A Novel Method for SoH Prediction of Batteries Based on Stacked LSTM with Quick Charge Data

Abstract

The transition to non-fossil fuels brings with its basic challenges in battery technologies. Due to their efficiency, one of the areas where Li-ion batteries are widely used is electric vehicles (EVs). Range estimation is one of the most important needs in a battery-powered electric vehicle (BEV). The range of BEVs directly depends on battery capacity and powertrain efficiency. Although the electrical performance of Li-ion batteries has significantly improved, it is still not possible to overcome their capacity degradation with aging. State of charge (SoC) and state of health (SoH) are two important measures for a battery. With accurate SoC and SoH estimates, a battery management system can prevent each cell in the battery pack from over-charging or over-discharging, and prolongs the life of the entire pack. The novel idea in this study is to estimate SoH with the data collected during the battery charging process. The most needed moment for SoH is the end of the charging process. With this information, the user can plan the job that the battery will be used with. In order to meet this need, a specially designed deep neural network (stacked LSTM) is trained and tested using measurements only from constant current charging phase of quick charge process. The test results show that this method is effectively applicable to quick chargers.