Abstract

Improvements in battery technology that enhance performance and prolong battery lifetime require on the spot interest to preserve up with the abrupt Electric Vehicle (EVs) adoption charge. With the increasing demand for electric powered motors, this paper discusses how critical it's far to optimise Battery Management System (BMS) with the assist of Artificial Intelligence (AI). Enhancing charging cycles to increase battery lifestyles, efficaciously looking forward to degradation, and minimising thermal impacts are key problems. In this paper the Artificial Intelligence-Powered Lifelong Estimation Learning Framework (AI-PLELF), which is supposed to address these concerns. This innovative method makes use of Machine Learning (ML) algorithms to forecast the state of the battery, improve strength control, and make charging tactics greater powerful. Providing accurate estimations of the State OfHealth (SOH) and State Of Charge (SOC) of a battery is the aim of the AI-PLELF system, which incorporates actual-time facts from battery sensors, past utilization styles, and environmental situations. The notified technique displays huge increases in battery performance and durability whilst as compared to conventional BMS by use of comprehensive simulation research. AI-PLELF has an extensive range of programs, ranging from the administration of character electric vehicles to the control of huge fleets, and it gives flexible responses for quite a few operational settings. According to the findings, a BMS this is powered with the aid of artificial intelligence has the ability to decorate the overallefficiency of electric vehicles and substantially convey down on the expenses of maintenance. This is a complete-size step in the direction of the development of environmentally friendly transportation alternatives