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This paper proposes a framework to perform design optimization of a series PHEV and investigates the impact of using real-world driving inputs on final design. Real-World driving is characterized from a database of naturalistic driving generated in Field Operational Tests. The procedure utilizes Markov chains to generate synthetic drive cycles representative of real-world driving. Subsequently, PHEV optimization is performed in two steps. First the optimal battery and motor sizes to most efficiently achieve a desired All Electric Range (AER) are determined. A synthetic cycle representative of driving over a given range is used for function evaluations. Then, the optimal engine size is obtained by considering fuel economy in the charge sustaining (CS) mode. The higher power/energy demands of real-world cycles lead to PHEV designs with substantially larger batteries and engines than those developed using repetitions of the federal urban cycle (UDDS).

The paper investigates the impact of the drive cycle choice on the Plug-in Hybrid Electric Vehicle (PHEV) design, and particularly the selection of component sizes. Models of representative Power-Split and Series PHEVs have been built and validated first. Then, the performance and energy/power usage metrics were obtained by simulating the vehicle behavior over real-world (naturalistic) drive cycles recorded during Field Operational Tests in South East Michigan. The PHEV performance predictions obtained with real-world driving cycles are in stark contrast to the results obtained by using a sequence of repeated federal drive cycles. Longer commutes require much higher peak power and consume much greater amount of energy per mile than EPA UDDS or HWFET cycle. The second part of the paper investigates the sensitivities of the PHEV attributes, such as the charge depleting range and the fuel economy in the charge sustaining mode, to component size variations.