E-bike load demand estimation for transport using cartographic data

Abstract

In remote areas of Sub-Saharan Africa, as well as in Rwanda, communities face the problem of finding an affordable and suitable way of transport for their daily life. Currently, the short-range transportation of people and goods rely on manual traction (e.g., bicycles, handmade wooden bicycle, etc.) and on small combustion engines. However, this region has a large renewable energy source (solar) which can help to mitigate this problem. Electric bicycles may be used to tackle the problem, although their use is still not largely widespread despite its potential. In addition, the local society does not have an easy way to accurately estimate the amount of energy consumed for a certain itinerary to be sure of how the electric vehicles perform. Thus, characteristic analysis of consumed energy is very important to analyze the performance of electric vehicle storage systems, model charging infrastructure, size vehicles, planning for itineraries, etc. This paper presents a methodology to estimate the power consumed by electric bikes for specific itineraries. In order to accomplish so, the WebPlotDigitizer tool and Google Maps were used to produce driving profile patterns. The results are compared against experimental data, showing the accuracy of the methodology presented. The simulated results show that the consumed power value differs by only 1.68% from the experimental recorded value; the difference can be explained by traffic congestion, the density of traffic, and the intersection that occurred during the experiment. The presented method of driving energy requirement estimation using WebPlotDigitizer is attractive, affordable, and easy to use.

Keywords

• Driving Pattern
• Electric vehicle
• Itinerary
• WebPlotDigitizer

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