A Novel Approach for Battery and Route Optimization for EV Vehicles

Electric vehicles (EVs) are a vital part of global strategies to reduce carbon emissions and address climate change. However, optimizing their battery usage and route efficiency presents considerable technical challenges. This study aims to tackle these issues by developing an integrated system that uses machine learning and real-time data for effective battery and route optimization. We implement Proximal Policy Optimization (PPO) for dynamic battery management, continuously adjusting based on real-time feedback to enhance both battery health and energy efficiency. For route optimization, we modify the A* algorithm to consider real-time factors such as traffic conditions, road quality, terrain, and weather. These modifications enable the system to adapt dynamically to changing conditions, ensuring optimal energy consumption and travel efficiency. The models are trained and validated using various datasets, including EV charging station information, battery performance data, and range prediction data. Our results show significant improvements in energy efficiency and travel time compared to traditional methods. In This research advances EV optimization by providing practical insights for developing more efficient and adaptive EV systems. The integration of real-time data and advanced machine learning techniques offers a robust framework for addressing the dynamic challenges of EV battery and route optimization.