Lithium-ion battery state of charge estimation based on equivalent circuit model

doi:10.19799/j.cnki.2095-4239.2024.0594

Abstract

Abstract:

The accurate and efficient assessment of the state of charge (SOC) of lithium-ion batteries is critical to ensuring the satisfactory performance and safety of electric vehicles and energy storage devices. The equivalent circuit model is considered to be effective for describing complex reaction processes inside Li-ion batteries. To use the equivalent circuit model to address the difficult trade-off between accuracy and complexity in SOC estimation, we use the first-order RC model as the foundation of this study. In order to improve the performance of the model over the SOC interval, the RC model is optimized according to electrochemical principles. By adding an improved error term for the solid-phase diffusion process inside the reactive battery to the open-circuit voltage (OCV) module of the first-order RC model, we reduce the computational complexity. By adding a modified error term that reflects the solid-phase diffusion process inside the cell to the first-order RC model of the OCV module, we also reduce the error between the equivalent circuit model and the more accurate mechanism model while ensuring that the computational complexity remains low. Then, based on the multiplicity test and pulse test data, a particle swarm algorithm is used to reduce the complexity and improve the accuracy of parameter identification through parameter decoupling. At the same time, a polynomial method is used to fit the OCV-SOC curve based on the OCV data from a small-multiplication test. Subsequently, based on the model parameter identification results, SOC estimation research is carried out. To address the insufficient accuracy of conventional Kalman filtering, a weighted sliding window is used with traceless Kalman filtering to improve the accuracy and robustness of the SOC estimation, and the Kalman filtering algorithm is verified based on the UDDS and DST dynamic test data. The final estimation results show excellent accuracy and robustness, unlike the traditional method. The results quickly converge to the accurate value when the initial SOC has a large deviation.

Key words: lithium-ion battery, fusion model, state of charge estimation, untracked Kalman filtering

CLC Number:

TM 912

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.

Figures/Tables 19

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Fig. 6

Fig. 7

Table 2

Fig. 8

Fig. 9

Table 3

Fig. 10

Fig. 11

Table 4

Fig. 12

Fig. 13

Fig. 14

Fig. 15

References 19

1	WANG Y J, TIAN J Q, SUN Z D, et al. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems[J]. Renewable and Sustainable Energy Reviews, 2020, 131: 110015. DOI: 10.1016/j.rser.2020.110015.
2	邓晔, 胡越黎, 滕华强. 锂电池开路电压的预估及SOC估算[J]. 仪表技术, 2015(2): 21-24. DOI: 10.19432/j.cnki.issn1006-2394. 2015. 02.007.
	DENG Y, HU Y L, TENG H Q. Open-circuit voltage prediction and SOC estimation of Li-ion battery[J]. Instrumentation Technology, 2015(2): 21-24. DOI: 10.19432/j.cnki.issn1006-2394.2015.02.007.
3	ZHENG F D, XING Y J, JIANG J C, et al. Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries[J]. Applied Energy, 2016, 183: 513-525. DOI: 10.1016/j.apenergy.2016.09.010.
4	SANTHANAGOPALAN S, WHITE R E. State of charge estimation using an unscented filter for high power lithium ion cells[J]. International Journal of Energy Research, 2010, 34(2): 152-163. DOI: 10.1002/er.1655.
5	YANG N X, ZHANG X W, LI G J. State-of-charge estimation for lithium ion batteries via the simulation of lithium distribution in the electrode particles[J]. Journal of Power Sources, 2014, 272: 68-78. DOI: 10.1016/j.jpowsour.2014.08.054.
6	SANTHANAGOPALAN S, WHITE R E. Online estimation of the state of charge of a lithium ion cell[J]. Journal of Power Sources, 2006, 161(2): 1346-1355. DOI: 10.1016/j.jpowsour.2006.04.146.
7	DI DOMENICO D, FIENGO G, STEFANOPOULOU A. Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model[C]// 2008 IEEE International Conference on Control Applications. IEEE, 2008: 702-707. DOI: 10.1109/CCA.2008.4629639.
8	TIAN Y, HUANG Z J, TIAN J D, et al. State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies[J]. Energy, 2022, 238: 121917. DOI: 10.1016/j.energy.2021.121917.
9	TANG X P, WANG Y J, CHEN Z H. A method for state-of-charge estimation of LiFePO₄ batteries based on a dual-circuit state observer[J]. Journal of Power Sources, 2015, 296: 23-29. DOI: 10.1016/j.jpowsour.2015.07.028.
10	CHEN Q Y, JIANG J C, RUAN H J, et al. Simply designed and universal sliding mode observer for the SOC estimation of lithium-ion batteries[J]. IET Power Electronics, 2017, 10(6): 697-705. DOI: 10.1049/iet-pel.2016.0095.
11	XIA B Z, ZHANG Z, LAO Z Z, et al. Strong tracking of a H-infinity filter in lithium-ion battery state of charge estimation[J]. Energies, 2018, 11(6): 1481. DOI: 10.3390/en11061481.
12	CHEN Z, FU Y H, MI C C. State of charge estimation of lithium-ion batteries in electric drive vehicles using extended Kalman filtering[J]. IEEE Transactions on Vehicular Technology, 2013, 62(3): 1020-1030. DOI: 10.1109/TVT.2012.2235474.
13	DOYLE M, FULLER T F, NEWMAN J. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell[J]. Journal of the Electrochemical Society, 1993, 140(6): 1526. DOI: 10.1149/1.2221597.
14	XIE Y Z, CHENG X M. A new solution to the spherical particle surface concentration of lithium-ion battery electrodes[J]. Electrochimica Acta, 2021, 399: 139391. DOI: 10.1016/j.electacta.2021.139391.
15	高文凯. 锂离子动力电池的短路故障诊断研究[D]. 上海: 上海理工大学, 2020. DOI: 10.27308/d.cnki.gslgu.2020.000009.
	GAO W K. Research on short-circuit fault diagnosis of lithium-ion power battery[D]. Shanghai: University of Shanghai for Science & Technology, 2020. DOI: 10.27308/d.cnki.gslgu.2020.000009.
16	ZHAN Z H, ZHANG J, LI Y, et al. Adaptive particle swarm optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39(6): 1362-1381. DOI: 10.1109/TSMCB.2009.2015956.
17	DOYLE M, NEWMAN J, GOZDZ A S, et al. Comparison of modeling predictions with experimental data from plastic lithium ion cells[J]. Journal of the Electrochemical Society, 1996, 143(6): 1890. DOI: 10.1149/1.1836921.
18	HAN X B, OUYANG M G, LU L G, et al. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification[J]. Journal of Power Sources, 2014, 251: 38-54. DOI: 10.1016/j.jpowsour.2013.11.029.
19	CAI Y F, WANG Q T, QI W. D-UKF based state of health estimation for 18650 type lithium battery[C]// 2016 IEEE International Conference on Mechatronics and Automation. IEEE, 2016: 754-758. DOI: 10.1109/ICMA.2016.7558657.

