Electric power-assisted bicycle pedal force detection method and system based on wave peak positioning

Abstract

The application relates to the technical field of bicycle detection, and particularly discloses a pedal force detection method and system for an electric power-assisted bicycle based on wave peak positioning, which comprises the following steps: monitoring the working process of the electric power-assisted bicycle, and collecting pedal force sensing signal data; performing wave peak positioning on a torque sensor voltage waveform, collecting period data between inner and outer sleeves of the torque sensor, and performing phase comparison on the period data of the inner and outer sleeves to obtain a phase difference between the inner and outer sleeves, and processing to obtain real-time pedal force data; and judging whether to perform feedback adjustment on the electric power assistance according to the real-time pedal force data. The pedal force detection method and system for the electric power-assisted bicycle based on wave peak positioning can ensure that the power assistance provided by the electric power-assisted bicycle matches the actual needs of a rider, further determines the optimal power assistance level of the electric power-assisted bicycle under different road conditions and riding speeds, and improves energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of bicycle testing technology, specifically to a method and system for detecting pedaling force in electric-assisted bicycles based on peak positioning. Background Technology

[0002] Currently, with the advancement of technology and people's increasing pursuit of cycling experience, the methods for detecting pedal force in electric-assisted bicycles are also constantly being improved and perfected. At present, various types of sensors and signal processing technologies have emerged on the market, which can meet the needs and scenarios of different users. At the same time, some high-end electric-assisted bicycle products also adopt advanced control systems and algorithms, which can more accurately sense the rider's pedal force and riding status, providing a smarter and more comfortable riding experience.

[0003] For example, the invention patent with announcement number CN110608902B is a method and test bench for testing the pedaling ability of an electric bicycle. The method includes: measuring the overall vehicle data of the electric bicycle to be tested; attaching the front wheel of the electric bicycle to be tested to a dynamometer; connecting a servo motor to the bottom bracket of the electric bicycle to be tested through the pedal connector of the electric bicycle pedaling ability test bench; setting a corresponding average standardized power for the bottom bracket according to the transmission ratio and crank length in the overall vehicle data; and simulating human pedaling on the electric bicycle pedaling ability test bench to measure the pedaling data and evaluate whether the pedaling ability of the electric bicycle to be tested is qualified.

[0004] For example, the invention patent with announcement number CN119246102A is a bicycle riding force parameter acquisition device, including a bicycle model body, pedal sensors mounted on the pedals, handlebar sensors mounted on the handlebars, and seat sensors mounted on the seat. Two sets of connecting blocks are connected to the bottom of the bicycle model body, and a horizontally set detection plate is fixedly connected to the lower end of the connecting blocks. The angle of the adjustment plate is adjusted by an angle adjustment structure to adjust the angle of the adjustment plate to a suitable position to simulate the slope of the road surface when the bicycle bumps. After the angle is adjusted, the rider sits on the seat of the bicycle model body to simulate the normal state of riding. When it is necessary to collect force data of the bicycle bumping, the electric pin is turned off, so that the detection plate falls and hits the adjustment plate, thereby facilitating the collection of the bicycle's force data.

[0005] However, in the process of implementing the technical solution of the present invention in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: At present, bicycle pedal force detection methods focus more on fixed threshold level detection methods, but due to the irregular jitter of the output voltage waveform, the stable period and phase difference of the waveform are not accurate enough, and the reliability and stability of pedal force detection are difficult to guarantee. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for detecting pedaling force in electric-assisted bicycles based on peak positioning, which can effectively solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a method for detecting pedal force of an electric-assisted bicycle based on peak positioning, comprising: monitoring the working process of the electric-assisted bicycle and collecting pedal force sensing signal data, wherein the pedal force sensing signal data includes the voltage waveform of a torque sensor and the frequency of the output voltage signal, the noise of the output voltage signal, and the number of output voltage peaks at each time point.

[0008] Peak location is determined for the voltage waveform of the torque sensor. Periodic data between the inner and outer sleeves of the torque sensor is collected, and the phase difference between the inner and outer sleeves is obtained by comparing the phase data of the inner and outer sleeves. Real-time pedaling force data is obtained by processing the phase difference between the inner and outer sleeves.

[0009] The system determines whether to adjust the electric power assist based on real-time pedaling force data.

[0010] As a further method, the specific processing procedure for acquiring the periodic data between the inner and outer sleeves of the torque sensor is as follows: the voltage waveform of the torque sensor includes the voltage signal waveform corresponding to the inner sleeve and the voltage signal waveform corresponding to the outer sleeve.

[0011] Mark the turning point where the peak changes from rising to falling in the voltage signal waveform corresponding to the inner and outer sleeves, and mark the interval between adjacent turning points as a signal change period to obtain the periodic data between the inner and outer sleeves. The periodic data includes the phase of the voltage signal waveform of the inner and outer sleeves at each time monitoring point.

[0012] As a further method, the real-time pedaling force data is obtained by processing the phase difference between the inner and outer sleeves. The specific processing procedure is as follows: the phase of the voltage signal waveform of the inner and outer sleeves at each time monitoring point is subtracted, and the absolute value of the difference is taken to obtain the phase difference between the inner and outer sleeves at each time monitoring point. The phase difference between the inner and outer sleeves at each time monitoring point is input into the electric bicycle database to match and obtain the pedaling force at each time monitoring point. The pedaling force at each time monitoring point is statistically analyzed and marked as real-time pedaling force data.

[0013] As a further method, the process of determining whether to adjust the electric assist based on real-time pedaling force data is as follows: extract the pedaling force threshold from the electric bicycle database, compare the pedaling force with the pedaling force threshold, and if the pedaling force is less than the pedaling force threshold, no additional operation is performed; if the pedaling force is greater than or equal to the pedaling force threshold, then the electric assist is adjusted based on feedback.

[0014] As a further method, the feedback adjustment of electric assist specifically involves: acquiring pedal force sensing signal data, bicycle road condition data, and bicycle usage characteristic data.

[0015] The stability evaluation index of the torque sensor is obtained by processing the pedal force sensing signal data.

[0016] The road condition complexity assessment index is obtained by processing the road condition data of bicycle travel.

[0017] The load adaptability assessment index is obtained by processing the bicycle usage characteristic data.

