Energy Consumption Control Strategy for Pure Electric Commercial Vehicles Based on DP Algorithm

Abstract

Reducing energy consumption and carbon emissions while effectively utilizing automotive resources is a crucial task for both the country and the automotive industry. To achieve this goal, this study employs the Dynamic Programming algorithm to optimize the control of pure electric commercial vehicles' driving process, thereby reducing their energy consumption. With the objective function of minimizing energy consumption and the constraints of vehicle power performance and economy, a simulation platform is built to analyze the driving energy consumption of pure electric commercial vehicles under typical working conditions. The experiment verified the effectiveness and feasibility of the driving energy consumption control strategy based on the Dynamic Programming algorithm. The results showed that compared to the traditional Dynamic Programming algorithm, the improved algorithm could save 1% - 2% more electricity. Additionally, when compared to the conventional PID algorithm, it could save about 1% - 7.5% of electricity. Compared to normal driving, optimized speed tracking reduced total energy consumption by 23.56%, while energy consumption during constant speed driving decreased by 6.62%. This indicates that the proposed energy consumption control strategy for pure electric commercial vehicles can achieve the goal of reducing driving energy consumption. The proposed driving energy consumption control strategy for pure electric commercial vehicles aims to plan the vehicle's driving speed, enabling it to travel on a reasonable speed track, and ultimately reducing driving energy consumption while improving driving economy.

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