Machine Learning based Battery Failure Prediction Using Random Forest Algorithm

Lithium-ion batteries having high energy and power densities, fast depleting cost, and multifaceted technological improvement lead to the first choice for electric transportation systems. Electric batteries are being more widely used in the automobile sector these days. As a result, the inner workings of these battery systems must be fully comprehended. There is currently no accurate model for predicting an electric car battery’s state of health (SOH). This project aims to use machine learning to develop a reliable SOH prediction model for batteries. A correct optimal method was also constructed to drive the modeling process in the right direction. Extensive simulations were performed to verify the accuracy of the suggested methodology. The machine learning (ML) algorithm creates a very accurate and dependable model for forecasting battery health in real-world scenarios. In this project, Random Forest algorithm is used for training the model and predicting the state of the lithium batteries by entering its parameters. The Random Forest algorithm, chosen for its robustness and ability to handle complex relationships within the data, is employed to train the predictive model. This algorithm excels in capturing intricate patterns and dependencies, making it well-suited for the diverse and dynamic nature of battery behavior. This model aims to give an accuracy of about 98%. Such accuracy is pivotal for EV manufacturers and users alike, as it facilitates proactive maintenance, optimizing battery life, and ensuring the overall efficiency and sustainability of electric transportation systems.