IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2023i1p206-d1310627.html
   My bibliography  Save this article

State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression

Author

Listed:
  • Emil Petkovski

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

  • Iacopo Marri

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

  • Loredana Cristaldi

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

  • Marco Faifer

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy)

Abstract

Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean R 2 value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean R 2 value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries.

Suggested Citation

  • Emil Petkovski & Iacopo Marri & Loredana Cristaldi & Marco Faifer, 2023. "State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression," Energies, MDPI, vol. 17(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:206-:d:1310627
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/1/206/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/1/206/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    2. Iacopo Marri & Emil Petkovski & Loredana Cristaldi & Marco Faifer, 2023. "Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach," Energies, MDPI, vol. 16(11), pages 1-13, May.
    3. Petit, Martin & Prada, Eric & Sauvant-Moynot, Valérie, 2016. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime," Applied Energy, Elsevier, vol. 172(C), pages 398-407.
    4. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(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. Bing Chen & Yongjun Zhang & Jinsong Wu & Hongyuan Yuan & Fang Guo, 2025. "Lithium-Ion Battery State of Health Estimation Based on Feature Reconstruction and Transformer-GRU Parallel Architecture," Energies, MDPI, vol. 18(5), pages 1-19, March.

    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. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    2. Diego Castanho & Marcio Guerreiro & Ludmila Silva & Jony Eckert & Thiago Antonini Alves & Yara de Souza Tadano & Sergio Luiz Stevan & Hugo Valadares Siqueira & Fernanda Cristina Corrêa, 2022. "Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization," Energies, MDPI, vol. 15(19), pages 1-21, September.
    3. Mehta, Rohit & Gupta, Amit, 2024. "Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    4. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    5. Che, Yunhong & Zheng, Yusheng & Wu, Yue & Sui, Xin & Bharadwaj, Pallavi & Stroe, Daniel-Ioan & Yang, Yalian & Hu, Xiaosong & Teodorescu, Remus, 2022. "Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network," Applied Energy, Elsevier, vol. 323(C).
    6. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    7. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    8. Park, Shina & Song, Youngbin & Kim, Sang Woo, 2024. "Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval," Energy, Elsevier, vol. 290(C).
    9. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    10. Liu, Yisheng & Fan, Guodong & Zhou, Boru & Chen, Shun & Sun, Ziqiang & Wang, Yansong & Zhang, Xi, 2023. "Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks," Applied Energy, Elsevier, vol. 351(C).
    11. 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).
    12. He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    13. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    14. Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
    15. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    16. Zhang, Junwei & Zhang, Weige & Sun, Bingxiang & Zhang, Yanru & Fan, Xinyuan & Zhao, Bo, 2024. "A novel method of battery pack energy health estimation based on visual feature learning," Energy, Elsevier, vol. 293(C).
    17. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    18. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    19. Zhang, Jianping & Zhang, Yinjie & Fu, Jian & Zhao, Dawen & Liu, Ping & Zhang, Zhiwei, 2024. "Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    20. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(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:2023:i:1:p:206-:d:1310627. 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.