Method and system for detecting pedal force of electric power-assisted bicycle based on wave crest positioning

Abstract

The invention relates to the technical field of bicycle detection, and particularly discloses an electric power-assisted bicycle pedal force detection method and system based on wave crest positioning, and the method comprises the steps: monitoring the working process of an electric power-assisted bicycle, and collecting pedal force sensing signal data; performing wave crest positioning on the voltage waveform of the torque sensor, collecting periodic data between an inner sleeve and an outer sleeve in the torque sensor, performing phase comparison on the periodic data of the inner sleeve and the outer sleeve to obtain a phase difference between the inner sleeve and the outer sleeve, and processing to obtain real-time pedal force data; and whether feedback adjustment is performed on the electric power is judged according to the real-time data of the pedal force. According to the electric power-assisted bicycle pedal force detection method and system based on wave crest positioning, it can be ensured that assistance provided by an electric power-assisted bicycle is matched with the actual demand of a rider, the optimal assistance level of the electric power-assisted bicycle under different road conditions and riding speeds is further determined, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of bicycle detection, and in particular to a method and system for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning. Background Art

[0002] At present, with the advancement of science and technology and people's pursuit of riding experience, the pedal force detection method of electric power-assisted bicycles is also constantly improving and perfecting. At present, there are many types of sensors and signal processing technologies on the market that can meet the needs and scenarios of different users. At the same time, some high-end electric power-assisted bicycle products also use advanced control systems and algorithms, which can more accurately sense the rider's pedaling 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 pedal riding ability of an electric bicycle. The method includes: measuring the whole vehicle data of the electric bicycle to be tested; mounting the front wheel of the electric bicycle to be tested on a dynamometer; connecting the servo motor to the middle shaft of the electric bicycle to be tested through the pedal connector of the electric bicycle pedal riding ability test bench; setting the corresponding average standardized power for the middle shaft according to the transmission ratio and crank length in the whole vehicle data; the electric bicycle pedal riding ability test bench simulates human riding, and the riding data is measured to evaluate whether the riding ability of the electric bicycle to be tested is qualified.

[0004] For example, the invention patent with the announcement number CN119246102A is a bicycle riding force parameter collection device, which includes a bicycle model body, a pedal sensor installed on the pedal, a handlebar sensor installed on the handlebar, and a seat sensor installed on the seat. Two groups of connecting blocks are connected to the bottom of the bicycle model body, and the lower ends of the connecting blocks are fixedly connected to a horizontally arranged detection plate; the angle of the adjustment plate is adjusted by relying on the angle adjustment structure, and the angle of the adjustment plate is adjusted to a suitable position to simulate the slope of the road surface hit by the bicycle when it is bumped; after the angle adjustment is completed, the rider sits on the seat on the bicycle model body to simulate the normal state when riding; when it is necessary to collect the force data of the bicycle with a large bump, the electric pin rod is turned off to make the detection plate fall and collide with the adjustment plate, so as to facilitate the collection of the force data of the bicycle.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it was found that the above technology has at least the following technical problems: the current bicycle pedal force detection method pays more attention to the fixed threshold level detection method, 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 the pedal force detection are difficult to guarantee. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for detecting pedal force of an electric power-assisted bicycle based on peak positioning, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an electric power-assisted bicycle pedal force detection method based on peak positioning, including: monitoring the working process of the electric power-assisted bicycle, collecting pedal force sensor signal data, the pedal force sensor signal data including the torque sensor voltage waveform and the output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node.

[0008] The peak of the torque sensor voltage waveform is located, the periodic data between the inner and outer sleeves in the torque sensor is collected, and the periodic data of the inner and outer sleeves are phase-compared to obtain the phase difference of the inner and outer sleeves. The real-time data of the pedal force is obtained based on the phase difference of the inner and outer sleeves.

[0009] Determine whether to make feedback adjustments to the electric power assist based on the real-time data of pedal force.

[0010] As a further method, the periodic data between the inner and outer sleeves in the torque sensor is collected, and the specific processing process is: the voltage waveform of the torque sensor includes a voltage signal waveform corresponding to the inner sleeve and a voltage signal waveform corresponding to the outer sleeve.

[0011] The turning point where the peak changes from rising to falling is marked in the voltage signal waveform corresponding to the inner and outer sleeves, and the period between adjacent turning points is marked as a signal change cycle to obtain the periodic data between the inner and outer sleeves, and 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 data of pedaling force is obtained according to the phase difference processing of the inner and outer sleeves. The specific processing process is: subtracting the phase of the inner and outer sleeve voltage signal waveforms at each time monitoring point, and taking the absolute value of the difference to obtain the phase difference of the inner and outer sleeves at each time monitoring point, inputting the phase difference of the inner and outer sleeves at each time monitoring point into the power-assisted bicycle database to match and obtain the pedaling force at each time monitoring point, and counting the pedaling force at each time monitoring point and marking it as the real-time data of pedaling force.

[0013] As a further method, the method determines whether to perform feedback adjustment on the electric power assistance based on the real-time pedaling force data. The specific judgment process is: extracting the pedaling force threshold from the power-assisted bicycle database, comparing the pedaling force with the pedaling force threshold, 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, feedback adjustment is performed on the electric power assistance.

[0014] As a further method, the feedback adjustment of the electric power assist is specifically processed as follows: obtaining pedal force sensor signal data, bicycle driving road condition data and bicycle use characteristic data.

[0015] The pedal force sensor signal data is processed to obtain the torque sensor stability evaluation index.

[0016] The bicycle driving road condition data is processed to obtain the road condition complexity evaluation index.

