Data learning based SoH estimation supported by modified charging algorithm for electric vehicle applications

As the demand for electric vehicles continues to surge, ensuring the longevity and efficiency of EV batteries becomes critical. The state of health (SoH) of these batteries serves as a critical metric, influenced by various factors such as State of Energy (SoE) and State of Power (SoP). Understanding the dynamics of EV battery health is essential for optimising performance and extending lifespan. The impedance values of battery cells (R and C values) serve as few of the fundamental parameters influencing SoE and SoP, inevitably degrading over time due to factors such as usage at varying C-rates, temperature fluctuations, and the number of charge cycles. This paper introduces an innovative approach for estimating the State of Health (SoH) of a battery. The charging algorithm is tailored to support the SoH algorithm, with modifications enabling the estimation of R & C parameters periodically throughout charging cycles, across a range of State of Charge (SoC) levels and various temperatures. These R & C parameters are utilized in a data learning-based SoH algorithm deployed on the cloud to determine the battery's SoH. This work on the SoH algorithm and modified charging algorithm is developed in the Matlab/Simulink environment and subsequently tested on a real vehicle. This provides us with much more accurate SoH predictions, which can in turn be used to modify various algorithms affected by battery health. Keywords: State of health (SoH), Charging algorithm, Battery parameter estimation