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2025 , Vol. 30 >Issue 2: 252 - 261

DOI: https://doi.org/10.1007/s12204-024-2727-y

Engieering and Technology

Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm-Extreme Learning Machine

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  • College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Accepted date: 2023-08-30

  Online published: 2025-03-21

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Abstract

To ensure a long-term safety and reliability of electric vehicle and energy storage system, an accurate estimation of the state of health (SOH) for lithium-ion battery is important. In this study, a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine (ELM). Input weights and hidden layer biases were generated randomly in traditional ELM. To improve the estimation accuracy of ELM, the differential evolution algorithm was used to optimize these parameters in feasible solution spaces. First, incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests. Then, the ELM based on differential evolution algorithm (DE-ELM model) was used for a lithium-ion battery SOH estimation. At last, four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model. Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.

Cite this article

Li Qingwei, Fu Can, Xue Wenli, Wei Yongqiang, Shen Zhiwen . Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm-Extreme Learning Machine[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(2) : 252 -261 . DOI: 10.1007/s12204-024-2727-y

References

[1] PETERS J F, BAUMANN M, ZIMMERMANN B, et al. The environmental impact of Li-Ion batteries and the role of key parameters: A review [J]. Renewable and Sustainable Energy Reviews, 2017, 67: 491-506.
[2] AGUSDINATA D B, LIUW J, EAKIN H, et al. Socioenvironmental impacts of lithium mineral extraction: Towards a research agenda [J]. Environmental Research Letters, 2018, 13(12): 123001.
[3] BERECIBAR M, GANDIAGA I, VILLARREAL I, et al. Critical review of state of health estimation methods of Li-ion batteries for real applications [J]. Renewable and Sustainable Energy Reviews, 2016, 56: 572- 587.
[4] RECHKEMMER S K, ZANG X Y, ZHANG W M, et al. Empirical Li-ion aging model derived from single particle model [J]. Journal of Energy Storage, 2019, 21: 773-786.
[5] ASHWIN T R, MCGORDON A, JENNINGS P A. Electrochemical modelling of Li-ion battery pack with constant voltage cycling [J]. Journal of Power Sources, 2017, 341: 327-339.
[6] GALEOTTI M, CIN`AL, GIAMMANCOC, et al. Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy [J]. Energy, 2015, 89: 678- 686.
[7] LIU B Y, TANG X P, GAO F R. Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model [J]. Electrochimica Acta, 2020, 344: 136098.
[8] LI J, ADEWUYI K, LOTFI N, et al. A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation [J]. Applied Energy, 2018, 212: 1178-1190.
[9] JIANG B, ZHU J G,WANG X Y, et al. A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries [J]. Applied Energy, 2022, 322: 119502.
[10] MAWONOU K S R, EDDAHECH A, DUMUR D, et al. Improved state of charge estimation for Li-ion batteries using fractional order extended Kalman filter [J]. Journal of Power Sources, 2019, 435: 226710.
[11] XU C, ZHANG E, JIANG K, et al. Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery [J]. Applied Energy, 2022, 327: 120091.
[12] CHANG C, WANG Q Y, JIANG J C, et al. Lithiumion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm [J]. Journal of Energy Storage, 2021, 38: 102570.
[13] MENG J H, CAI L, LUO G Z, et al. Lithium-ion battery state of health estimation with short-term current pulse test and support vector machine [J]. Microelectronics Reliability, 2018, 88/89/90: 1216-1220.
[14] ZHU H R, CHEN Z Q, YANG D Q. State of health estimation for Li-ion batteries based on differential thermal voltammetry and Gaussian process regression[ J]. Journal of Shanghai Jiao Tong University, 2024, 58(12): 1925-1934 (in Chinese).
[15] SAHA B, GOEBEL K, POLL S, et al. Prognostics methods for battery health monitoring using a Bayesian framework [J]. IEEE Transactions on Instrumentation and Measurement, 2009, 58(2): 291-296.
[16] SHEN S, SADOUGHI M, LI M, et al. Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries [J]. Applied Energy, 2020, 260: 114296.
[17] NI Y L, XU J N, ZHU C B, et al. Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model [J]. Applied Energy, 2022, 305: 117922.
[18] HUANG G B, ZHU Q Y, SIEWC K. Extreme learning machine: A new learning scheme of feedforward neural networks [C]//2004 IEEE International Joint Conference on Neural Networks. Budapest: IEEE, 2004: 985- 990.
[19] PAN H H, L¨U Z Q, WANG H M, et al. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine [J]. Energy, 2018, 160: 466-477.
[20] CHEN L, WANG H M, LIU B H, et al. Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation [J]. Energy, 2021, 215: 119078.
[21] LI Q W, FU C. Novel state-of-health estimation of lithium-ion battery based on the incremental capacity analysis method and extreme learning machine [C]// International Conference on Computer, Artificial Intelligence, and Control Engineering. Guangzhou: SPIE, 2023: 742-748.
[22] NIU P, LI J, LIU N, et al. NOx emission optimization of a boiler based on improved flower pollination algorithm and extreme learning machine [J]. Journal of Chinese Society of Power Engineering, 2018, 38(10): 782-787 (in Chinese).
[23] LI G Q, NIU P F, LIU C, et al. Enhanced combination modeling method for combustion efficiency in coal-fired boilers [J]. Applied Soft Computing, 2012, 12(10): 3132-3140.
[24] ZHANG S Z, ZHAI B Y, GUO X, et al. Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks [J]. Journal of Energy Storage, 2019, 26: 100951.
[25] BLOOM I, JANSEN A N, ABRAHAM D P, et al. Differential voltage analyses of high-power, lithiumion cells. 1. Technique and application [J]. Journal of Power Sources, 2005, 139(1): 295-303.
[26] HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications [J]. Neurocomputing, 2006, 70(1/2/3): 489-501.
[27] STORN R, PRICE K. Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces [J]. Journal of Global Optimization, 1997, 11: 341-359.
[28] WANG X, ZHAO S G. Differential evolution algorithm for high dimensional optimization problem [J]. Journal of Computer Applications, 2014, 34(1): 179-181 (in Chinese).
[29] BOLE B, KULKARNI C S, DAIGLE M. Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use [C]//Annual Conference of the PHM Society. Fort Worth: PHM Society, 2014: 1-9.

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