[0018] Based on the stability evaluation index of the torque sensor, the road condition complexity evaluation index, the load adaptability evaluation index, and the real-time pedaling force data, a comprehensive analysis is conducted to obtain the bicycle assist demand evaluation value. The electric assist is then adjusted based on the feedback of the bicycle assist demand evaluation value.

[0019] As a further method, the road condition data of the bicycle is processed to obtain a road condition complexity assessment index. The specific processing process is as follows: the road condition data of the bicycle includes the bicycle lean angle, the bicycle vertical acceleration and the bump frequency.

[0020] The critical bicycle lean angle, critical bicycle vertical acceleration, and critical bump frequency were extracted from the e-bike database, and a road condition complexity assessment index was obtained through comprehensive analysis.

[0021] As a further method, the bicycle usage characteristic data is processed to obtain a load adaptability assessment index. The specific processing procedure is as follows: the bicycle usage characteristic data includes bicycle speed, rider weight, and wheel deformation.

[0022] Critical bicycle speed, critical rider weight, and critical wheel deformation are extracted from the electric bicycle database. A comprehensive analysis is conducted to obtain the load adaptability evaluation index, and the filter cutoff frequency is adjusted based on the load adaptability evaluation index.

[0023] As a further method, the electric assist is adjusted based on the bicycle assist demand assessment value. The specific process is as follows: the bicycle assist demand assessment value is input into the electric assist bicycle database to match the corresponding bicycle assist demand; the bicycle assist demand is compared with the actual bicycle assist and the difference is obtained to obtain the bicycle assist difference value; the electric assist is adjusted based on the bicycle assist difference value.

[0024] As a further method, the specific numerical expression for the bicycle assistance demand assessment value is as follows:

[0025] ;

[0026] Wherein, ZN represents the bicycle assist demand assessment value, DS represents the torque sensor stability assessment index, LF represents the road condition complexity assessment index, FS represents the load adaptability assessment index, FO represents the pedaling force, and FO0 represents the critical pedaling force. This indicates the bicycle assist demand assessment influencing factor corresponding to the set torque sensor stability assessment index. 2 represents the bicycle assistance demand assessment influencing factor corresponding to the set road condition complexity assessment index. This indicates the bicycle assist demand assessment influencing factor corresponding to the set load adaptability assessment index. This indicates the impact factor on the bicycle assist demand assessment corresponding to the set pedal force.

[0027] The second aspect of the present invention provides a pedal force detection system for electric-assisted bicycles based on peak positioning, comprising: a data acquisition module for monitoring the working process of the electric-assisted bicycle and acquiring pedal force sensing signal data, wherein the pedal force sensing signal data includes the voltage waveform of the torque sensor and the frequency of the output voltage signal, the noise of the output voltage signal, and the number of output voltage peaks at each time point.

[0028] The real-time pedaling force data analysis module is used to locate the peak of the voltage waveform of the torque sensor, collect the periodic data between the inner and outer sleeves of the torque sensor, compare the phase of the periodic data of the inner and outer sleeves to obtain the phase difference between the inner and outer sleeves, and process the phase difference between the inner and outer sleeves to obtain the real-time pedaling force data.

[0029] The electric power assist feedback adjustment module is used to determine whether to adjust the electric power assist based on real-time pedaling force data.

[0030] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0031] (1) By providing a method and system for detecting pedal force of electric-assisted bicycle based on peak positioning, this invention can ensure that the assistance provided by the electric-assisted bicycle matches the actual needs of the rider, improve the stability and safety of riding, and further determine the optimal assistance level of the electric-assisted bicycle under different road conditions and riding speeds, thereby reducing unnecessary energy consumption and improving energy utilization efficiency.

[0032] (2) By evaluating the stability evaluation index of the torque sensor, the present invention can promptly detect and resolve potential faults or performance degradation problems, reduce unexpected risks during riding, ensure the rapid response of the power assist system, enable riders to obtain immediate support when assistance is needed, thereby improving the rider's controllability and safety, and can also more accurately control the output of the power assist system.

[0033] (3) By evaluating the road condition complexity assessment index, the present invention can more intelligently adjust the assist strategy to adapt to different road conditions, can more accurately control the output power of the motor, thereby optimizing the energy distribution, and can also help electric assist bicycles judge the safety of the current road to reduce the possibility of accidents, thus helping to provide riders with a more personalized riding experience. Attached Figure Description

[0034] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0036] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] Reference Figure 1 As shown, the first aspect of the present invention provides a method for detecting pedal force of an electric-assisted bicycle based on peak positioning, comprising: monitoring the working process of the electric-assisted bicycle and collecting pedal force sensing signal data, wherein the pedal force sensing signal data includes the voltage waveform of a torque sensor and the frequency of the output voltage signal, the noise of the output voltage signal, and the number of output voltage peaks at each time point.

[0039] Peak location is determined for the voltage waveform of the torque sensor. Periodic data between the inner and outer sleeves of the torque sensor is collected, and the phase difference between the inner and outer sleeves is obtained by comparing the phase data of the inner and outer sleeves. Real-time pedaling force data is obtained by processing the phase difference between the inner and outer sleeves.

[0040] The system determines whether to adjust the electric power assist based on real-time pedaling force data.

[0041] Furthermore, periodic data between the tips of the inner and outer sleeves in the torque sensor is collected. Specifically, the torque sensor consists of a pair of active and passive sleeves with crenellated gears rotating relative to each other at a certain angle, a circuit board containing a microcontroller, and a Hall element. This sensor is installed at the bottom bracket of the electric bicycle. The active sleeve is directly driven by the pedal shaft. A side cover is installed at the right end of the active sleeve, and the circuit board is fixed inside the side cover. The Hall element is located on the left side of the circuit board. During riding, the rider's pedaling force drives the active sleeve to rotate forward via the chain. The active sleeve drives the passive sleeve, which in turn propels the bicycle forward. This relative motion changes the magnetic field inside the sensor, i.e., the magnetic flux of the Hall element, thus causing a change in the voltage signal. The Hall element not only senses changes in the magnetic field but also converts these changes into electrical signal outputs, i.e., different waveform outputs. The active sleeve is fitted outside the passive sleeve, where the active sleeve is the outer sleeve and the passive sleeve is the inner sleeve. The voltage waveform of the torque sensor includes the voltage signal waveform corresponding to the inner sleeve and the voltage signal waveform corresponding to the outer sleeve, which can be measured by the microcontroller in the circuit board. The turning point from rising to falling peaks is marked in the voltage signal waveforms corresponding to the inner and outer sleeves, and the interval between adjacent turning points is marked as a signal change cycle. The periodic data between the inner and outer sleeves is obtained. The periodic data includes the phase of the voltage signal waveforms of the inner and outer sleeves at each time monitoring point. The phase refers to the time point of the voltage signal within one cycle.

