Smart e‐bike monitoring system: real‐time open source and open hardware GPS assistance and sensor data for electrically‐assisted bicycles

Abstract

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.

Why cycling matters for electric mobility: towards diverse, active and sustainable e-mobilities

“…The material used in this paper stems from lending these bikes to 80 commuters for six to eight week periods. The quantitative material is discussed elsewhere (Cairns et al 2017;Kiefer and Behrendt 2015). The material presented in this paper has not been previously published.…”

Usage Patterns of Electric Bicycles: An Analysis of the WeBike Project

“…Of these, our work is most similar to Kiefer and Behrendt [17], Fyhri and Fearnley [18], Paefgen and Michahelles [19], and Flüchter and Wortmann [20] which also focus on usage patterns. Kiefer and Behrendt collected GPS and motor assistance level data, but they emphasized developing a hardware system reusable by other research groups rather than data analysis.…”

An Overview of Existing Experiences with Solar-Powered E-Bikes

“…As such, a lot of research might be needed to cover this new field of interest. Gorenflo et al (2017) [35], Kiefer and Behrendt (2016) [38], Fyhri and Fearnley (2015) [19], Fluchter and Wortmann (2014) [39], as well as Paefgen and Michahelles (2010) [40], focused, for instance, on usage patterns. Gorenflo et al (2017) conducted a 3-year field trial with 33 sensor-equipped e-bikes [35].…”

“…Gorenflo et al (2017) found that the students that participated in this field trial usually let their e-bike's batteries to get totally empty and charged them when the battery status was around 15-45%, while university staff members usually charged their batteries when they were above 70% [35]. Kiefer and Behrendt (2016) used GPS and motor assistance level data [38]. Fyhri and Fearnley (2015) analyzed odometer data collected by the e-bikes [19].…”