Electric vehicle battery state-of-charge estimation based on optimized deep learning strategy with varying temperature at different C Rate

Abstract

The estimation of the state-of-charge (SOC) in battery technology is a vital task for the battery management system (BMS). In this study, a modeling framework is presented for SOC estimation that makes use of methodologies that involve machine learning (ML). Aside from providing the user with real-time feedback on the battery's remaining capacity, precise knowledge of SOC imposes further control over the charging/discharging process, hence elongating the battery's useful lifespan. This decreases the possibility of over-voltage. As a result, the proposed work creates a foundation for the development of realistic training data at different temperature and different C rate. This analysis demonstrates how the discharge voltage curves change at various temperatures and various C Rate. Change in voltage, internal resistance and SOC is analyzed at various temperature and various C Rate. This paves the way for a more exact SOC estimation that is driven by data-driven methods. According to the findings of this study, the energy storage capacity is greater when the C rate of charging is lower, and it is possible to deliver more energy when the C rate of discharging is lower. According to the findings of this study, the discharge of the battery is linear for the SoC values that are less than 90%. The error in the SoC estimation is brought down to 0.835% by using this approach.