A real-time energy management control strategy of hybrid energy system for a pure electric loader

To enhance the efficiency and prolong the battery life of hybrid energy systems equipped with battery and supercapacitor for a pure electric loader, this paper proposes a real-time energy management strategy based on model predictive control (MPC). First, the working characteristics of the loader and the topology structure of the hybrid energy are analyzed. Second, considering the repetitive operation model of electric loaders, a neural network algorithm based on long short-term memory is used to predict power and a dynamic programming algorithm is used as the rolling optimization solution for MPC strategy. Finally, the MPC is verified using the simulation in MATLAB/Simulink. The results indicate that compared with other strategies, the MPC strategy can improve hybrid energy efficiency and reduce the maximum battery current fluctuations. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system. A real-time model predictive control (MPC) is proposed for hybrid energy systems, incorporating battery and supercapacitor in electric loader. In the MPC, the input reference signal utilizes the power predicton from long short-term memory , while the rolling optimization employs a dynamic programming algorithm. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system.image