The Smart E-Bike Monitoring System (SEMS) is an open-source platform for the acquisition of usage data from electrically-assisted bicycles (also called pedelecs). The system can monitor location, rider control data and other custom sensor input in real time. SEMS is designed to run from the e-bike battery, and requires no intervention from the rider.
The SEMS data feeds an online interface for (1) data analysis and (2) for riders to view their own data. The smart e-bike monitoring system is designed as an autonomous, modular and flexible system – the basic system can be replicated by other researchers and can be extended with modules to explore various issues in e-bike research.
The source code and hardware design are publicly available under General Public License, for non-commercial use: http://dx.doi.org/10.17033/DATA.00000016
The following paper discusses the monitoring system in detail:
Kiefer C, Behrendt F (2015) Smart E-Bike Monitoring System: realtime open-source and open hardware GPS, assistance and sensor data for electrically-assisted bicycles. Journal IET Intelligent Transport Systems: 1-10. http://dx.doi.org/10.1049/iet-its.2014.0251 and http://eprints.brighton.ac.uk/14321/
Abstract: The smart e-bike monitoring system (SEMS) is a platform for the real-time acquisition of usage data from electrically-assisted bikes (also called pedelecs or e-bikes). It is autonomous (runs off the bike battery), replicable (open source and open hardware), scalable (different fleet sizes) and modular (sensors can be added), so it can be used for further research and development. The system monitors location (global positioning system), rider control data (level of assistance) and other custom sensor input in real time. The SEMS data feeds an online interface for data analysis, for riders to view their own data and for sharing on social media. The basic system can be replicated by other researchers and can be extended with modules to explore various issues in e-bike research. The source code and hardware design are publicly available, under the General Public License, for non-commercial use. SEMS was implemented on 30 bikes and collected data during 10 months of real-word trials in the UK. This study details the design and implementation of the hardware and software, discusses the system use and explores features for future design iterations. The SEMS turns singular e-bikes into a networked fleet and is an example of the internet of things in the cycling context.