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

[0018] According to the torque sensor stability evaluation index, road condition complexity evaluation index, load adaptability evaluation index and pedal force real-time data, a comprehensive analysis is conducted to obtain the bicycle power assistance demand evaluation value, and the electric power assistance is feedback-adjusted according to the bicycle power assistance demand evaluation value.

[0019] As a further method, the bicycle driving road condition data is processed to obtain a road condition complexity evaluation index, and the specific processing process is: the bicycle driving road condition data includes a bicycle inclination angle, a bicycle vertical acceleration and a bump frequency.

[0020] The critical bicycle inclination angle, critical bicycle vertical acceleration and critical bump frequency were extracted from the power-assisted bicycle database, and the road condition complexity evaluation index was obtained through comprehensive analysis.

[0021] As a further method, the bicycle usage characteristic data is processed to obtain the load adaptability evaluation index, and the specific processing process is: the bicycle usage characteristic data includes the bicycle moving speed, the rider's weight and the wheel deformation.

[0022] The critical bicycle moving speed, critical rider weight and critical wheel deformation are extracted from the power-assisted bicycle database, and the load adaptability evaluation index is obtained through comprehensive analysis. The filter cutoff frequency is adjusted according to the load adaptability evaluation index.

[0023] As a further method, the electric power assistance is feedback-adjusted according to the bicycle power assistance demand evaluation value. The specific process is: according to the bicycle power assistance demand evaluation value, the corresponding bicycle power assistance demand is matched into the power-assisted bicycle database, the bicycle power assistance demand is compared with the actual bicycle power assistance and the difference is made to obtain the bicycle power assistance difference, and the electric power assistance is feedback-adjusted according to the bicycle power assistance difference.

[0024] As a further method, the bicycle power assistance demand evaluation value is specifically expressed as: in, represents the bicycle power assistance demand assessment value, represents the torque sensor stability evaluation index, represents the road complexity evaluation index, Represents the load adaptability evaluation index, Indicates pedal force, represents the critical pedal force, Indicates the bicycle assistance demand assessment influencing factor corresponding to the set torque sensor stability assessment index, It represents the impact factor of bicycle assistance demand assessment corresponding to the set road complexity assessment index. Indicates the bicycle assistance demand assessment impact factor corresponding to the set load adaptability assessment index, Indicates the impact factor of bicycle assistance demand assessment corresponding to the set pedaling force.

[0025] The second aspect of the present invention provides an electric power-assisted bicycle pedal force detection system based on peak positioning, including: a data acquisition module for monitoring the working process of the electric power-assisted bicycle and collecting pedal force sensor signal data, wherein the pedal force sensor signal data includes a torque sensor voltage waveform and an output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node.

[0026] The pedal force real-time 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 in the torque sensor, and perform phase comparison on the periodic data of the inner and outer sleeves to obtain the phase difference between the inner and outer sleeves, and obtain the real-time pedal force data based on the phase difference between the inner and outer sleeves.

[0027] The electric power steering feedback adjustment module is used to determine whether to perform feedback adjustment on the electric power steering according to the real-time data of the pedal force.

[0028] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a method and system for detecting pedal force of an electric power-assisted bicycle based on peak positioning, thereby ensuring that the power assistance provided by the electric power-assisted bicycle matches the actual needs of the rider, improving riding stability and safety, and further determining the optimal power assistance level of the electric power-assisted bicycle under different road conditions and riding speeds, thereby reducing unnecessary energy consumption and improving energy utilization efficiency.

[0029] (2) By evaluating the stability evaluation index of the torque sensor, the present invention can timely discover and solve potential faults or performance degradation problems, reduce the risk of accidents during riding, ensure the rapid response of the power-assist system, and enable the rider to get support immediately when power-assist is needed, thereby improving the rider's controllability and safety, and can also more accurately control the output of the power-assist system.

[0030] (3) By evaluating the road condition complexity evaluation index, the present invention can more intelligently adjust the power assist strategy to adapt to different road conditions, more accurately control the output power of the motor, thereby optimizing the distribution of energy, and help the electric power-assisted bicycle to judge the safety of the current road to reduce the possibility of accidents, thereby helping to provide riders with a more personalized riding experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0032] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0033] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0034] Figure 3 It is a schematic diagram of the functional relationship between the bicycle power-assistance demand evaluation value and the road condition complexity evaluation index of the present invention. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] Reference Figure 1 As shown, the first aspect of the present invention provides an electric power-assisted bicycle pedal force detection method based on peak positioning, including: monitoring the working process of the electric power-assisted bicycle, collecting pedal force sensor signal data, the pedal force sensor signal data including the torque sensor voltage waveform and the output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node.

[0037] The peak of the torque sensor voltage waveform is located, the periodic data between the inner and outer sleeves in the torque sensor is collected, and the periodic data of the inner and outer sleeves are phase-compared to obtain the phase difference of the inner and outer sleeves. The real-time data of the pedal force is obtained based on the phase difference of the inner and outer sleeves.

[0038] Determine whether to make feedback adjustments to the electric power assist based on the real-time data of pedal force.

