Electric Vehicle Battery Drain Prediction Using Artificial Intelligence

Electric vehicles (EVs) are generally regarded as a more environmentally beneficial means of transportation due to their low environmental effect and user-friendliness. Due to its high energy density and long lifespan, lithium-ion (Li-ion) cells are widely employed in battery-powered EVs. No matter how well a system is designed, it will eventually break down due to the effects of time and/or use. Li-ion batteries have a very high replacement cost if they fail. To ensure the safety and dependability of EVs for various uses, it will suffice to assess the current state of the batteries and provide an estimate of the remaining life. Inaccurate predictions were commonly made in the past due to the nonlinear decline in battery capacity with very modest fluctuation in the first few cycles. In this study, we introduce a reliable method for estimating lifespan that is based on artificial intelligence. To acquire this, gather the necessary information, scrub it of extraneous details, and then isolate the top eight traits. The AI models used here include artificial neural networks (ANNs) and convolutional neural networks (CNNs). Each model undergoes 15 training iterations. Models are compared based on their performance using the accuracy and loss functions. The CNN achieves a remarkable accuracy of 97.21 percent with a small mistake of 0.0913 percent.