Utilising Machine Learning for the Prediction of the Hysteresis Factor in Lithium-Ion Batteries Through Incorporation of Driving Cycles

Abstract

The management of electric vehicle (EV) batteries involves accurately estimating their state-of-charge (SOC), which indicates remaining usable energy. Precise SOC estimation extends battery life, increases usable capacity, and enhances vehicle performance by allowing a greater depth of discharge without increasing battery weight or size. Traditional SOC estimation via Coulomb counting accumulates errors and requires correction using the Open-Circuit-Voltage (OCV). Modern silicon graphite anodes exhibit voltage hysteresis, making accurate SOC determination difficult. Introducing a hysteresis factor, ranging from -1 to 1, helps interpret OCV values within this hysteresis region.

This thesis proposes using a black box approach and machine learning models to predict the hysteresis factor for SOC correction more accurately. The research aims to enhance SOC accuracy in real-world EV environments, beyond laboratory settings. Improved SOC estimation through machine learning could significantly advance battery management systems (BMS), yielding both economic and environmental benefits by increasing EV efficiency. The study addresses the existing gaps in SOC estimation for voltage hysteresis, considering the vehicle as a system by incorporating driving cycles, BMS technical limitations, and cell chemistry. By improving EV reliability and efficiency, this research aims to promote broader acceptance and contribute to a sustainable future in personal transportation.