There is a lot of data-driven research being released on EVs. However, a lot of this research is based on assumptions of EV usage rather than actual data, because buying a fleet of EVs in order to collect its data is very expensive. Also, there isn’t an abundance of public data on EV usage available to researchers.
Our idea was to model EVs using eBike data. This would be a much cheaper solution than purchasing an entire fleet of vehicles. In this study, we analyzed 70GB of usage data from eBikes, as well as data logged from pure EVs and hybrids to determine whether eBike usage could model EV usage.
We found that EVs actually cannot be modelled using eBike data, because they are used differently. This includes the time of charging, the state of charge when charging begins, duration of charging, destinations, and trip durations. Moreover, the distribution of months of operation and trip lengths differ at a fundamental level.
Access the paper here
C5. Simon Dominik Fink and Lukasz Golab and Srinivasan Keshav and Hermann de Meer. (2017). How Similar is the Usage of Electric Cars and Electric Bicycles?. ACM eEnergy EVSys Workshop, (334–340)
What would be the return on investment of transitioning a taxi company’s fleet to electric vehicles?
We built a Bayesian model to calculate the return on investment when switching to electric vehicles. The model can be configured with several input parameters, including the type of vehicle to be tested, electricity and gasoline prices, and roadside charging/battery switching infrastructure assumptions.
We evaluated our model using location data collected from over 500 Yellow Cab San Francisco (YCSF) taxis. Using prices at the time of the study, 2014, we found that transitioning YCSF’s fleet to battery electric vehicles and hybrid vehicles was indeed profitable. Moreover, given that gasoline prices in San Francisco were only 5.4% higher than the rest of the US, but electricity prices were 75% higher, taxi companies with similar mobility patterns in other cities were likely to profit more than YCSF. Generally, when gas prices rise above a certain level, EVs will give a better return on investment.
We’d like to note that at the time of this study, we made the assumption that there would be battery swapping stations set up around San Francisco to allow EVs to recharge immediately (see: Better Place). As part of our study, we determined that the optimal place to put these swapping stations would be at the airport, where longer taxi trips would likely drain the battery of an EV. Unfortunately, the infrastructure for these stations was never realized.
Access the paper here
J20. T Carpenter*, AR Curtis*, S Keshav. (2014). The return on investment for taxi companies transitioning to electric vehicles. Transportation. 41(4)
Sizing Finite-Population Vehicle Pools
Range anxiety, or the fear that a battery will run out before reaching a destination, is a factor that prevents customers from buying electric cars. Solutions like battery switching, adding more charging outlets, and larger batteries have been proposed, but in 2014 BMW offered a simpler solution: what if when a dealership sells an electric car, they automatically “subscribe” the buyer to a program that allows them to borrow gasoline cars for longer trips? That is, whenever an EV car owner needs to make a longer trip that will outlast the car’s battery range, they can come to the dealership, trade in a subscription coupon for a gasoline car, and rent out that gasoline car free of charge that day.
The question then becomes: how many gasoline cars does a dealership need to reserve for this program? In this study, we analyzed four different methods of sizing this vehicle pool, one of which doesn’t need any prior data or training. The following paper describes these methods in detail.
J16. Tommy Carpenter and Srinivasan Keshav and Johnny Wong. (2014). Sizing Finite-Population Vehicle Pools. IEEE Trans. Intelligent Transportation Systems. PP(99)
Bikeshare Pool Sizing for Bike-And-Ride Multimodal Transit
In shared bike-and-ride systems, commuters can ride a shared bicycle from home to a public transportation station, drop the bicycle off in a pool, take public transportation, pick up another bicycle from a pool at their destination stop, then ride to their final destination. So, how many bicycles would be needed at each transportation stop? A naive solution would be to have two bicycles for each commuter, one at the stop they board public transportation, and one at the stop they get off. The caveat is that this would be prohibitively expensive. So, what would be the smallest number of bikes that should be available at each public transportation station for this to work?
It turns out that the mathematical problem for sizing these bikeshares has the same structure as the mathematical problem for sizing vehicle pools, which was described above. By using this method, we were able to reduce the number of bicycles in each pool by between 39% to 75%, compared to having two bikes per commuter.
J6. G. Tang and S. Keshav and L. Golab and K. Wui. (2018). Bikeshare Pool Sizing for Bike-And-Ride Multimodal Transit. IEEE Transactions on Intelligent Transportation Systems.
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.
