IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v274y2023ics0360544223007727.html
   My bibliography  Save this article

Data-driven state-of-health estimation for lithium-ion battery based on aging features

Author

Listed:
  • Li, Xining
  • Ju, Lingling
  • Geng, Guangchao
  • Jiang, Quanyuan

Abstract

Reliable state-of-health (SOH) estimation is crucial to the safe operation of lithium-ion battery. Data-driven SOH estimation becomes a hot research topic with the booming of high-performance machine learning algorithms. The effectiveness of a data-driven approach will be enhanced significantly if the input features are extracted properly. In order to improve the SOH estimation accuracy, the features highly associated with battery degradation should be utilized in the data-driven model. In this paper, an aging feature extraction method based on electrochemical model (EM) is proposed to account for the battery degradation mechanisms. The aging features of EM such as charge transfer resistance, solid phase diffusion coefficient and electrode volume fraction are defined as internal health features (IHFs) for SOH estimation. Moreover, external health features (EHFs) are directly extracted from the voltage and temperature curves that split into multiple stages. Then, IHFs and multi-stage EHFs are selected appropriately for SOH estimation in offline and online application scenarios. Finally, two well-known machine learning algorithms are employed to construct data-driven SOH estimation model using IHFs and EHFs. Experimental data are used to prove that the proposed method can effectively improve the accuracy of SOH estimation under different application scenarios and battery charge–discharge modes.

Suggested Citation

  • Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223007727
    DOI: 10.1016/j.energy.2023.127378
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223007727
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127378?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. Jing Xie & Yi-Chun Lu, 2020. "A retrospective on lithium-ion batteries," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
    2. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    3. Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    4. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    5. Shu Zhang & Wenying Chen, 2022. "Assessing the energy transition in China towards carbon neutrality with a probabilistic framework," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    6. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    7. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    8. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
    9. Ospina Agudelo, Brian & Zamboni, Walter & Postiglione, Fabio & Monmasson, Eric, 2023. "Battery State-of-Health estimation based on multiple charge and discharge features," Energy, Elsevier, vol. 263(PA).
    10. Xiao-Guang Yang & Teng Liu & Chao-Yang Wang, 2021. "Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles," Nature Energy, Nature, vol. 6(2), pages 176-185, February.
    11. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    12. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Chen, Junxiong & Hu, Yuanjiang & Zhu, Qiao & Rashid, Haroon & Li, Hongkun, 2023. "A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging," Energy, Elsevier, vol. 282(C).
    3. 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).
    4. Zhang, Wencan & He, Hancheng & Li, Taotao & Yuan, Jiangfeng & Xie, Yi & Long, Zhuoru, 2024. "Lithium-ion battery state of health prognostication employing multi-model fusion approach based on image coding of charging voltage and temperature data," Energy, Elsevier, vol. 296(C).
    5. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
    6. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
    7. Li, Xiaoyu & Lyu, Mohan & Li, Kuo & Gao, Xiao & Liu, Caixia & Zhang, Zhaosheng, 2023. "Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning," Energy, Elsevier, vol. 282(C).
    8. Chang, Chun & Pan, Yaliang & Wang, Shaojin & Jiang, Jiuchun & Tian, Aina & Gao, Yang & Jiang, Yan & Wu, Tiezhou, 2024. "Fast EIS acquisition method based on SSA-DNN prediction model," Energy, Elsevier, vol. 288(C).
    9. Xiong, Ran & Wang, Shunli & Huang, Qi & Yu, Chunmei & Fernandez, Carlos & Xiao, Wei & Jia, Jun & Guerrero, Josep M., 2024. "Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy stor," Energy, Elsevier, vol. 292(C).
    10. 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).

    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. Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
    2. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    3. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
    4. Huang, Huanyang & Meng, Jinhao & Wang, Yuhong & Feng, Fei & Cai, Lei & Peng, Jichang & Liu, Tianqi, 2022. "A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve," Applied Energy, Elsevier, vol. 322(C).
    5. Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    6. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
    7. 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).
    8. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    9. 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).
    10. 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).
    11. Karimi, Danial & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2023. "A comprehensive coupled 0D-ECM to 3D-CFD thermal model for heat pipe assisted-air cooling thermal management system under fast charge and discharge," Applied Energy, Elsevier, vol. 339(C).
    12. Lin, Chuanping & Xu, Jun & Shi, Mingjie & Mei, Xuesong, 2022. "Constant current charging time based fast state-of-health estimation for lithium-ion batteries," Energy, Elsevier, vol. 247(C).
    13. Xiong, Ran & Wang, Shunli & Huang, Qi & Yu, Chunmei & Fernandez, Carlos & Xiao, Wei & Jia, Jun & Guerrero, Josep M., 2024. "Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy stor," Energy, Elsevier, vol. 292(C).
    14. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    15. Liu, Chenghao & Deng, Zhongwei & Zhang, Xiaohong & Bao, Huanhuan & Cheng, Duanqian, 2024. "Battery state of health estimation across electrochemistry and working conditions based on domain adaptation," Energy, Elsevier, vol. 297(C).
    16. 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).
    17. Kim, Jaewon & Sin, Seunghwa & Kim, Jonghoon, 2024. "Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system," Energy, Elsevier, vol. 297(C).
    18. Song, Ziyou & Hou, Jun & Li, Xuefeng & Wu, Xiaogang & Hu, Xiaosong & Hofmann, Heath & Sun, Jing, 2020. "The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection," Energy, Elsevier, vol. 193(C).
    19. Zhou, Yong & Dong, Guangzhong & Tan, Qianqian & Han, Xueyuan & Chen, Chunlin & Wei, Jingwen, 2023. "State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression," Energy, Elsevier, vol. 262(PB).
    20. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(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:energy:v:274:y:2023:i:c:s0360544223007727. 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.journals.elsevier.com/energy .

    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.