A Pedaling Torque Observation Approach for Sensorless Electric Bicycles
This study proposes an innovative unknown input observation approach based on Kalman filtering to estimate the cycling torque and provide assistance in electrically powered bicycles. Specifically, a constant and a sinusoidal pedaling torque model are compared, underlining the need for an enhanced ma...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10839373/ |
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| author | Riccardo Mandriota Niklas Konig Emanuele Grasso Matthias Nienhaus |
| author_facet | Riccardo Mandriota Niklas Konig Emanuele Grasso Matthias Nienhaus |
| author_sort | Riccardo Mandriota |
| collection | DOAJ |
| description | This study proposes an innovative unknown input observation approach based on Kalman filtering to estimate the cycling torque and provide assistance in electrically powered bicycles. Specifically, a constant and a sinusoidal pedaling torque model are compared, underlining the need for an enhanced mathematical description to improve system performance. Using a nonlinear model of the bicycle longitudinal dynamics, the cycling torque is reconstructed with an Extended Kalman Filter. Also, an online low-computational effort road slope estimation method based on Kalman filtering, that accounts for cornering effect errors, is proposed. The considered approaches, that utilize wheel speed, inertial, and motor current measurements, are tested in an outdoor setting with variable slopes and curves. Differently from the current state-of-the-art, the estimation performances are not only expressed in terms of pedaling torque estimation error minimization. This work presents a novel pedaling power and delivered energy analysis to evaluate the provided electrical assistance and the consequent pedaling effort decrease. The experimental results show that a cycling endeavor reduction, similar to what can be achieved when electrical assistance is provided employing a torque sensor, is possible, especially when relying on improved pedaling modeling. |
| format | Article |
| id | doaj-art-e90aca88e93545198707e67133fb43bf |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e90aca88e93545198707e67133fb43bf2025-01-21T00:00:58ZengIEEEIEEE Access2169-35362025-01-0113106191063710.1109/ACCESS.2025.352930710839373A Pedaling Torque Observation Approach for Sensorless Electric BicyclesRiccardo Mandriota0https://orcid.org/0000-0001-8406-4308Niklas Konig1https://orcid.org/0000-0001-8038-1162Emanuele Grasso2https://orcid.org/0000-0003-4723-3381Matthias Nienhaus3https://orcid.org/0000-0002-8020-7110Lehrstuhl für Antriebstechnik, Universität des Saarlandes, Saarbrücken, GermanyLehrstuhl für Antriebstechnik, Universität des Saarlandes, Saarbrücken, GermanyLehrstuhl für Antriebstechnik, Universität des Saarlandes, Saarbrücken, GermanyLehrstuhl für Antriebstechnik, Universität des Saarlandes, Saarbrücken, GermanyThis study proposes an innovative unknown input observation approach based on Kalman filtering to estimate the cycling torque and provide assistance in electrically powered bicycles. Specifically, a constant and a sinusoidal pedaling torque model are compared, underlining the need for an enhanced mathematical description to improve system performance. Using a nonlinear model of the bicycle longitudinal dynamics, the cycling torque is reconstructed with an Extended Kalman Filter. Also, an online low-computational effort road slope estimation method based on Kalman filtering, that accounts for cornering effect errors, is proposed. The considered approaches, that utilize wheel speed, inertial, and motor current measurements, are tested in an outdoor setting with variable slopes and curves. Differently from the current state-of-the-art, the estimation performances are not only expressed in terms of pedaling torque estimation error minimization. This work presents a novel pedaling power and delivered energy analysis to evaluate the provided electrical assistance and the consequent pedaling effort decrease. The experimental results show that a cycling endeavor reduction, similar to what can be achieved when electrical assistance is provided employing a torque sensor, is possible, especially when relying on improved pedaling modeling.https://ieeexplore.ieee.org/document/10839373/Electric bicyclesKalman filteringpedaling torque estimationroad slope estimationsensorless controlstate observation |
| spellingShingle | Riccardo Mandriota Niklas Konig Emanuele Grasso Matthias Nienhaus A Pedaling Torque Observation Approach for Sensorless Electric Bicycles IEEE Access Electric bicycles Kalman filtering pedaling torque estimation road slope estimation sensorless control state observation |
| title | A Pedaling Torque Observation Approach for Sensorless Electric Bicycles |
| title_full | A Pedaling Torque Observation Approach for Sensorless Electric Bicycles |
| title_fullStr | A Pedaling Torque Observation Approach for Sensorless Electric Bicycles |
| title_full_unstemmed | A Pedaling Torque Observation Approach for Sensorless Electric Bicycles |
| title_short | A Pedaling Torque Observation Approach for Sensorless Electric Bicycles |
| title_sort | pedaling torque observation approach for sensorless electric bicycles |
| topic | Electric bicycles Kalman filtering pedaling torque estimation road slope estimation sensorless control state observation |
| url | https://ieeexplore.ieee.org/document/10839373/ |
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