[0042] Specifically, the real-time pedaling force data is obtained by processing the phase difference between the inner and outer sleeves. The specific processing procedure is as follows: the phase of the voltage signal waveform of the inner and outer sleeves at each time monitoring point is subtracted, and the absolute value of the difference is taken to obtain the phase difference between the inner and outer sleeves at each time monitoring point. The phase difference between the inner and outer sleeves at each time monitoring point is input into the electric bicycle database to match and obtain the pedaling force at each time monitoring point. The pedaling force at each time monitoring point is statistically analyzed and marked as real-time pedaling force data.

[0043] In one specific embodiment, a mapping set of the phase difference between the inner and outer sleeves and the corresponding pedal force is constructed by using the relationship between the phase difference between the inner and outer sleeves and the pedal force in historical data. The real-time phase difference between the inner and outer sleeves is input, and the corresponding pedal force is obtained from the mapping set.

[0044] It should be explained that in this embodiment, the phase difference between the inner and outer sleeves is obtained by subtracting their phases. The change in the phase difference between the inner and outer sleeves is related to the torque generated by the pedaling force. Taking a torsion spring sensor as an example, the two metal alloy discs of the torsion spring sensor are connected by a spring. When a person pedals, the crank drives the connected discs, causing the spring to compress, and the two discs will have relative displacement. During the spring compression process, according to Hooke's Law, the deformation length of the spring is linearly related to the magnitude of the applied force. Therefore, by reading the change in the phase difference value, the change in the magnitude of the pedaling force can be predicted. The phase difference between the inner and outer sleeves can be input into the electric bicycle database to match the corresponding pedaling force. The phase difference data obtained by the peak wave positioning method effectively eliminates the jitter factor of the voltage waveform caused by the fixed threshold level detection method, thereby obtaining the stable period and phase difference value of the waveform. Due to the different physical characteristics and force conditions of the inner and outer sleeves, their movement speed and mode when responding to torque will differ. In the application of power-assisted bicycles, when pedaling force is suddenly applied, the outer sleeve, being directly connected to the drive shaft, will start rotating first. However, the inner sleeve, due to factors such as mechanical friction or the elasticity of connecting parts, will rotate with a delay. This delay leads to asynchrony in the relative position changes of the inner and outer sleeves. From the perspective of voltage signals, the voltage signal corresponding to the outer sleeve changes first due to its movement, while the voltage signal corresponding to the inner sleeve changes slightly later, resulting in a phase difference. This phase difference actually reflects the time difference and the difference in relative position changes of the inner and outer sleeves under the action of torque.

[0045] Furthermore, the system determines whether to adjust the electric assist based on real-time pedaling force data. The specific determination process is as follows: extract the pedaling force threshold from the electric bicycle database, compare the pedaling force with the pedaling force threshold, and if the pedaling force is less than the pedaling force threshold, no adjustment is made to the electric assist; if the pedaling force is greater than or equal to the pedaling force threshold, feedback adjustment is made to the electric assist.

[0046] Specifically, the feedback adjustment of electric assist involves acquiring pedaling force sensor data, bicycle road condition data, and bicycle usage characteristic data.

[0047] The stability evaluation index of the torque sensor is obtained by processing the pedal force sensing signal data.

[0048] The road condition complexity assessment index is obtained by processing the road condition data of bicycle travel.

[0049] The load adaptability assessment index is obtained by processing the bicycle usage characteristic data.

[0050] Based on the stability evaluation index of the torque sensor, the road condition complexity evaluation index, the load adaptability evaluation index, and the real-time pedaling force data, a comprehensive analysis is conducted to obtain the bicycle assist demand evaluation value. The electric assist is then adjusted based on the feedback of the bicycle assist demand evaluation value.

[0051] Specifically, the stability evaluation index of the torque sensor is obtained by processing the bicycle pedal force monitoring data. The specific processing process is as follows: the pedal force sensing signal data includes the output voltage signal frequency, output voltage signal noise and the number of output voltage peaks at each time point.

[0052] The torque sensor stability evaluation index is obtained by extracting the critical output voltage signal noise, the number of reference output voltage peaks, and the number of allowable deviation output voltage peaks from the electric bicycle database and conducting a comprehensive analysis.

[0053] In a specific embodiment, the output voltage signal frequency of the torque sensor refers to the frequency value of its output voltage signal when the torque sensor senses the torque applied to the pedal by the rider. The output voltage signal noise is the amplitude of the noise signal accompanying the output voltage signal of the torque sensor relative to the main signal strength. The number of output voltage peaks refers to the number of peaks in the output voltage signal of the torque sensor. Both can be obtained by the microcontroller in the circuit board.

[0054] Furthermore, the torque sensor stability evaluation index is specifically expressed as follows:

[0055] ;

[0056] Wherein, DS represents the torque sensor stability evaluation index. The frequency of the output voltage signal at the x-th time node is represented by fo, which represents the critical output voltage signal frequency. X Let NO0 represent the output voltage signal noise at time point x, NO0 represent the critical output voltage signal noise, e represent the natural constant, and Bn represent the output voltage signal noise at time point x. X Bn represents the number of output voltage peaks at the x-th time node. o ΔBn represents the number of output voltage peaks relative to the reference standard, and ΔBn represents the number of output voltage peaks with allowable deviation. This indicates the stability evaluation influencing factor of the torque sensor corresponding to the set output voltage signal frequency. This indicates the influence factor on the stability assessment of the torque sensor corresponding to the set output voltage signal noise. The value represents the torque sensor stability evaluation influence factor corresponding to the set number of output voltage peaks, where x represents the time node number, x=1,2,3,...,y, and y represents the total number of time nodes.

