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Environmental performance of plug-in hybrid electric vehicles: a case study for the Greater Toronto Area

Leon Raykin,   University of Toronto
Heather MacLean,   University of Toronto
Matthew J. Roorda,   University of Toronto

Plug-in hybrid electric vehicles (PHEVs) use electricity received from the grid and stored in large on-board batteries to partially displace liquid fuels as an energy source for propulsion.  These vehicles represent one potential approach to reducing some of the negative environmental impacts of light duty vehicles.  However, the environmental performance of PHEVs depends on the electricity source used for charging (1), the driving and charging patterns (2, 3), the vehicle characteristics (4), as well as the full life cycle of activities associated with vehicle production, use, and end-of-life (5, 6).

The current study uses a life cycle approach to analyze the environmental performance of PHEVs and compares this performance to that of conventional gasoline vehicles (CVs) and gasoline hybrid electric vehicles (HEVs).  Comparisons are made across a representative sample of drivers in the Greater Toronto Area (GTA) whose commutes differ in terms of start time, distance traveled, and driving conditions.  In this way, we establish the sensitivity of the results to variation in driving and charging patterns.  The environmental metrics examined include petroleum energy use and greenhouse gas (GHG) emissions.

Our preliminary results indicate that a PHEV with a 30 kilometer all-electric range charged nightly during off-peak hours and used by a typical commuter in Ontario can reduce life cycle GHG emissions (including vehicle production and use) by 50 – 70% compared to a CV and 25 – 50% compared to a HEV, depending on the assumed electricity supply mix.  Further analyses will examine alternative PHEV configurations and employ disaggregated GTA travel data to determine the impact of driving and charging patterns on the results.

This study examines the environmental performance of PHEVs and demonstrates the sensitivity of the results to variation in several important parameters.  These findings are expected to inform policies related to improving the environmental sustainability of the personal transportation sector.   


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