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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.
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.
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).
As grid-provided electricity continues to rise in cost, individuals, small companies, and building operators are increasingly looking to offset grid usage using solar photovoltaic (PV) panels and energy storage. When constrained by a budget however, it is difficult to predict the optimal ratio of solar PV panels and storage that one should buy to completely or partially offset grid usage.
We tested three ways to determine the possible sizing(*) required to meet the anticipated load in an off-grid system. Out of the three methods–simulation, optimization, and stochastic network calculus–we found that the easiest method, simulation, also provided the best results.
(*) sizing: the power or energy size of the storage in kW/kWh, and the size of solar generation in kWp.
We are currently building a web application to help predict the best sizing for a solar energy system. The tool will be linked here when completed.
F. Kazhamiaka, S. Keshav, and C. Rosenberg, Robust and Practical Approaches for Solar PV and Storage Sizing, Proc. ACM eEnergy 2018, June 2018.
The best-known lithium-ion battery model whose parameters can be calibrated entirely from a battery’s manufacturer-provided specifications (ie. internal resistance, nominal capacity, voltage at full charge, etc) was proposed by Tremblay et al and is widely use. However, it has some shortcomings. For instance, it has low fidelity at high charge and discharge rates. Moreover, it only models a cell, and does not model the battery management system. This makes it unsuitable for evaluating practical storage systems.
We propose an alternative, the Power-based Integrated (PI) model, whose parameters can also be calibrated entirely from manufacturer specifications, but has much higher fidelity across a wide range of charge/discharge rates. This model is freely available in the public domain as a Matlab system block compatible with Simulink simulation software. Access the block here.
We suggest using our model when:
- Simulating an energy system with storage by modeling its power flows. In this case, it is desirable to have a battery model that uses power as input because power is conserved (note that the Tremblay model uses current as input). The PI model uses power as input.
- Modeling the battery management system (BMS) – the BMS protects the cells in a battery from being damaged due to improper use such as under/over-charging. Modelling the BMS in the PI model prevents the simulated battery from being used in unrealistic ways.
- Low error – based on validation performed in the experiment, we see that the PI model has a mean absolute voltage error of less than 0.1V across a wide range of C-rates.
F. Kazhamiaka, S. Keshav, C. Rosenberg, and K.-H. Pettinger, ”Simple Spec-Based Modelling of Lithium-Ion Batteries, ” IEEE Transactions on Energy Conversion, Vol 33, No. 4, December 2018.