This paper centers on the estimation of the total energy consumption for personal transportation in the United States, to include fossil fuel and/or electricity consumption, depending on vehicle type. The bottom-up sector-based estimation method introduced here contributes to a computational tool under development at The Ohio State University for assisting decision making in energy policy, pricing, and investment. In this work, driving patterns are classified into two categories: commuting to work, and driving for leisure and shopping. For commuting, distribution of distance data is available in the literature. Leisure/shopping driving durations are estimated using activity patterns for a driving population, modeled using a heterogeneous Markov chain. A backward vehicle dynamic simulator is used to compute energy consumption for different vehicle types. Key findings of the current study include: (i) Independent of the total number of miles driven annually, the higher the vehicle electrification the lower the total primary energy consumption. (ii) With the modeling in this work, the percentage of trips that purely electric vehicles are unable to complete varies from 7% to 13% for driving distances up to 20000 miles per year. The percentage increases significantly for driving distances over that threshold, owing to intrinsic limitations of the battery.
Personal Transportation Energy Consumption
MARANO, VINCENZO;
2012-01-01
Abstract
This paper centers on the estimation of the total energy consumption for personal transportation in the United States, to include fossil fuel and/or electricity consumption, depending on vehicle type. The bottom-up sector-based estimation method introduced here contributes to a computational tool under development at The Ohio State University for assisting decision making in energy policy, pricing, and investment. In this work, driving patterns are classified into two categories: commuting to work, and driving for leisure and shopping. For commuting, distribution of distance data is available in the literature. Leisure/shopping driving durations are estimated using activity patterns for a driving population, modeled using a heterogeneous Markov chain. A backward vehicle dynamic simulator is used to compute energy consumption for different vehicle types. Key findings of the current study include: (i) Independent of the total number of miles driven annually, the higher the vehicle electrification the lower the total primary energy consumption. (ii) With the modeling in this work, the percentage of trips that purely electric vehicles are unable to complete varies from 7% to 13% for driving distances up to 20000 miles per year. The percentage increases significantly for driving distances over that threshold, owing to intrinsic limitations of the battery.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.