Application of Dynamic Programming Algorithm in Route Planning and Energy Consumption Optimization of New Energy Vehicles

With the large-scale promotion of new energy vehicles, range anxiety and insufficient energy efficiency have become the core bottlenecks restricting their popularization. Integrating route planning and energy consumption optimization has emerged as one of the major technical directions in solving this problem. The currently used route planning techniques largely emphasize the shortest path, disregarding the relationship between the energy consumption properties of new energy vehicles and dynamic road conditions, and this makes it hard to strike the balance between the accuracy of optimization and real-time operation. This paper first reviews relevant research on dynamic programming algorithms and energy consumption optimization for new energy vehicles, analyzing the shortcomings of existing research. Second, it constructs a new energy vehicle energy consumption model integrating multiple constraints, designs a path planning and energy consumption co-optimization scheme based on improved dynamic programming, and achieves dual optimization of path and energy consumption through phased iterative solutions. Finally, a simulation experimental platform is built, and verification is carried out using real road condition data. Experimental results show that the dynamic programming algorithm achieves significant energy-saving effects in all test scenarios, with an average energy consumption reduction of 21%, effectively adapting to complex dynamic road conditions and providing technical support for the efficient operation of new energy vehicles.