[0057] This embodiment's algorithm combines the output voltage signal frequency, output voltage signal noise, and the number of output voltage peaks at each time point to comprehensively analyze and obtain a torque sensor stability evaluation index. Under normal circumstances, the output voltage signal frequency and the number of output voltage peaks of the torque sensor are positively correlated. When the torque changes faster, the output voltage signal frequency is higher, and the number of output voltage peaks will increase accordingly. This is because rapid changes in torque cause rapid fluctuations in the sensor's output signal, resulting in more peaks. At the same time, output voltage signal noise will interfere with the torque sensor's output signal, causing fluctuations in the output voltage signal frequency and the number of output voltage peaks. Comprehensive analysis can yield a more comprehensive torque sensor stability evaluation index.

[0058] It should be explained that this embodiment considers three key factors: the output voltage signal frequency, output voltage signal noise, and the number of output voltage peaks at each time point. This helps the sensor respond to torque changes in real time, thereby quickly adjusting the power assist control strategy to ensure the stability and comfort of the electric-assisted bicycle during riding. It can optimize the power assist control strategy, reduce unnecessary energy consumption, and promptly detect potential sensor faults or anomalies, facilitating early repair or replacement of the sensor and preventing the impact of malfunctions on riding safety and performance. By standardizing the output voltage signal frequency, output voltage signal noise, and number of output voltage peaks at each time point, they are compared on the same order of magnitude, improving the fairness and comparability of the evaluation. By weighting the impact of the output voltage signal frequency, output voltage signal noise, and number of output voltage peaks at each time point, their relative importance in the evaluation index is reflected. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is easy to see that the smaller the deviation in output voltage signal frequency, output voltage signal noise, or number of output voltage peaks, the larger the torque sensor stability evaluation index. By evaluating the stability index of the torque sensor, potential faults or performance degradation can be detected and resolved in a timely manner, reducing the risk of accidents during riding. It can ensure the rapid response of the power assist system, allowing riders to receive immediate support when needed, thereby improving the rider's control and safety. It can also more accurately control the output of the power assist system, avoiding unnecessary energy consumption. It helps to detect signs of sensor wear or performance degradation in a timely manner, thereby taking preventive measures and further enhancing the product's market competitiveness.

[0059] In a specific embodiment, the values ​​of the torque sensor stability evaluation influence factors corresponding to the output voltage signal frequency, output voltage signal noise, and number of output voltage peaks range from 0 to 1. These values ​​represent the degree of influence of the output voltage signal frequency, output voltage signal noise, and number of output voltage peaks on the torque sensor stability evaluation index. Each torque sensor stability evaluation influence factor can be obtained from the electric bicycle database. By adjusting the values ​​of the influence factors, the degree of influence of different factors on the final torque sensor stability evaluation index can be flexibly adjusted. The correspondence can be a pre-set mapping relationship. For example, the output voltage signal frequency, output voltage signal noise, and number of output voltage peaks form a mapping set with the weight factors corresponding to the output voltage signal frequency, output voltage signal noise, and number of output voltage peaks preset in the electric bicycle database. The real-time output voltage signal frequency, output voltage signal noise, and number of output voltage peaks are substituted into the mapping set to obtain the weight factors corresponding to the output voltage signal frequency, output voltage signal noise, and number of output voltage peaks. The mapping relationship can be one-to-one or many-to-one.

[0060] Furthermore, the process of determining whether the torque sensor needs to be inspected and repaired is based on the torque sensor stability evaluation index. Specifically, the torque sensor stability evaluation threshold is extracted from the electric bicycle database, and the torque sensor stability evaluation index is compared with the torque sensor stability evaluation threshold. If the torque sensor stability evaluation index is greater than or equal to the torque sensor stability evaluation threshold, no further action is taken. If the torque sensor stability evaluation index is less than the torque sensor stability evaluation threshold, relevant personnel are immediately notified to inspect and repair the torque sensor.

[0061] Specifically, the road condition data of bicycle travel is processed to obtain the road condition complexity assessment index. The specific processing process is as follows: the bicycle travel road condition data includes the bicycle lean angle, the bicycle vertical acceleration and the bump frequency.

[0062] The critical bicycle lean angle, critical bicycle vertical acceleration, and critical bump frequency were extracted from the e-bike database, and a road condition complexity assessment index was obtained through comprehensive analysis.

[0063] In one specific embodiment, bicycle lean angle refers to the tilt angle of the bicycle body relative to the ground. By precisely controlling the bicycle lean angle, the rider can maintain stability when turning or encountering complex road conditions, reducing the risk of loss of control due to excessive lean angle. Bicycle lean angle can be monitored by a lean angle sensor. Bicycle vertical acceleration refers to the acceleration experienced by the bicycle in the vertical direction. A decrease in vertical acceleration means a reduction in the impact force generated when the vehicle encounters bumps or uneven road surfaces, which helps protect the rider from injury. This can be monitored in real time by an acceleration sensor. Bump frequency refers to the number of times the bicycle's vertical acceleration exceeds a critical value within the monitoring period. A lower bump frequency means a smoother ride, reducing rider discomfort or safety hazards caused by frequent bumps. This can be obtained by statistically analyzing the number of changes in acceleration sensor data.

[0064] Furthermore, the road condition complexity assessment index is specifically expressed as follows:

[0065] ;

[0066] Where LF represents the road condition complexity assessment index, An represents the bicycle lean angle, An0 represents the critical bicycle lean angle, a represents the bicycle's vertical acceleration, a0 represents the critical bicycle vertical acceleration, f represents the bump frequency, and f0 represents the critical bump frequency. γ1 represents the road condition complexity assessment influence factor corresponding to the set bicycle lean angle, γ2 represents the road condition complexity assessment influence factor corresponding to the set bicycle acceleration, and γ3 represents the road condition complexity assessment influence factor corresponding to the set bump frequency.

