Realtime Application of Progressive Optimal Search and Adaptive Dynamic Programming in Multi-Source HEVs

In multi-source hybrid electric vehicles (HEVs), more degrees of freedom are introduced to the power management optimization problem. Formulation of the optimal control problem and selection of solution method significantly affect the realtime applicability of the developed strategy. Moreover, elaborate definition of objective cost function and control variables are the essences to approach the global optimal solution in real-time. In this paper, two novel developed concepts are introduced: Adaptive Dynamic Programming (ADP) and Progressive Optimal Search (POS). The problem definition and solution method are structured in each method to yield minimum fuel consumption and suit realtime applicability. The results are comparatively analyzed with respect to previous developed methods of rule-based (RB) and adaptive RB. Experimental application of the developed methods is conducted using a hardware-in-the-loop (HIL) test-rig to validate the control modules. The comparative analysis emphasizes the advantages of each method from the perspectives of trip cost minimization, charge sustenance, and realtime applicability.