Adaptive E-Bike Cadence Control for Varying Terrain
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Solution Overview
Problem
Existing motor-assisted bicycles, such as e-bikes, have limited ability to adjust cycling parameters like cadence, leading to potential strain and inefficiency, especially in varying terrain conditions.
Innovation Solution
An adaptive cadence control system that utilizes a computing system to regulate cycling parameters based on sensor inputs and an adaptive cadence control algorithm, adjusting cadence and torque to optimize rider comfort and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the e-bike allows manual pedaling with fixed cadence parameters, then the rider has full control over pedaling, but the rider experiences strain and inefficiency in varying terrain conditions
Solution Approach 1:
The system dynamically adjusts cadence parameters based on real-time terrain conditions and rider input. The cadence can transition between manual rider control and automated system control, with the computing system continuously monitoring operational parameters (speed, torque, gradient) and adjusting cadence setpoints accordingly. This dynamic adaptation resolves the contradiction by providing both rider control and terrain adaptability.
Solution Approach 2:
The system changes cadence parameters (speed, torque, gradient thresholds) based on detected terrain conditions. When the computing system detects uphill terrain or adverse conditions, it automatically adjusts the cadence setpoint to optimize rider effort and efficiency. This parameter adaptation allows the e-bike to maintain ease of operation while becoming versatile across different terrain types.
2Measurement precision
If the computing system continuously receives cycling inputs to regulate parameters, then the control accuracy is high, but the computing load increases
Solution Approach 1:
The computing system regulates cadence parameters at periodic intervals rather than continuously. It receives cycling inputs, processes them against stored terrain data and rider preferences, and outputs adjusted cadence setpoints at discrete time points. This periodic regulation maintains parameter accuracy while significantly reducing computing load and energy consumption compared to continuous processing.
Solution Approach 2:
The system performs preliminary actions by pre-storing terrain data, rider preferences, and control algorithms in memory before actual cycling begins. During operation, the computing system retrieves and applies these pre-prepared parameters rather than computing everything in real-time. This preliminary preparation maintains high regulation accuracy while minimizing the computing energy required during active cycling.
3Device complexity
If the e-bike uses fixed gear ratios, then the mechanical structure is simple, but the cycling efficiency decreases in varying terrain
Solution Approach 1:
The transmission system transitions from fixed gear ratios to a dynamic variable ratio system. The computing system monitors terrain conditions and rider cadence, then automatically adjusts gear ratios in real-time to optimize mechanical advantage. This dynamic transmission maintains simple mechanical structure while dramatically improving cycling efficiency across varying terrain by providing the optimal gear ratio for each condition.
4Adaptability or versatility
If the system adjusts cadence frequently to respond to terrain changes, then the adaptability increases, but the operational component lifespan decreases due to increased wear
Solution Approach 1:
The system applies partial cadence adjustments rather than maximum adjustments for every terrain change. The computing system evaluates terrain conditions and applies only the necessary degree of cadence change to maintain efficient cycling, avoiding excessive adjustments. This partial action approach maintains terrain response capability while reducing mechanical stress and extending component lifespan.
Solution Approach 2:
The system implements beforehand cushioning by implementing smooth transition protocols for cadence adjustments. Before making cadence changes, the computing system calculates gradual transition paths that minimize mechanical shock and stress on transmission components. This prior cushioning approach allows frequent terrain-responsive adjustments while protecting operational components from excessive wear.
Data Source
AI summary
A motor-assisted bicycle includes a transmission, an electric motor coupled to the transmission, one or more sensors, and an adaptive cadence control system communicatively coupled to the one or more sensors, the transmission and the electric motor. The adaptive cadence control system obtains initialization parameters, programming initial settings of the motor-assisted bicycle based on the initialization parameters, obtains operational signals of the motor-assisted bicycle, obtains cycling parameters, generates individual cadence adjustments, generates an overall cadence adjustment based on the individual cadence adjustments, generates one or more controlled variable settings based on the overall cadence adjustment, and controlling one or more operational control components of the motor-assisted bicycle based on the one or more controlled variable settings.