State of charge estimation of lithium batteries using adaptive unscented H infinity filter

doi:10.19799/j.cnki.2095-4239.2024.0434

Abstract

Abstract:

The state of charge (SOC) is a crucial metric for assessing the remaining power of lithium batteries, playing a significant role in optimizing battery usage and ensuring safety. To address the challenge of SOC estimation using the H infinity frilter (HIF), which offers high robustness but limited accuracy, this study proposes an adaptive unscented H infinity filter (AU_HIF) to enhance estimation precision. The dual polarization equivalent circuit model, known for its balanced accuracy and complexity, is selected to develop the new estimation algorithm. The unscented Kalman filter (UKF), which is more suitable for nonlinear state estimation compared to the traditional extended Kalman filter, is combined with a novel fading factor designed based on the prior error covariance matrix. This design minimizes the impact of outdated measurements on estimation results, improving the tracking capability and accuracy of the filtering algorithm. The effectiveness of the proposed AU_HIF is validated through simulations using data collected from a custom-built experimental platform. Results demonstrate that the adaptive unscented HIF outperforms traditional H infinity filtering, the standard UKF, and other modified H infinity filtering algorithms in terms of estimation accuracy and robustness. This research significantly enhances SOC estimation for battery systems used in new energy vehicles and energy storage power stations.

Key words: lithium battery, SOC, H infinity filter, dual polarization model, fading factor

CLC Number:

TM 912.8

Wei QIAN, Dazhong ZHAO, Xiangwei GUO, Yafeng WANG, Wenjing LI. State of charge estimation of lithium batteries using adaptive unscented H infinity filter[J]. Energy Storage Science and Technology, 2024, 13(11): 4078-4088.

