Prediction of battery temperature in electric bus charging stage based on CNN-LSTM

Abstract

New energy electric vehicles play an important role in reducing carbon emissions, reducing fossil energy consumption, and promoting the development of electrified transportation. As an important energy storage and driving source for pure electric vehicles, the safety of power batteries in the charging process has always attracted much attention. During use, the thermal effect of the battery will affect the temperature and electrochemical properties of the battery, greatly affecting the safety and service life of the battery. This article uses the historical data of the real electric bus in operation and selects the real driving data collected on 2 electric buses. A battery temperature prediction method based on CNN-LSTM hybrid nerve in the charging stage of electric bus is proposed. Finally, compared with other models, the results show that the model can effectively predict the short-term battery temperature change in the future.

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

“…These data not only include battery states data, but also include vehicle actual scene data and environmental meteorological data [48]. Wang used real driving data from two electric buses to predict the battery temperature during the charging phase of an electric bus [49]. With the development of artificial intelligence and big data technology, natural driving data may become the main source of data for estimating the status of batteries in engineering practice.…”