SOC Estimation of E-Vehicle Using Large Dataset for Vehicle Energy Consumption

Abstract

The sale of electric vehicles in the market still faces a challenge due to their limited driving range, in addition to the time required for charging batteries and, in many cases, the limited infrastructure of charging stations. Each time, before starting a journey, the driver has to assess whether the available charge on the battery is enough or not. Frequently, this estimation is based on the travelling distance and past consumption. There are two aspects that can have a very significant influence on the battery consumption: the route and the driving style. Accuracy of battery charge status (SOC) estimation plays a significant role in the management of electric vehicle power batteries. However, recently abrupt changes from SOC data often occurs in the actual operation of electric vehicles and some errors appear in the establishment of battery models which gives rise to poorly adaptive and robust performance of traditional algorithms in the process of SOC estimation. The work proposed in this paper uses a RNN-LSTM based model which can effectively improve the accuracy of models and SOC estimation of lithium ion batteries.