IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v207y2017icp354-362.html
   My bibliography  Save this article

Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods

Author

Listed:
  • Zhao, Yang
  • Liu, Peng
  • Wang, Zhenpo
  • Zhang, Lei
  • Hong, Jichao

Abstract

This paper presents a novel fault diagnosis method for battery systems in electric vehicles based on big data statistical methods. According to machine learning algorithm and 3σ multi-level screening strategy (3σ-MSS), the abnormal changes of cell terminal voltages in a battery pack can be detected and calculated in the form of probability. Applying the neural network algorithm, this paper combines fault and defect diagnosis results with big data statistical regulation to construct a more complete battery system fault diagnosis model. Through analyzing the abnormalities hidden beneath the surface, researchers can see the design flaws in battery systems and provide feedback on the upstream of designing. Furthermore, the local outlier factor (LOF) algorithm and clustering outlier diagnosis algorithm are applied to verifying the calculation results. To further validate the effectiveness of the diagnosis method, a corresponding analysis between statistical diagnosis results and actual vehicle is given. To test the big data diagnosis model, the diagnosis results based on the actual vehicle operating data for the whole year is shown.

Suggested Citation

  • Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    • Handle: RePEc:eee:appene:v:207:y:2017:i:c:p:354-362
    DOI: 10.1016/j.apenergy.2017.05.139
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917306931
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.05.139?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. Yuan, Xinmei & Li, Lili & Gou, Huadong & Dong, Tingting, 2015. "Energy and environmental impact of battery electric vehicle range in China," Applied Energy, Elsevier, vol. 157(C), pages 75-84.
    2. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    3. Li, Yue & Chattopadhyay, Pritthi & Xiong, Sihan & Ray, Asok & Rahn, Christopher D., 2016. "Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge," Applied Energy, Elsevier, vol. 184(C), pages 266-275.
    4. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    5. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    6. Zhao, Xin & Doering, Otto C. & Tyner, Wallace E., 2015. "The economic competitiveness and emissions of battery electric vehicles in China," Applied Energy, Elsevier, vol. 156(C), pages 666-675.
    7. Zhang, Shuo & Xiong, Rui & Cao, Jiayi, 2016. "Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system," Applied Energy, Elsevier, vol. 179(C), pages 316-328.
    8. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    9. Hua Zhang & Lei Pei & Jinlei Sun & Kai Song & Rengui Lu & Yongping Zhao & Chunbo Zhu & Tiansi Wang, 2016. "Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group," Energies, MDPI, vol. 9(5), pages 1-18, May.
    10. 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.
    11. Mingant, R. & Bernard, J. & Sauvant-Moynot, V., 2016. "Novel state-of-health diagnostic method for Li-ion battery in service," Applied Energy, Elsevier, vol. 183(C), pages 390-398.
    12. Bi, Jun & Zhang, Ting & Yu, Haiyang & Kang, Yanqiong, 2016. "State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter," Applied Energy, Elsevier, vol. 182(C), pages 558-568.
    13. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    14. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    15. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    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. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    3. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    4. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.
    5. Dafen Chen & Jiuchun Jiang & Xue Li & Zhanguo Wang & Weige Zhang, 2016. "Modeling of a Pouch Lithium Ion Battery Using a Distributed Parameter Equivalent Circuit for Internal Non-Uniformity Analysis," Energies, MDPI, vol. 9(11), pages 1-18, October.
    6. Xiaofeng Ding & Jiawei Cheng & Feida Chen, 2017. "Impact of Silicon Carbide Devices on the Powertrain Systems in Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-17, April.
    7. Lin, Cheng & Mu, Hao & Xiong, Rui & Cao, Jiayi, 2017. "Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy," Applied Energy, Elsevier, vol. 194(C), pages 560-568.
    8. Ding, Xiaofeng & Chen, Feida & Du, Min & Guo, Hong & Ren, Suping, 2017. "Effects of silicon carbide MOSFETs on the efficiency and power quality of a microgrid-connected inverter," Applied Energy, Elsevier, vol. 201(C), pages 270-283.
    9. Zhang, Zutao & Zhang, Xingtian & Chen, Weiwu & Rasim, Yagubov & Salman, Waleed & Pan, Hongye & Yuan, Yanping & Wang, Chunbai, 2016. "A high-efficiency energy regenerative shock absorber using supercapacitors for renewable energy applications in range extended electric vehicle," Applied Energy, Elsevier, vol. 178(C), pages 177-188.
    10. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    11. Xiangyu Cui & Zhu Jing & Maji Luo & Yazhou Guo & Huimin Qiao, 2018. "A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-21, January.
    12. Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhou, Long & Zhang, Zhendong, 2016. "Recording frequency optimization for massive battery data storage in battery management systems," Applied Energy, Elsevier, vol. 183(C), pages 380-389.
    13. Yonggang Liu & Jie Li & Ming Ye & Datong Qin & Yi Zhang & Zhenzhen Lei, 2017. "Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information," Energies, MDPI, vol. 10(4), pages 1-20, March.
    14. Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
    15. Li, Jun-qiu & Fang, Linlin & Shi, Wentong & Jin, Xin, 2018. "Layered thermal model with sinusoidal alternate current for cylindrical lithium-ion battery at low temperature," Energy, Elsevier, vol. 148(C), pages 247-257.
    16. Wang, WenWei & Yang, Sheng & Lin, Cheng, 2017. "Clay-like mechanical properties for the jellyroll of cylindrical Lithium-ion cells," Applied Energy, Elsevier, vol. 196(C), pages 249-258.
    17. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    18. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    19. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    20. Motoaki, Yutaka & Yi, Wenqi & Salisbury, Shawn, 2018. "Empirical analysis of electric vehicle fast charging under cold temperatures," Energy Policy, Elsevier, vol. 122(C), pages 162-168.

    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:207:y:2017:i:c:p:354-362. 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.