Research on Hierarchical Optimization of Intelligent Connected New Energy Vehicles Based on Dynamic Programming

With the intensification of the global energy crisis and environmental pollution, the research and development of intelligent connected new energy vehicles have become a hot topic. This study focuses on the hierarchical optimization control method of intelligent connected new energy vehicles, aiming to achieve efficient energy management and utilization through dynamic programming algorithms. The study first constructed the dynamic model and energy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{flow}$</tex> model of intelligent connected new energy vehicles, providing a theoretical basis for hierarchical optimization control. Subsequently, dynamic programming algorithms were used to optimize the energy management strategy, and the Bellman principle of reverse optimization was employed to search for the global optimal solution, in order to achieve rational allocation and efficient utilization of energy. The simulation experiment results show that the hierarchical optimization control method based on dynamic programming can significantly improve the fuel economy and emission performance of intelligent connected new energy vehicles, providing strong support for the intelligent and efficient development of new energy vehicles. This study not only has important theoretical value, but also provides useful reference for the practical application and promotion of intelligent connected new energy vehicles.