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A plug-in hybrid electric vehicle (PHEV) can improve fuel economy and emission reduction significantly compared to hybrid electric vehicles and conventional internal combustion engine (ICE) vehicles. Currently there lacks an efficient and effective approach to identify the optimal combination of the battery pack size, electric motor, and engine for PHEVs in the presence of multiple design objectives such as fuel economy, operating cost, and emission. This work proposes a design approach for optimal PHEV hybridization. Through integrating the Pareto set pursuing (PSP) multiobjective optimization algorithm and powertrain system analysis toolkit (PSAT) simulator on a Toyota Prius PHEV platform, 4480 possible combinations of design parameters (20 batteries, 14 motors, and 16 engines) were explored for PHEV20 and PHEV40 powertrain configurations. The proposed approach yielded the optimal solution in a small fraction of computational time, as compared to an exhaustive search. This confirms the efficiency and applicability of PSP to problems with discrete variables. In the design context we have found that battery, motor, and engine collectively define the optimal hybridization scheme, which also varies with the drive cycle and all electric range (AER). The proposed method and software platform could be applied to optimize other powertrain designs.

Increase of demand on product variety has pushed companies to think about offering more and more product variants in order to take more market shares. However, product variation can lead to cost increase for design and production, as well as the lead time for new variants. As a result, a proper tradeoff is required between cost-effectiveness of manufacturing and satisfying diverse demands. Such tradeoff has been shown to be manageable effectively by exploiting product family design (PFD) and platform-based product development. These strategies have been widely studied during the past decades, and a large number of approaches have been proposed for covering different issues and steps related to design and development of product families and platforms. Verification and performance of such approaches have also been traced through practical case studies applied to several industries. This paper focuses on a review of the research in this field and efforts to classify the recent advancements relevant to product family design and platform development issues. A comprehensive review on the state-of-the-art research in this field was done by Jiao et al. in 2007; therefore the main focus of this paper is on the research activities from 2006 to present. Mainly, the effort of this paper is to identify new achievements in regard with different aspects of product family design such as customer involvement in design, market driven studies, new indices and metrics for assessing families and developing the desired platforms, issues relevant to product family optimization (i.e., new algorithms and optimization approaches applied to different PFD problems along with their benefits and limitations in comparison to previously developed approaches), issues relevant to development of platforms (i.e., platform configuration approaches, joint platform design and optimization, and factors effective on forming proper platform types), and issues relevant to knowledge management and modeling of families and platforms for facilitating and supporting future design efforts. Through a comparison with previous research, new achievements are discussed and the remaining challenges and potential new research areas in this field are addressed.

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