Model Predictive Control-Assisted Energy Management Strategy for Hybrid Mining Dump Trucks Based on Speed and Slope Prediction

This article proposes an innovative energy management strategy for hybrid multi-source dump trucks operating under real slope conditions in mining areas. Although previous studies have addressed the energy management issues of hybrid vehicles, few studies have taken into account complex environmental factors such as slopes under actual working conditions. The article overcomes this limitation by integrating a radial basis function (RBF) neural network to directly and accurately predict future vehicle demand power, thereby optimizing the DP-MPC strategy and improving energy efficiency. The results indicate that, compared with the traditional MPC strategy, the proposed strategy reduces fuel consumption by 3.34% and engine start-stop events by 76.2%. Additionally, when compared with another strategy that uses historical data to predict future speed and slope, calculates the vehicle’s future power demand, and incorporates it into the DP-MPC algorithm, the proposed strategy achieves comparable fuel consumption while also reducing engine start-stop events by 69.7%. Notably, the average calculation time for each step is 43.85 ms, which is substantially less than the sampling time of 1 s. To further confirm the real-time performance of the strategy, a hardware-in-the-loop (HIL) test is conducted.