This dissertation presents a new method for solving for the optima of the Plug-in HEV's overall system parameters. Different from the existing HEV optimization approaches shown in the literature that are mainly control strategies focused, our study suggested that the powertrain sizing optimization is also a crucial factor for achieving minimum fuel consumption and emissions. To solve this multi-objective problem, the dissertation research featured a concurrent approach that simultaneously optimizes both HEV powertrain sizing parameters and control logics. The novelty is using probabilistic algorithms to attack this large-scale and nonlinear problem. Such a derivative-free approach has gained high efficiency in handling the high-order, noisy and discontinuous objective functions, and nonlinear constraints of the Plug-in HEV optimization problem.This dissertation presents a new method for solving for the optima of the Plug-in HEVa#39;s overall system parameters.
|Title||:||Concurrent Multi-objective Optimization of Plug-in Parallel HEV by a Hybrid Evolution Algorithm|
|Publisher||:||ProQuest - 2007|