IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p132-d1012352.html
   My bibliography  Save this article

Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network

Author

Listed:
  • Xin Zhang

    (School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, China)

  • Jiawei Hou

    (School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, China)

  • Zekun Wang

    (School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, China)

  • Yueqiu Jiang

    (School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China)

Abstract

The traditional ampere-hour (Ah) integration method ignores the influence of battery health (SOH) and considers that the battery capacity will not change over time. To solve the above problem, we proposed a joint SOH-SOC estimation model based on the GWO-BP neural network to optimize the Ah integration method. The method completed SOH estimation through the GWO-BP neural network and introduced SOH into the Ah integration method to correct battery capacity and improve the accuracy of state of charge (SOC) estimation. In addition, the method also predicted the SOH of the battery, so the driver could have a clearer understanding of the battery aging level. In this paper, the stability of the joint SOH-SOC estimation model was verified by using different battery data from different sources. Comparative experimental results showed that the estimation error of the joint SOH-SOC estimation model could be stabilized within 5%, which was smaller compared with the traditional ampere integration method.

Suggested Citation

  • Xin Zhang & Jiawei Hou & Zekun Wang & Yueqiu Jiang, 2022. "Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network," Energies, MDPI, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:132-:d:1012352
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/132/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/132/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Saleh Mohammed Shahriar & Erphan A. Bhuiyan & Md. Nahiduzzaman & Mominul Ahsan & Julfikar Haider, 2022. "State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence," Energies, MDPI, vol. 15(21), pages 1-26, October.
    2. Guoqing Jin & Lan Li & Yidan Xu & Minghui Hu & Chunyun Fu & Datong Qin, 2020. "Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions," Energies, MDPI, vol. 13(7), pages 1-17, April.
    3. Biao Yang & Yinshuang Wang & Yuedong Zhan, 2022. "Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 15(13), pages 1-18, June.
    4. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
    5. Enguang Hou & Yanliang Xu & Xin Qiao & Guangmin Liu & Zhixue Wang, 2021. "State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter," Energies, MDPI, vol. 14(17), pages 1-14, September.
    6. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    7. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    8. Wei, Zhongbao & Zhao, Difan & He, Hongwen & Cao, Wanke & Dong, Guangzhong, 2020. "A noise-tolerant model parameterization method for lithium-ion battery management system," Applied Energy, Elsevier, vol. 268(C).
    9. Xin Qiao & Zhixue Wang & Enguang Hou & Guangmin Liu & Yinghao Cai, 2022. "Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion," Energies, MDPI, vol. 15(12), pages 1-22, June.
    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. Xinfeng Zhang & Xiangjun Li & Kaikai Yang & Zhongyi Wang, 2023. "Lithium-Ion Battery Modeling and State of Charge Prediction Based on Fractional-Order Calculus," Mathematics, MDPI, vol. 11(15), pages 1-15, August.

    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. Tae-Won Noh & Junghoon Ahn & Byoung Kuk Lee, 2022. "Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-14, February.
    2. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Mohammadmahdi Ghiji & Vasily Novozhilov & Khalid Moinuddin & Paul Joseph & Ian Burch & Brigitta Suendermann & Grant Gamble, 2020. "A Review of Lithium-Ion Battery Fire Suppression," Energies, MDPI, vol. 13(19), pages 1-30, October.
    4. 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.
    5. 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).
    6. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    7. Yonghong Xu & Cheng Li & Xu Wang & Hongguang Zhang & Fubin Yang & Lili Ma & Yan Wang, 2022. "Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation," Sustainability, MDPI, vol. 14(23), pages 1-25, November.
    8. Sung-Min Cho & Jin-Su Kim & Jae-Chul Kim, 2019. "Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation," Energies, MDPI, vol. 12(9), pages 1-21, May.
    9. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections," Mathematics, MDPI, vol. 10(16), pages 1-16, August.
    10. Md Ohirul Qays & Yonis Buswig & Md Liton Hossain & Ahmed Abu-Siada, 2020. "Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System," Energies, MDPI, vol. 13(13), pages 1-16, July.
    11. Sungwoo Jo & Sunkyu Jung & Taemoon Roh, 2021. "Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge," Energies, MDPI, vol. 14(21), pages 1-16, November.
    12. Angelo Bonfitto, 2020. "A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks," Energies, MDPI, vol. 13(10), pages 1-13, May.
    13. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    14. Chehade, Abdallah & Savargaonkar, Mayuresh & Krivtsov, Vasiliy, 2022. "Conditional Gaussian mixture model for warranty claims forecasting," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    15. Bingyu Sang & Zaijun Wu & Bo Yang & Junjie Wei & Youhong Wan, 2024. "Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter," Energies, MDPI, vol. 17(7), pages 1-16, March.
    16. Sergi Obrador Rey & Juan Alberto Romero & Lluis Trilla Romero & Àlber Filbà Martínez & Xavier Sanchez Roger & Muhammad Attique Qamar & José Luis Domínguez-García & Levon Gevorkov, 2023. "Powering the Future: A Comprehensive Review of Battery Energy Storage Systems," Energies, MDPI, vol. 16(17), pages 1-21, September.
    17. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    18. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    19. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    20. Panpan Hu & W. F. Tang & C. H. Li & Shu-Lun Mak & C. Y. Li & C. C. Lee, 2023. "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies, MDPI, vol. 16(14), pages 1-19, July.

    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:16:y:2022:i:1:p:132-:d:1012352. 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.