This paper addresses the problem of motor activation on electric-power-assisted cycles that are not equipped with pedal torque sensors. The algorithm is based on inertial and motor velocity measurements along with pedal cadence. The problem is solved by a system that detects when the rider is on the saddle. Three different dynamics (longitudinal, lateral, and vertical) are analyzed, showing differences in their characteristics when the rider is on the saddle and when she is walking the bicycle. The problem is framed as a fault detection problem based on three independent analyses that are then fused. Experimental validation on a single-gear bicycle shows that the proposed solution outperforms the traditional cadence-based activation logic both reliability and reactiveness standpoints. The proposed system improves reactiveness compared with the standard cadence-based method without affecting safety.

An IMU-Driven Rider-on-Saddle Detection System for Electric-Power-Assisted Bicycles

Corno, Matteo;Berretta, Daniele;Savaresi, Sergio M.
2016-01-01

Abstract

This paper addresses the problem of motor activation on electric-power-assisted cycles that are not equipped with pedal torque sensors. The algorithm is based on inertial and motor velocity measurements along with pedal cadence. The problem is solved by a system that detects when the rider is on the saddle. Three different dynamics (longitudinal, lateral, and vertical) are analyzed, showing differences in their characteristics when the rider is on the saddle and when she is walking the bicycle. The problem is framed as a fault detection problem based on three independent analyses that are then fused. Experimental validation on a single-gear bicycle shows that the proposed solution outperforms the traditional cadence-based activation logic both reliability and reactiveness standpoints. The proposed system improves reactiveness compared with the standard cadence-based method without affecting safety.
2016
Electric power assisted bicycles; human electric hybrid vehicles; human powered vehicles; single track vehicle dynamics; Automotive Engineering; Mechanical Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1036662
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