Energy Management Strategy for Hybrid Mining Trucks Based on Global SOC Planning and Neural Network Optimal Control

Hybrid mining trucks, as core equipment for mine transportation, face high energy consumption and significant fluctuations in power demand during cyclic operations due to prolonged exposure to demanding operating conditions characterized by heavy loads and variable working conditions. To address the issues of high energy consumption and significant fluctuations in power demand during the cyclic operation of mining trucks, this paper proposes a hybrid mining truck energy management strategy based on global SOC (State of Charge) planning and neural network optimization control. First, a powertrain model was developed for a typical operating cycle of a hybrid mining truck, and its accuracy was validated by comparing it with experimental data. Using dynamic programming algorithms to plan the SOC for single-cycle operations provides a rational reference for energy allocation across different operational phases of mining trucks during a single cycle. Next, using the powerful nonlinear mapping and self-learning capabilities of neural networks, the system learns the optimal power output sequence of the range extender during offline computation. This enables rapid decision-making and adaptive adjustment of power allocation between the range extender and battery in real-time applications, thereby effectively reducing energy consumption under cyclic operating conditions. Design a SOC tracking controller to adjust the output of the neural network optimization controller, thereby achieving tracking of the planned global SOC. Finally, validated through simulations and real vehicle tests, the proposed energy management strategy can improve fuel economy, achieving a 1.27% fuel saving compared to traditional rule-based strategies in real vehicle tests. This study provides a feasible energy management scheme for the green and efficient operation of heavy mining vehicles.