Energy Management Strategies for Hybrid Loaders: Classification, Comparison and Prospect

Abstract

As one of the effective and crucial ways to achieve the energy saving and emission reduction of loaders, hybrid technology has attracted the attention of many scholars and manufacturers. Selecting an appropriate energy management strategy (EMS) is essential to reduce fuel consumption and emissions for hybrid loaders (HLs). In this paper, firstly, the application status of drivetrain configuration of HLs is presented. Then, the working condition characteristics of loaders are analyzed. On the basis of this, the configurations of HLs are classified, and the features and research status of each configuration are described. Next, taking the energy consumption optimization of HLs as the object, the implementation principle and research progress of the proposed rule strategy and optimization strategy are compared and analyzed, and the differences of existing EMSs and future development challenges are summarized. Finally, combining the advantages of intelligent control and optimal control, the future prospective development direction of EMSs for HLs is considered. The conclusions of the paper can be identified in two points: firstly, although the existing EMSs can effectively optimize the energy consumption of HLs, the dependence of the strategy on the mechanism model and the vehicle parameters can reduce the applicability of the strategy to heterogeneous HLs and the robustness to a complex working condition. Secondly, combining the advantages of intelligent control and optimal control, designing an intelligent EMS not depending on the vehicle analytical model will provide a new method for solving the energy consumption optimization problem of HL.

Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results

“…Two rule-based control strategies, i.e., the Engine-Dominant and Motor-Dominant strategies, are proposed for a power-split configuration and compared regarding fuel consumption and emissions under a cityhighway combined driving cycle [26]. Using the off-line optimization results, the new RB can improve the vehicle's fuel economy, but there may be a problem of driving condition adaptability [27][28][29]. RB is not fuel efficient but requires less computing power, and DP is fuel efficient, but computation might be more complex.…”