Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation

Jinrui Nan, Bo Deng, Wanke Cao, Jianjun Hu, Yuhua Chang, Yili Cai and Zhiwei Zhong
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Jinrui Nan: National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Bo Deng: National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Wanke Cao: National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Jianjun Hu: China North Vehicle Research Institute, Beijing 100072, China
Yuhua Chang: Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland
Yili Cai: National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Zhiwei Zhong: National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

Energies, 2022, vol. 15, issue 15, 1-19

Abstract: Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning.

Keywords: short-text mining; grey correlation; early fault warning; electric bus; big data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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