基于大数据的电动汽车用户行为对电池老化影响分析

In order to explore the influence of users' behaviors on the battery aging of electric vehicles, a statistical analysis method based on big data is proposed to study the influence of the region user's vehicle located, the charging mode preference and driving style of users on battery aging, relying on a large number of high-quality user and vehicle data on the enterprise supervision platform. The results show that with the increase of the average operating temperature of battery, the attenuation of battery capacity reduces first and then increases. The battery aging degree of Beijing users is 10.59%~19.09% higher than that of Guangdong users. With the rise of fast charging frequency, the attenuation rate of battery capacity gradually increases, but with the increasing rate declining. The battery aging of the user preferring fast charging is 33.45%~56.24% faster than that of users preferring slow charging. Aggressive driving mode may accelerate battery aging, with an aging rate 1.73%~10.37% faster than gentle driving mode. The research results have reference significance for vehicle enterprises to optimize vehicle function design and guide the users to drive healthily.