Keywords
Electric vehicle, Charging, Air quality, Emissions
Start Date
16-9-2020 11:00 AM
End Date
16-9-2020 11:20 AM
Abstract
The deployment of passenger EVs is producing a profound impact on both the transport and electricity sectors. Previous studies have not had access to the new wealth of data on how EVs are charged and the resulting effect on the mix of electricity generation sources and upstream emissions. In this study, charging profiles are taken from the two largest demonstrations of passenger Plug-in EVs in the UK and were combined with a concurrent dataset of the time-varying electricity generation mix. Calculations are made of both greenhouse gas and air quality pollutant emissions produced due to the charging of EVs. Smart charging profiles from the new data are explored alongside retrospective theoretical smart charging use cases. A partial Well-to-Wheel method is used to compare the results of this study with equivalent internal combustion engine vehicles. A Battery-EV (BEV) in the UK produced 41g CO2, 30mg NOX, and 0.75mg primary PM2.5 per km in 2019 due to the generation of electricity. The mix of sources supplying BEV charging was close to the UK average. Real world and theoretical smart charging were found to reduce CO2 emissions by 10% and 15%, respectively. Population exposure to these air pollutants was then assessed using an air quality simulation tool, the U.K Integrated Assessment Model. The contribution of a BEV’s air pollutant emissions from the generation of electricity to the average population’s exposure to NOx was 94% lower than a compliant Euro 6 diesel vehicle’s exhaust emissions (per kilometre). However, as the largest contribution to particulate emissions for both vehicle types was non-exhaust emissions, only a 26% reduction was found for PM2.5. This study shows the emissions resulting from BEV use in a decarbonising electricity system are sensitive to the time of day the vehicle is charged and currently contribute less to the population’s exposure to air pollutants than traditional vehicle technologies.
Holistic Assessment of Air Pollution and Climate Impacts from Electric Vehicle Charging
The deployment of passenger EVs is producing a profound impact on both the transport and electricity sectors. Previous studies have not had access to the new wealth of data on how EVs are charged and the resulting effect on the mix of electricity generation sources and upstream emissions. In this study, charging profiles are taken from the two largest demonstrations of passenger Plug-in EVs in the UK and were combined with a concurrent dataset of the time-varying electricity generation mix. Calculations are made of both greenhouse gas and air quality pollutant emissions produced due to the charging of EVs. Smart charging profiles from the new data are explored alongside retrospective theoretical smart charging use cases. A partial Well-to-Wheel method is used to compare the results of this study with equivalent internal combustion engine vehicles. A Battery-EV (BEV) in the UK produced 41g CO2, 30mg NOX, and 0.75mg primary PM2.5 per km in 2019 due to the generation of electricity. The mix of sources supplying BEV charging was close to the UK average. Real world and theoretical smart charging were found to reduce CO2 emissions by 10% and 15%, respectively. Population exposure to these air pollutants was then assessed using an air quality simulation tool, the U.K Integrated Assessment Model. The contribution of a BEV’s air pollutant emissions from the generation of electricity to the average population’s exposure to NOx was 94% lower than a compliant Euro 6 diesel vehicle’s exhaust emissions (per kilometre). However, as the largest contribution to particulate emissions for both vehicle types was non-exhaust emissions, only a 26% reduction was found for PM2.5. This study shows the emissions resulting from BEV use in a decarbonising electricity system are sensitive to the time of day the vehicle is charged and currently contribute less to the population’s exposure to air pollutants than traditional vehicle technologies.
Stream and Session
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