[0039] Furthermore, the periodic data between the tooth tips of the inner and outer sleeves in the torque sensor are collected. The specific process is as follows: the torque sensor is composed of a pair of active sleeves with battlement-like gears that rotate relative to each other at a certain angle, a passive sleeve, a circuit board containing a single-chip microcomputer, and a Hall element. The sensor is installed at the center axis of the power-assisted bicycle. The active sleeve is directly driven by the center axis of the pedal. A side cover is provided at the right end of the active sleeve. A circuit board is fixed inside the side cover. The Hall element is located on the left side of the circuit board. During riding, the force of the rider's pedal drives the active sleeve to rotate forward through the chain, and the active sleeve drives the passive sleeve to drive the bicycle forward. This relative motion changes the magnetic field inside the sensor, that is, the magnetic flux of the Hall element, thereby causing the voltage signal to change. The Hall element not only senses changes in the magnetic field, but also converts this change into an electrical signal output, that is, a different waveform output. The active sleeve is sleeved on the outside of the passive sleeve, wherein the active sleeve is the outer sleeve and the passive sleeve is the inner sleeve. The torque sensor voltage waveform includes a voltage signal waveform corresponding to the inner sleeve and a voltage signal waveform corresponding to the outer sleeve, which can be measured by the single-chip microcomputer in the circuit board; the turning point where the peak changes from rising to falling is marked in the voltage signal waveform corresponding to the inner and outer sleeves, and the period between adjacent turning points is marked as a signal change cycle, and the period data between the inner and outer sleeves is obtained. The period data includes the phase of the voltage signal waveform of the inner and outer sleeves at each time monitoring point, and the phase refers to the time point at which the voltage signal is located in a cycle.

[0040] Specifically, the real-time data of pedaling force is obtained according to the phase difference of the inner and outer sleeves. The specific processing process is: subtracting the phase of the inner and outer sleeve voltage signal waveforms at each time monitoring point, and taking the absolute value of the difference to obtain the phase difference of the inner and outer sleeves at each time monitoring point, inputting the phase difference of the inner and outer sleeves at each time monitoring point into the power-assisted bicycle database to match and obtain the pedaling force at each time monitoring point, and counting the pedaling force at each time monitoring point as the real-time data of pedaling force.

[0041] In a specific embodiment, a mapping set of the phase difference between the inner and outer sleeves and the corresponding pedaling force is constructed based on the relationship between the phase difference between the inner and outer sleeves and the pedaling force in historical data, the real-time phase difference between the inner and outer sleeves is input, and the corresponding pedaling force is obtained from the mapping set.

[0042] It should be explained that in this embodiment, the phase difference of the inner and outer sleeves is obtained by subtracting the phase of the inner and outer sleeves. The change of the phase difference of the inner and outer sleeves is related to the torque generated by the external force of the pedal. Taking the torsion spring sensor as an example, the spring is used as an adapter between the two metal alloy discs of the torsion spring sensor. When a person steps on the pedal, the crank drives the connected disc to compress the spring, and the two discs will produce relative displacement. In the process of spring compression, according to Hooke's law, the deformation length of the spring is linearly related to the magnitude of the force applied. Therefore, by reading the change of the phase difference value, the change of the magnitude of the pedal force can be predicted. The phase difference of the inner and outer sleeves can be input into the database of the power-assisted bicycle to match the corresponding pedal 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, and then obtains the stable period and phase difference value of the waveform. Due to the different physical properties and force conditions of the inner and outer sleeves, there will be differences in their movement speed and mode when responding to torque. In power-assisted bicycle applications, when pedal force is suddenly applied, the outer sleeve starts to rotate first because it is directly connected to the drive shaft, while the inner sleeve will rotate with a delay due to some mechanical friction or the elasticity of the connecting parts. This delay causes the inner and outer sleeves to change asynchronously in relative position. From the perspective of voltage signals, the voltage signal corresponding to the outer sleeve starts to change 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 in the movement of the inner and outer sleeves under the action of torque and the difference in relative position changes.

[0043] Furthermore, it is determined whether to perform feedback adjustment on the electric power assistance based on the real-time data of the pedaling force. The specific judgment process is: extracting the pedaling force threshold from the power-assisted bicycle database, comparing the pedaling force with the pedaling force threshold, if the pedaling force is less than the pedaling force threshold, then the electric power assistance is not adjusted; if the pedaling force is greater than or equal to the pedaling force threshold, then the electric power assistance is feedback adjusted.

[0044] Specifically, the electric power assist is feedback-regulated, and the specific processing process is: obtaining pedal force sensor signal data, bicycle driving road condition data and bicycle use characteristic data.

[0045] The pedal force sensor signal data is processed to obtain the torque sensor stability evaluation index.

[0046] The bicycle driving road condition data is processed to obtain the road condition complexity evaluation index.

[0047] The load adaptability evaluation index is obtained by processing the bicycle usage characteristic data.

[0048] According to the torque sensor stability evaluation index, road condition complexity evaluation index, load adaptability evaluation index and pedal force real-time data, a comprehensive analysis is conducted to obtain the bicycle power assistance demand evaluation value, and the electric power assistance is feedback-adjusted according to the bicycle power assistance demand evaluation value.

[0049] Specifically, the bicycle pedal force monitoring data is processed to obtain the torque sensor stability evaluation index, and the specific processing process is: the pedal force sensor signal data includes the output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node.

[0050] The critical output voltage signal noise, the reference output voltage peak number and the allowable deviation output voltage peak number were extracted from the power-assisted bicycle database, and the torque sensor stability evaluation index was obtained through comprehensive analysis.

[0051] In a specific embodiment, the output voltage signal frequency of the torque sensor refers to the frequency value corresponding to the output voltage signal of the torque sensor when it senses the torque applied by the rider on the pedals. 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 output voltage peak number refers to the number of peaks in the output voltage signal of the torque sensor, all of which can be obtained through the single-chip microcomputer in the circuit board.

