The adoption of electric vehicles has increased over the past few years, partly due to different government policies such purchase rebates, charging station rebates, high-occupancy lane access for EVs, and more. However, it isn’t obvious which of these policies are the most successful in increasing EV adoption. Another thing to consider is that although rapid EV adoption is desirable, it has potential drawbacks including increased grid load and the need to build additional expensive charging stations.
We developed a Python-based tool that predicts EV adoption and usage, by simulating people’s behaviour when purchasing, driving, and charging EVs in a city. This tool can be used for purposes such as the following:
- Policy makers can estimate the impact of different policies on EV adoption
- Electrical utilities can estimate the spatial and temporal changes in energy demand on the electric grid, based on different levels of EV adoption and different EV technologies
- Charging station planners can estimate how different levels of EV adoption affect public charging station activity
- Battery manufacturers can determine how battery sizes would affect EV adoption and electrical load
If you are interested in using this tool, please contact Prof. Keshav at firstname.lastname@example.org.
We first used the tool to model EV adoption in San Francisco, tuning the parameters using data from a prior survey of people’s transportation needs. We did a ‘what-if’ analysis using the tool – for example, what are the effects of different policies and technology changes on EV adoption and usage in San Francisco? We found that:
- Increasing rebates by $2000 had the highest impact.
- Range anxiety is not an important deciding factor in EV adoption since most driving distances are short as in San Francisco.
- A consumer education program focused on the lower lifetime costs of EVs may not be worthwhile.
- Increasing the battery sizes of EVs results in increased charging load due to less gasoline use and less public charging station activity.
Adepetu, V. Arya, and S. Keshav. (2016). An Agent-Based Electric Vehicle Ecosystem Model: San Francisco Case Study. Transport Policy. 46: 109-122.
We also used the tool to model EV adoption in Los Angeles, California to determine the impact of high-capacity batteries and EV rebates on EV adoption. Simulating the different cases of battery costs and prices, we find that even in Los Angeles, a geographically spread out city, quintupling the battery size of cars at no additional cost improves EV adoption by only 5%. Since the price of EVs is a more significant barrier to adoption than range, to promote EV adoption, policy makers should focus more on affordability than giving manufacturers credits for improving EV range.
Adepetu and S. Keshav. (2015). The Relative Importance of Price and Driving Range on Electric Vehicle Adoption: Los Angeles Case Study. Transportation Journal. : 1-21.
The above two studies, written in 2015 and 2016, were conducted at a time when EV costs were relatively high. This meant that a large percentage of EV adopters were more affluent, and could afford to live close to their workplaces. This in turn meant that EV owners had very short commutes to work, causing battery size and range to be a minor consideration when purchasing a vehicle.
At this moment EVs are still not the cheapest alternative, so we will continue seeing a trend where car prices are a higher barrier to EV adoption than car range. We’re predicting that range will become a major factor in EV adoption in North America only when EV prices fall on par with gasoline car prices.