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Electric Vehicle Owners Drive Less Than We Thought

Analysis of home electricity usage reveals how much people charge – and therefore drive – their EVs.

Today’s post is co-authored with Fiona Burlig, James Bushnell & David Rapson.

It’s hard to overstate how different the vehicle market will be in 2035 if automakers like General Motors and Volvo stick to their plans to produce mostly electric vehicles (EVs) and stop selling gasoline-powered cars. EVs have been anointed as the replacement technology, but have a long way to go, comprising less than 1% of the vehicle fleet today. 

Meeting these lofty goals without imposing serious costs on consumers will require EVs to serve as close substitutes for gasoline cars. But is this the case so far? That’s an important question to answer as California leads the way on this transition and the Biden administration signals its intention to make the shift to EVs nationally. Yet the truth is we are far from understanding how much it will cost to fully transition to EVs. 

One way to start to answer this question is to look at how much people are driving their EVs. If households are putting as many miles on their EVs as on their gas-guzzling counterparts, this is a good sign for the EV revolution. On the other hand, if households only drive their EVs sparingly, it might be an indication that current EV technology simply isn’t as good. That could signal more innovation will be needed before consumers will make that switch en masse, or else the switch away from gasoline could be more costly than we hope.  


Despite the importance of this question, it turns out to be surprisingly hard to gather comprehensive data on how much EVs are driven (something we have lamented before!). Some researchers have used survey evidence, but these results come from people who are particularly excited to talk about their new EVs and may not represent average drivers. Even California policymakers are choosing to rely on just a few hundred households that have installed a dedicated electricity meter for their EVs to guess the behavior of hundreds of thousands of EV drivers. These meters are costly to install, so the households that install them probably don’t represent average drivers. The financial stakes for the economy are real: these estimates of EV electricity consumption feed into infrastructure planning and determine how Low Carbon Fuel Standard credits are allocated.

Measuring electric vehicle miles traveled

In a new working paper (which we will be presenting at next month’s Energy Institute POWER Conference), we provide the first at-scale estimates of “electric vehicle miles traveled” (eVMT) that represent the overall EV population in California. We combine nearly 12 billion hours of electricity meter measurements—a representative sample of roughly 10% of residential electricity meters in California’s largest utility, Pacific Gas & Electric—with household-level EV registration records from 2014 to 2017 to estimate how much EV charging is occurring at home. We then adjust this result to account for away-from-home charging to retrieve a measure of average eVMT. 

We find that when a household gets a new EV, electricity demand increases sharply, and then stays at this higher level. However, the increase we see is less than half the amount state regulators have assumed based on households with dedicated meters: only 2.9 kilowatt-hours (kWh) per day. That translates into about 5,300 miles of driving per year—roughly half as far as regulators’ estimates, and only half as far as people on average drive their gas-powered cars. Lucas found something similar using data from a nationally representative survey, although he’s looking at total miles driven, not eVMT, a distinction that matters for plug-in hybrid electric vehicles.

All told, this suggests that households may not yet view EVs as a good substitute for their gasoline-powered cars and that, unless there are major improvements in EV technology, regulators and policy-makers have more work to do to convince drivers to abandon their gasoline-powered cars for EVs.

Can this be right?

Our estimate has been reported on recently (see, e.g., here, here and here) and a number of people have raised questions about the results. We were surprised by our findings as well, and have taken a number of steps to stress-test them. Here are some of the details behind our estimates:

  • But, EV owners also charge away from home.  This is important, and we account for it in our estimates. We start by estimating the change in residential electricity use when households install EVs. We convert these estimates into eVMT in two steps: (i) we apply a fuel efficiency conversion (to go from home kWh to home-charged eVMT), which accounts for the fact that 1 kWh translates to different amounts of eVMT across models; and (ii) we use aggregate data on non-residential charging from the California Air Resource Board’s (CARB’s) Low Carbon Fuel Standard (LCFS) program to estimate out-of-home charging. CARB’s data include all nonresidential metered charging that earns an LCFS credit. Charging providers have a strong incentive to report to CARB: these LCFS credits are worth approximately $0.20 – $0.25 per kWh. Thus, we expect these data to cover the bulk of non-residential charging (including, for example, Tesla’s Supercharger network). Non-metered charging is necessarily excluded from these data. Even if non-metered charging were to make up 10% of all non-residential charging, however, our overall annual eVMT estimate would remain under 5,500. Using the CARB data, we calculate that EVs drive around 5,300 eVMT per year. We estimate that 75% of this, or approximately 3,975 eVMT come from at-home charging, and 25% or 1,325 eVMT come from out-of-home charging.
  • But, a lot of EV owners have rooftop solar. It is important to account for solar PV, as approximately 20% of the EV owners in our sample also have solar panels. We observe whether each household in our sample has a solar interconnection and when this interconnection occurred. We find that installing solar PV reduces a household’s (net) kWh consumed by 0.8 kWh per hour on average. We see that this reduction occurs during daytime hours only, giving us confidence that this control is working properly and that we’re accounting for rooftop solar in our estimates.
  • But, all EVs are not created equally. We recognize this and estimate home charging for three distinct groups of EVs: Teslas, non-Tesla battery-electric vehicles (like the Nissan Leaf and Chevy Bolts), and plug-in hybrid electric vehicles (like the Ford C-Max). Teslas increase household load by 0.24 kWh per hour on average. The non-Tesla battery electric vehicles like Leafs increase load by 0.10 kWh per hour, and the plug-in hybrids like the C-Max  increase load by 0.09 kWh per hour. This reflects both the fact that Teslas consume more electricity per eVMT than the other battery electric vehicles  and that Teslas in our sample are driven further than other vehicles off of home charging. We account for these differences – and the composition of vehicle types in our sample – when we calculate the average eVMT in the sample.
  • But, things have changed since 2017. This is certainly true, and there are reasons to believe that people who bought EVs over the last 3 years may drive them more than people who bought EVs from 2014-2017 (e.g., the newer vehicles have longer ranges, and people who drive a lot and were worried about range waited to buy their EVs). But, we do not see any detectable changes in our results from 2014 to 2017, and some of the same factors were at play over this time period. This makes us think that newer data might not be dramatically different, but we don’t know. 

What does this mean for policy?

Whatever the explanation for the lower EV miles driven, there are clear lessons for policymakers. First, EV manufacturers should be required to make eVMT data available to regulators and researchers, so that our results can be replicated in other settings. We spent years gaining access to the data for this study – but this process could be easier. EV manufacturers digitally record data from the cars that they sell – but they haven’t been required to share the information and they have little to no incentive to share it voluntarily.

Even utilities don’t know how much power is used by the cars in their service territories. Meanwhile, they are spending hundreds of millions of dollars upgrading electricity charging and distribution infrastructure and the companies making these investments, and the regulators approving them, have limited information about where the cars are, let alone how much electricity they are using. For example, in California, revisions to the Low Carbon Fuel Standard allow vehicle manufacturers to claim “incremental” credits for the electricity their cars use, but these regulations are set up in ways that continue to keep key decision makers out of the loop. We would support rules that require the car manufacturers to report usage data to all of the relevant government agencies. This could be made a condition for qualifying for publicly supported incentives. We also believe that agencies should be able to share such data with researchers (under confidentiality arrangements) who will perform analyses that are critical to improve EV policy.  

Second, much more policy innovation is needed to move 100% of road travel to electric. Rather than relying on bans and mandates to sell EVs, we could try giving drivers the right incentives. Pricing vehicle emissions would be a good start. At the moment, incentives are backwards. Electricity prices in the United States are low where the grid is dirtiest and high where the grid is cleanest. Some EV owners in California pay several times more to charge than their neighbors due to the vagaries of utility service territory boundaries. This is both inefficient and unfair. 

Collectively, we are only beginning to learn some of the most basic facts about the costs and benefits of transportation electrification. To inform efficient policy decisions going forward, we must democratize access to key data sources (like eVMT and charging behavior), acknowledge the fact that there is much we do not yet know, and create conditions that allow us to course-correct as new information becomes available.

Keep up with Energy Institute blogs, research, and events on Twitter @energyathaas.

Suggested citation: Burlig, Fiona, Bushnell, James, Rapson, David and Wolfram, Catherine. “Electric Vehicle Owners Drive Less Than We Thought” Energy Institute Blog, UC Berkeley, February 16, 2021,


Catherine Wolfram View All

Catherine Wolfram is Associate Dean for Academic Affairs and the Cora Jane Flood Professor of Business Administration at the Haas School of Business, University of California, Berkeley. ​She is the Program Director of the National Bureau of Economic Research's Environment and Energy Economics Program, Faculty Director of The E2e Project, a research organization focused on energy efficiency and a research affiliate at the Energy Institute at Haas. She is also an affiliated faculty member of in the Agriculture and Resource Economics department and the Energy and Resources Group at Berkeley.

Wolfram has published extensively on the economics of energy markets. Her work has analyzed rural electrification programs in the developing world, energy efficiency programs in the US, the effects of environmental regulation on energy markets and the impact of privatization and restructuring in the US and UK. She is currently implementing several randomized controlled trials to evaluate energy programs in the U.S., Ghana, and Kenya.

She received a PhD in Economics from MIT in 1996 and an AB from Harvard in 1989. Before joining the faculty at UC Berkeley, she was an Assistant Professor of Economics at Harvard.