[0052] Furthermore, the torque sensor stability evaluation index has a specific numerical expression as follows: in, represents the torque sensor stability evaluation index, represents the output voltage signal frequency at the xth time node, Indicates the critical output voltage signal frequency, represents the output voltage signal noise at the xth time node, represents the critical output voltage signal noise, e represents the natural constant, Indicates the number of output voltage peaks at the xth time node, Indicates the number of peaks of the reference standard output voltage. Indicates the number of allowable deviation output voltage peaks, Indicates the torque sensor stability assessment influencing factor corresponding to the set output voltage signal frequency, Indicates the torque sensor stability assessment impact factor corresponding to the set output voltage signal noise, It represents the torque sensor stability evaluation influencing factor corresponding to the set number of output voltage peaks. x represents the number of each time node, x=1,2,3,...,y, and y represents the total number of time nodes.

[0053] The algorithm of this embodiment combines the output voltage signal frequency, output voltage signal noise and output voltage peak number of each time node, and comprehensively analyzes to obtain the torque sensor stability evaluation index. Under normal circumstances, the output voltage signal frequency of the torque sensor is positively correlated with the number of output voltage peaks. When the torque changes faster, the output voltage signal frequency is higher, and the number of output voltage peaks will increase accordingly, because the rapid change of torque will cause rapid fluctuations in the sensor output signal, thereby generating more peaks; at the same time, the output voltage signal noise will interfere with the output signal of the torque sensor, causing fluctuations in the output voltage signal frequency and the number of output voltage peaks. Comprehensive analysis can obtain a more comprehensive torque sensor stability evaluation index.

[0054] It should be explained that in this embodiment, three key factors are considered, namely, the output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node, which helps the sensor to respond to the change of torque in real time, so as to quickly adjust the power-assisted control strategy, ensure the stability and comfort of the electric power-assisted bicycle during riding, optimize the power-assisted control strategy, reduce unnecessary energy consumption, and timely discover the potential faults or abnormal conditions of the sensor, which helps to take measures in advance to repair or replace the sensor, and avoid the impact of faults on riding safety and performance. By standardizing the output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node, it is ensured that they are compared at the same magnitude, which improves the fairness and comparability of the evaluation. By weighting the influence of the output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that when the output voltage signal frequency deviation or the output voltage signal noise or the output voltage peak number deviation is smaller, the torque sensor stability evaluation index is larger. By evaluating the torque sensor stability assessment index, potential failures or performance degradation problems can be discovered and resolved in a timely manner, reducing unexpected risks during riding. This can ensure a quick response from the power-assist system so that riders can get immediate support when they need it, thereby improving rider control and safety. It can also more accurately control the output of the power-assist system and avoid unnecessary energy consumption, which helps to promptly detect signs of sensor wear or performance degradation so that preventive measures can be taken, further enhancing the market competitiveness of the product.

[0055] In a specific embodiment, the value range of the torque sensor stability assessment influencing factor corresponding to the output voltage signal frequency, the output voltage signal noise and the output voltage peak number is between 0 and 1, which represents the numerical value of the influence degree of the output voltage signal frequency, the output voltage signal noise and the output voltage peak number on the torque sensor stability assessment index. Each torque sensor stability assessment influencing factor can be obtained from the power-assisted bicycle database. By adjusting the value of the influencing factor, the influence degree of different factors on the final torque sensor stability assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the output voltage signal frequency, the output voltage signal noise and the output voltage peak number are formed with the weight factors corresponding to the output voltage signal frequency, the output voltage signal noise and the output voltage peak number preset in the power-assisted bicycle database to form a mapping set. The real-time output voltage signal frequency, the output voltage signal noise and the output voltage peak number are brought into the mapping set to obtain the weight factors corresponding to the output voltage signal frequency, the output voltage signal noise and the output voltage peak number. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0056] Furthermore, whether the torque sensor needs to be inspected and repaired is determined based on the torque sensor stability evaluation index. The specific process is: extracting the torque sensor stability evaluation threshold from the power-assisted bicycle database, comparing the torque sensor stability evaluation index 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 additional operation is performed; if the torque sensor stability evaluation index is less than the torque sensor stability evaluation threshold, immediately notify relevant staff to inspect and repair the torque sensor.

[0057] Specifically, the bicycle driving road condition data is processed to obtain the road condition complexity evaluation index, and the specific processing process is: the bicycle driving road condition data includes the bicycle inclination angle, the bicycle vertical acceleration and the bump frequency.

[0058] The critical bicycle inclination angle, critical bicycle vertical acceleration and critical bump frequency were extracted from the power-assisted bicycle database, and the road condition complexity evaluation index was obtained through comprehensive analysis.

[0059] In a specific embodiment, the inclination angle of a bicycle refers to the inclination angle of the bicycle body relative to the ground. By precisely controlling the inclination angle of the bicycle, the rider can maintain stability when turning or encountering complex road conditions, reducing the risk of loss of control due to excessive inclination, and the inclination angle of the bicycle can be monitored by an inclination sensor; the vertical acceleration of the bicycle refers to the acceleration of the bicycle in the vertical direction. The reduction in the vertical acceleration means that the impact force generated by the vehicle when encountering bumps or uneven roads is reduced, which helps to protect the rider from injury. It can be monitored in real time by an acceleration sensor; the bump frequency refers to the number of times the vertical acceleration of the bicycle exceeds the critical value during the monitoring period. A lower bump frequency means a smoother ride, reducing the rider's physical discomfort or safety hazards caused by frequent bumps, and can be obtained by counting the number of changes in the acceleration sensor data.

[0060] Furthermore, the road condition complexity evaluation index has a specific numerical expression as follows: in, represents the road complexity evaluation index, represents the bicycle inclination angle, represents the critical bicycle inclination angle, is the vertical acceleration of the bicycle, represents the critical bicycle vertical acceleration, represents the turbulence frequency, represents the critical turbulence frequency, It represents the impact factor of the road complexity assessment corresponding to the set bicycle inclination angle, It represents the impact factor of the road complexity assessment corresponding to the set bicycle acceleration. Indicates the impact factor of the road condition complexity assessment corresponding to the set bump frequency.