Figures/Tables 11

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References 34

1	余传祥, 张英健, 潘傲然, 等. 基于IFO-GA-IUKF算法的锂电池多温度SOC估计[J/OL]. 储能科学与技术: 1-13[2024-05-07].https://doi.org/10.19799/j.cnki.2095-4239.2024.0001.
	YU C X, ZHANG Y J, PAN A R, et al. Research on state of charge estimation of power battery in wide temperature range[J/OL]. Energy Storage Science and Technology, 1-13[2024-05-07]. https://doi.org/10.19799/j.cnki.2095-4239.2024.0001.
2	MAHESHWARI A, NAGESWARI S. Real-time state of charge estimation for electric vehicle power batteries using optimized filter[J]. Energy, 2022, 254: 124328. DOI: 10.1016/j.energy. 2022. 124328.
3	CHEN L, YU W T, CHENG G Y, et al. State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter[J]. Energy, 2023, 271: 127007. DOI: 10.1016/j.energy.2023.127007.
4	CHEN L P, GUO W L, LOPES A M, et al. State-of-charge estimation for lithium-ion batteries based on incommensurate fractional-order observer[J]. Communications in Nonlinear Science and Numerical Simulation, 2023, 118: 107059. DOI: 10.1016/j.cnsns.2022.107059.
5	袁照凯, 范秋华, 王冬青, 等. 基于MIAEKF的多温度下锂电池SOC估计[J]. 储能科学与技术, 2024, 13(2): 680-690. DOI: 10.19799/j.cnki.2095-4239.2023.0533.
	YUAN Z K, FAN Q H, WANG D Q, et al. State of charge estimation for lithium-ion batteries under multiple temperatures based on the MIAEK algorithm[J]. Energy Storage Science and Technology, 2024, 13(2): 680-690. DOI: 10.19799/j.cnki.2095-4239.2023.0533.
6	YU Q Q, HUANG Y K, TANG A H, et al. OCV-SOC-temperature relationship construction and state of charge estimation for a series–parallel lithium-ion battery pack[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 6362-6371. DOI: 10.1109/TITS.2023.3252164.
7	MOHAMMADI F. Lithium-ion battery State-of-charge estimation based on an improved coulomb-counting algorithm and uncertainty evaluation[J]. Journal of Energy Storage, 2022, 48: 104061. DOI: 10.1016/j.est.2022.104061.
8	CHEN J X, ZHANG Y, WU J, et al. SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output[J]. Energy, 2023, 262: 125375. DOI: 10.1016/j.energy.2022.125375.
9	WANG Q, YE M, WEI M, et al. Deep convolutional neural network based closed-loop SOC estimation for lithium-ion batteries in hierarchical scenarios[J]. Energy, 2023, 263: 125718. DOI: 10.1016/j.energy.2022.125718.
10	HOSSAIN M, HAQUE M E, ARIF M T. Kalman filtering techniques for the online model parameters and state of charge estimation of the Li-ion batteries: A comparative analysis[J]. Journal of Energy Storage, 2022, 51: 104174. DOI: 10.1016/j.est. 2022.104174.
11	GE C A, ZHENG Y P, YU Y. State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm[J]. Journal of Energy Storage, 2022, 55: 105474. DOI: 10.1016/j.est. 2022. 105474.
12	HE L, WANG Y Y, WEI Y J, et al. An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery[J]. Energy, 2022, 244: 122627. DOI: 10.1016/j.energy.2021.122627.
13	WANG L M, GAO K X, HAN J Y, et al. Battery pack SOC estimation by Noise Matrix Self Adjustment-Extended Kalman Filter algorithm based on cloud data[J]. Journal of Energy Storage, 2024, 84: 110706. DOI: 10.1016/j.est.2024.110706.
14	QIAN W, LI W, GUO X W, et al. A switching gain adaptive sliding mode observer for SOC estimation of lithium-ion battery[J]. Energy, 2024, 292: 130585. DOI: 10.1016/j.energy.2024.130585.
15	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.
16	LIU F, YU D, SU W X, et al. Adaptive multitimescale joint estimation method for SOC and capacity of series battery pack[J]. IEEE Transactions on Transportation Electrification, 2024, 10(2): 4484-4502. DOI: 10.1109/TTE.2023.3314050.
17	HOSSAIN M, HAQUE M E, ARIF M T. Online model parameter and state of charge estimation of Li-ion battery using unscented Kalman filter considering effects of temperatures and C-rates[J]. IEEE Transactions on Energy Conversion, 2022, 37(4): 2498-2511. DOI: 10.1109/TEC.2022.3178600.
18	XIAO R X, HU Y W, JIA X G, et al. A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment[J]. Energy, 2022, 243: 123072. DOI: 10.1016/j.energy.2021.123072.
19	REZAEI O, MOGHADDAM H A, PAPARI B. A fast sliding-mode-based estimation of state-of-charge for Lithium-ion batteries for electric vehicle applications[J]. Journal of Energy Storage, 2022, 45: 103484. DOI: 10.1016/j.est.2021.103484.