19 thoughts on “Electric Vehicle Owners Drive Less Than We Thought Leave a comment

  1. I’m surprised that this paper expresses ‘surprise’ at the EV mileage findings. Prior papers on EV’s from the Institute and others have already documented that those purchasing EVs have‘above average’ incomes, which is also consistent with federal and state tax credit incentives which favor higher incomes. It might be expected that higher income individuals would be more than likely to live closer to their area of employment and also own multiple vehicles, where non-EV vehicles would be used for longer duration travel.
    Second, it was disturbing to see in your introduction that the incentives to push more EV sales could only be met by “imposing serious costs on consumers”. Your partial redemption came late in your presentation when you stated that “Rather than relying on bans and mandates to sell EVs, we could try giving drivers the right incentives”. Great point – positive incentives to encourage a preferred behavior rather than a negative incentive which will hurt all consumers. Unfortunately, your paper then reverts back to punative incentives by suggesting the need for additional emission penalties (which will punish all consumers) and strangely, better electricity pricing that seems to favor clean rather than dirty generation.
    Better electricity pricing is absolutely necessary but be careful what you wish for when it comes to EV’s. The recent paper by Severin Borenstein might not prove completelysupportive of EV’s especially when the cost of distribution, home charging capabilities, and other grid improvements to support increased demand from EV’s is factored in.
    Finally, if the economic policy focus is on carbon reduction, why focus on EV’s and not on more reliable, cleaner long term generation options like hydro and nuclear?

    • We can’t meet our emission targets by focusing only on the electricity sector. Transportation is nearly half of our emissions, and building emissions are another sizable chunk. Electricity being 100% clean will only make a significant dent if we electrify these other sectors. Truly zero emission vehicles are the only option in the future.

  2. 2X errors in estimates of EV consumption are big enough to cause lots of poor decisions, both by regulators and by private investors.
    “The financial stakes for the economy are real: these estimates of EV electricity consumption feed into infrastructure planning and …..”

    As your blog has mentioned multiple times, there are lots of issues arising from the night-time load created by recharging EVs. Rate structures, the value of central storage, cold-weather peak loads (relevant this week in Texas), various subsidy/rebate programs, inter-customer rate equity, and many other calculations incorporate estimates of EV penetration and energy consumption many years from now. You have done good work to notice and create preliminary estimates of the problem.

    Mandating better data reporting seems more politically acceptable than it would have been a year ago. But information about EV penetration and use will continue to be commercially sensitive, as auto companies chase what they hope is a major market. I suspect that confidentiality of individuals’ data is not what holds them back from sharing, and guaranteeing it won’t be enough. Perhaps some sort of third party aggregator can create data that removes information about manufacturers, yet is still useful for forecasting and policy.

  3. Were other sources of mileage data considered? DMV records odometer readings on transfer of ownership. Insurance companies seem to know approximate miles per year without owners providing them – collected from maintenance activities, tire checks/repair, etc? I was surprised when getting auto insurance quotes that the companies knew my rough mileage.

  4. This is fascinating research on a very topical issue. I fully agree with the data access issues. In Australia there is likely to be a very high correlation of EV ownership and access to rooftop solar (surely also true to California), and very powerful incentives to self-consume (in my own case, I save a dollar a day my diverting my excess rooftop PV into my little PHEV – which only stores 10 kWh). The diversion is easily done. I do wonder how much this might explain your findings ?

  5. Three Teslas, so far no change in my driving pattern, except slightly higher usage than the previous three ICE cars. As of now, here’s how it looks:
    Mileage in All Months Held Estm Annual Miles
    2012 Tesla S Signature 31,100 41 9,102
    2016 Tesla S 90D 65,000 57 13,684
    2021 Tesla Y Long Range 2,711 3 10,844 (1,300 supercharged miles, so 30% thus far)
    Average per year 11,210

    2010 Mercury Milan 20,580 33 7,484
    2006 Merc E320 Diesel 23,108 39 7,110
    2000 Mercedes S430 81,000 89 10,921
    Average per year 8,505

  6. I saw this shared on Facebook. Here were my comments.

    What a great (and easy) read, Katya. Thanks for sharing.
    I will add new thoughts into the mix
    • charging from 110v outlets in parking garages at work and MURBs. Most of this trickle charging wouldnt be accounted for.
    • 2017 is the inflection point in BEV range tech at competitive price points.
    … prior to this, there is a significant bias in early adopters who – live in more
    Complete communities, – fewer trips (eg retired), – more eco conscious to begin with.
    • related: driving EVs has been shown to make drivers more conscious of their trips, distances, and this likely leads to more efficiency journeys (combining trips, thinking twice about discretionary far-off destinations, etc) due to all the data on screen in the vehicle/dashboard/smart App.

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