Optimization of New Energy Vehicle Battery Charging Schemes via Integration of Artificial Intelligence and Real-World Scenarios

Abstract

With the increasing global adoption of New Energy Vehicles (NEVs), battery charging efficiency, lifespan, and safety have emerged as critical constraints on industry development. Addressing the shortcomings of traditional charging strategies, this research proposes a multi-objective charging optimization framework for NEV batteries, based on the deep integration of Artificial Intelligence (AI) and real-world scenarios. By leveraging deep learning and reinforcement learning technologies, this framework enables real-time perception of battery states and dynamic adjustment of charging parameters, effectively reducing charging duration, mitigating battery capacity degradation, and balancing grid load. The results demonstrate that AI technology significantly enhances charging efficiency and user experience, promotes the intelligent upgrade of charging infrastructure, facilitates the transformation of the energy structure towards sustainability, addresses the challenge of imbalanced charging facility distribution, and fosters a secure and reliable charging ecosystem. This study provides a significant reference for the intelligent development of the NEV industry.