IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i3p639-d1328544.html
   My bibliography  Save this article

SOC - SOH Estimation and Balance Control Based on Event-Triggered Distributed Optimal Kalman Consensus Filter

Author

Listed:
  • Xiaohan Fang

    (Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

  • Moran Xu

    (Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

  • Yuan Fan

    (Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

Abstract

The inconsistency in state-of-charge ( SOC ) for electric vehicle batteries will cause component damage and lifespan reduction of batteries. Meanwhile, the consistency in the state-of-health ( SOH ) also negatively influences the consensus of SOC . To ensure the consensuses of SOC and SOH simultaneously, this paper introduces an innovative distributed optimal Kalman consensus filter (KCF) approach to battery management systems. In addition, at the stage where sensors transmit information to each other, a new event-triggering mechanism (ETM) based on dynamic information is proposed to reduce communication overhead effectively. Theoretical analysis verifies the optimality of the algorithm. By numerical simulations, the proposed event-triggered distributed optimal KCF (ET-DOKCF) method can improve the performance of SOC - SOH estimation and save communication resources.

Suggested Citation

  • Xiaohan Fang & Moran Xu & Yuan Fan, 2024. "SOC - SOH Estimation and Balance Control Based on Event-Triggered Distributed Optimal Kalman Consensus Filter," Energies, MDPI, vol. 17(3), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:639-:d:1328544
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/3/639/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/3/639/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Chao & Yin, Wanjun & Wen, Tao, 2024. "An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid," Energy, Elsevier, vol. 287(C).
    2. Dorokhova, Marina & Martinson, Yann & Ballif, Christophe & Wyrsch, Nicolas, 2021. "Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation," Applied Energy, Elsevier, vol. 301(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. Saeed Alyami, 2024. "Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach," Sustainability, MDPI, vol. 16(14), pages 1-15, July.
    2. Jiao, Feixiang & Zou, Yuan & Zhou, Yi & Zhang, Yanyu & Zhang, Xibeng, 2023. "Energy management for regional microgrids considering energy transmission of electric vehicles between microgrids," Energy, Elsevier, vol. 283(C).
    3. Liu, Junling & Li, Mengyue & Xue, Liya & Kobashi, Takuro, 2022. "A framework to evaluate the energy-environment-economic impacts of developing rooftop photovoltaics integrated with electric vehicles at city level," Renewable Energy, Elsevier, vol. 200(C), pages 647-657.
    4. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).
    5. Soobok Yoon & Roger Dargaville, 2024. "The Optimal Infrastructure Design for Grid-to-Vehicle (G2V) Service: A Case Study Based on the Monash Microgrid," Energies, MDPI, vol. 17(10), pages 1-26, May.
    6. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
    7. Demir, Sumeyra & Stappers, Bart & Kok, Koen & Paterakis, Nikolaos G., 2022. "Statistical arbitrage trading on the intraday market using the asynchronous advantage actor–critic method," Applied Energy, Elsevier, vol. 314(C).
    8. He, Lin & Hu, Xingwen & Yin, Guangwei & Shao, Xingguo & Liu, Jichao & Shi, Qin, 2023. "A voltage dynamics model of lithium-ion battery for state-of-charge estimation by proportional-integral observer," Applied Energy, Elsevier, vol. 351(C).
    9. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging," Energy, Elsevier, vol. 293(C).
    10. Sun, Chuyu & Zhao, Xiaoli & Qi, Binbin & Xiao, Weihao & Zhang, Hongjun, 2022. "Economic and environmental analysis of coupled PV-energy storage-charging station considering location and scale," Applied Energy, Elsevier, vol. 328(C).
    11. Huang, Wenxin & Wang, Jianguo & Wang, Jianping & Zeng, Haiyan & Zhou, Mi & Cao, Jinxin, 2025. "Assessment of the technical economic viability and carbon reduction potential of urban-scale photovoltaic generation for electric vehicle charging station," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    12. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Zhang, Tao & Li, Shaojie & Yang, Zhi & Liu, Xiaohua & Jiang, Yi, 2023. "Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy," Applied Energy, Elsevier, vol. 341(C).
    13. Max Faßbender & Nicolas Rößler & Markus Eisenbarth & Jakob Andert, 2025. "An Electric Vehicle Charging Simulation to Investigate the Potential of Intelligent Charging Strategies," Energies, MDPI, vol. 18(11), pages 1-31, May.
    14. Yueyang Xu & Yibo Wang & Chuang Liu & Jian Xiong & Mo Zhou & Yang Du, 2025. "Adaptive Robust Optimal Scheduling of Combined Heat and Power Microgrids Based on Photovoltaic Mechanism/Data Fusion-Driven Power Prediction," Energies, MDPI, vol. 18(3), pages 1-23, February.
    15. Yin, Linfei & Xiong, Yi, 2024. "Incremental learning user profile and deep reinforcement learning for managing building energy in heating water," Energy, Elsevier, vol. 313(C).
    16. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    17. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    18. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    19. Zhou, Wenbing & Chi, Yuanying & Tang, Songlin & Hu, Yu & Wang, Zhengzao & Zhang, Xufeng, 2024. "The evolution analysis of low-carbon power transition strategies and carbon emission decoupling based on carbon neutrality target," Energy, Elsevier, vol. 311(C).
    20. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(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:2024:i:3:p:639-:d:1328544. 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.