Automatic Bicycle Gear Shift Learning From Rider Shift Behavior
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Solution Overview
Problem
Automatic gear shifts for electric and non-electric bicycles have been niche products due to the complexity of detecting the currently set gear, requiring additional sensors and lacking user-specific adaptation, which hinders widespread adoption.
Innovation Solution
A method and device that adapt the automatic gear shift of a two-wheeler by creating a characteristic map based on individual user information, using sensor variables like speed, cadence, and torque to initiate gear changes without driver intervention, and learning from manual shift requests to adjust the gear assignments in a database for improved efficiency and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors are used to detect the currently set gear, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses existing sensors (speed sensor, cadence sensor, torque sensor) to automatically determine the currently engaged gear through calculation and logic, rather than requiring dedicated gear position sensors. The control unit determines the current gear by evaluating sensor data against stored gear characteristics, making the system self-sufficient using already-available components.
Solution Approach 2:
Existing sensors serve multiple functions: speed sensors detect both vehicle speed and cadence, torque sensors measure both pedaling torque and power output. This multi-functionality eliminates the need for separate dedicated sensors for each measurement, reducing overall system complexity while maintaining measurement precision.
2Adaptability or versatility
If user-specific adaptation is implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system incorporates feedback mechanisms where the control unit continuously monitors sensor data, compares it with stored user profiles and gear characteristics, and automatically adjusts gear shifting decisions. This feedback loop enables user-specific adaptation by learning from individual riding patterns and preferences while maintaining manageable system complexity through algorithmic processing.
Solution Approach 2:
User profiles and gear characteristics are pre-stored in the control unit's memory before actual riding occurs. This preliminary preparation of data structures and reference information allows the system to quickly adapt to individual users without requiring complex real-time calculations, reducing operational complexity while maintaining high adaptability.
3Adaptability or versatility
If manual shift requests are used for teaching, then adaptability is improved, but loss of time increases
Solution Approach 1:
The system updates the characteristic map periodically based on manual shift requests rather than requiring complete re-teaching each time. The control unit accumulates teaching data over multiple riding sessions and performs periodic updates to the gear shift characteristics, reducing the time burden on users while maintaining adaptability through incremental learning.
Data Source
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AI summary
The invention relates to a method for teaching an automatic gear shift mechanism of a two-wheeler, and to a device for carrying out said method, and a two-wheeler having such a device that changes a present characteristic map for controlling the gear shift mechanism on the basis of the individual use information. Optionally, the method can also be used to produce a characteristic map for controlling the gear shift mechanism, in that the user information is registered during shifting processes and is used for further automatic shifting operations.