Modeling Hybrid Electric Vehicle Energy Management Strategy Using Dynamic Programming Algorithm

In this article, we discuss the methodology for computational problem-solving after modeling hybrid electric vehicle energy management strategies using dynamic programming algorithms. Hybrid electric vehicles utilize dual power systems combining internal combustion engines and electric motors to enhance fuel efficiency and reduce emissions. To optimize HEV operational efficiency, we develop energy management strategies ensuring optimal energy utilization at any given timepoint. The dynamic programming algorithm serves as our optimization technique to identify optimal solutions under specified constraints. In our research implementation, we typically structure the DP approach by defining state variables (such as battery State of Charge), control variables (power split between engine and motor), and transition equations representing system dynamics. The backward DP computation involves solving the Bellman equation recursively from final to initial states, while forward implementation applies optimal policies derived from the DP solution. Key functions in our MATLAB implementation include state discretization using mesh grids, cost function calculation incorporating fuel consumption and battery degradation, and policy extraction through value iteration. We evaluate strategy performance using metrics like fuel economy, computational efficiency, and constraint satisfaction. This research provides valuable references for designing energy management strategies in hybrid electric vehicles, with practical code implementation focusing on discretization techniques and optimization horizon handling.