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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.

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This brief deals with the improvement of a vehicle's pass-through of predictively known urban driving situations concerning its fuel consumption. Today's technology enables the prediction of information about traffic events. This information can be used to identify efficient driving strategies. The main aim is to reduce the dynamics in the velocity profiles of driving situations and with it the corresponding fuel consumption in urban traffic. An algorithm has been built to calculate fuel consumption optimized driving trajectories. Input parameters are temporal and spatial depending constraints of the driving situation as well as other restrictions like a speed-limit. The main objective of the function was to enable a situation adaptive reaction to every predictively known forthcoming traffic event. Thus an optimized driving trajectory can be steadily calculated for the next route section of the vehicle provided that predictive information about the traffic events are available. The higher the availability of information the better an optimization of the driving strategies will be possible.Fuel characteristics and other energetically relevant data for a real-world vehicle have been created by detailed simulation to evaluate the fuel consumption of the driving strategies. For demonstration purposes the common driving situation "traffic light" was chosen. The fuel consumption calculated for the predictive driving strategies is compared to the consumption of a simulated average driver without predictive information.The calculated potentials have been verified by measuring the fuel consumption of an experimental vehicle for the simulated driving strategies.