IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2658-d787343.html
   My bibliography  Save this article

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Author

Listed:
  • Mengying Chen

    (School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia)

  • Fengling Han

    (School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia)

  • Long Shi

    (School of Engineering, RMIT University, Melbourne, VIC 3000, Australia)

  • Yong Feng

    (School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Chen Xue

    (School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Weijie Gao

    (Beijing Intell-Sun Technology Limited, Beijing 100012, China)

  • Jinzheng Xu

    (Research and Development Center, Anhui Huasun Energy Co., Ltd., Xuancheng 242000, China)

Abstract

Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model to enable accurate SoC estimation. The proposed method is evaluated using two dynamic battery tests: the dynamic street test (DST) and the federal urban driving schedule (FUDS) test. The simulation results show that the proposed method achieved high estimation accuracy and fast response speed. Additionally, real-time battery information, including battery output voltage and SoC, was acquired and displayed by an automatic monitoring system. The designed system is valuable for all battery application cases.

Suggested Citation

  • Mengying Chen & Fengling Han & Long Shi & Yong Feng & Chen Xue & Weijie Gao & Jinzheng Xu, 2022. "Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model," Energies, MDPI, vol. 15(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2658-:d:787343
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2658/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2658/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diouf, Boucar & Pode, Ramchandra, 2015. "Potential of lithium-ion batteries in renewable energy," Renewable Energy, Elsevier, vol. 76(C), pages 375-380.
    2. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
    3. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    4. Linghu, Jinqing & Kang, Longyun & Liu, Ming & Luo, Xuan & Feng, Yuanbin & Lu, Chusheng, 2019. "Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter," Energy, Elsevier, vol. 189(C).
    5. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.
    6. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    7. 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.
    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. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.

    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. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    2. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    3. Kim, Minho & Chun, Huiyong & Kim, Jungsoo & Kim, Kwangrae & Yu, Jungwook & Kim, Taegyun & Han, Soohee, 2019. "Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search," Applied Energy, Elsevier, vol. 254(C).
    4. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    5. Xu, Yiming & Ge, Xiaohua & Guo, Ruohan & Shen, Weixiang, 2025. "Recent advances in model-based fault diagnosis for lithium-ion batteries: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    6. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    8. Kuo Yang & Yugui Tang & Zhen Zhang, 2021. "Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter," Energies, MDPI, vol. 14(4), pages 1-15, February.
    9. 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).
    10. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    11. 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).
    12. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    13. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    14. Yigeng Huangfu & Jiani Xu & Dongdong Zhao & Yuntian Liu & Fei Gao, 2018. "A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer," Energies, MDPI, vol. 11(5), pages 1-21, May.
    15. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    16. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    17. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    18. Jie Xing & Peng Wu, 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    19. Fan, Guodong & Li, Xiaoyu & Zhang, Ruigang, 2021. "Global Sensitivity Analysis on Temperature-Dependent Parameters of A Reduced-Order Electrochemical Model And Robust State-of-Charge Estimation at Different Temperatures," Energy, Elsevier, vol. 223(C).
    20. Ni, Zichuan & Xiu, Xianchao & Yang, Ying, 2022. "Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis," Energy, Elsevier, vol. 254(PC).

    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:15:y:2022:i:7:p:2658-:d:787343. 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.