Dynamic Programming-based Optimization of Control Variables of an Extended Range Electric Vehicle

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.