Transcripción del resumen del autor: This paper proposed a set of representative real-world driving cycles in Midwestern US, which are capable of capturing the dependence of driving patterns on driving distance. Recent analyses of the real-world driving in USA show that most of certification cycles lead to underestimation of energy consumption per mile compared to the naturalistic driving patterns. Real-world driving is a mix of local driving and highway driving. Furthermore, the driving patterns show high dependency on the driving distance. To cover the wide range of real-world driving distances, five synthetic cycles are generated ranging from 4.78 miles to 40.71 miles following the real-world driving distance distribution. Each individual cycle is constructed by a stochastic process using the extracted driving information from the naturalistic trip data in the Midwestern US. While constructing the cycle set, the statistical criteria for validating the cycle representativeness are processed to capture the clear distance dependency and remove random variations. The synthesized cycles are subsequently used for Plug-in Hybrid Electric Vehicle (PHEVs) or Hybrid Electric Vehicle (HEVs) design and control studies for the assessment of the impact of electrified vehicles on the grid.

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