A control method and system for a power-assisted bicycle based on gear ratio self-learning

Abstract

The application discloses a control method and system of an assisted bicycle based on gear ratio self-learning, and the method comprises the following steps: reading historical gear ratio, judging whether the self-learning condition is met, and performing gear ratio self-learning according to the current driving state of the assisted bicycle, and assigning the gear ratio obtained through self-learning to the current gear ratio; when it is judged that the self-learning condition is not met, setting the current gear ratio as the historical gear ratio; controlling the motor output of the assisted bicycle according to the current gear ratio, and updating the historical gear ratio as the current gear ratio. The application can keep excellent assisting effect through self-learning gear ratio.

Description

Technical Field

[0001] This invention belongs to the field of electric bicycles, and more specifically to the field of power-assisted bicycles, and specifically relates to a control method and system for power-assisted bicycles based on gear ratio self-learning. Background Technology

[0002] As a new type of transportation, e-bikes are lightweight, easy to ride, and offer smooth assistance, making them a very popular mode of transportation in Europe and America, and gradually gaining popularity. E-bikes generally consist of an electrical system including a torque sensor, controller, motor, and battery. The torque sensor, installed at the bottom bracket, provides information on the pedaling torque and cadence. Single-speed or multi-speed e-bikes come in various forms, and the gear ratio between the bottom bracket and rear wheel will differ, thus affecting the assistance control parameters provided by the control system.

[0003] Existing technology, such as Chinese patent document with patent application number CN201810427669.7, provides a method for controlling the power output of an electric bicycle. This method detects the torque magnitude and pedaling frequency using a bottom bracket torque sensor, and calculates the current power output by the rider based on the torque and pedaling frequency. It then selects the assist ratio based on the pedaling frequency and finally calculates the required output power of the motor. However, this method does not consider the bicycle's gear ratio, therefore the assist effect will vary significantly for electric bicycles with different gear ratios.

[0004] Existing technology, such as Chinese patent document CN202210760024.1, provides a starting control method for a power-assisted bicycle. This starting control method includes: acquiring pedal torque and pedal speed; determining whether the pedal torque and pedal speed meet preset starting conditions; if so, acquiring a pre-stored chain drive ratio, current assist ratio, current user weight, and ground slope; determining a torque output command based on the pre-stored chain drive ratio, current assist ratio, current user weight, ground slope, and pedal torque; and outputting the torque output command to the motor. While this approach achieves a comfortable start, it requires a preset drive ratio, thus the controller is not universally compatible with bicycles of different drive ratios. Summary of the Invention

[0005] One of the objectives of this invention is to provide a power-assisted bicycle control method based on gear ratio self-learning, which maintains excellent assist effect through self-learning of gear ratio.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for controlling a power-assisted bicycle based on gear ratio self-learning, the method comprising:

[0008] After reading the historical gear ratio and determining that the self-learning conditions are met, the gear ratio is self-learned based on the current driving state of the electric bicycle. The gear ratio obtained through self-learning is then assigned to the current gear ratio. If the self-learning conditions are not met, the current gear ratio is set to the historical gear ratio.

[0009] The motor output of the power-assisted bicycle is controlled according to the current gear ratio, and the historical gear ratio is updated to the current gear ratio.

[0010] Several alternative methods are provided below, but they are not intended as additional limitations on the overall solution above. They are merely further additions or optimizations. Provided there are no technical or logical contradictions, each alternative method can be combined individually with respect to the overall solution above, or multiple alternative methods can be combined with each other.

[0011] Preferably, the self-learning condition is that the torque information collected by the center axis torque sensor is greater than a preset torque threshold, and the center axis torque sensor is installed on the center axis of the electric bicycle.

[0012] Preferably, the step of performing gear ratio self-learning based on the current riding state of the electric bicycle includes:

[0013] Based on the cadence sensor installed on the bottom bracket of the electric bicycle, the period between two adjacent pulses of the cadence sensor is obtained as T1, and based on the wheel speed sensor installed on the rear wheel of the electric bicycle, the period between two adjacent pulses of the wheel speed sensor is obtained as T2.

