IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipds0306261924020075.html
   My bibliography  Save this article

A framework for joint SOC and SOH estimation of lithium-ion battery: Eliminating the dependency on initial states

Author

Listed:
  • Zeng, Xiaoyong
  • Sun, Yaoke
  • Xia, Xiangyang
  • Chen, Laien

Abstract

Model-based methods are prevalently used for estimating battery states, which forms the foundation of battery management systems. However, the efficacy of these methods relies on accurate initial state settings, and inaccuracies can precipitate substantial instability and even divergence, thereby posing serious threats to battery safety. This issue is particularly acute in the joint estimation of state of charge (SOC) and state of health (SOH) due to their interdependence. This study endeavors to obviate this dependency on initial states. Initially, two radial basis function auto-regressive models with exogenous inputs (RBF-ARXMs) are formulated to capture battery nonlinear dynamics and establish correlations between SOC, SOH, and observations. Building on these models, we derive effective target distributions and advocate for applying a Markov chain Monte Carlo method to sample and infer initial SOC and SOH values. Finally, a co-estimation method for SOC and SOH is developed, utilizing the unscented Kalman filter in conjunction with an RBF-ARXM. Validation on the Oxford and NASA degradation datasets demonstrates that the proposed methods achieve reliable initial state inference and SOC-SOH co-estimation throughout the batteries’ life cycle. Additionally, validation on the A123 dynamic driving dataset shows that our methods provide accurate initial SOC inference and SOC estimation under complex conditions.

Suggested Citation

  • Zeng, Xiaoyong & Sun, Yaoke & Xia, Xiangyang & Chen, Laien, 2025. "A framework for joint SOC and SOH estimation of lithium-ion battery: Eliminating the dependency on initial states," Applied Energy, Elsevier, vol. 377(PD).
  • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020075
    DOI: 10.1016/j.apenergy.2024.124624
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924020075
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.124624?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lin, Mingqiang & Yan, Chenhao & Wang, Wei & Dong, Guangzhong & Meng, Jinhao & Wu, Ji, 2023. "A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance," Energy, Elsevier, vol. 277(C).
    2. Bizhong Xia & Bo Ye & Jianwen Cao, 2022. "Polarization Voltage Characterization of Lithium-Ion Batteries Based on a Lumped Diffusion Model and Joint Parameter Estimation Algorithm," Energies, MDPI, vol. 15(3), pages 1-21, February.
    3. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    4. Huang, Haichi & Bian, Chong & Wu, Mengdan & An, Dong & Yang, Shunkun, 2024. "A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries," Energy, Elsevier, vol. 288(C).
    5. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    6. Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    7. Zhao, Xu & Chen, Yongan & Chen, Luowen & Chen, Ning & Wang, Hao & Huang, Wei & Chen, Jiayao, 2023. "On full-life-cycle SOC estimation for lithium batteries by a variable structure based fractional-order extended state observer," Applied Energy, Elsevier, vol. 351(C).
    8. Fan, Xinyuan & Zhang, Weige & Zhang, Caiping & Chen, Anci & An, Fulai, 2022. "SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture," Energy, Elsevier, vol. 256(C).
    9. Yu, Quanqing & Xiong, Rui & Yang, Ruixin & Pecht, Michael G., 2019. "Online capacity estimation for lithium-ion batteries through joint estimation method," Applied Energy, Elsevier, vol. 255(C).
    10. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    11. Lin, Mingqiang & Wu, Jian & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, Elsevier, vol. 268(C).
    12. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    13. Zhang, Kai & Bai, Dongxin & Li, Yong & Song, Ke & Zheng, Bailin & Yang, Fuqian, 2024. "Robust state-of-charge estimator for lithium-ion batteries enabled by a physics-driven dual-stage attention mechanism," Applied Energy, Elsevier, vol. 359(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. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    2. Wan, Sicheng & Yang, Haojing & Lin, Jinwen & Li, Junhui & Wang, Yibo & Chen, Xinman, 2024. "Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM," Energy, Elsevier, vol. 310(C).
    3. Liu, Zheng & Zhao, Zhenhua & Qiu, Yuan & Jing, Benqin & Yang, Chunshan & Wu, Huifeng, 2023. "Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction," Energy, Elsevier, vol. 283(C).
    4. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    5. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    6. Chen, Bingyang & Zeng, Xingjie & Liu, Chao & Xu, Yafei & Cao, Heling, 2025. "Health management of power batteries in low temperatures based on Adaptive Transfer Enformer framework," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    7. Wu, Jiang & Lei, Dong & Liu, Zelong & Zhang, Yan, 2024. "A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 311(C).
    8. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    9. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
    10. Zhang, Jie & Xiao, Bo & Niu, Geng & Xie, Xuanzhi & Wu, Saixiang, 2024. "Joint estimation of state-of-charge and state-of-power for hybrid supercapacitors using fractional-order adaptive unscented Kalman filter," Energy, Elsevier, vol. 294(C).
    11. Sulaiman, Mohd Herwan & Mustaffa, Zuriani & Zakaria, Nor Farizan & Saari, Mohd Mawardi, 2023. "Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle," Energy, Elsevier, vol. 279(C).
    12. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    13. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).
    14. Quanqing Yu & Changjiang Wan & Junfu Li & Lixin E & Xin Zhang & Yonghe Huang & Tao Liu, 2021. "An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 14(7), pages 1-22, March.
    15. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    16. Xu, Kangkang & He, Tailong & Yang, Pan & Meng, Xianbing & Zhu, Chengjiu & Jin, Xi, 2024. "A new online SOC estimation method using broad learning system and adaptive unscented Kalman filter algorithm," Energy, Elsevier, vol. 309(C).
    17. Yixin Liu & Ao Lei & Chunyang Yu & Tengfei Huang & Yuanbin Yu, 2024. "An Improved Collaborative Estimation Method for Determining The SOC and SOH of Lithium-Ion Power Batteries for Electric Vehicles," Energies, MDPI, vol. 17(13), pages 1-22, July.
    18. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    19. Qiao, Jialu & Wang, Shunli & Yu, Chunmei & Yang, Xiao & Fernandez, Carlos, 2023. "A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance," Energy, Elsevier, vol. 263(PE).
    20. Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(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:eee:appene:v:377:y:2025:i:pd:s0306261924020075. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.