Easy E-Bike

The Easy E-Bike project uses the results of previous research and development activities on energy and range factors for electric driven vehicles. The goal is to develop an algorithm for predictive energy and range management of e-bikes, which will result in user-related energy and range solutions for e-bikes. This should result in applications and systems that make e-bike riding even easier, better and more beginner-friendly. The project is being carried out independently by IEEM.

As for drives with e-vehicles, the question of the remaining range also arises before starting e-bike rides. In particular, it is necessary to decide whether the available battery charge is sufficient for driving the planned route or whether the desired destination can be reached. Currently, only information about the remaining possible range is available as distance values with reference to specific support intensities. This does not take into account route-specific influences on energy consumption during the ride, nor does it take into account the use of different support intensities by the e-bike riders.

In place of simple and unspecific range information, energy and dynamics prediction is used to calculate predictions of the expected route- and driver-specific energy consumption and of the required battery charge for planned routes. For this purpose there must be known in advance the route to be driven as well as all route characteristics relevant for the dynamics and energy analysis. With this data, the energy and dynamics prediction is then able to calculate parameters such as the electrical energy consumption, the speed curve, the effort required by the riders, etc.. If additional information about the athleticism of the e-bike riders and the technical characteristics of their e-bikes is known, more precise predictions can be made. In addition, the algorithm estimates the expected use of the available support intensities based on the calculated data. Thus, it is possible with the algorithm to support e-bike riders in planning their rides with a prediction of the expected need for battery charging.

The Online E-Bike Planner enables the planning of e-bike rides by using the energy and dynamics prediction to predict the expected energy demand. For this purpose, the planner offers an interactive map as well as the possibility of specific calculations by configuring and specifying the characteristics of the rider and his e-bike. Furthermore, it can be set the desired effort of the planned ride, so that a distinction can be made between sporty ambitious and everyday rides.

The predictive energy and range management (VERM) of the IEEM extends the energy and dynamics prediction by taking into account the current battery charge level of the e-bike as well as the energy available for the e-bike ride. Thus, before and during the ride, an automated calculation is done to determine the sufficiency of the available battery charge for the planned route. If it is not sufficient, the use of the support intensities is adjusted during the ride in order to support the riders specifically with the available electrical energy. Based on this, the support intensity can then be adjusted manually by the rider or automatically by the e-bike drive system during the ride. The energy and range management is performed regularly in order to react to deviations between the calculation and the actual ride.

This allows the app to collect the required battery charge level as well as other data recorded by the e-bike drive system, such as speed or pedalling frequency. During the ride, the current support mode to be selected is then displayed, which must be adjusted accordingly by the rider. In addition, the current speed, pedalling frequency, battery charge level and current position are displayed on a map. The map can also be expanded by a navigation function.

In addition to the transfer of knowledge from research, the project also offers students the opportunity to participate in the development process in the form of project work or theses.