Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC

Abstract

The application of Hybrid Electric Wheel Loaders (HEWL) represents an attractive option for future industrial development. In order to reduce the equivalent fuel consumption and extend the lifetime of the battery, a real time whale optimal algorithm and general regression neural network (WOA-GRNN) based hierarchical model predictive control (WG-MPC) energy management strategy for HEWL is proposed. The structure of the WG-MPC consists of three layers, namely, the prediction layer, the economic layer and the control layer. In the prediction layer, a prediction model based on WOA-GRNN is proposed for the complex demand power prediction. In the economic layer, the equivalent diesel cost, diesel generator operation cost, the lifetime cost of the diesel generator and the battery pack are considered. The control layer is designed based on improved MPC. Through the simulation analysis, the proposed WOA-GRNN prediction model achieves the best prediction accuracy compared with the traditional prediction methods. Compared with FT-MPC and ECMS, the total economic cost of WG-MPC is reduced by 3.42% and 5.12%, respectively. The simulation results demonstrate that the proposed WG-MPC strategy has good control performance, high computational efficiency, low equivalent diesel consumption, longer battery lifetime and good use cost economy, which is of great significance for practical applications.