ELM- and MCSCKF-based state of charge estimation for lithium-ion batteries

WANG Qiao; YE Min; WEI Meng; LIAN Gao-qi; WU Chen-guang

doi:10.13374/j.issn2095-9389.2022.05.10.003

Volume 45 Issue 6

May 2023

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Article Navigation > Chinese Journal of Engineering > 2023 > 45(6): 995-1002

WANG Qiao, YE Min, WEI Meng, LIAN Gao-qi, WU Chen-guang. ELM- and MCSCKF-based state of charge estimation for lithium-ion batteries[J]. Chinese Journal of Engineering, 2023, 45(6): 995-1002. doi: 10.13374/j.issn2095-9389.2022.05.10.003

Citation:

WANG Qiao, YE Min, WEI Meng, LIAN Gao-qi, WU Chen-guang. ELM- and MCSCKF-based state of charge estimation for lithium-ion batteries[J]. Chinese Journal of Engineering, 2023, 45(6): 995-1002. doi: 10.13374/j.issn2095-9389.2022.05.10.003

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ELM- and MCSCKF-based state of charge estimation for lithium-ion batteries

doi: 10.13374/j.issn2095-9389.2022.05.10.003

WANG Qiao^1
,,
YE Min^{1
,
,},
WEI Meng^{1, 2},
LIAN Gao-qi¹,
WU Chen-guang¹

1.
National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an 710064, China
2.
Department of Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore

More Information

Corresponding author: E-mail: mingye@chd.edu.cn
Received Date: 2022-05-10
Available Online: 2022-07-05
Publish Date: 2023-05-31

Abstract

Abstract

Lithium-ion batteries are widely used in electric vehicles and energy storage systems. As a prerequisite for the safe and efficient application of lithium-ion batteries, battery management systems have received extensive attention worldwide. Among these prerequisites, the state of charge (SOC), as the basic parameter of battery management system online application, is crucial for the safe and efficient operation of battery management systems. However, measurement noise decreases the accuracy and robustness of the state of charge estimation. To reduce the impact of noise on the state of charge estimation of lithium-ion batteries, a novel SOC estimation method based on an extreme learning machine and a maximum correlation entropy square root volumetric Kalman filter is proposed in this paper. First, the extreme learning machine is used as the measurement equations of the Kalman filter because of its good generalization and fast running speed, and the voltage and current are selected as the model input; second, on the basis of the gray wolf optimization algorithm, the extreme learning machine hyperparameters are thoroughly optimized to improve the accuracy of the state of charge estimation for lithium-ion batteries; finally, on the basis of the framework of the maximum correlation entropy square root volumetric Kalman filter, a closed-loop estimation is realized to further reduce the state of charge estimation error caused by the measurement noise of voltage and current. The proposed method can simplify the time-consuming parameter adjustment of an extreme learning machine and show superior robustness under low-quality measurement. The proposed method is validated under multiple drive cycles and a wide temperature range to verify its generalization performance. The test results show that the proposed method substantially improves the accuracy of lithium-ion battery state of charge estimation. At the same time, the average running time of the proposed method is only one-third of that of long short memory neural networks and gate recurrent unit neural networks. Under complex driving conditions and a large temperature range, the root mean square error of the proposed method is less than 1%, and the maximum error is less than 3%. Furthermore, two case experiments are performed to evaluate the robustness of the proposed closed-loop estimation approach, and the results obtained when data have an initial state of charge error and measurement noise verify the superior robustness of the proposed approach compared with long short memory neural networks and gate recurrent unit neural networks.
- lithium-ion battery,
- state of charge,
- extreme learning machine,
- grey wolf optimizer,
- Kalman filter,
- closed-loop estimation

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References(26)

