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A dynamic programming-based algorithm is developed and used for off-line optimization of range extended electric vehicle power train control variables over standardized certification driving cycles. The aim is to minimize the fuel consumption subject to battery state-of-charge constraints and physical limits of different power train variables. The control variables to be optimized include engine torque and electric machine speed, as well as a variable that selects the power train operating mode. The optimization results are presented for four characteristic certification driving cycles and characteristic vehicle operating regimes including electric driving during charge depleting mode, hybrid driving during charge sustaining mode, and combined/blended regime.

In order to reduce air and noise pollution in urban environments, low emission zones (LEZ) are being introduced in many cities worldwide. This paper deals with design of a LEZ-anticipating control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) given in a P2-type parallel powertrain configuration. A control-oriented backward-looking model of the PHEV powertrain is used as a design basis. The core control strategy is based on combining a rule-based (RB) controller including an explicit battery state-of-charge (SoC) controller and an equivalent consumption minimization strategy (ECMS), and it is superimposed by generating an optimal SoC reference trajectory aimed at enabling pure electric driving through forthcoming LEZs and minimizing the overall fuel consumption. The optimal SoC reference trajectory is generated by minimizing its length over travelled distance.