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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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It has been demonstrated that considering the knowledge of drive cycle as a priori in the PHEV control strategy can improve its performance. The concept of power cycle instead of drive cycle is introduced to consider the effect of noise factors in the prediction of future drivetrain power demand. To minimize the effect of noise factors, a practical solution for developing a power-cycle library is introduced. A control strategy is developed using the predicted power cycle which inherently improves the optimal operation of engine and consequently improves the vehicle performance. Since the control strategy is formed exclusively for each PHEV rather than a preset strategy which is designed by OEM, the effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered for each PHEV. Simulation results show that the control strategy based on the driver library of power cycle would improve both vehicle performance and battery health.I.