[0067] This embodiment's algorithm combines bicycle tilt angle, vertical acceleration, and bump frequency to comprehensively analyze and obtain a road condition complexity assessment index. When a bicycle tilts during travel, a centrifugal force is generated in the tilt direction. To maintain balance, the bicycle needs to steer or adjust its speed to generate an opposite centripetal force to counteract the centrifugal force. During this process, the bicycle's vertical acceleration changes in response to the change in tilt angle. If the road surface is uneven during travel, the wheels will be subjected to impact forces from different directions. These impact forces will cause the bicycle to tilt and produce bumps. The frequency of bumps depends on the degree of road unevenness and the bicycle's travel speed. When an electric-assisted bicycle is subjected to bumps during travel, it will produce changes in vertical acceleration. This change in acceleration is a result of vibration transmission and reflects the impact of road unevenness on the vehicle. Comprehensive analysis can yield a more comprehensive road condition complexity assessment index.

[0068] It should be explained that this embodiment considers three key factors: bicycle lean angle, bicycle vertical acceleration, and bump frequency. These factors help riders better control the vehicle, improving handling precision and response speed. They can reduce vibration and discomfort during riding, enhancing riding comfort. Furthermore, they can reduce wear on components such as wheels, suspension, and frame, contributing to vehicle stability. They can also reduce the extra energy consumption of the motor when dealing with bumps, improving overall energy efficiency. By standardizing bicycle lean angle, bicycle vertical acceleration, and bump frequency, they are compared on the same order of magnitude, improving the fairness and comparability of the evaluation. The settings of Ano, ao, and fo avoid bicycle safety issues caused by excessively high bicycle lean angle, bicycle vertical acceleration, and bump frequency. By weighting the influence of bicycle lean angle, bicycle vertical acceleration, and bump frequency, their relative importance in the evaluation index is reflected. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is easy to see that the larger the bicycle lean angle, bicycle vertical acceleration, or bump frequency, the larger the road condition complexity evaluation index. By assessing the road complexity index, the assist strategy can be adjusted more intelligently to adapt to different road conditions. The output power of the motor can be controlled more precisely, thereby optimizing energy distribution. It can also help electric-assisted bicycles judge the safety of the current road to reduce the possibility of accidents, and help provide riders with a more personalized riding experience.

[0069] In a specific embodiment, the values ​​of the road condition complexity assessment influencing factors corresponding to bicycle lean angle, bicycle vertical acceleration, and bump frequency range from 0 to 1, representing the numerical values ​​of the degree of influence of bicycle lean angle, bicycle vertical acceleration, and bump frequency on the road condition complexity assessment index. Each road condition complexity assessment influencing factor can be obtained from the electric bicycle database. By adjusting the values ​​of the influencing factors, the degree of influence of different factors on the final road condition complexity assessment index can be flexibly adjusted. The correspondence can be a pre-set mapping relationship. For example, bicycle lean angle, bicycle vertical acceleration, and bump frequency form a mapping set with the pre-set weighting factors corresponding to bicycle lean angle, bicycle vertical acceleration, and bump frequency in the electric bicycle database. The real-time bicycle lean angle, bicycle vertical acceleration, and bump frequency are substituted into the mapping set to obtain the weighting factors corresponding to bicycle lean angle, bicycle vertical acceleration, and bump frequency. The mapping relationship can be one-to-one or many-to-one.

[0070] Specifically, the load adaptability assessment index is obtained by processing the bicycle usage characteristic data. The specific processing procedure is as follows: the bicycle usage characteristic data includes bicycle speed, rider weight, and wheel deformation.

[0071] Critical bicycle speed, critical rider weight, and critical wheel deformation are extracted from the electric bicycle database. A comprehensive analysis is conducted to obtain the load adaptability evaluation index, and the filter cutoff frequency is adjusted based on the load adaptability evaluation index.

[0072] In one specific embodiment, a reasonable speed setting helps reduce vehicle wear and tear, extending its service life. The bicycle's speed can be monitored in real time using a speedometer. The rider's weight can be measured using a scale, and by taking the rider's weight into account, the vehicle can be adjusted and optimized to improve comfort and performance. Wheel deformation can be monitored in real time using a deformation sensor. When the wheel is subjected to external force, the wheel material deforms, resulting in strain. The deformation sensor converts this strain into an electrical signal output, thereby enabling real-time monitoring of wheel deformation. Real-time monitoring of wheel deformation helps to detect wheel wear and damage in a timely manner, allowing for prompt repair or replacement.

[0073] Furthermore, the load adaptability assessment index is specifically expressed as follows:

[0074] ;

[0075] Where FS represents the load adaptability assessment index, e represents the natural constant, N represents the bicycle speed, N0 represents the critical bicycle speed, G represents the rider's weight, G0 represents the critical rider's weight, Ca represents the wheel deformation degree, Ca0 represents the critical wheel deformation degree, and μ1 represents the load adaptability assessment influence factor corresponding to the set bicycle speed. This indicates the load adaptability assessment influencing factor corresponding to the set rider weight. This indicates the load adaptability assessment influence factor corresponding to the set wheel deformation degree.

[0076] This embodiment's algorithm combines bicycle speed, rider weight, and wheel deformation to comprehensively analyze and obtain a load adaptability assessment index. Rider weight is a crucial factor affecting the speed of an e-bike. Heavier riders may increase the load on the e-bike, thus affecting its acceleration performance and top speed. Wheel deformation can affect the rolling resistance and stability of the e-bike, thereby impacting its speed. Severe wheel deformation can lead to increased rolling resistance, thus reducing speed. The rider's weight may exert additional pressure on the wheels, leading to increased wheel deformation. Furthermore, heavier riders may accelerate wheel wear and aging, increasing the risk of wheel deformation. Comprehensive analysis yields a more complete load adaptability assessment index.

[0077] It should be explained that this embodiment considers three key factors: bicycle speed, rider weight, and wheel deformation. This ensures the vehicle's stability and handling under complex road conditions, reduces the risk of traffic accidents, helps optimize vehicle design, ensures good riding performance and safety across various weight ranges, and allows for timely detection and handling of wheel problems, ensuring vehicle stability and safety during operation. Standardizing bicycle speed, rider weight, and wheel deformation ensures they are compared on the same order of magnitude, improving the fairness and comparability of the evaluation. Weighting the impact of bicycle speed, rider weight, and wheel deformation reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is easy to see that the higher the bicycle speed, the lower the rider weight, or the lower the wheel deformation, the higher the load adaptability evaluation index. By evaluating the load adaptability assessment index, potential safety hazards of the vehicle when the load changes can be identified in a timely manner. This helps riders take precautions to avoid dangers during riding, thereby improving riding safety. It can also reflect the durability and wear of components of the electric-assist bicycle under load changes, so that timely repairs or replacements can be carried out. This helps to improve the overall performance and quality of the vehicle and enhance its market competitiveness.

