Design of intelligent energy management system for electric vehicles based on multi-objective optimization

This study proposes an intelligent energy management system for electric vehicles. This system uses multi-objective optimization to overcome the limitations of existing electric vehicles, including limited range, battery life degradation, and low energy utilization efficiency. The research aims to comprehensively optimize the vehicle’s power, battery life, and energy utilization efficiency. The method involves creating an energy management strategy based on multi-objective optimization that incorporates the Pontryagin minimum principle and deep Q-Network. This method uses the Pontryagin minimum principle to create an initial optimization framework and adjusts it in real time using a deep Q-network to address the complex, dynamic characteristics of an electric vehicle’s energy management system. The simulation results demonstrated that the proposed system achieved significant improvements. Compared to mainstream energy management systems, it had the lowest fuel cell and power cell degradation rates of 19.21% and 40.28%, respectively. Additionally, the system exhibited an average acceleration time of 5.38 s and an average hill climbing ability of 25.91%. These outcomes demonstrate the effectiveness of the proposed EMS in optimizing power, extending battery life, and improving energy utilization efficiency. This makes it an innovative solution for developing electric vehicle energy management systems.