Search Results

In an authors' previous SAE publication, an energy management control strategy has been proposed for the basic, charge-depleting/charge-sustaining (CD/CS) regime of an Extended Range Electric Vehicle (EREV). The strategy is based on combining a rule-based controller, including a state-of-charge regulator, with an equivalent consumption minimization strategy. This paper presents an extension of the control strategy, which can provide a favorable vehicle behavior in the more general blended (BLND) operating regime, as well. Dynamic programming-based control variables optimization is first conducted to gain an insight into the vehicle optimal behavior in the BLND regime, facilitate the feedback control strategy development/extension, and serve as a benchmark for the control strategy verification. Next, a parameter optimization method is applied to find optimal values of critical engine on/off thresholds.

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.

Driving cycles are usually defined by vehicle speed as a function of time and they are typically used to estimate fuel consumption and pollutant emissions. Currently, certification driving cycles are mainly used for this purpose. Since they are artificially generated, the resulting estimates and analyzes can generally be biased. In order to address these shortcomings, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded real-world data. To this end, this paper focuses on synthesis of multidimensional driving cycles using the Markov chain-based method and particularly on their validation. The synthesis is based on Markov chain of fourth order, where the road slope is accounted, as well. The corresponding transition probability matrix is implemented in the form of a sparse matrix parameterized with a rich set of recorded city bus driving cycles.