[0014] The angular velocities of the center axle and rear wheels are calculated as follows:

[0015]

[0016]

[0017] In the formula, ω1 is the angular velocity of the central axle, N1 is the number of pulses generated by the pedal frequency sensor per revolution, ω2 is the angular velocity of the rear wheel, and N2 is the number of pulses generated by the wheel speed sensor per revolution.

[0018] The calculation of gears is as follows:

[0019]

[0020] In the formula, G is the gear ratio obtained through self-learning.

[0021] Preferably, the step of assigning a value to the current gear ratio based on the gear ratio obtained through self-learning includes:

[0022] The gear ratio obtained through self-learning is low-pass filtered. If the gear ratio obtained through self-learning is not filtered, the current gear ratio is set to the gear ratio obtained through self-learning; if the gear ratio obtained through self-learning is filtered, the current gear ratio is set to the historical gear ratio.

[0023] Preferably, the step of controlling the motor output of the power-assisted bicycle according to the current gear ratio includes:

[0024] By taking the reference gear ratio and the reference phase current applied by the motor controller under standard reference conditions, a reference total output power model for the electric bicycle is established.

[0025] Take the current gear ratio under the current driving state and set the current phase current applied by the motor controller to establish the current total output power model of the electric bicycle;

[0026] Based on the condition of maintaining the same assist effect, establish a relationship model between the reference total output power model and the current total output power model;

[0027] Solve the relationship model to obtain the current phase current applied by the motor controller, and control the motor output of the power-assisted bicycle according to the current phase current applied by the motor controller.

[0028] Preferably, the reference gear ratio and reference phase current applied by the motor controller under standard reference conditions are used to establish a reference total output power model for the electric bicycle, including:

[0029]

[0030] In the formula, Pout_total_con is the reference total output power model, Pout_man_con is the reference human power, Pout_motor_con is the reference motor power, Torque is the human pedaling torque, ω2 is the rear wheel angular velocity, G0 is the reference gear ratio, and U p The phase voltage applied to the motor controller, I p0 The reference phase current applied to the motor controller, It is the impedance angle.

[0031] Preferably, the step of establishing a relationship model between the reference total output power model and the current total output power model based on the condition of maintaining the same assist effect includes:

[0032]

[0033] In the formula, Pout_total_rea is the current total output power model, Pout_man_rea is the current human power, Pout_motor_rea is the current motor power, Torque is the human pedaling torque, ω2 is the rear wheel angular velocity, G1 is the current gear ratio, and Up The phase voltage applied to the motor controller, I p1 The current phase current applied to the motor controller with the solution. It is the impedance angle.

[0034] Preferably, the conditions for maintaining the same assist effect are: the total output power remains the same, the pedaling torque remains the same, and the rear wheel angular velocity remains the same when the same wheel speed is reached.

[0035] Preferably, the relationship model is such that the reference total output power model is equal to the current total output power model.

[0036] This invention provides a gear ratio self-learning-based control method for electric bicycles. The method continuously learns the gear ratio of the electric bicycle and automatically adjusts the control parameters accordingly, continuously and automatically adjusting the motor torque to achieve the same assist effect and improve the user's riding experience. Furthermore, the program can perform adaptive control for different bicycle models, enabling controller standardization.

[0037] The second objective of this invention is to provide a power-assisted bicycle control system based on gear ratio self-learning, which maintains excellent assist performance through self-learning of gear ratios.

[0038] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0039] A power-assisted bicycle control system based on gear ratio self-learning, wherein the power-assisted bicycle control method based on gear ratio self-learning includes:

[0040] The self-learning module is used to read historical gear ratios, determine if the self-learning conditions are met, perform gear ratio self-learning based on the current riding state of the electric bicycle, and set the current gear ratio to the self-learned gear ratio. If the self-learning conditions are not met, the current gear ratio is set to the historical gear ratio.

