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Hybrid electric vehicles have shown significant improvement for both fuel efficiency and emission reduction, and attracted many researchers. Paramount for the fuel efficiency of HEVs is the energy management control strategies. ECMS (equivalent consumption minimization strategy) is one of the well-known real time power management strategies and has been used extensively in different works; however, its intrinsic difficulty is to find the optimal equivalent factor, which in theory is determined by the a priori knowledge of the complete driving cycle. Different methods have been proposed to solve this issue, but each one has its own pros. and cons. Especially, the applicability of each method for different cycles as well as the computation overhead are two main concerns in the methods presented so far. In this paper, a new method is presented for calculating equivalent factor in the ECMS method. The method does not rely on any prediction nor the a priori knowledge of driving cycles. Its robustness is demonstrated through different driving cycles with distinct characteristics. Our new method will improve the effectiveness and robustness of the ECMS method.

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Abstract-The engine cooling system in trucks is one of the main sources of parasite load. Thus fuel efficiency can be improved by optimal control of engine thermal management system considering fuel consumption minimization as the objective. Although several optimal control methods have been proposed for the engine cooling system, their main emphasize is on regulating engine and coolant temperature in an acceptable range rather than minimizing fuel consumption. In contrast, this paper investigates the fuel saving potential of predictive optimal control methods for the engine cooling system of conventional trucks. Our method exploits the idea of energy buffers in the automotive system, where the engine cooling system and the battery serve as energy buffers. The advantages of this approach are the recovery of brake energy and the balance of energy sources so that the total energy loss is minimized. A model predictive controller is used as the real time controller, and the results are compared with a simple state feedback controller and a global optimal solution obtained by dynamic programming. The results show limited but notable improvement in fuel efficiency. The results also construct a base for ongoing research on energy buffer control in conventional heavy trucks.