Battery pack capacity prediction using deep learning and data compression technique: A method for real-world vehicles

Based on EV charging data, this work presents data compression and capacity labeling methods, achieving accurate battery capacity prediction using a deep learning model. The accurate prediction of battery pack capacity in electric vehicles (EVs) is crucial for ensuring safety and optimizing performance. Despite extensive research on predicting cell capacity using laboratory data, predicting the capacity of onboard battery packs from field data remains challenging due to complex operating conditions and irregular EV usage in real-world settings. Most existing methods rely on extracting health feature parameters from raw data for capacity prediction of onboard battery packs, however, selecting specific parameters often results in a loss of critical information, which reduces prediction accuracy. To this end, this paper introduces a novel framework combining deep learning and data compression techniques to accurately predict battery pack capacity onboard. The proposed data compression method converts monthly EV charging data into feature maps, which preserve essential data characteristics while reducing the volume of raw data. To address missing capacity labels in field data, a capacity labeling method is proposed, which calculates monthly battery capacity by transforming the ampere-hour integration formula and applying linear regression. Subsequently, a deep learning model is proposed to build a capacity prediction model, using feature maps from historical months to predict the battery capacity of future months, thus facilitating accurate forecasts. The proposed framework, evaluated using field data from 20 EVs, achieves a mean absolute error of 0.79 Ah, a mean absolute percentage error of 0.65%, and a root mean square error of 1.02 Ah, highlighting its potential for real-world EV applications.