A significant barrier to the adoption of e-bikes is range anxiety, or the fear of running out of battery with no place to recharge. Currently, e-bikes do not display the estimated range available. Instead, a digital display shows battery voltage, but it is difficult to estimate the remaining range of a bike based on this value. Though e-bike manufacturers do publish the maximum range of their models, we found that this number is not an accurate predictor for all riders, depending on how aggressively they ride. Note that any additional hardware required for range prediction must be inexpensive, in order to keep overall prices low.
Using data from a fleet of 31 sensor-equipped e-bikes used in the University of Waterloo WeBike project, combined with OpenStreetMap data, we evaluate two range prediction models for e-bikes. The first model is a simple one, based on the average battery consumption 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 a simple on-board prediction technique can be implemented by measuring battery voltage, battery current, and mileage. Since most e-bikes have an odometer built in, by making this odometer data accessible and deploying additional sensors at the battery, our technique can be implemented inexpensively.
L. Gebhard, L. Golab. S. Keshav, and H. de Meer, “Range prediction for electric bicycles,” Proc. ACM e-Energy 2016.
Electric vehicles (EVs) pose a challenge to the electrical grid in two ways.
- First, large-scale introductions of EVs pose a significant load to the grid. An EV can be charged with a load of up to 19.2kW (with Level 2 chargers), whereas a typical North American home has an average load of 1kW – this means a single EV could impose a load as large as that imposed by nearly twenty average homes.
- Secondly, the load posed by an EV is variable by time and location: its load on a grid will unpredictably disappear when it is being driven. It might then charge at a different location, re-appearing at a different part of the electricity distribution network.
Since a typical EV charger is located within 3km of the nearest substation, the transmission delay between any charger and its connected substation is less than 1ms. As such, we can design a distributed control algorithm that adjusts the charging rate of an EV every few milliseconds, in response to the load being placed on the overall distribution system. For example, if an EV is charging at a rate that affects the reliability of the grid, its charging rate can be decreased.
Three papers were written on this subject. The first paper introduces the problem and describes how the congestion control problem for a grid distribution system is similar to the congestion control problem in the Internet.
- O. Ardakanian, C. Rosenberg, and S. Keshav. Real Time Distributed Congestion Control for Electrical Vehicle Charging (invited paper), ACM SIGMETRICS Performance Evaluation Review 40.3 (2012): 38-42.
By using a mathematical framework originally developed for rate control in the Internet (TCP), each EV charger in the grid can independently update its charging rate, while ensuring that the overall load on the grid stays at an ideal level, the allocated rates for each charger are proportionally fair, and that these allocations are optimal. The second paper in this series focuses on a static network scenario, in which the non-EV load is constant, and a fixed number of EVs are connected to chargers.
- O. Ardakanian, C. Rosenberg, and S. Keshav. Distributed Control of Electric Vehicle Charging, Proc. ACM e-Energy, May 2013. Winner of Best Paper Award.
The third paper goes into detail about the dynamic network scenario, which involves variable home loads and number of plugged-in EVs. Since the dynamic network scenario can be decomposed into a series of static intervals, the static control algorithm described above can be extended to be used in a dynamic network.
- O. Ardakanian, S. Keshav, C. Rosenberg. Real-Time Distributed Control for Smart Electric Vehicle Chargers: From a Static to a Dynamic Study, IEEE Transactions on Smart Grid, vol.5, no.5, pp. 2295-2305, Sept. 2014.
We show that in a test setting, only 70 EVs could be fully charged without control, whereas up to around 700 EVs can be fully charged using our control algorithm. This work was further extended to integrate EV charging control with control of distributed storage, while accounting for distributed solar generation. Details can be found here: O. Ardakanian, S. Keshav, C. Rosenberg, “Integration of Renewable Generation and Elastic Loads into Distribution Grids,” Springer, 2016.