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 realworld (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. The results provide quantitative guidance pertaining to design decisions in the context of driving patterns.
While much of the previous research relies on FederalDriving Schedules originally developed for emission certification tests of conventional vehicles, consumer acceptance and market penetration will depend on PHEV performance under realistic driving conditions. Therefore, characterizing the actual driving is essential for PHEV design and control studies, and for establishing realistic forecasts pertaining to vehicle energy consumption and charging requirements. To achieve this goal, we analyze naturalistic driving data generated in Field Operational Tests (FOT) of passenger vehicles in Southeast Michigan. The FOT were originally conceived for evaluating driver interaction with advanced safety systems, but the databases are rich with information pertaining to vehicle energy. After the initial statistical analysis of the vehicle speed histories, the naturalistic driving schedules are used as input to the PHEV computer simulation to predict energy usage as a function of trip length. The highest specific energy, i.e. energy per mile, is critical for battery and motor sizing. As an illustration of the impact of actual driving, the low-energy and high-energy driving patterns would require PHEV20 battery sizes of 6.12 kWh and 13.6 kWh, respectively. This is determined assuming that the minimum state of charge (SOC) is 40%. In addition, the naturalistic driving databases are mined for information about vehicle resting time, i.e. time spent at typical locations during the 24-hour period. The locations include "home", "work", "large-business" such as a large retail store, and "small business", such as a gas station, and finally "residential" other than home. The characterization of vehicle daily missions supports analysis of charging schedules, as it indicates times spent at given locations as well as the likely battery SOC at the time of arrival.
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). This is a finding of high relevance for forecasting technology diffusion, consumer acceptance, and impact of PHEVs on power grid. These differences increase progressively with desired AER due to increasing energy/mile usage of real world driving with distance.
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