Optimization Study of Energy Management of Intelligent Unloading Device Based on Supercharged Power System

To cope with the dual demands of loading and unloading efficiency and environmental protection in the modern logistics industry, this paper investigates an intelligent unloading device energy management method based on a supercharged power system. By combining a diesel generator with a power battery to form a dual-source energy supply architecture, it can provide sufficient power when the unloading vehicle is under high load and shave peaks and fill valleys during the low load or idle phase, realizing a comprehensive balance between fuel economy and emission control. The paper first establishes a system simulation platform including a diesel fuel consumption model and a battery SOC model, and then factors such as fuel consumption, emission, and battery life are incorporated into a multi-objective optimization framework to explore the performance differences among strategies based on rule-based control, offline dynamic programming (DP), and model predictive control (MPC). Simulation results show that the optimization approach can reduce fuel consumption by 5% to 9%, and significantly smooth battery SOC and reduce emissions compared with the traditional empirical strategy. The hardware-in-the-loop test preliminarily verifies the effectiveness of the proposed method, which supports the subsequent popularization and application under more complex or high-power working conditions.