[0078] In a specific embodiment, the load adaptability assessment influencing factors corresponding to bicycle speed, rider weight, and wheel deformation range from 0 to 1, representing the degree of influence of bicycle speed, rider weight, and wheel deformation on the load adaptability assessment index. Each load adaptability assessment influencing factor can be obtained from the electric bicycle database. By adjusting the values ​​of the influencing factors, the degree of influence of different factors on the final load adaptability assessment index can be flexibly adjusted. The correspondence can be a pre-set mapping relationship. For example, bicycle speed, rider weight, and wheel deformation form a mapping set with the pre-set weighting factors corresponding to bicycle speed, rider weight, and wheel deformation in the electric bicycle database. The real-time bicycle speed, rider weight, and wheel deformation are substituted into the mapping set to obtain the weighting factors corresponding to bicycle speed, rider weight, and wheel deformation. The mapping relationship can be one-to-one or many-to-one.

[0079] Furthermore, the filter cutoff frequency is adjusted based on the load adaptability evaluation index. Specifically, the load adaptability evaluation threshold is extracted from the electric bicycle database. Simultaneously, the load adaptability evaluation index is input into the database to match the corresponding filter cutoff frequency correction value. The load adaptability evaluation index is compared with the load adaptability evaluation threshold. If the load adaptability evaluation index is greater than or equal to the load adaptability evaluation threshold, the filter cutoff frequency is subtracted from the filter cutoff frequency correction value to obtain the updated filter cutoff frequency. If the load adaptability evaluation index is less than the load adaptability evaluation threshold, the filter cutoff frequency is added to the filter cutoff frequency correction value to obtain the updated filter cutoff frequency. The cutoff frequency of the filter allows electric-assist bicycles to better adapt to riding needs under different load conditions. For example, when riding under heavy load or uphill, the vehicle requires more power support. In this case, the cutoff frequency of the filter can be appropriately increased to reduce the filtering of high-frequency noise, making the motor response faster and more accurate, thereby improving the riding experience. At the same time, a reasonable cutoff frequency setting helps to reduce unnecessary energy loss. When riding under light load or on flat roads, lowering the cutoff frequency of the filter can reduce the response to high-frequency signals, thereby reducing the energy consumption of the motor and improving energy efficiency. This is of great significance for extending the range of electric-assist bicycles.

[0080] Specifically, the numerical expression for the bicycle assistance demand assessment value is as follows:

[0081] ;

[0082] Wherein, ZN represents the bicycle assist demand assessment value, DS represents the torque sensor stability assessment index, LF represents the road condition complexity assessment index, FS represents the load adaptability assessment index, FO represents the pedaling force, and FO0 represents the critical pedaling force. This indicates the bicycle assist demand assessment influencing factor corresponding to the set torque sensor stability assessment index. 2 represents the bicycle assistance demand assessment influencing factor corresponding to the set road condition complexity assessment index. This indicates the bicycle assist demand assessment influencing factor corresponding to the set load adaptability assessment index. This indicates the impact factor on the bicycle assist demand assessment corresponding to the set pedal force.

[0083] This embodiment's algorithm combines road condition complexity assessment index, load adaptability assessment index, and pedal force and torque sensor stability assessment index to comprehensively analyze and obtain a bicycle assist demand assessment value. Changes in road condition complexity directly affect the load adaptability of electric-assisted bicycles. For example, in complex road conditions, such as steep mountain roads or congested city streets, electric-assisted bicycles need to withstand greater loads and more frequent acceleration and deceleration operations. This requires electric-assisted bicycles to have better load adaptability and power performance to meet these challenges. The quality of load adaptability directly affects the stability and accuracy of pedal force and torque sensors. When electric-assisted bicycles are driven under conditions of large load changes, such as climbing hills or accelerating, pedal force and torque sensors need to withstand greater stress and changes. If the stability of the sensors is insufficient, it may lead to measurement errors or signal distortion, thereby affecting the control effect of the electric assist system and the riding experience. Comprehensive analysis can obtain a more comprehensive bicycle assist demand assessment value.

[0084] It should be explained that this embodiment considers four key factors: road condition complexity assessment index, load adaptability assessment index, pedaling force, and torque sensor stability assessment index. This allows for understanding the performance of the electric-assist bicycle under different road conditions, enabling targeted optimization. This helps improve the vehicle's load capacity, ensuring stability and efficiency under varying loads. It also ensures accurate torque information under various conditions, resulting in smoother and more efficient riding. Furthermore, it allows for more precise pedaling force control, making riding easier and more comfortable, further improving the reliability and durability of the electric-assist bicycle, thereby reducing the failure rate. By weighting the influence of the road condition complexity assessment index, load adaptability assessment index, pedaling force, and torque sensor stability assessment index, their relative importance in the assessment index is reflected. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is easy to see that the larger the road condition complexity assessment index, or the smaller the load adaptability assessment index, or the greater the pedaling force, or the smaller the torque sensor stability assessment index, the greater the bicycle's assist requirement assessment value. By evaluating the assistance demand of electric bicycles, it is possible to ensure that the assistance provided by electric bicycles matches the actual needs of riders. This helps to optimize the handling performance of electric bicycles, improve riding stability and safety, and further determine the optimal assistance level of electric bicycles under different road conditions and riding speeds, thereby reducing unnecessary energy consumption and improving energy efficiency.

