State-of-charge estimation of lead-carbon batteries based on the PNGV model and an adaptive Kalman filter algorithm

doi:10.19799/j.cnki.2095-4239.2022.0697

Abstract

Abstract:

Energy storage batteries are widely used, and accurate estimation of the state of charge (SOC) of these batteries is of great significance in improving the state of battery health. In scenarios like energy storage power stations, lead-carbon batteries have drawn increasing interest as a novel-type energy storage battery with high performance, low cost, and high safety. However, there is still no lead-carbon battery SOC calculation available. Following the galvanostatic intermittent titration technique, this study first explores the relationship between the SOC and open circuit voltage of lead-carbon batteries. Then, voltammetry characteristic data of lead-carbon batteries are obtained through hybrid pulse power characteristic test, the equivalent circuit models of first-order Thevenin and first-order PNGV are established, and the parameters of the two equivalent circuit models are identified using a random algorithm based on the surrogate model and sensitivity analysis. On this basis, the extended Kalman filter (EKF) algorithm is used to estimate the SOC of lead-carbon batteries, and noise interference is considered during the estimation process. When the initial value of the lead-carbon battery SOC is unknown, the EKF algorithm cannot accurately estimate its SOC. Therefore, to address the drawbacks of EKF, this study proposes an adaptive extended Kalman filter to estimate the state of lead-carbon batteries. Results show that in the presence of noise and an unknown initial SOC value, the adaptive extended Kalman filter algorithm can estimate the SOC of lead-carbon batteries more accurately than the EKF algorithm and ampere-hour integration method, and the error is the smallest (3.91%) when the initial SOC value is 0.9, which verifies the effectiveness and applicability of the algorithm and improves the accuracy and reliability of SOC estimation of lead-carbon batteries.

Key words: lead-carbon batteries, state of charge, PNGV model, adaptive Kalman filter

CLC Number:

TM 911

Zheng CHEN, Zhide WANG, Wenbiao MOU, Peiwang ZHU, Gang XIAO. State-of-charge estimation of lead-carbon batteries based on the PNGV model and an adaptive Kalman filter algorithm[J]. Energy Storage Science and Technology, 2023, 12(3): 941-950.

Figures/Tables 13

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SOC	R₀/Ω	R₁/Ω	C₁/F
1.000000000	0.0035077	0.0036562	594064.985
0.953552525	0.0023634	0.0058163	353350.099
0.907105095	0.0022207	0.0002574	48700958.0
0.860657505	0.0022558	0.0017198	561751.000
0.814209879	0.0021980	0.0012835	810726.848
0.767762391	0.0022167	0.0018784	1917591.16
0.721314711	0.0021778	0.0016324	1430473.00
0.674866805	0.0021851	0.0014300	3136893.55
0.628419057	0.0022140	0.0013259	4676282.87
0.581971227	0.0022148	0.0021573	688854.461
0.535523416	0.0022439	0.0012376	3174760.20
0.489075763	0.0022323	0.0026272	956666.572
0.442628056	0.0022226	0.0016542	764816.472
0.396180415	0.0023833	0.0014345	918403.892
0.349732742	0.0023764	0.0051263	1474842.57
0.303284917	0.0024833	0.0013775	1078338.44
0.256837484	0.0025445	0.0035349	325334.762
0.210389979	0.0027384	0.0013796	302885.799
0.163943146	0.0023821	0.0317291	187861.310

SOC	R₀/Ω	R₁/Ω	C₁/F	C_b/F
1.000000000	0.0034758	0.0020972	401309.403	8071306.712
0.953552525	0.0023069	0.0039969	543196.674	9369423.626
0.907105095	0.0022438	0.0007140	1016062.04	9794221.409
0.860657505	0.0022558	0.0010946	864939.380	8672535.774
0.814209879	0.0021721	0.0009153	929354.354	9792360.335
0.767762391	0.0022041	0.0008917	968423.365	9646873.602
0.721314711	0.0021806	0.0010278	601850.242	10143786.58
0.674866805	0.0021864	0.0006958	992081.546	8802985.421
0.628419057	0.0021747	0.0008738	642572.923	8435115.878
0.581971227	0.0022019	0.0007245	649194.289	7673325.622
0.535523416	0.0022029	0.0006679	760457.169	9519066.290
0.489075763	0.0022251	0.0005150	966198.550	5729502.913
0.442628056	0.0022204	0.0007484	385541.450	8509847.276
0.396180415	0.0022933	0.0004257	920412.082	9897711.303
0.349732742	0.0023631	0.0006738	398232.771	8798002.850
0.303284917	0.0024343	0.0006458	970885.344	7665918.121
0.256837484	0.0025785	0.0004425	903690.436	9768447.136
0.210389979	0.0028942	0.0020679	990817.685	4440181.059
0.163943146	0.0025317	0.0038396	281331.860	10015772.20

方法	假定电池初始SOC	RMSE
安时积分(Ah)法	0.9	10.00%
	0.8	20.00%
	0.7	30.00%
扩展卡尔曼滤波(EKF)算法	0.9	18.57%
	0.8	19.18%
	0.7	19.71%
自适应扩展卡尔曼滤波(AEKF)算法	0.9	3.91%
	0.8	5.10%
	0.7	6.70%

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