Health Monitoring of E-Vehicle Battery Using Machine Learning

Transportation is an essential aspect of modern life, and cars have become a widely prevalent mode of transportation in nearly every household. However, the majority of cars currently in use rely on internal combustion engines fueled by gasoline or diesel, which contribute significantly to pollution by releasing harmful gases during the combustion process. Research indicates that cars and trucks are responsible for approximately 25% of greenhouse gas emissions. In response to growing environmental consciousness, the development of alternative energy sources, such as batteries, has gained momentum. This has led to the emergence of electric vehicles (EVs) that utilize batteries as their primary source of energy. Governments have recently introduced various policy incentives to promote the adoption of electric vehicles, with a focus on the long driving range, easy recharging, affordability, and safety. However, the batteries in these vehicles undergo gradual degradation over time and require periodic replacement. Consequently, battery maintenance is a significant concern, as it constitutes a substantial portion of an electric vehicle's cost (around 40%). To avoid sudden vehicle failures, it is crucial to periodically monitor the battery's health.