Hochschule Karlsruhe Hochschule Karlsruhe - University of Applied Sciences
Hochschule Karlsruhe Hochschule Karlsruhe - University of Applied Sciences

Easy E-Bike

E-bike riding without range worries for a better riding experience

Getting around by bicycle or e-bike has the potential to partially replace motorised transport and contribute to reducing CO2 emissions from transportation. E-bikes in particular encourage people to switch to bicycles, as an electric drive in combination with muscle power helps them to overcome adverse environmental conditions such as strong winds or large differences in altitude. Thus, depending on the sportiness several miles with a varied scenery can be driven in a shorter time - but relaxed. This has also made the e-bike very popular as an outdoor sports device. However, the fun ends as soon as the battery is not sufficient. Therefore, knowing how far you can ride with the available energy of the battery is useful. In addition, if it is known how to use the electric support optimally and the available energy as efficiently as possible during range-critical journeys, this is even better. It is precisely for this problem that the approach of a predictive energy and range management system specially developed for e-bikes offers an energy control, so that an e-bike ride without range worries guarantees riding pleasure.

IEEM is developing a predictive energy and range management system specifically for e-bikes

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.

Range information for e-bikes

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.

Energy and dynamics prediction for e-bikes

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.

Online E-Bike Planner

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.

Try it now!
Online Planner

Any suggestions for improvement?
Then write to:
rudolf.schneespam prevention@h-ka.de

Predictive energy and range management

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.


For the use of the predictive energy and range management with a direct technical connection to the e-bike and its electronics, a demonstrator is being developed in form of a smartphone app. The app allows the planning of e-bike rides and already offers a selection of existing routes. For the usage of the VERM algorithm, information about the e-bike and the rider have to be entered by the user. The app and the drive system of the e-bike which is used for the development are connected via Bluetooth. For this purpose, a specially developed CAN-to-Bluetooth adapter (CAN2BT) is used.

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.


CAN accesses on the e-bike data with a CAN2Bluetooth adapter, which was specially developed by a student group (Andreas Bank, Christian Schmitt and Jonas Gentner).

Project participation

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.


Contact person for Development and VERM-App
Yannick Rauch, M.Sc.
Tel.: +49 (0)721 925-1657 
yannick.rauchspam prevention@h-ka.de

address & post


Contact person for the Online E-Bike Planner
Dipl.-Inform. Frank May
Tel.: +49(0)721 925-1658
frank.mayspam prevention@h-ka.de

address & post

Main focus

Energy and range management system
Intelligent bike functions
Support algorithm

Key data

Type of project: Academic research and development project
Duration of the project: since 04/2019
Project management: Prof. Dr.-Ing. Reiner Kriesten
Research & Development: Yannick Rauch, M.Sc.
Development: Michael Weber, M.Sc.
Online E-Bike Planner: Dipl.-Inform. Frank May
Public Relations: Dr. Rudolf Schnee
Funder: University of Karlsruhe

Institute of Energy Efficient Mobility (IEEM)
Moltkestr. 30
76133 Karlsruhe

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Institute of Energy Efficient Mobility (IEEM)
Postfach 2440
76012 Karlsruhe