20	XIONG R, YU Q Q, WANG L Y, et al. A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter[J]. Applied Energy, 2017, 207: 346-353. DOI: 10.1016/j.apenergy. 2017. 05.136.
21	CHEN C, XIONG R, SHEN W X. A lithium-ion battery-in-the-loop approach to test and validate multiscale dual H infinity filters for state-of-charge and capacity estimation[J]. IEEE Transactions on Power Electronics, 2018, 33(1): 332-342. DOI: 10.1109/TPEL. 2017.2670081.
22	SHU X, CHEN Z, SHEN J W, et al. State of charge estimation for lithium-ion battery based on hybrid compensation modeling and adaptive H-infinity filter[J]. IEEE Transactions on Transportation Electrification, 2023, 9(1): 945-957. DOI: 10.1109/TTE. 2022. 3180077.
23	ZHAO L H, LIU Z Y, JI G H. Lithium-ion battery state of charge estimation with model parameters adaptation using H ∞ extended Kalman filter[J]. Control Engineering Practice, 2018, 81: 114-128. DOI: 10.1016/j.conengprac.2018.09.010.
24	CHEN Z, ZHAO H Q, SHU X, et al. Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter[J]. Energy, 2021, 228: 120630. DOI: 10.1016/j.energy.2021.120630.
25	LIU F, YU D, SU W X, et al. Multi-state joint estimation of series battery pack based on multi-model fusion[J]. Electrochimica Acta, 2023, 443: 141964. DOI: 10.1016/j.electacta.2023.141964.
26	DONG G Z, XU Y, WEI Z B. A hierarchical approach for finite-time H∞ state-of-charge observer and probabilistic lifetime prediction of lithium-ion batteries[J]. IEEE Transactions on Energy Conversion, 2022, 37(1): 718-728. DOI: 10.1109/TEC. 2021. 3109896.
27	LI K Q, ZHOU F, CHEN X, et al. State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-thermal model[J]. Energy, 2023, 263: 125763. DOI: 10.1016/j.energy. 2022. 125763.
28	ZHANG S S, WAN Y H, DING J, et al. State of charge (SOC) estimation based on extended exponential weighted moving average H∞ filtering[J]. Energies, 2021, 14(6): 1655. DOI: 10.3390/en14061655.
29	ZHAO J B, MILI L. A theoretical framework of robust H-infinity unscented Kalman filter and its application to power system dynamic state estimation[J]. IEEE Transactions on Signal Processing, 2019, 67(10): 2734-2746. DOI: 10.1109/TSP. 2019. 2908910.
30	NING Z S, DENG Z W, LI J W, et al. Co-estimation of state of charge and state of health for 48 V battery system based on cubature Kalman filter and H-infinity[J]. Journal of Energy Storage, 2022, 56: 106052. DOI: 10.1016/j.est.2022.106052.
31	郭向伟, 邢程, 司阳, 等. RLS锂电池全工况自适应等效电路模型[J]. 电工技术学报, 2022, 37(16): 4029-4037. DOI: 10.19595/j.cnki.1000-6753.tces.210384.
	GUO X W, XING C, SI Y, et al. RLS adaptive equivalent circuit model of lithium battery under full working condition[J]. Transactions of China Electrotechnical Society, 2022, 37(16): 4029-4037. DOI: 10.19595/j.cnki.1000-6753.tces.210384.
32	ZHANG S Z, GUO X, ZHANG X W. An improved adaptive unscented Kalman filtering for state of charge online estimation of lithium-ion battery[J]. Journal of Energy Storage, 2020, 32: 101980. DOI: 10.1016/j.est.2020.101980.
33	CHEN L P, WU X B, LOPES A M, et al. Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter[J]. Energy, 2022, 252: 123972. DOI: 10.1016/j.energy.2022.123972.
34	LIU Y Y, WANG S L, XIE Y X, et al. A novel adaptive H-infinity filtering method for the accurate SOC estimation of lithium-ion batteries based on optimal forgetting factor selection[J]. International Journal of Circuit Theory and Applications, 2022, 50(10): 3372-3386. DOI: 10.1002/cta.3339.

参数	初始值
系统状态量 x_k	[0.9 0 0] ^T
状态估计误差协方差矩阵 P	[1 0 0;0 0.0001 0;0 0 0.0001]
系统白噪声协方差 Q	[10^-11 0 0;0 10^-9 0;0 0 10^-10]
测量白噪声协方差R	0.00008
性能边界γ	2

估计方法	工况	MAE/%	RMSE/%
UKF	WLTC	0.8611	1.4228
UKF	DST	0.9264	1.2699

E_HIF	WLTC	0.7725	1.1554
E_HIF	DST	0.6996	1.3836

文献[34]	WLTC	0.5990	0.9383
文献[34]	DST	0.4365	0.7060

AU_HIF	WLTC	0.2105	0.3064
AU_HIF	DST	0.2193	0.3828

Method	Condition	MAE/%	RMSE/%
UKF	WLTC	0.8713	1.4349
UKF	DST	0.9473	1.2833

E_HIF	WLTC	0.8487	1.1934
E_HIF	DST	0.8277	1.4161

文献[34]	WLTC	0.6798	0.9776
文献[34]	DST	0.5639	0.7492

AU_HIF	WLTC	0.3329	0.3547
AU_HIF	DST	0.3503	0.4159

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