参数	磷酸铁锂
标称容量/Ah	20
标称电压/V	3.2
内阻/mΩ	≤6
充电上限电压/V	3.65±0.05
放电下限电压/V	2
连续充电最大电流/C	2
连续放电最大电流/C	2
充放电温度	25
标准充电制度	CCCV
循环次数/次	1500

算法	工况	RMSE	MaE	MAE
粒子群算法	DST	0.0615	0.0487	0.1340
粒子群算法	UDDS	0.0478	0.0348	0.1260

解耦算法	DST	0.0391	0.0088	0.0750
解耦算法	UDDS	0.0135	0.0348	0.0597

模型	工况	RMSE	MaE	MAE
一阶RC模型	DST	0.0413	0.0124	0.1402
一阶RC模型	UDDS	0.0186	0.0427	0.0735

改进模型	DST	0.0391	0.0088	0.0850
改进模型	UDDS	0.0135	0.0348	0.0597

工况	算法	RMSE	MaE	MAE
DST	UKF	0.86%	0.52%	0.75%
DST	加权UKF	0.45%	0.29%	0.39%

UDDS	UKF	1.26%	0.98%	1.53 %
UDDS	加权UKF	0.33%	0.27%	0.45%

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	Guozheng MA, Jinwei CHEN, Xingyu XIONG, Zhenzhong YANG, Gang ZHOU, Rengzong HU. High-rate lithium storage performance of SnSb-Li₄Ti₅O₁₂ composite anode for Li-ion batteries at low-temperature [J]. Energy Storage Science and Technology, 2024, 13(7): 2107-2115.
[13]	Wentao WANG, Yifan WEI, Kun HUANG, Guowei LV, Siyao ZHANG, Xinya TANG, Zeyan CHEN, Qingyuan LIN, Zhipeng MU, Kunhua WANG, Hua CAI, Jun CHEN. Testing standards and developmental advances for low-temperature Li-ion batteries [J]. Energy Storage Science and Technology, 2024, 13(7): 2300-2307.
[14]	Guangyu CHENG, Xinwei LIU, Shuo LIU, Haitao GU, Ke WANG. Controlling electrolyte solvent components to enhance cycle life of LCO/C low-temperature 18650 batteries [J]. Energy Storage Science and Technology, 2024, 13(7): 2171-2180.
[15]	Shuping WANG, Xiankun YANG, Changhao LI, Ziqi ZENG, Yifeng CHENG, Jia XIE. Diethyl ethylphosphonate-based flame-retardant wide-temperature-range electrolyte in lithium-ion batteries [J]. Energy Storage Science and Technology, 2024, 13(7): 2161-2170.