Deep-learning framework for state of charge (SoC) estimation of electric vehicle batteries using a Pynq board

Abstract

In recent years, carbon emissions are increasing worldwide due to excessive usage fossil fuels. To overcome these emissions, lithium-ion (Li-ion) batteries have become more prominent alternative. Li-ion batteries are used as primary component of energy storage systems for sustainable energy in response to rising global carbon gases. Battery Management System (BMS) in Electric Vehicles (EVs) is an important aspect and is indicated by two parameters called State of Charge (SoC) and State of Health (SoH). Of these two, SoC value is related to energy distribution, charging and discharging of batteries. Hence Estimating SoC value is of high important in BMS for optimum usage of batteries. Recent trends in Artificial Intelligence and Deep Learning provides a way for new developments in algorithms for estimating SoC. At the same time, the use of programmable devices like Field Programmable Gate Arrays (FPGAs) for data processing applications provides acceleration in time and optimal use of hardware. Pynq boards which are Zynq dependent and one variant of FPGAs are capable of executing python programs directly on hardware. This paper focuses on developing different DNN architectures for estimating SoC of a Li-ion battery of an EV and realizing on Pynq Z2 board.