E-Bike Torque Curve Prediction for Low-Torque Gear Shifting
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Solution Overview
Problem
Existing e-bike transmissions experience malfunctions or friction due to high torque generated by riders during shifting, which existing control methods like US2021/061414A1 do not adequately address.
Innovation Solution
A method for predicting future extreme values in a torque curve by determining torque values at multiple points in time, measuring elapsed time and pedal angle, and using these to predict minimal torque points for optimal transmission shifting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If transmission shifting is performed during high torque periods, then shifting speed is improved, but transmission reliability deteriorates due to malfunctions and friction
Solution Approach 1:
The system predicts future extreme torque values and schedules transmission shifting to occur before high torque periods begin. By performing preliminary scheduling based on predicted torque curves, the shifting operation is timed to occur during low torque periods, preventing transmission malfunctions and friction while maintaining efficient shifting execution
Solution Approach 2:
The system continuously monitors actual torque values and compares them with predicted values, adjusting the shifting schedule in real-time. This feedback mechanism ensures that shifting operations are dynamically timed to avoid unexpected high torque events, thereby maintaining transmission reliability while achieving fast shifting when conditions are favorable
2Reliability
If transmission shifting is delayed to avoid high torque periods, then transmission reliability is improved, but shifting responsiveness deteriorates
Solution Approach 1:
The system performs preliminary prediction of torque curve extreme values and proactively schedules shifting operations during anticipated low torque periods. This advance planning eliminates the need for reactive delays, as the system is already positioned to execute shifting at optimal moments without sacrificing responsiveness
Solution Approach 2:
The shifting schedule is made dynamic and adaptive rather than fixed. The system continuously adjusts the timing of shifting operations based on real-time torque measurements and predicted torque curve evolution, allowing the system to seize optimal shifting opportunities as they arise without unnecessary delays
3Measurement precision
If torque measurement frequency is increased to improve prediction accuracy, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system uses partial measurement action by determining torque values at selectively spaced time points rather than continuous high-frequency measurement. This partial sampling approach provides sufficient precision for predicting extreme values and scheduling shifting operations while significantly reducing energy consumption compared to continuous high-frequency measurement
Solution Approach 2:
The system leverages the natural periodicity of the torque curve and the vehicle's operating characteristics to determine optimal measurement intervals. By using the system's own operational patterns to guide measurement timing, it achieves accurate prediction of extreme values with minimal measurement activity, thereby reducing energy consumption while maintaining measurement precision
Data Source
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AI summary
The invention relates to 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, comprising the steps of: - determining a torque value at several points in time, - determining an extreme value of the determined torque values at each point in time, - measuring an elapsed time and/or an angle traveled by a pedal of the vehicle since the point in time at which the extreme value was determined, - determining a period of the torque curve, and - predicting at least one point in time of a future extreme value based on the determined period as well as the measured time and/or the angle traveled.