Abstract
The hybrid electric vehicle (HEV) offers superior fuel economy (FE) compared to the conventional counterpart; yet, it is costlier. To optimize an HEV design, both energy storage cost—one of the major contributors in overall cost—and FE are often set as objective functions in optimization processes. The techniques applied to manage the power flow between powertrain energy sources and sinks significantly affect the results of this optimization. In this paper, an advanced bandwidth-based control strategy teamed up with a duty ratio control strategy is applied and a Pareto Frontier, including energy storage system (ESS) costs and FEs, for a series HEV (SHEV) with limited all-electric-mode is introduced. The bandwidth-based control allows the SHEV’s advantage in engine efficiency management to be extended to the lighter ESS as compared to the ESS sizes in vehicles available in the market. To produce the Pareto Frontier, a vehicle model created in the powertrain modeling environment Autonomie is customized and used in parallel-mode multiobjective genetic algorithm (MOGA) optimization. It is noted that the approach proposed here is not claimed to be a global optimal solution but instead is an improvement over typical solutions used in real vehicles based on constraints such as the need to implement on a practical real-time controller.
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