Power-Assisted Bicycle Torque Control for Adaptive Rider Assistance
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Solution Overview
Problem
Conventional power-assisted bicycles provide a monotonous and invariant auxiliary force, which may not be suitable for ordinary users due to differences in cycling habits, riding techniques, and physical fitness, leading to an uncomfortable and fatiguing riding experience.
Innovation Solution
An auxiliary force control system and method that utilizes a sensing device, a mobile computing device with Artificial Neural Network (ANN) models, and controllers to dynamically adjust the motor's auxiliary force based on user-specific data, historical riding data, and environmental conditions, ensuring a tailored assistance for each rider.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional power-assisted bicycles provide a fixed auxiliary force determined by manufacturer testing, then the system structure is simple and easy to manufacture, but the auxiliary force is not suitable for ordinary users with different cycling habits, body shapes, and stamina levels
Solution Approach 1:
The patent implements dynamic adjustment of auxiliary force by equipping the bicycle with sensors that detect user parameters (weight, height, age) and riding conditions, then automatically adjusts motor output in real-time through a control system. This transforms the fixed auxiliary force into a dynamic, adaptive parameter that responds to user needs.
Solution Approach 2:
The system incorporates feedback mechanisms where sensors continuously monitor user characteristics and riding status, transmit this data to a controller, and the controller adjusts the motor's auxiliary force accordingly. This closed-loop feedback enables the system to adapt to different users automatically without manual intervention.
2Ease of operation
If the auxiliary force is increased to provide effort-saving effects, then users with lower stamina benefit, but users with higher stamina find the auxiliary force too high and exercise effects are reduced
Solution Approach 1:
The system changes the parameter of auxiliary force based on detected user parameters. By inputting user data (weight, height, age) and riding conditions into the control algorithm, the system calculates and adjusts the appropriate auxiliary force level, ensuring it provides effort-saving effects for low-stamina users while maintaining exercise benefits for high-stamina users.
3Ease of operation
If a throttle handle is provided for user control, then the user can manually adjust motor output, but the user's wrist has to continuously exert force to rotate and maintain the position, causing fatigue on long trips
Solution Approach 1:
The system eliminates the need for manual throttle control by implementing self-service functionality. Sensors automatically detect user intent and riding conditions, and the control system autonomously adjusts the motor's auxiliary force without requiring the user to physically manipulate any controls. The system serves itself by making intelligent decisions based on sensed data.
4Adaptability or versatility
If the auxiliary force is decreased to maintain exercise effects, then health benefits are preserved, but users with lower stamina do not achieve sufficient effort-saving effects
Solution Approach 1:
The system dynamically changes the auxiliary force parameter based on detected user characteristics. For users with lower stamina, the system increases auxiliary force to provide effort-saving effects, while for users with higher stamina, it reduces auxiliary force to maintain exercise benefits. This parameter adaptation resolves the contradiction between exercise maintenance and effort-saving effects.
Data Source
AI summary
An auxiliary force control system and a method for a power-assisted bicycle are disclosed. The system has a sensing device, a mobile computing device, a first controller, and a second controller. The sensing device receives a riding torque and a riding speed. The mobile computing device generates a tuning factor via a first and a second ANN model. Personal data and historical riding data are input data of the first ANN model. Predicted grade outputted by the first ANN model, the personal data, and environment data are input data of the second ANN model. The first controller generates a final factor according to the tuning factor, a mode factor, and a gap-range factor. The second controller outputs a motor driver current according to a parameter of target output of the motor, which is generated based on the final factor, to the motor to drive the motor.


