An EV charging in the driveway in front of a house and garage.

New Report Finds Emission-Optimized Charging Can Make Electric Vehicles Nearly 20 Percent Cleaner Annually

 September 10, 2019 

WattTime analysis shows smart timing of EV charging can reduce associated grid emissions throughout the U.S. and support renewable energy growth

OAKLAND, CA – September 10, 2019 — Environmental tech nonprofit WattTime today announced the release of a new report, How Emissions-Optimized EV Charging Enables Cleaner Electric Vehicles.

The analysis explores how correctly implemented emissions-optimized charging for electric vehicles (EVs) can further improve their environmental performance, as measured by grid emissions associated with EV charging. It also estimates aggregate environmental benefit based on 2030 adoption rates for EVs. 

While all EVs are cleaner than the average internal combustion engine auto—even when charged on a dirty grid—WattTime’s report found that smarter charging can make EVs even cleaner. Top report takeaways include: 

  • Emissions-optimized EV charging can further reduce associated emissions nearly 20 percent annually and up to 90 percent on individual days, above and beyond standard EV charging (which is already cleaner than gasoline-burning cars)
  • The most promising opportunities for emissions reductions exist on grids with highly variable emissions rates—a scenario likely to become more and more common as states add more renewable energy to legacy fossil-fueled grids
  • In addition to helping decarbonize the transportation sector, emissions-optimized charging can aid renewable energy grid integration, reduce curtailment of surplus wind and solar, and has the potential to decrease pollution regionally on air quality alert days 

“Our research shows that—without ever having to touch the electric powertrain’s efficiency—this smarter method of charging essentially gives an instant ‘MPGe boost’ to EVs,” said Christy Lewis, WattTime analyst and lead author of the report. “There’s a lot of talk about how EVs can act as a grid asset, and now we have analysis that shows a compelling value proposition for using software intelligence to shift charging to moments of clean power; for once, there is an ‘eco easy-button’ that EVs can use to automatically reduce emissions at scale.” 

The analysis compares emissions-optimized charging vs. baseline EV charging in multiple regions of the U.S. The report examines four U.S. grids in varying degrees of transition from fossil fuels to renewables, including grids in California, New York, the Midwest, and the Southwest. Two common charging scenarios were compared: daytime workplace charging and overnight at-home charging. In addition, WattTime considered both average-mileage and high-mileage driver profiles. 

What makes this analysis particularly valuable is WattTime’s use of real-time marginal emissions data—as opposed to annualized average emissions numbers—to get a more-granular look at EV charging and quantify its true emissions impact. Through that lens, the report then examines the effects of implementing a time-based emissions signal, like Automated Emissions Reduction (AER) software, to automatically shift charge times to moments of cleaner power. AER uses past, present, and predictive grid data—combined with sophisticated algorithms and machine learning—to allow any internet-connected smart device to optimize its energy use in order to reduce CO₂ or other pollutants. 

Three major, converging trends prompted WattTime’s analysis:

  1. Electric vehicle adoption continues to surge in the United States, and is predicted to leap from 1 million in 2018 to nearly 19 million in 2030
  2. In tandem with that growth, a proliferation of smart, level 2 EV charging stations in homes, offices, and public spaces will offer opportunities to optimize charge timing, since they typically require less time than the full charge window to recharge an EV battery.
  3. Grid emissions rates are becoming increasingly variable thanks to the addition of more renewable energy sources to the nation’s grids. This constant variation—occurring as much as every five minutes—presents an opportunity to sync EV charging with times of clean energy and avoid dirty energy.

The potential for sizable carbon emissions reductions revealed by the report is clear: If smarter charging were to be deployed across California’s target of 5 million zero-emissions vehicles (ZEV) by 2030, it would result in the equivalent of taking over 180,000 gasoline-burning cars off the road. Similarly, if applied to New York’s target of 2 million EVs by 2030, smarter charging would result in the equivalent removal of almost 48,000 gasoline-burning cars. These numbers are above and beyond any baseline emissions benefits provided by EVs.

“Although emissions reductions are probably the most exciting direct result of smarter EV charging, the ripple effect is truly outstanding,” said Lewis. “We can use EV adoption as a tool to drive the clean energy transition. By allowing EV chargers to sync with times of renewable energy on the grid, we can make them even more competitive and cost-effective than fossil-fueled power.”

Emissions-optimized charging via AER software has already been implemented commercially by Enel X (formerly eMotorwerks), which offers the feature in their JuiceBox Green 40 EV charger.

About WattTime
WattTime is a nonprofit with a software tech startup DNA, dedicated to giving everyone everywhere the power to choose clean energy. We invented Automated Emissions Reduction (AER), which allows utilities, IoT device and energy storage companies, and any end user to effortlessly reduce emissions from energy, when and where they happen. Our cutting-edge insights and algorithms, coupled with machine learning, can shift the timing of flexible electricity use to sync with times of cleaner energy and avoid times of dirtier energy. We sell solutions that make it easy for anyone to achieve emissions reductions without compromising cost and user experience. WattTime was founded by PhD researchers from the University of California, Berkeley, and in 2017 became a subsidiary of Rocky Mountain Institute. For more information, please visit

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