Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach
Ramakrishna S S Nuvvula, Polamarasetty P Kumar, Alighazi Siddiqui, S. Thamizharasan, Chai Ching Tan, Raaid Alubady, Baseem Khan
Abstract
This study introduces an innovative strategy to enhance electric vehicle (EV) fleet operations by combining predictive analytics and renewable energy sources. Leveraging real-world data from an active EV fleet, our method encompasses predictive maintenance, charging schedule optimization, and route planning, all empowered by advanced machine learning techniques. The results demonstrate a significant 15% reduction in greenhouse gas emissions, substantial annual cost savings exceeding $550,000, and noteworthy adaptability. This approach aligns with global sustainability targets and delivers valuable insights for fleet operators, policymakers, and researchers, promoting a more environmentally friendly and sustainable future.