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Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles

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  • Wang, Zhenpo
  • Hong, Jichao
  • Liu, Peng
  • Zhang, Lei

Abstract

Monitoring of battery systems is of critical importance for guaranteeing safe and reliable operation of electric vehicles (EVs). Fault diagnosis is responsible for discovering multifarious faults at both pack and cell levels, and accordingly alerting drivers. This paper proposes an in-situ voltage fault diagnosis method based on the modified Shannon entropy, which is capable of predicting the voltage fault in time through monitoring battery voltage during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the Service and Management Center for Electric Vehicles (SMC-EV) in Beijing, and used to verify the effectiveness of the presented diagnosis method. The validation results show that the proposed method can accurately forecast both the time and location of voltage fault within battery packs. Furthermore, a security management strategy is devised on the basis of the Z-score approach, and the abnormity coefficient is set to make real-time evaluation of voltage abnormity.

Suggested Citation

  • Wang, Zhenpo & Hong, Jichao & Liu, Peng & Zhang, Lei, 2017. "Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles," Applied Energy, Elsevier, vol. 196(C), pages 289-302.
    • Handle: RePEc:eee:appene:v:196:y:2017:i:c:p:289-302
    DOI: 10.1016/j.apenergy.2016.12.143
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