Adaptive Battery Management System Using Machine Learning

A new age of electric vehicles (EVs) comes with great demand for efficient battery management systems that ensure an optimal level of performance as well as durability. In this work, we present the developed battery management system (DABMS) and the system adapts battery usage to an individual driver and environment efficient battery usage and approved more machine learning techniques for the driving experience integration is good in the development process. The state of charge (SoC) of a battery is monitored using an artificial neural network, which serves to project how much battery power will remain at any given time and when maintenance will be effective viewing the battery’s state of power. Additionally, the incorporated recommendation engine enables the user to classify and find optimal energy efficient electric vehicles (EVs) according to averages of vehicle health, range of motion, and performance characteristics given by the statistics. This system encapsulates the concepts of classification, prediction, and recommendation in one battery management system, thus increasing user satisfaction, vehicle effectiveness, and battery endurance