Range anxiety is a significant reason why people are hesitant to buy e-bikes – they’re afraid of running out of battery in the middle of a ride, with no place to recharge. It doesn’t help that e-bikes often don’t have a gauge that shows how much distance is left in a bike battery. Instead, most e-bikes have a display that reveals battery voltage—though we can all agree that it’s difficult to determine the remaining range on an e-bike from this number.
Although e-bike manufacturers do publish the maximum range of their models, we found that this number isn’t accurate for all riders, since some people ride more aggressively than others.
Using data from a fleet of 31 sensor-equipped e-bikes used in the University of Waterloo WeBike project, combined with OpenStreetMap data, we evaluated two range prediction methods for e-bikes. The first model is a simple one, since it just takes into account the average battery usage from past trips. The second model is a more complex linear regression model that considers the characteristics of the anticipated route (such as off-road percentage, the number of stop signs, and the number of traffic lights), as well as battery temperature.
We found that the more complex linear regression model didn’t perform much better than the simpler one. Using real trip data, our predictions using the simple model were usually within a 10% of the actual remaining range at the end of the trip.
These results should be of interest to e-bike manufacturers because the simple model we tested, which gave promising results, can be implemented as a simple on-board prediction technique. Since most e-bikes have an odometer built in, making this odometer data accessible and adding the ability to measure battery voltage and current are the only main additions needed to implement our technique inexpensively.
L. Gebhard, L. Golab. S. Keshav, and H. de Meer, “Range prediction for electric bicycles,” Proc. ACM e-Energy 2016.
E‐bikes are revolutionizing transportation in China and parts of Europe, yet little is known about user patterns in North America, particularly Canadian cities where uptake tends to lag. To fill this gap in knowledge, the University of Waterloo studied a sample of e-bike riders over three years in Kitchener-Waterloo, Ontario, Canada. The field trial, called WeBike, amassed over 150GB of data on e-bike usage by faculty, staff, and students from the summer of 2014 until spring of 2017.
Three papers were published by the University of Waterloo analyzing this data – the first two are quantitative in nature, and the last one is qualitative.
Quantitative data collected from the e-bikes included GPS, acceleration, and battery charge and discharge data. Based on our analysis, we draw several conclusions:
- Over 6000 trips, students made more e-bike trips than faculty and staff members, were more likely to ride in the evening, and had lower average speed trips.
- The primary purpose of e-bikes in the trial were used for commuting.
- Most trips lasted less than 20 minutes.
- Most trips had an average speed of 15–23 km/h (while in motion) with a mean of 18.9 km/h.
- The most prominent charging times for all riders was between 4pm and 7pm (likely coinciding with when riders returned home from school or work).
- Participation in the WeBike field trial did not significantly change participants’ sentiments towards various modes of transportation.
- There was little correlation between anticipated use of the e-bikes and actual use.
I. Rios, L. Golab. and S. Keshav, “Analyzing the Usage Patterns of Electric Bicycles,” EV-SYS Workshop at ACM e-Energy, 2016.
C. Gorenflo, I. Rios, L. Golab, S. Keshav, “Usage Patterns of Electric Bicycles: An Analysis of the WeBike Project,” Journal of Advanced Transportation, October 2017.
Some notable findings in the qualitative study include:
- Over time, nearly all participants self‐reported an increase in the total number of trips taken on their e‐bike, due to
- 1) Increased level of comfort with the technology and battery range
- 2) The motor allowing them to travel further than a regular bike or by foot
- 3) The e‐bike being generally viewed as faster than walking, cycling, and public transit
- Security concerns, specifically theft, were consistently cited. Participants felt the e‐bike was more susceptible to theft or vandalism because it looked expensive.
- Participants felt that e-bike usage facilitated more physical activity than driving a car.
- All participants stated that once the study was over, they would continue to ride the e‐bike they had been given as compensation for their participation in the study.
S. Edge, J. Dean, M. Cuomo, and S. Keshav, “Exploring e-bikes as a mode of sustainable transport: a temporal qualitative study of the perspectives of a sample of novice riders in a Canadian city“, Canadian Geographer/Le Geographe Canadien, April 2018.
- The general population in Canada is still unaware of e-bikes and their potential, so e-bike manufacturers should consider educating potential customers on e-bike usage.
- E-bike use does not cease in the winter months. Hence, e-bike manufacturers in countries with winter weather should consider offering built-in fenders and lights for safer winter cycling.
- E-bike manufacturers should target sales to non-bike users, such as seniors, rather than trying to displace sales of regular bicycles and for increasing physical activity for individuals with health conditions or limited mobility (e.g., those related to aging).