IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v185y2017ip2p2033-2044.html
   My bibliography  Save this article

Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter

Author

Listed:
  • Liu, Zhentong
  • He, Hongwen

Abstract

This paper presents an effective model-based sensor fault detection and isolation (FDI) scheme for a series battery pack with low computational effort. The large number of current and voltage sensors in the battery pack, make it of high computational complexity. The major purpose of sensor FDI is to guarantee the healthy operations of the battery management system (BMS), and thus to prevent the battery from over-charge and over-discharge. In the voltage sensors fault scenarios, the most possibly being over-charged and over-discharged cells are these two cells with the maximum and minimum voltage respectively. Within the proposed scheme, these two cells are monitored in real time to diagnose the pack current sensor fault, or a voltage sensor fault of these two cells, while the rest cells are monitored offline with a long time interval, guaranteeing other voltage sensors working normally. For the scheme implementation, adaptive extended Kalman filter (AEKF) is used to estimate the battery states of each individual cell, and the estimated output voltage is compared with the measured voltage to generate a residual. Then the residuals are evaluated by a statistical inference method that determines the presence of the fault. Finally, the effectiveness of the proposed sensor FDI scheme is experimentally validated with a series battery pack under the UDDS driving cycles.

Suggested Citation

  • Liu, Zhentong & He, Hongwen, 2017. "Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter," Applied Energy, Elsevier, vol. 185(P2), pages 2033-2044.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:2033-2044
    DOI: 10.1016/j.apenergy.2015.10.168
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915014105
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.10.168?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. He, Hongwen & Xiong, Rui & Zhao, Kai & Liu, Zhentong, 2013. "Energy management strategy research on a hybrid power system by hardware-in-loop experiments," Applied Energy, Elsevier, vol. 112(C), pages 1311-1317.
    2. Zhentong Liu & Hongwen He, 2015. "Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles," Energies, MDPI, vol. 8(7), pages 1-19, June.
    3. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
    4. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai & Xie, Jing & Zhang, Xu, 2015. "A novel active equalization method for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 36-42.
    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. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    2. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.
    3. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    4. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    5. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    6. Ru-Jen Lin & Rong-Huei Chen & Thao-Minh Ho, 2013. "Market Demand, Green Innovation, and Firm Performance: Evidence from Hybrid Vehicle Industry," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    7. Das, Kaushik & Kumar, Roushan & Krishna, Anurup, 2024. "Analyzing electric vehicle battery health performance using supervised machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    8. Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    9. Yi Zhang & Qiang Guo & Jie Song, 2023. "Internet-Distributed Hardware-in-the-Loop Simulation Platform for Plug-In Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 16(18), pages 1-17, September.
    10. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    11. Joanna Kott & Marek Kott, 2019. "Generic Ontology of Energy Consumption Households," Energies, MDPI, vol. 12(19), pages 1-19, September.
    12. Abu Hanifah, Rabiatuladawiyah & Toha, Siti Fauziah & Hassan, Mohd Khair & Ahmad, Salmiah, 2016. "Power reduction optimization with swarm based technique in electric power assist steering system," Energy, Elsevier, vol. 102(C), pages 444-452.
    13. Romo, R. & Micheloud, O., 2015. "Power quality of actual grids with plug-in electric vehicles in presence of renewables and micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 189-200.
    14. Yu Jia & Shasha Li & Yu Shi, 2018. "An Analytical and Numerical Study of Magnetic Spring Suspension with Energy Recovery Capabilities," Energies, MDPI, vol. 11(11), pages 1-15, November.
    15. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    16. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    17. Odile Capron & Ahmadou Samba & Noshin Omar & Peter Van Den Bossche & Joeri Van Mierlo, 2015. "Thermal Behaviour Investigation of a Large and High Power Lithium Iron Phosphate Cylindrical Cell," Energies, MDPI, vol. 8(9), pages 1-26, September.
    18. Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Tan, Kang Miao & Mithulananthan, N., 2015. "A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 365-385.
    19. Alfredo Alvarez-Diazcomas & Adyr A. Estévez-Bén & Juvenal Rodríguez-Reséndiz & Miguel-Angel Martínez-Prado & Roberto V. Carrillo-Serrano & Suresh Thenozhi, 2020. "A Review of Battery Equalizer Circuits for Electric Vehicle Applications," Energies, MDPI, vol. 13(21), pages 1-29, October.
    20. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.

    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:appene:v:185:y:2017:i:p2:p:2033-2044. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.