h i g h l i g h t s < PHEVs' optimal energy management strategy (EMS) is highly influenced by temperature. < DP algorithm considers both battery charge and engine temperature state variables. < Optimal charge depletion trajectory represents an optimal engine temperature trajectory. < Real-time sub-optimal EMS can be realised by following the optimal charge trajectory. Keywords:Energy management strategy Temperature Thermal management Plug-in hybrid electric vehicle Optimal control Dynamic programing a b s t r a c t In plug-in hybrid electric vehicles (PHEVs), the engine temperature declines due to reduced engine load and extended engine off period. It is proven that the engine efficiency and emissions depend on the engine temperature. Also, temperature influences the vehicle air-conditioner and the cabin heater loads. Particularly, while the engine is cold, the power demand of the cabin heater needs to be provided by the batteries instead of the waste heat of engine coolant. The existing energy management strategies (EMS) of PHEVs focus on the improvement of fuel efficiency based on hot engine characteristics neglecting the effect of temperature on the engine performance and the vehicle power demand. This paper presents a new EMS incorporating an engine thermal management method which derives the global optimal battery charge depletion trajectories. A dynamic programming-based algorithm is developed to enforce the charge depletion boundaries, while optimizing a fuel consumption cost function by controlling the engine power. The optimal control problem formulates the cost function based on two state variables: battery charge and engine internal temperature. Simulation results demonstrate that temperature and the cabin heater/air-conditioner power demand can significantly influence the optimal solution for the EMS, and accordingly fuel efficiency and emissions of PHEVs.

In this paper an approach for the real time optimal control of vehicles with more than one source for providing traction power (e.g. HEVs) is presented. After a short classification of existing approaches and their respective characteristics the new modular concept as a combination of two algorithms is introduced. For the basic online management an equivalent consumption minimization strategy (ECMS) is implemented. The adaption towards changing driving conditions is realized by an independent calculation and adjustment of the main decision criterion of the ECMS towards the predicted operational profile using a calculation time optimized dynamic programming approach.

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