Research Article
Big-data Analysis and Knowledge Discovery of Battery Fault in Numerous Real-world Electric Vehicles
@INPROCEEDINGS{10.4108/eai.17-6-2022.2322760, author={Guangyu Zhao and Qingle Sun}, title={Big-data Analysis and Knowledge Discovery of Battery Fault in Numerous Real-world Electric Vehicles}, proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2022}, month={10}, keywords={big-data analysis knowledge discovery electric vehicles (evs) correlation analysis hypothesis testing}, doi={10.4108/eai.17-6-2022.2322760} }- Guangyu Zhao
Qingle Sun
Year: 2022
Big-data Analysis and Knowledge Discovery of Battery Fault in Numerous Real-world Electric Vehicles
ICIDC
EAI
DOI: 10.4108/eai.17-6-2022.2322760
Abstract
Battery safety and aging have been considered as most import issues restricting the further deployment and development of electric vehicles in real-world applications. Due to differences in technology, materials, groups and physical and chemical reactions inside the battery, lithium-ion concentration gradient will be formed inside the battery, which is directly reflected in the inconsistency of external parameters. To study the correlation between battery faults and external parameters, in this study, all-life-cycle big-data of 41 electric vehicles are analysed by data mining. Firstly, the characteristic parameters representing voltage consistency are studied, and then the selected related parameters are statistically analysed in each dimension to explore the factors affecting the safety and reliability of batteries. The interesting knowledge discovered by this study can provide follow-up support for battery safety.