[0061] The algorithm of this embodiment combines the bicycle's inclination angle, the bicycle's vertical acceleration and the bump frequency, and comprehensively analyzes to obtain the road condition complexity evaluation index. When the bicycle tilts during driving, a centrifugal force in the direction of the tilt will be generated. In order to maintain balance, the bicycle needs to generate an opposite centripetal force by turning or adjusting the speed to offset the centrifugal force. During this process, the bicycle's vertical acceleration will change in response to the change in inclination angle; if the road surface is uneven during driving, the wheels will be subjected to impact forces in different directions. These impact forces will cause the bicycle to tilt and produce bumps. The frequency of bumps depends on the unevenness of the road surface and the driving speed of the bicycle; if an electric power-assisted bicycle is bumped during driving, a change in acceleration in the vertical direction will occur. This acceleration change is the result of vibration transmission, which reflects the impact of the uneven road surface on the vehicle. A more comprehensive road condition complexity evaluation index can be obtained through comprehensive analysis.

[0062] It should be explained that the present embodiment takes into account three key factors, namely, the bicycle inclination angle, the bicycle vertical acceleration, and the bump frequency, which can help the rider to better control the vehicle, improve the control accuracy and response speed, reduce vibration and discomfort during riding, improve riding comfort, reduce wear on components such as wheels, suspension systems, and frames, help maintain vehicle stability, and reduce the extra energy consumption of the motor when dealing with bumps, thereby improving overall energy efficiency. By standardizing the bicycle inclination angle, bicycle vertical acceleration, and bump frequency, ensuring that they are compared at the same magnitude, the fairness and comparability of the evaluation are improved, and at the same time , and The setting can avoid bicycle safety problems caused by excessive bicycle inclination angle, bicycle vertical acceleration and bump frequency. By weighting the influence of bicycle inclination angle, bicycle vertical acceleration and bump frequency, it 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 not difficult to see that the greater the bicycle inclination angle, bicycle vertical acceleration or bump frequency, the greater the road condition complexity evaluation index. By evaluating the road condition complexity evaluation index, the power assist strategy can be adjusted more intelligently to adapt to different road conditions, the output power of the motor can be controlled more accurately, thereby optimizing the distribution of energy, and it can also help electric power-assisted bicycles judge the safety of the current road to reduce the possibility of accidents, which helps to provide riders with a more personalized riding experience.

[0063] In a specific embodiment, the value range of the road condition complexity assessment influencing factor corresponding to the bicycle inclination angle, bicycle vertical acceleration and bump frequency is between 0 and 1, which represents the numerical value of the influence degree of the bicycle inclination 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 power-assisted bicycle database. By adjusting the value of the influencing factor, the influence degree of different factors on the final road condition complexity assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the bicycle inclination angle, bicycle vertical acceleration and bump frequency and the weight factors corresponding to the bicycle inclination angle, bicycle vertical acceleration and bump frequency preset in the power-assisted bicycle database form a mapping set, and the real-time bicycle inclination angle, bicycle vertical acceleration and bump frequency are brought into the mapping set to obtain the weight factors corresponding to the bicycle inclination angle, bicycle vertical acceleration and bump frequency. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0064] Specifically, the bicycle usage characteristic data is processed to obtain the load adaptability evaluation index, and the specific processing process is as follows: the bicycle usage characteristic data includes the bicycle moving speed, the rider's weight and the wheel deformation.

[0065] The critical bicycle moving speed, critical rider weight and critical wheel deformation are extracted from the power-assisted bicycle database, and the load adaptability evaluation index is obtained through comprehensive analysis. The filter cutoff frequency is adjusted according to the load adaptability evaluation index.

[0066] In a specific embodiment, reasonable speed setting helps to reduce vehicle wear and consumption and extend the service life of the vehicle. The bicycle movement speed can be monitored in real time through a meter; the rider's weight can be measured by a weight scale. By considering the rider's weight, the vehicle can be adjusted and optimized in a targeted manner to improve the vehicle's comfort and driving performance; the wheel deformation can be monitored in real time through a deformation sensor. When the wheel is subjected to external force, the wheel material will deform and generate strain. The deformation sensor converts this strain into an electrical signal output, thereby realizing real-time monitoring of the wheel deformation. Real-time monitoring of the wheel deformation helps to promptly detect wheel wear and damage so that timely repair or replacement can be carried out.

[0067] Furthermore, the load adaptability evaluation index has a specific numerical expression as follows: in, represents the load adaptability evaluation index, e represents the natural constant, represents the speed of the bicycle. represents the critical bicycle moving speed, Indicates the rider's weight, represents the critical rider weight, Indicates the wheel deformation, represents the critical wheel deformation, It represents the load adaptability assessment impact factor corresponding to the set bicycle moving speed, Indicates the load adaptability assessment impact factor corresponding to the set rider weight. Indicates the load adaptability assessment impact factor corresponding to the set wheel deformation.

[0068] The algorithm of this embodiment combines the bicycle moving speed, the rider's weight and the wheel deformation, and comprehensively analyzes to obtain the load adaptability evaluation index. The rider's weight is an important factor affecting the speed of the electric power-assisted bicycle. A heavier rider may increase the load of the electric power-assisted bicycle, thereby affecting its acceleration performance and maximum speed; the wheel deformation may affect the rolling resistance and stability of the electric power-assisted bicycle, and thus affect its speed. If the wheel deformation is serious, it may cause an increase in rolling resistance, thereby reducing the speed; the rider's weight may exert additional pressure on the wheel, thereby increasing the wheel deformation, and the heavier the rider, the more likely it is that the wheel will wear and age faster, thereby increasing the risk of wheel deformation. A comprehensive analysis can obtain a more comprehensive load adaptability evaluation index.

