Approach for an Assistance System for E-Bikes to Implement Rider-Adaptive Support Karlsruhe University of Applied Sciences

Format:

Book

Conference/Event

Author/Creator:

Rauch, Yannick, author.

Contributor:

Kriesten, Reiner

Conference Name:

2024 Stuttgart International Symposium (2024-07-02 : Stuttgart, Germany)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

When riding an e-bike, riders are faced with the question of whether there is enough energy left in the battery to reach the destination with the desired level of support. Therefore, e-bike riders have range anxiety. Specifically, this describes the fear that the battery charge will be exhausted before there is an opportunity to recharge it and that it will no longer be possible to use the electric support. However, e-bike riders have so far had to decide for themselves whether the available battery charge is sufficient for riding the planned route or whether the desired destination can be reached. In this context, the challenge is to decide how much electric propulsion support can be used so that an appropriate amount of effort can be achieved for the entire ride. In order to assist e-bike riders with this problem, the objective of this paper is to present an approach towards a system that provides rider-adaptive support over the entire ride of a defined route. This involves using the propulsion support in such a way that the rider requires an appropriate level of effort. The rider-adaptive support is to be implemented via an automatic mode of the e-bike propulsion system, which automatically sets the corresponding support intensity. The assistance system is designed to ensure that a planned destination can be reached using the rider-adaptive support. To achieve this, the use of the propulsion support is optimized and automatically adjusted according to the available energy and the route to be cycled. The implementation will be carried out as a predictive energy management system. This calculates an optimized support strategy based on an energy demand prediction for the route to be cycled and the available energy of the e-bike battery

Notes:

Vendor supplied data

Publisher Number:

2024-01-2979

Access Restriction:

Restricted for use by site license