Innovative Energy Management Strategies for Electric Vehicles: Optimizing Efficiency and Sustainability in Dynamic Operating Environments

This paper conducts an in-depth investigation into advanced energy management strategies for electric vehicles (EVs), focusing on enhancing efficiency and sustainability in dynamic operating conditions. Leveraging data from authoritative sources such as the National Renewable Energy Laboratory (NREL), Electric Vehicle Database (EVD), OpenStreetMap (OSM), and Weather Data API, we developed a hybrid model that integrates machine learning and mathematical optimization techniques. The machine learning component utilizes a Support Vector Machine (SVM) to predict energy consumption based on historical data, while the optimization phase employs dynamic programming to minimize energy usage. Our methodology includes comprehensive steps of data preprocessing, model development, optimization, and validation, ensuring robust and accurate outcomes. The results reveal substantial improvements in energy consumption, state-of-charge (SOC) management, and prediction accuracy. Specifically, our optimized approach achieved an average efficiency enhancement of 13.8%, maintained SOC within optimal limits, and demonstrated low Root Mean Square Error (RMSE) values in energy consumption predictions. These findings highlight the efficacy of our strategies in significantly advancing the efficiency and sustainability of EVs across diverse operational scenarios.