AI-based Predictive Framework for Lithium-Ion Battery Life Cycle Estimation for optimizing Electric Vehicle Consistency

The research focuses on the optimization of the life cycle of lithium-ion batteries in electric vehicles through the utilization of advanced machine learning models. Using the comprehensive dataset including battery performance metrics, environmental conditions, driving patterns, and load conditions, several machine learning models including Support Vector Machines, Deep Neural Networks, Long Short-Term Memory networks, and Convolutional Neural Networks were trained and validated. Based on the thorough experimentation and validation, the predictive capacity of each model is estimated discussing the precision, recall, F1 score, AUC-ROC, and similar metrics. The results obtained show that SVM is the most attractive model, and the most accurate and robust in predicting the life cycle of the battery. The real-time testing also confirmed the efficacy of the SVM model since the battery life could increase when driving an electric vehicle by optimizing the operating conditions of motors based on real-time input data. In other words, while the battery drained out after 4 hours without the application of the SVM model, its integration allowed extending the life cycle of the battery to 5.5 hours. The results can be used for developing the best machine learning models in predictive medicine. The future research should focus on the improvement of machine learning models, integration of additional sensor data sources, and inclusive of other alterations that can improve the role of the battery within the electric vehicle and enhance its life cycle.