量	2.2 Ah
充电截止电压	4.2 V
放电截止电压	2.75 V

温度	EKF		UKF		RUKF		ARUKF		MIARUKF		MIARUKF+UKF
温度	AAE	MAE	AAE	MAE	AAE	MAE	AAE	MAE	AAE	MAE	AAE	MAE
-10 ℃	2.68%	5.13%	1.99%	4.93%	1.77%	2.44%	1.29%	2.22%	0.93%	1.76%	0.42%	1.05%
25 ℃	1.73%	3.40%	1.68%	2.85%	1.47%	2.12%	1.20%	1.91%	0.53%	1.12%	0.25%	0.70%
40 ℃	1.60%	3.55%	1.47%	2.63%	1.43%	1.90%	1.04%	1.70%	0.60%	1.10%	0.23%	0.59%

[1]	Zhonglin SUN, Jiabo LI, Di TIAN, Zhixuan WANG, Xiaojing XING. Useful life prediction for lithium-ion batteries based on COA-LSTM and VMD [J]. Energy Storage Science and Technology, 2024, 13(9): 3254-3265.
[2]	Jizhong LU, Simin PENG, Xiaoyu LI. State-of-health estimation of lithium-ion batteries based on multifeature analysis and LSTM-XGBoost model [J]. Energy Storage Science and Technology, 2024, 13(9): 2972-2982.
[3]	Siyuan SHEN, Yakun LIU, Donghuang LUO, Yujun LI, Wei HAO. Transient overvoltage protection design and circuit development for energy storage lithium-ion battery modules [J]. Energy Storage Science and Technology, 2024, 13(9): 3277-3286.
[4]	Yuan CHEN, Siyuan ZHANG, Yujing CAI, Xiaohe HUANG, Yanzhong LIU. State-of-health estimation of lithium batteries based on polynomial feature extension of the CNN-transformer model [J]. Energy Storage Science and Technology, 2024, 13(9): 2995-3005.
[5]	Ruihe XING, Suting WENG, Yejing LI, Jiayi ZHANG, Hao ZHANG, Xuefeng WANG. AI-assisted battery material characterization and data analysis [J]. Energy Storage Science and Technology, 2024, 13(9): 2839-2863.
[6]	Hongsheng GUAN, Cheng QIAN, Bo SUN, Yi REN. Predicting capacity degradation trajectory for lithium-ion batteries under limited data conditions [J]. Energy Storage Science and Technology, 2024, 13(9): 3084-3093.
[7]	Xue KE, Huawei HONG, Peng ZHENG, Zhicheng LI, Peixiao FAN, Jun YANG, Yuzheng GUO, Chunguang KUAI. Estimating lithium-ion battery health using automatic feature extraction and channel attention mechanisms for multi-timescale modeling [J]. Energy Storage Science and Technology, 2024, 13(9): 3059-3071.
[8]	Chengwen TIAN, Bingxiang SUN, Xinze ZHAO, Zhicheng FU, Shichang MA, Bo ZHAO, Xubo ZHANG. Accelerated life prediction of lithium-ion batteries using data-driven approaches [J]. Energy Storage Science and Technology, 2024, 13(9): 3103-3111.
[9]	Qingbo LI, Maohui ZHANG, Ying LUO, Taolin LYU, Jingying XIE. Lithium-ion battery state of charge estimation based on equivalent circuit model [J]. Energy Storage Science and Technology, 2024, 13(9): 3072-3083.
[10]	Bingxiang SUN, Xin YANG, Xingzhen ZHOU, Shichang MA, Zhihao WANG, Weige ZHANG. Comparative parametric study of metaheuristics based on impedance modeling for lithium-ion batteries [J]. Energy Storage Science and Technology, 2024, 13(9): 2952-2962.
[11]	Yufeng HUANG, Huanchao LIANG, Lei XU. Kalman filter optimize Transformer method for state of health prediction on lithium-ion battery [J]. Energy Storage Science and Technology, 2024, 13(8): 2791-2802.
[12]	Zheng CHEN, Bo YANG, Zhigang ZHAO, Jiangwei SHEN, Renxin XIAO, Xuelei XIA. State of charge estimation considering lithium battery temperature and aging [J]. Energy Storage Science and Technology, 2024, 13(8): 2813-2822.
[13]	Guohe CHEN, Peizhao LYU, Menghan LI, Zhonghao RAO. Research progress on thermal runaway propagation characteristics of lithium-ion batteries and its inhibiting strategies [J]. Energy Storage Science and Technology, 2024, 13(7): 2470-2482.
[14]	Chengxin LIU, Ziheng LI, Zeyu CHEN, Pengxiang LI, Qingyi TAO. Characterization study on overheat-induced thermal runaway for lithium-ion battery in energy storage [J]. Energy Storage Science and Technology, 2024, 13(7): 2425-2431.
[15]	Shijie LIAO, Ying WEI, Yunhui HUANG, Renzong HU, Henghui XU. 1，3-Difluorobenzene diluent-stabilizing electrode interface for high-performance low-temperature lithium metal batteries [J]. Energy Storage Science and Technology, 2024, 13(7): 2124-2130.