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Energies 2017, 10(7), 919; doi:10.3390/en10070919

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

1,2,* and 1,2,*
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Beijing Co-Innovation Center for Electric Vehicles Lecturer, Beijing 100081, China
Authors to whom correspondence should be addressed.
Academic Editor: Hailong Li
Received: 15 May 2017 / Revised: 25 June 2017 / Accepted: 26 June 2017 / Published: 4 July 2017
(This article belongs to the Special Issue Advanced Energy Storage Technologies and Their Applications (AESA))
View Full-Text   |   Download PDF [15729 KB, uploaded 4 July 2017]   |  


A thermal runaway prognosis scheme for battery systems in electric vehicles is proposed based on the big data platform and entropy method. It realizes the diagnosis and prognosis of thermal runaway simultaneously, which is caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data is derived from the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing. Furthermore, a thermal security management strategy for thermal runaway is presented under the Z-score approach. The abnormity coefficient is introduced to present real-time precautions of temperature abnormity. The results illustrated that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. The presented method is flexible in all disorder systems and possesses widespread application potential in not only electric vehicles, but also other areas with complex abnormal fluctuating environments. View Full-Text
Keywords: thermal runaway; battery systems; big data platform; National Service and Management Center for Electric Vehicles thermal runaway; battery systems; big data platform; National Service and Management Center for Electric Vehicles

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hong, J.; Wang, Z.; Liu, P. Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles. Energies 2017, 10, 919.

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