References

[1]	郜浩楠, 徐俊, 蒲曉暉, 等. 面向新能源汽車的懸架振動能量回收在線控制方法. 西安交通大學學報, 2020, 54(4):19 doi: 10.7652/xjtuxb202004003 Gao H N, Xu J, Pu X H, et al. An online control method for energy recovery of suspension vibration of new energy vehicles. J Xi’an Jiaotong Univ, 2020, 54(4): 19 doi: 10.7652/xjtuxb202004003
[2]	Shrivastava P, Soon T K, Idris M Y I B, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renew Sustain Energy Rev, 2019, 113: 109233 doi: 10.1016/j.rser.2019.06.040
[3]	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. Renew Sustain Energy Rev, 2020, 131: 110015 doi: 10.1016/j.rser.2020.110015
[4]	Xiong R, Cao J Y, Yu Q Q, et al. Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access, 2017, 6: 1832
[5]	Zhang S Z, Guo X, Dou X X, et al. A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery. Sustain Energy Technol Assess, 2020, 40: 100752
[6]	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. Appl Energy, 2017, 207: 346 doi: 10.1016/j.apenergy.2017.05.136
[7]	王曉蘭, 靳皓晴, 劉祥遠. 基于融合模型的鋰離子電池荷電狀態在線估計. 工程科學學報, 2020, 42(9):1200 Wang X L, Jin H Q, Liu X Y. Online estimation of the state of charge of a lithium-ion battery based on the fusion model. Chin J Eng, 2020, 42(9): 1200
[8]	Zhang Q, Cui N X, Li Y, et al. Fractional calculus based modeling of open circuit voltage of lithium-ion batteries for electric vehicles. J Energy Storage, 2020, 27: 100945 doi: 10.1016/j.est.2019.100945
[9]	Feng F, Teng S L, Liu K L, et al. Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model. J Power Sources, 2020, 455: 227935 doi: 10.1016/j.jpowsour.2020.227935
[10]	Zhu R, Duan B, Zhang J M, et al. Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter. Appl Energy, 2020, 277: 115494 doi: 10.1016/j.apenergy.2020.115494
[11]	Liu X, Qu H, Zhao J H, et al. Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems. ISA Trans, 2018, 80: 195 doi: 10.1016/j.isatra.2018.05.001
[12]	Almeida G C S, Souza A C Z, Ribeiro P F. A neural network application for a lithium-ion battery pack state-of-charge estimator with enhanced accuracy. Proceedings, 2020, 58(1): 33
[13]	Wang J, Yang Y Q, Wang T, et al. Big data service architecture: a survey. J Internet Technol, 2020, 21(2): 393
[14]	Reddy G T, Reddy M P K, Lakshmanna K, et al. Analysis of dimensionality reduction techniques on big data. IEEE Access, 2020, 8: 54776 doi: 10.1109/ACCESS.2020.2980942
[15]	Gozde O S, Milutin P, Zafer S, et al. Battery state-of-charge estimation based on regular/recurrent Gaussian process regression. IEEE Trans. Ind. Electron., 2018, 65(5): 4311 doi: 10.1109/TIE.2017.2764869
[16]	Li X Y, Yuan C G, Li X H, et al. State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression. Energy, 2020, 190: 116467 doi: 10.1016/j.energy.2019.116467
[17]	Ren X Q, Liu S L, Yu X D, et al. A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM. Energy, 2021, 234: 121236 doi: 10.1016/j.energy.2021.121236
[18]	Jiao M, Wang D Q, Qiu J L. A GRU-RNN based momentum optimized algorithm for SOC estimation. J Power Sources, 2020, 459: 228051 doi: 10.1016/j.jpowsour.2020.228051
[19]	Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1-3): 489 doi: 10.1016/j.neucom.2005.12.126
[20]	Jiao M, Wang D Q, Yang Y, et al. More intelligent and robust estimation of battery state-of-charge with an improved regularized extreme learning machine. Eng Appl Artif Intell, 2021, 104: 104407 doi: 10.1016/j.engappai.2021.104407
[21]	Hossain Lipu M S, Hannan M A, Hussain A, et al. Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm. IEEE Trans Ind Appl, 2019, 55(4): 4225 doi: 10.1109/TIA.2019.2902532
[22]	Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Adv Eng Softw, 2014, 69: 46 doi: 10.1016/j.advengsoft.2013.12.007
[23]	Deng Z W, Hu X S, Lin X K, et al. Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression. Energy, 2020, 205: 118000 doi: 10.1016/j.energy.2020.118000
[24]	Wei Z B, Hu J, Li Y, et al. Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries. Appl Energy, 2022, 307: 118246 doi: 10.1016/j.apenergy.2021.118246
[25]	Wei Z B, Zhao D F, He H W, et al. A noise-tolerant model parameterization method for lithium-ion battery management system. Appl Energy, 2020, 268: 114932 doi: 10.1016/j.apenergy.2020.114932
[26]	Wei Z B, Dong G Z, Zhang X N, et al. Noise-immune model identification and state-of-charge estimation for lithium-ion battery using bilinear parameterization. IEEE Trans Ind Electron, 2021, 68(1): 312 doi: 10.1109/TIE.2019.2962429