Multi-objective optimized energy management strategy using an artificial tree algorithm for extended range hybrid loaders

Abstract

Aiming at the problems that existing energy management strategies (EMS) are rarely applied to 100-ton loaders and the engine start-stops frequently under complex driving conditions, this paper proposes a novel EMS for 100-ton extended range hybrid loaders based on an artificial tree algorithm (AT). Firstly, using the equivalent fuel consumption minimization strategy (ECMS) as a foundation, a penalty function is designed to restrict the range extender’s start-stop frequency and integrated into the ECMS control framework. Secondly, a real-time driving condition recognition model based on AT optimized back propagation (BP) Neural Network is proposed. Finally, with the equivalent factor, scale factor of state of charge (SOC) penalty function and range extender start-stop penalty function as optimization variables, and fuel economy, SOC stability, and range extender start-stop frequency as optimization objectives, AT is used for multi-objective optimization to obtain the optimal control parameters corresponding to the identified driving conditions. The simulation results demonstrate that compared with ECMS and Proportional-Integral-Derivative (PID) based ECMS, the proposed strategy is more effective for maintaining SOC stability. Besides, it improves the fuel economy by 5.937% and 1.353%, respectively, and decreases the number of range extender start-stops by 50.000% and 55.556%, respectively.