Artificial Intelligence in Electric Vehicle Battery Management System: A Techniques for Better Energy Storage

The automobile industry is currently undergoing a huge transition from IC Engine based systems to electric based mobility systems. Battery technology based on Li ion has made interesting move towards popularization of EV in world market. battery management system (BMS) forms one of the major constituents of this technology. Battery pack as a whole is the most sought-after component of EV which needs intensive monitoring and control. Battery parameters such as State of Health (SOH) and State of Charge (SOC) needs precise measurement and calculation. Monitoring them directly is a difficult task. In the present work methodologies and approaches for estimating the batteries parameters using Artificial Intelligent methods were investigated. It was found that six machine learning algorithms are popularly utilized for state estimation. The employed methods are linear, random forest, gradient boost, light gradient boosting (light-GBM), extreme gradient boosting (XGB), and support vector machine (SVM) regressors. A comparation of all methods is made and further. Prediction of state of Li-ion batteries is precisely done using AI methods. It is found that AI combined with a battery management system can improve engery usage with further electric vehicle performance improvement.