Pedal Torque Peak Prediction for Low-Wear E-Bike Shifting
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Solution Overview
Problem
E-bikes with assistance drives experience impairment or friction in transmissions due to high torque during shifting, which can be exacerbated by manual or automatic transmissions.
Innovation Solution
A method for predicting the point in time of future extreme values in a torque curve by ascertaining torque values, determining extreme values, measuring elapsed time and angle, and predicting future extreme values to optimize transmission shifting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If transmission shifting is performed during high torque periods, then shifting speed can be maintained, but transmission impairment and friction increase
Solution Approach 1:
The system predicts future extreme torque values and schedules transmission shifting to occur before these peaks are reached. By performing the shifting action in advance of the high torque period, the transmission components can be engaged or disengaged while torque is still low, preventing impairment while maintaining operational continuity
Solution Approach 2:
The system dynamically adjusts the timing of transmission shifting based on real-time prediction of torque curve extrema. Rather than using fixed shifting schedules, the shifting timing is continuously optimized based on predicted future torque conditions, allowing the system to adapt to varying rider input and cycling conditions
2Reliability
If transmission shifting is delayed until torque is minimized, then transmission wear is reduced, but shifting timing precision must be improved
Solution Approach 1:
The system performs preliminary detection and prediction of torque curve extrema, identifying future minimum torque points before they occur. This advance prediction allows the control system to plan and execute shifting operations with precise timing, ensuring shifts occur at the optimal moment rather than reacting after the fact
Solution Approach 2:
The system continuously monitors actual torque values and compares them against predicted torque curves. This feedback mechanism allows the system to verify prediction accuracy and adjust future predictions accordingly, improving timing precision over time while maintaining reliable shift execution
3Productivity
If torque values are continuously monitored to predict extreme values, then shifting optimization is achieved, but computational complexity increases
Solution Approach 1:
The system extracts only the critical features needed for prediction from the continuous torque signal - specifically identifying extreme values (maxima and minima) and their timing patterns. Rather than processing the entire continuous torque curve, the system focuses on these key characteristic points, reducing computational requirements while maintaining prediction accuracy
Solution Approach 2:
The system pre-calculates and stores the periodic pattern of torque extrema based on historical data. By establishing this baseline pattern in advance, the system can quickly compare real-time torque values against the predicted pattern without performing complex real-time calculations, thereby reducing computational complexity during actual operation
Data Source
AI summary
A method for predicting a point in time of a future extreme value in a torque curve of a vehicle, in particular a single-track vehicle such as an E-bike includes (i) ascertaining a torque value at several points in time, (ii) determining an extreme value of the ascertained torque values at any point in time, (iii) measuring an elapsed time and/or an angle travelled by a pedal of the vehicle since the point in time at which the extreme value was determined, (iv) ascertaining a period duration of the torque curve, and (v) predicting at least one point in time of a future extreme value based on the ascertained period duration and the measured time and/or the angle travelled.