[0069] It should be explained that three key factors, namely bicycle moving speed, rider weight and wheel deformation, are considered in this embodiment, which can ensure the stability and maneuverability of the vehicle under complex road conditions, reduce the risk of traffic accidents, help optimize the design of the vehicle, ensure that good driving performance and safety can be maintained within various weight ranges, and promptly discover and deal with wheel problems to ensure the stability and safety of the vehicle during driving. By standardizing bicycle moving speed, rider weight and wheel deformation, it is ensured that they are compared at the same magnitude, which improves the fairness and comparability of the evaluation. By weighting the influence of bicycle moving speed, rider weight and wheel deformation, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that the greater the bicycle moving speed or the smaller the rider weight or the smaller the wheel deformation, the greater the load adaptability evaluation index. By evaluating the load adaptability assessment index, potential safety hazards when the vehicle's load changes can be discovered in a timely manner, which helps riders take measures in advance to avoid danger during riding, thereby improving riding safety. It can also reflect the durability and wear of electric assisted bicycle components when the load changes, so that timely repairs or replacements can be made, which helps to improve the overall performance and quality of the vehicle and enhance its market competitiveness.

[0070] In a specific embodiment, the value range of the load adaptability assessment influencing factor corresponding to the bicycle moving speed, the rider's weight and the wheel deformation is between 0 and 1, which represents the numerical value of the influence degree of the bicycle moving speed, the rider's weight and the wheel deformation on the load adaptability assessment index. Each load adaptability assessment influencing factor can be obtained from the power-assisted bicycle database. By adjusting the value of the influencing factor, the influence degree of different factors on the final load adaptability assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the bicycle moving speed, the rider's weight and the wheel deformation and the weight factors corresponding to the bicycle moving speed, the rider's weight and the wheel deformation preset in the power-assisted bicycle database form a mapping set, and the real-time bicycle moving speed, the rider's weight and the wheel deformation are brought into the mapping set to obtain the weight factors corresponding to the bicycle moving speed, the rider's weight and the wheel deformation. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0071] Furthermore, the filter cutoff frequency is adjusted according to the load adaptability evaluation index. The specific process is as follows: extracting the load adaptability evaluation threshold from the power-assisted bicycle database, and at the same time inputting the load adaptability evaluation index into the power-assisted bicycle database to match and obtain the corresponding filter cutoff frequency correction value, comparing the load adaptability evaluation index with the load adaptability evaluation threshold, if the load adaptability evaluation index is greater than or equal to the load adaptability evaluation threshold, subtracting the filter cutoff frequency from the filter cutoff frequency correction value to obtain an updated filter cutoff frequency, and if the load adaptability evaluation index is less than the load adaptability evaluation threshold, adding the filter cutoff frequency to the filter cutoff frequency correction value to obtain an updated filter cutoff frequency. Filter cutoff frequency. By adjusting the filter cutoff frequency, electric power-assisted bicycles can better adapt to riding needs under different load conditions. For example, when overloaded or uphill, the vehicle needs more power support. At this time, the filter cutoff frequency can be appropriately increased to reduce the filtering of high-frequency noise, making the power-assisted motor respond more quickly and accurately, thereby improving the riding experience. At the same time, a reasonable cutoff frequency setting helps to reduce unnecessary energy loss. When riding with a light load or on a flat road, lowering the filter cutoff frequency can reduce the response to high-frequency signals, thereby reducing the energy consumption of the power-assisted motor and improving energy utilization efficiency. This is of great significance for extending the range of electric power-assisted bicycles.

[0072] Specifically, the bicycle assistance demand evaluation value is expressed as follows: in, represents the bicycle power assistance demand assessment value, represents the torque sensor stability evaluation index, represents the road complexity evaluation index, Represents the load adaptability evaluation index, Indicates pedal force, represents the critical pedal force, Indicates the bicycle assistance demand assessment influencing factor corresponding to the set torque sensor stability assessment index, It represents the impact factor of bicycle assistance demand assessment corresponding to the set road complexity assessment index. Indicates the bicycle assistance demand assessment impact factor corresponding to the set load adaptability assessment index, Indicates the impact factor of bicycle assistance demand assessment corresponding to the set pedaling force.

[0073] like Figure 3 As shown, in a specific embodiment, = =0.3, = =0.4, =10 degrees, =20 degrees, = 0.5, when =0.1, the functional relationship between the bicycle assistance demand evaluation value and the road condition complexity evaluation index is shown in curve a; when =0.5, the functional relationship between the bicycle assistance demand evaluation value and the road condition complexity evaluation index is shown in curve b; when =1, the functional relationship between the bicycle assistance demand evaluation value and the road condition complexity evaluation index is shown in curve c.

[0074] The algorithm of this embodiment combines the road condition complexity evaluation index, the load adaptability evaluation index, and the pedal force and torque sensor stability evaluation index, and comprehensively analyzes to obtain the bicycle assistance demand evaluation value. Changes in road condition complexity will directly affect the load adaptability of the electric power-assisted bicycle. For example, under complex road conditions, such as steep mountain roads or crowded city streets, electric power-assisted bicycles need to withstand heavier loads and more frequent acceleration and deceleration operations, which requires electric power-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 the pedal force and torque sensors. When the electric power-assisted bicycle is traveling under large load changes, such as climbing or accelerating, the pedal force and torque sensors need to withstand greater stress and changes. If the sensor is not stable enough, it may cause measurement errors or signal distortion, thereby affecting the control effect and riding experience of the electric power-assisted system. Comprehensive analysis can obtain a more comprehensive bicycle assistance demand evaluation value.

