AI based Battery Life Estimation of Electric Vehicle

Electric vehicles (EVs) is the most useful by-product of renewable technologies in transportation domain due to ecofriendly and user-friendly nature. EVs that run on batteries rely on Lithium-ion (Li-ion) cells due to their high capacity for energy and longer lifecycle. Over time or with continued use, any system, no matter how intricately constructed, the batteries will degrade. The expense associated with a failed Li-ion battery is considerable. For these uses, determining battery health and estimating battery life is the sole essential feature required to improve EV reliability and protection. Because of the nonlinear battery capacity fading with small fluctuation in early cycles, most previous research yielded unreliable predictions for the future. In this research, we present a comprehensive Artificial Intelligence (AD based framework for making reliable predictions about battery life cycles. This is accomplished by acquiring data and then removing unwanted features, and extracting eight of the most salient features. Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNN) are the two types of AI models employed here. A total of 15 epochs are used to train both models. Using the accuracy and loss function, we determine which model performs the best. The CNN has a very good accuracy of 97.21% and a loss of 0.0913%.