SOC estimation of lithium-ion batteries based on BP-UKF algorithm

doi:10.19799/j.cnki.2095-4239.2022.0574

Abstract

Abstract:

The state of charge (SOC) of batteries is one of the most important indicators for battery management. An accurate SOC estimation is necessary to ensure the safe and effective operations of lithium-ion batteries. To improve the accuracy of the SOC estimation of lithium-ion batteries, this paper proposes a SOC estimation method by adopting the integration of the unscented Kalman filter (UKF) and the back propagation (BP) neural network, based on the second-order Thevenin equivalent model.By obtaining the model parameters through hybrid pulse power characteristic tests, the UKF algorithm is used to estimate the initial SOC of the battery. The nonlinear point transformation method is used to avoid the accuracy loss caused by the system linearization process in the extended Kalman filter (EKF). Then, a three-layer BP neural network is constructed, and the estimation errors are corrected by considering the charging and discharging voltage and current, along with other parameters of lithium-ion batteries. The estimation errors are then excluded from the initial estimation results to achieve more accurate results. The charging and discharging results of lithium-ion batteries in the dynamic stress test were collected by the battery charging and discharging tester. The BP-UKF algorithm proposed in this paper was compared with the EKF algorithm and the traditional UKF algorithm under different noise environments. The experimental results show that the maximum error of the proposed BP-UKF algorithm is within 2.18%, the mean absolute percentage error is within 0.54%, and the root mean square error is within 0.0044, demonstrating evident improvements compared with the other two algorithms. In addition, the accuracy of the BP-UKF algorithm is improved more significantly under the condition of large environmental noises.

Key words: SOC estimation, unscented Kalman filter algorithm, lithium-ion battery, second-order Thevenin model, BP neural network

CLC Number:

TM 911

Fan YANG, Jiarui HE, Ming LU, Lingxia LU, Miao YU. SOC estimation of lithium-ion batteries based on BP-UKF algorithm[J]. Energy Storage Science and Technology, 2023, 12(2): 552-559.

Figures/Tables 10

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References 16

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SOC/%	R₀/Ω	R₁/Ω	R₂/Ω	C₁/F	C₂/F
10	0.0151	0.1261	0.0186	3.7881	603.5809
20	0.0035	0.0167	0.018	35.2726	884.75
30	0.0027	0.016	0.016	29.2451	907.7039
40	0.0019	0.0043	0.0154	235.5616	995.8001
50	0.0025	0.0065	0.0167	136.4359	881.1511
60	0.0028	0.0064	0.0183	203.6628	744.9572
70	0.0047	0.0174	0.0184	40.0957	550.8873
80	0.0039	0.0122	0.0168	68.0778	651.8157
90	0.0046	0.0203	0.0155	30.9716	688.2432
100	0.0049	0.0283	0.0222	18.3537	651.1505

算法	q_k=q₀, r_k=r₀			q_k=5q₀, r_k=r₀			q_k=q₀, r_k=2r₀			q_k=5q₀, r_k=2r₀
算法	MAPE	RMSE	最大误差	MAPE	RMSE	最大误差	MAPE	RMSE	最大误差	MAPE	RMSE	最大误差
EKF	0.99%	0.0055	11.51%	1.11%	0.0062	12.1%	1.43%	0.0080	13.92%	1.45%	0.0081	15.2
UKF	0.57%	0.0015	3.86%	0.84%	0.0050	6.07%	0.56%	0.0038	4.19%	1.05%	0.0060	7.46%
BP-UKF	0.34%	0.0006	1.30%	0.40%	0.0029	1.32%	0.33%	0.0022	1.74%	0.54%	0.0044	2.18%

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