IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5746-d1522790.html
   My bibliography  Save this article

Lithium-Ion Battery Health Management and State of Charge (SOC) Estimation Using Adaptive Modelling Techniques

Author

Listed:
  • Houda Bouchareb

    (LISA Laboratory, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Khadija Saqli

    (LISA Laboratory, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Nacer Kouider M’sirdi

    (LIS-SASV and HyRES Lab, Aix Marseille University, 13399 Marseille, France)

  • Mohammed Oudghiri Bentaie

    (LISA Laboratory, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

Abstract

Effective health management and accurate state of charge (SOC) estimation are crucial for the safety and longevity of lithium-ion batteries (LIBs), particularly in electric vehicles. This paper presents a health management system (HMS) that continuously monitors a 4s2p LIB pack’s parameters—current, voltage, and temperature—to mitigate risks such as overcurrent and thermal runaway while ensuring balanced charge distribution between cells. An improved online battery model (IOBM) is developed to enhance SOC estimation accuracy. The system utilises forgetting factor recursive least squares (FFRLS) for real-time parameter updates, an adaptive nonlinear sliding mode observer (ANSMO) for SOC estimation, and a long short-term memory (LSTM) network to dynamically adjust capacity based on operating conditions. Validation using the urban dynamometer driving schedule (UDDS) test demonstrated high accuracy, with the proposed battery model achieving a root mean square error (RMSE) of 12.13 mV and the LSTM achieving an RMSE of 0.0118 Ah. Regular updates to the battery’s current capacity, along with the proposed IOBM, significantly improved SOC estimation performance, maintaining estimation errors within 1.08%.

Suggested Citation

  • Houda Bouchareb & Khadija Saqli & Nacer Kouider M’sirdi & Mohammed Oudghiri Bentaie, 2024. "Lithium-Ion Battery Health Management and State of Charge (SOC) Estimation Using Adaptive Modelling Techniques," Energies, MDPI, vol. 17(22), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5746-:d:1522790
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5746/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/22/5746/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lan-Rong Dung & Hsiang-Fu Yuan & Jieh-Hwang Yen & Chien-Hua She & Ming-Han Lee, 2016. "A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions," Energies, MDPI, vol. 9(1), pages 1-21, January.
    2. Li, Shuangqi & He, Hongwen & Su, Chang & Zhao, Pengfei, 2020. "Data driven battery modeling and management method with aging phenomenon considered," Applied Energy, Elsevier, vol. 275(C).
    3. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    2. Mazhar Abbas & Eung-sang Kim & Seul-ki Kim & Yun-su Kim, 2016. "Comparative Analysis of Battery Behavior with Different Modes of Discharge for Optimal Capacity Sizing and BMS Operation," Energies, MDPI, vol. 9(10), pages 1-19, October.
    3. Huang, Jiangfan & An, Qing & Zhou, Mingyu & Tang, Ruoli & Dong, Zhengcheng & Lai, Jingang & Li, Xin & Yang, Xiangguo, 2024. "A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics," Applied Energy, Elsevier, vol. 361(C).
    4. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    5. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
    6. Li, Shuangqi & Gu, Chenghong & Zeng, Xianwu & Zhao, Pengfei & Pei, Xiaoze & Cheng, Shuang, 2021. "Vehicle-to-grid management for multi-time scale grid power balancing," Energy, Elsevier, vol. 234(C).
    7. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. Zhang, Wencan & Ouyang, Nan & Yin, Xiuxing & Li, Xingyao & Wu, Weixiong & Huang, Liansheng, 2022. "Data-driven early warning strategy for thermal runaway propagation in Lithium-ion battery modules with variable state of charge," Applied Energy, Elsevier, vol. 323(C).
    9. Gomez, William & Wang, Fu-Kwun & Chou, Jia-Hong, 2024. "Li-ion battery capacity prediction using improved temporal fusion transformer model," Energy, Elsevier, vol. 296(C).
    10. Iván Sanz-Gorrachategui & Pablo Pastor-Flores & Antonio Bono-Nuez & Cora Ferrer-Sánchez & Alejandro Guillén-Asensio & Carlos Bernal-Ruiz, 2021. "Lithium-Ion Battery Parameter Identification via Extremum Seeking Considering Aging and Degradation," Energies, MDPI, vol. 14(22), pages 1-12, November.
    11. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Health-Conscious vehicle battery state estimation based on deep transfer learning," Applied Energy, Elsevier, vol. 316(C).
    12. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2024. "An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2," Energy, Elsevier, vol. 304(C).
    13. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    14. Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).
    15. Biggio, Luca & Bendinelli, Tommaso & Kulkarni, Chetan & Fink, Olga, 2023. "Ageing-aware battery discharge prediction with deep learning," Applied Energy, Elsevier, vol. 346(C).
    16. Cristobal Morales & Augusto Lismayes & Hector Chavez & Harold R. Chamorro & Lorenzo Reyes-Chamorro, 2021. "The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards," Energies, MDPI, vol. 14(21), pages 1-13, November.
    17. Xi Luo & Jorge Varela Barreras & Clementine L. Chambon & Billy Wu & Efstratios Batzelis, 2021. "Hybridizing Lead–Acid Batteries with Supercapacitors: A Methodology," Energies, MDPI, vol. 14(2), pages 1-27, January.
    18. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
    19. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    20. Wang, Hao & He, Hongwen & Bai, Yunfei & Yue, Hongwei, 2022. "Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles," Applied Energy, Elsevier, vol. 320(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5746-:d:1522790. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.