[0075] It should be explained that in this embodiment, four key factors are considered, namely, the road condition complexity evaluation index, the load adaptability evaluation index, the pedal force and the torque sensor stability evaluation index, so that the performance of the electric power-assisted bicycle under different road conditions can be understood, so as to carry out targeted optimization, which is helpful to improve the load capacity of the vehicle, so that it can remain stable and efficient under different loads, and can also ensure that it can provide accurate torque information under various conditions, so as to achieve a smoother and more efficient riding, and can also achieve more accurate pedal force control, so that riding is easier and more comfortable, and further improve the reliability and durability of the electric power-assisted bicycle, so as to reduce the failure rate. By weighting the influence of the road condition complexity evaluation index, the load adaptability evaluation index, the pedal force and the torque sensor stability evaluation index, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that when the road condition complexity evaluation index is larger or the load adaptability evaluation index is smaller or the pedal force is larger or the torque sensor stability evaluation index is smaller, the bicycle assistance demand evaluation value is larger. By evaluating the bicycle power-assist demand assessment value, we can ensure that the power assistance provided by the electric power-assist bicycle matches the actual needs of the rider, which helps to optimize the handling performance of the electric power-assist bicycle, improve riding stability and safety, and further determine the optimal power assistance level of the electric power-assist bicycle under different road conditions and riding speeds, thereby reducing unnecessary energy consumption and improving energy utilization efficiency.

[0076] In a specific embodiment, the value range of the bicycle assistance demand evaluation influencing factor corresponding to the road condition complexity evaluation index, the load adaptability evaluation index, the pedal force and the torque sensor stability evaluation index is between 0 and 1, which represents the numerical value of the influence degree of the road condition complexity evaluation index, the load adaptability evaluation index, the pedal force and the torque sensor stability evaluation index on the bicycle assistance demand evaluation value. Each bicycle assistance demand evaluation influencing factor can be obtained from the power-assisted bicycle database. By adjusting the value of the influencing factor, the influence degree of different factors on the final bicycle assistance demand evaluation value can be flexibly adjusted. The corresponding relationship can be a pre-set mapping. A mapping relationship is formed, for example, the road condition complexity assessment index, the load adaptability assessment index, the pedal force and torque sensor stability assessment index and the weight factors corresponding to the road condition complexity assessment index, the load adaptability assessment index, and the pedal force and torque sensor stability assessment index preset in the power-assisted bicycle database form a mapping set, and the real-time road condition complexity assessment index, the load adaptability assessment index, and the pedal force and torque sensor stability assessment index are brought into the mapping set to obtain the road condition complexity assessment index, the load adaptability assessment index, and the pedal force and torque sensor stability assessment index corresponding to the weight factors, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0077] Specifically, the electric power assistance is feedback-adjusted according to the bicycle power assistance demand evaluation value. The specific process is: according to the bicycle power assistance demand evaluation value, the corresponding bicycle power assistance demand is matched into the power-assisted bicycle database, the bicycle power assistance demand is compared with the actual bicycle power assistance and the difference is made to obtain the bicycle power assistance difference, and the electric power assistance is feedback-adjusted according to the bicycle power assistance difference.

[0078] Furthermore, the electric power assist is feedback-adjusted according to the bicycle power assist difference. The specific process is: if the bicycle power assist difference is greater than zero, the actual bicycle power assist is increased until the bicycle power assist requirement is met, that is, the bicycle power assist difference is input into the power-assisted 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 an updated motor output power, and the motor power is output according to the updated motor output power to provide stronger power assist; if the bicycle power assist difference is equal to zero, no additional operation is performed; if the bicycle power assist difference is less than zero, the actual bicycle power assist is reduced until the bicycle power assist requirement is met, that is, the bicycle power assist difference is input into the power-assisted 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 an updated motor output power, and the motor power is output according to the updated motor output power to provide weaker power assist.

[0079] In a specific embodiment, a mapping set of bicycle assist difference values ​​and corresponding motor output power adjustment values ​​is constructed based on the relationship between bicycle assist difference values ​​and motor output power adjustment values ​​in historical data, and the real-time bicycle assist difference value is input to obtain the corresponding motor output power adjustment value from the mapping set.

[0080] Reference Figure 2 As shown, the second aspect of the present invention provides an electric power-assisted bicycle pedal force detection system based on peak positioning, including: a data acquisition module, used to monitor the working process of the electric power-assisted bicycle, and collect pedal force sensor signal data, the pedal force sensor signal data including the torque sensor voltage waveform and the output voltage signal frequency, output voltage signal noise and output voltage peak number at each time node.

[0081] The pedal force real-time 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 in the torque sensor, and perform phase comparison on the periodic data of the inner and outer sleeves to obtain the phase difference between the inner and outer sleeves, and obtain the real-time pedal force data based on the phase difference between the inner and outer sleeves.

[0082] The electric power steering feedback adjustment module is used to determine whether to perform feedback adjustment on the electric power steering according to the real-time data of the pedal force.

[0083] The power-assisted bicycle database is used to store power-assisted bicycle related data, including: critical output voltage signal noise, reference standard output voltage peak number, allowable deviation output voltage peak number, critical bicycle inclination angle, critical bicycle vertical acceleration, critical bump frequency, critical bicycle moving speed, critical rider weight, critical wheel deformation, bicycle power-assisted demand assessment influencing factor corresponding to a set torque sensor stability assessment index, bicycle power-assisted demand assessment influencing factor corresponding to a set road condition complexity assessment index, bicycle power-assisted demand assessment influencing factor corresponding to a set load adaptability assessment index, bicycle power-assisted demand assessment influencing factor corresponding to a set pedal force, and torque sensor stability assessment threshold and other indicators.

