SoC Estimation for an Electric Two-Wheeler Using Machine Learning Techniques

Due to the energy efficiency and commitment to the environment, electric vehicles (EVs) have significantly increased in popularity. Accurately estimating the battery's state of charge (SoC), which indicates the remaining energy available for vehicle operation, is a crucial component of EV operation. For efficient battery management, maximum vehicle range, and avoiding sudden battery discharge, accurate SoC calculation is crucial. In this abstract, a simple method for calculating the SoC of an electric two-wheeler vehicle is presented. The suggested methodology makes use of machine learning methods to predict the intricate connections between several vehicle characteristics and the battery's SoC. Machine learning's capacity to recognize patterns and generate precise predictions based on past data is its main benefit. An extensive dataset encompassing data on the battery voltage, current, temperature, speed, and other pertinent factors is gathered in order to create the SoC estimation model. The SoC estimation model is trained using a variety of machine-learning approaches, such as linear regression and logistic regression. It has been found that the linear regression model provides more accurate predictions as compared to the logistic regression model.