[0085] In a specific embodiment, the values ​​of the bicycle assist demand assessment influencing factors corresponding to the road condition complexity assessment index, load adaptability assessment index, and pedal force and torque sensor stability assessment index range from 0 to 1. These values ​​represent the degree of influence of the road condition complexity assessment index, load adaptability assessment index, and pedal force and torque sensor stability assessment index on the bicycle assist demand assessment value. Each bicycle assist demand assessment influencing factor can be obtained from an electric bicycle database. By adjusting the values ​​of these influencing factors, the degree of influence of different factors on the final bicycle assist demand assessment value can be flexibly adjusted. The correspondence can be a pre-defined mapping. The mapping relationship can be as follows: for example, the road condition complexity assessment index, load adaptability assessment index, and pedal force and torque sensor stability assessment index are mapped to the weight factors corresponding to the road condition complexity assessment index, load adaptability assessment index, and pedal force and torque sensor stability assessment index preset in the electric bicycle database. The real-time road condition complexity assessment index, load adaptability assessment index, and pedal force and torque sensor stability assessment index are then substituted into the mapping set to obtain the weight factors corresponding to the road condition complexity assessment index, load adaptability assessment index, and pedal force and torque sensor stability assessment index. The mapping relationship can be one-to-one or many-to-one.

[0086] Specifically, the electric assist is adjusted based on the bicycle assist demand assessment value. The process is as follows: the bicycle assist demand assessment value is input into the electric assist bicycle database to match the corresponding bicycle assist demand; the bicycle assist demand is compared with the actual bicycle assist and the difference is calculated to obtain the bicycle assist difference value; the electric assist is adjusted based on the bicycle assist difference value.

[0087] Furthermore, the electric assist is adjusted based on the difference in bicycle assist power. The specific process is as follows: If the difference in bicycle assist power is greater than zero, the actual bicycle assist power is increased until the bicycle assist power requirement is met. That is, the difference in bicycle assist power is input into the electric bicycle database to match the corresponding motor output power adjustment value. The actual motor output power is added to the motor output power adjustment value to obtain the updated motor output power. The motor power output is then adjusted based on the updated motor output power to provide stronger assist. If the difference in bicycle assist power is equal to zero, no additional operation is performed. If the difference in bicycle assist power is less than zero, the actual bicycle assist power is decreased until the bicycle assist power requirement is met. That is, the difference in bicycle assist power is input into the electric bicycle database to match the corresponding motor output power adjustment value. The actual motor output power is subtracted from the motor output power adjustment value to obtain the updated motor output power. The motor power output is then adjusted based on the updated motor output power to provide weaker assist.

[0088] In one specific embodiment, a mapping set between the bicycle assist difference and the corresponding motor output power adjustment value is constructed by using the relationship between the bicycle assist difference and the motor output power adjustment value in historical data. The real-time bicycle assist difference is input, and the corresponding motor output power adjustment value is obtained from the mapping set.

[0089] Reference Figure 2 As shown, the second aspect of the present invention provides a pedal force detection system for electric-assisted bicycles based on peak positioning, comprising: a data acquisition module for monitoring the working process of the electric-assisted bicycle and acquiring pedal force sensing signal data, wherein the pedal force sensing signal data includes the voltage waveform of the torque sensor and the frequency of the output voltage signal, the noise of the output voltage signal, and the number of output voltage peaks at each time point.

[0090] The real-time pedaling force data analysis module is used to locate the peak of the voltage waveform of the torque sensor, collect the periodic data between the inner and outer sleeves of the torque sensor, compare the phase of the periodic data of the inner and outer sleeves to obtain the phase difference between the inner and outer sleeves, and process the phase difference between the inner and outer sleeves to obtain the real-time pedaling force data.

[0091] The electric power assist feedback adjustment module is used to determine whether to adjust the electric power assist based on real-time pedaling force data.

[0092] The electric bicycle database stores relevant data, including: critical output voltage signal noise, number of peaks in the reference standard output voltage, number of peaks in the allowable deviation output voltage, critical bicycle lean angle, critical bicycle vertical acceleration, critical bump frequency, critical bicycle speed, critical rider weight, critical wheel deformation, bicycle assist demand assessment influencing factors corresponding to the set torque sensor stability assessment index, bicycle assist demand assessment influencing factors corresponding to the set road condition complexity assessment index, bicycle assist demand assessment influencing factors corresponding to the set load adaptability assessment index, bicycle assist demand assessment influencing factors corresponding to the set pedaling force, and torque sensor stability assessment thresholds, among other indicators.

[0093] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for detecting pedaling force in an electric-assisted bicycle based on peak localization, characterized in that, include: The working process of the electric-assisted bicycle is monitored, and pedal force sensing signal data is collected. The pedal force sensing signal data includes the voltage waveform of the torque sensor, as well as the frequency of the output voltage signal, the noise of the output voltage signal, and the number of output voltage peaks at each time point. Peak location of the voltage waveform of the torque sensor is determined, periodic data between the inner and outer sleeves of the torque sensor is collected, and the periodic data of the inner and outer sleeves are compared in phase to obtain the phase difference between the inner and outer sleeves. Real-time pedal force data is obtained by processing the phase difference between the inner and outer sleeves. The system determines whether to adjust the electric power assist based on real-time pedaling force data. The specific process for feedback adjustment of the electric power assist is as follows: Acquire pedal force sensor signal data, bicycle road condition data, and bicycle usage characteristic data; The stability evaluation index of the torque sensor is obtained by processing the pedal force sensing signal data. The road condition complexity assessment index is obtained by processing the road condition data of bicycle travel; The load adaptability assessment index is obtained by processing bicycle usage characteristic data; Based on the stability evaluation index of the torque sensor, the road condition complexity evaluation index, the load adaptability evaluation index, and the real-time pedaling force data, a comprehensive analysis is conducted to obtain the bicycle assist demand evaluation value, and the electric assist is adjusted based on the bicycle assist demand evaluation value. The specific numerical expression for the bicycle assistance demand assessment value is as follows: ； Wherein, ZN represents the bicycle assist demand assessment value, DS represents the torque sensor stability assessment index, LF represents the road condition complexity assessment index, FS represents the load adaptability assessment index, FO represents the pedaling force, and FO0 represents the critical pedaling force. This indicates the bicycle assist demand assessment influencing factor corresponding to the set torque sensor stability assessment index. 2 represents the bicycle assistance demand assessment influencing factor corresponding to the set road condition complexity assessment index. This indicates the bicycle assist demand assessment influencing factor corresponding to the set load adaptability assessment index. This indicates the bicycle assist demand assessment influencing factor corresponding to the set pedal force; The torque sensor stability evaluation index, specifically expressed as follows: ； Wherein, DS represents the torque sensor stability evaluation index. The frequency of the output voltage signal at the x-th time node is represented by fo, which represents the critical output voltage signal frequency. X Let NO0 represent the output voltage signal noise at time point x, NO0 represent the critical output voltage signal noise, e represent the natural constant, and Bn represent the output voltage signal noise at time point x. X Bn represents the number of output voltage peaks at the x-th time node. o ΔBn represents the number of output voltage peaks relative to the reference standard, and ΔBn represents the number of output voltage peaks with allowable deviation. This indicates the stability evaluation influencing factor of the torque sensor corresponding to the set output voltage signal frequency. This indicates the influence factor on the stability assessment of the torque sensor corresponding to the set output voltage signal noise. The value represents the torque sensor stability evaluation influence factor corresponding to the set number of output voltage peaks, where x represents the time node number, x=1,2,3,...,y, and y represents the total number of time nodes; The road condition complexity assessment index, specifically expressed as follows: ； Where LF represents the road condition complexity assessment index, An represents the bicycle lean angle, An0 represents the critical bicycle lean angle, a represents the bicycle's vertical acceleration, a0 represents the critical bicycle vertical acceleration, f represents the bump frequency, and f0 represents the critical bump frequency. γ1 represents the road condition complexity assessment influence factor corresponding to the set bicycle lean angle, γ2 represents the road condition complexity assessment influence factor corresponding to the set bicycle acceleration, and γ3 represents the road condition complexity assessment influence factor corresponding to the set bump frequency. The load adaptability assessment index, specifically expressed as follows: ； Where FS represents the load adaptability assessment index, e represents the natural constant, N represents the bicycle speed, N0 represents the critical bicycle speed, G represents the rider's weight, G0 represents the critical rider's weight, Ca represents the wheel deformation degree, Ca0 represents the critical wheel deformation degree, and μ1 represents the load adaptability assessment influence factor corresponding to the set bicycle speed. This indicates the load adaptability assessment influencing factor corresponding to the set rider weight. This indicates the load adaptability assessment influence factor corresponding to the set wheel deformation degree.