[0084] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning, characterized in that: include: The working process of the electric power-assisted bicycle is monitored to collect pedal force sensor signal data, wherein the pedal force sensor signal data includes a torque sensor voltage waveform and an output voltage signal frequency, an output voltage signal noise, and an output voltage peak number at each time node; The peak of the torque sensor voltage waveform is located, the periodic data between the inner and outer sleeves in the torque sensor is collected, and the periodic data of the inner and outer sleeves are compared in phase to obtain the phase difference of the inner and outer sleeves, and the real-time data of the pedal force is obtained according to the phase difference of the inner and outer sleeves; Determine whether to make feedback adjustments to the electric power assist based on the real-time data of pedal force.

2. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 1, characterized in that: The specific processing process of collecting the periodic data between the inner and outer sleeves in the torque sensor is as follows: The torque sensor voltage waveform includes a voltage signal waveform corresponding to the inner sleeve and a voltage signal waveform corresponding to the outer sleeve; The turning point where the peak changes from rising to falling is marked in the voltage signal waveform corresponding to the inner and outer sleeves, and the period between adjacent turning points is marked as a signal change cycle to obtain the periodic data between the inner and outer sleeves, and the periodic data includes the phase of the voltage signal waveform of the inner and outer sleeves at each time monitoring point.

3. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 1, characterized in that: The real-time data of pedal force is obtained by processing the phase difference between the inner and outer sleeves, and the specific processing process is as follows: The phases of the inner and outer sleeve voltage signal waveforms at each time monitoring point are subtracted, 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 the pedaling force at each time monitoring point, and the pedaling force at each time monitoring point is counted and marked as real-time pedaling force data.

4. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 1, characterized in that: The specific judgment process of judging whether to perform feedback adjustment on the electric power assist according to the real-time data of the pedaling force is as follows: A pedal force threshold is extracted from a power-assisted bicycle database, and the pedal force is compared with the pedal force threshold. If the pedal force is less than the pedal force threshold, no additional operation is performed. If the pedal force is greater than or equal to the pedal force threshold, feedback adjustment is performed on the electric power assist.

5. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 4, characterized in that: The feedback adjustment of the electric power assist is specifically processed as follows: Acquiring pedal force sensor signal data, bicycle driving road condition data and bicycle use characteristic data; The pedal force sensor signal data is processed to obtain the torque sensor stability evaluation index; Processing bicycle driving road condition data to obtain a road condition complexity evaluation index; Processing bicycle usage characteristic data to obtain a load adaptability evaluation index; According to the torque sensor stability evaluation index, road condition complexity evaluation index, load adaptability evaluation index and pedal force real-time data, a comprehensive analysis is conducted to obtain the bicycle power assistance demand evaluation value, and the electric power assistance is feedback-adjusted according to the bicycle power assistance demand evaluation value.

6. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 5, characterized in that: The bicycle driving road condition data is processed to obtain the road condition complexity evaluation index, and the specific processing process is as follows: The bicycle driving road condition data includes bicycle inclination angle, bicycle vertical acceleration and bump frequency; The critical bicycle inclination angle, critical bicycle vertical acceleration and critical bump frequency were extracted from the power-assisted bicycle database, and the road condition complexity evaluation index was obtained through comprehensive analysis.

7. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 5, characterized in that: The load adaptability evaluation index is obtained by processing the bicycle usage characteristic data, and the specific processing process is as follows: The bicycle usage characteristic data includes bicycle moving speed, rider weight and wheel deformation; The critical bicycle moving speed, critical rider weight and critical wheel deformation are extracted from the power-assisted bicycle database, and the load adaptability evaluation index is obtained through comprehensive analysis. The filter cutoff frequency is adjusted according to the load adaptability evaluation index.

8. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 5, characterized in that: The feedback adjustment of the electric power assist according to the bicycle power assist demand evaluation value is specifically performed as follows: According to the bicycle power assistance demand evaluation value, the corresponding bicycle power assistance demand is matched in the power-assisted bicycle database, the bicycle power assistance demand is compared with the actual bicycle power assistance and the difference is made to obtain the bicycle power assistance difference, and the electric power assistance is feedback-adjusted according to the bicycle power assistance difference.

9. The method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning according to claim 5, characterized in that: The bicycle power assistance demand evaluation value is specifically expressed as: in, represents the bicycle power assistance demand assessment value, represents the torque sensor stability evaluation index, represents the road complexity evaluation index, Represents the load adaptability evaluation index, Indicates pedal force, represents the critical pedal force, represents the bicycle assistance demand assessment influencing factor corresponding to the set torque sensor stability assessment index, It represents the impact factor of bicycle assistance demand assessment corresponding to the set road complexity assessment index. Indicates the bicycle assistance demand assessment impact factor corresponding to the set load adaptability assessment index, Indicates the impact factor of bicycle assistance demand assessment corresponding to the set pedaling force.

10. A system using the method for detecting pedal force of an electric power-assisted bicycle based on wave peak positioning as claimed in any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to monitor the working process of the electric power-assisted bicycle and collect pedal force sensor signal data, wherein the pedal force sensor signal data includes a torque sensor voltage waveform and an output voltage signal frequency, an output voltage signal noise and an output voltage peak number at each time node; The pedal force real-time 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 in the torque sensor, and perform phase comparison on the periodic data of the inner and outer sleeves to obtain the phase difference between the inner and outer sleeves, and obtain the pedal force real-time data according to the phase difference between the inner and outer sleeves; The electric power steering feedback adjustment module is used to determine whether to perform feedback adjustment on the electric power steering according to the real-time data of the pedal force.

Images