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)
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).