2. The method for detecting pedaling force of an electric-assisted bicycle based on peak positioning according to claim 1, characterized in that: The specific processing procedure for the periodic data between the inner and outer sleeves in the torque sensor is as follows: The voltage waveform of the torque sensor includes the voltage signal waveform corresponding to the inner sleeve and the voltage signal waveform corresponding to the outer sleeve; Mark the inflection point where the peak changes from rising to falling in the voltage signal waveform corresponding to the inner and outer sleeves, and mark the interval between adjacent inflection points as one signal change period to obtain the periodic data between the inner and outer sleeves. The periodic data includes the voltage signals of the inner and outer sleeves. The phase of the waveform at each time monitoring point.

3. The method for detecting pedaling force of an electric-assisted bicycle based on peak positioning according to claim 1, characterized in that: The real-time pedaling force data is obtained by processing the phase difference between the inner and outer sleeves. The specific processing procedure is as follows: The phase difference of the inner and outer sleeve voltage signal waveforms at each time monitoring point is calculated, and the absolute value of the difference is taken to obtain the phase difference of the inner and outer sleeves at each time monitoring point. The phase difference of the inner and outer sleeves at each time monitoring point is input into the power-assisted bicycle database to match and obtain the pedaling force at each time monitoring point. The pedaling force at each time monitoring point is statistically analyzed and marked as real-time pedaling force data.

4. The method for detecting pedaling force of an electric-assisted bicycle based on peak positioning according to claim 1, characterized in that: The process of determining whether to adjust the electric power assist based on real-time pedaling force data is as follows: The system extracts pedal force thresholds from the electric bicycle database and compares the pedal force with these thresholds. If the pedal force is less than the threshold, no further action is taken. If the pedal force is greater than or equal to the threshold, the electric assist is adjusted accordingly.

5. The method for detecting pedaling force of an electric-assisted bicycle based on peak positioning according to claim 1, characterized in that: The road condition complexity assessment index is obtained by processing the bicycle travel data. The specific processing procedure is as follows: The bicycle road condition data includes bicycle lean angle, bicycle vertical acceleration, and bump frequency; The critical bicycle lean angle, critical bicycle vertical acceleration, and critical bump frequency were extracted from the e-bike database, and a road condition complexity assessment index was obtained through comprehensive analysis.

6. The method for detecting pedaling force of an electric-assisted bicycle based on peak localization according to claim 1, characterized in that: The process of processing bicycle usage characteristic data to obtain the load adaptability evaluation index is as follows: The bicycle usage characteristic data includes bicycle speed, rider weight, and wheel deformation. Critical bicycle speed, critical rider weight, and critical wheel deformation are extracted from the electric bicycle database. A comprehensive analysis is conducted to obtain the load adaptability evaluation index, and the filter cutoff frequency is adjusted based on the load adaptability evaluation index.

7. The method for detecting pedaling force of an electric-assisted bicycle based on peak localization according to claim 1, characterized in that: The specific process of adjusting the electric assist based on the bicycle's assist demand assessment is as follows: The corresponding bicycle assistance demand is obtained by inputting the bicycle assistance demand assessment value into the electric bicycle database. The bicycle assistance demand is compared with the actual bicycle assistance and the difference is calculated to obtain the bicycle assistance difference value. The electric assistance is adjusted based on the bicycle assistance difference value.

8. A system applying the wave crest localization-based electric bicycle pedal force detection method as described in any one of claims 1-7, characterized in that: include: The data acquisition module is used to monitor the working process of the electric-assisted bicycle and collect pedal force sensing signal data. The pedal force sensing signal data includes the voltage waveform of the torque sensor and the frequency of the output voltage signal, the noise of the output voltage signal, and the number of output voltage peaks at each time point. The real-time pedal force data analysis module is used to locate the peak of the torque sensor voltage waveform, collect the periodic data between the inner and outer sleeves of the torque sensor, and compare the phase of the periodic data of the inner and outer sleeves to obtain the phase difference between them. Real-time pedaling force data is obtained by processing the phase difference between the inner and outer sleeves; The electric power assist feedback adjustment module is used to determine whether to adjust the electric power assist based on real-time pedaling force data.

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