[0041] The self-adjusting control module is used to control the motor output of the power-assisted bicycle according to the current gear ratio;

[0042] The parameter update module is used to update the historical gear ratio to the current gear ratio.

[0043] This invention proposes a gear ratio self-learning-based power-assisted bicycle control system. By continuously learning the gear ratio of the power-assisted bicycle and automatically adjusting control parameters accordingly, it can continuously and automatically adjust the motor output torque to achieve the ideal assist effect. This control system can implement control strategies for different gear ratios, significantly improving the user experience. The main benefits are as follows:

[0044] (1) For control systems, a set of control programs can be adapted to different vehicle models to achieve the same assist effect, realize the standardization and universalization of controllers, improve product quality, reduce risks, reduce repetitive work and waste, reduce costs, and improve market response efficiency.

[0045] (2) For multi-speed bicycles, compared with single control parameters, this self-learning control strategy can achieve adaptive power assist control parameters, which greatly improves the user's riding experience. Attached Figure Description

[0046] Figure 1 A schematic diagram of an existing electric bicycle structure;

[0047] Figure 2 This is a schematic diagram showing the installation of the shaft torque sensor and wheel speed sensor in this invention;

[0048] Figure 3 This is a flowchart of the power-assisted bicycle control method based on gear ratio self-learning according to the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0051] The gear ratio is defined as the ratio of the number of teeth on the driving gear to the number of teeth on the driven gear. C represents the number of teeth on the driving gear, F represents the number of teeth on the driven gear, and G is the gear ratio. The relationship between them is expressed by the formula: G = C ÷ F. For example... Figure 1 In the electric bicycle shown, the driving wheel is the chainring, and the driven wheel is the freewheel.

[0052] The transmission ratio (transmission coefficient) is defined as: gear ratio multiplied by the rear wheel diameter. Let D represent the transmission ratio and B represent the rear wheel diameter. The relationship between them is expressed by the formula: D = C ÷ F × B = GB.

[0053] For single-speed bicycles, due to changes in market demand, there are various types of bicycles, and the gear ratios of these bicycles cannot be kept consistent; for multi-speed bicycles, the gear ratios also change according to the rider's adjustments. Therefore, for bicycles, the gear ratios are not fixed.

[0054] For the same set of fixed parameters, when the gear ratios are inconsistent, the relationship between the rider's cadence and wheel speed will also be inconsistent, resulting in inconsistent assist effects. When the gear ratio is larger, that is, the diameter of the driving wheel is larger, the chain speed is higher, and the driven wheel is smaller, the power will be greater, but the rider will need to exert more force, making it more strenuous. Therefore, in this case, it is necessary to increase the motor's output torque to achieve easy assistance. Conversely, when the gear ratio is smaller, the rider will need to exert less effort, and under the same conditions, the motor's output torque can be reduced.

[0055] To address this, this embodiment proposes a gear ratio self-learning assist control strategy. This strategy continuously learns the gear ratio of the electric bicycle and automatically adjusts the control parameters accordingly, continuously and automatically adjusting the motor torque to achieve the same assist effect and improve the user's riding experience. Furthermore, this control strategy can adaptively control different bicycle models, enabling controller standardization.

[0056] like Figure 2 As shown, to meet the power assist control strategy proposed in this embodiment, a center-axle torque sensor that integrates torque and cadence measurement is installed on the center-axle of the power-assisted bicycle, and a wheel speed sensor is installed on the rear wheel. It is easy to understand that the installation of the center-axle torque sensor and wheel speed sensor in this embodiment is mainly to obtain the center-axle torque, cadence, and rear wheel speed. Provided that the required information can be obtained, the sensors are not strictly limited; for example, a torque sensor and a cadence sensor can be installed separately on the center-axle, both of which fall within the scope of protection of this invention. This embodiment only uses the center-axle torque sensor and wheel speed sensor as examples for illustration.

[0057] like Figure 3 As shown, the gear ratio self-learning-based power-assisted bicycle control method of this embodiment includes the following steps:

[0058] Step 1: Read the historical gear ratios. If the self-learning conditions are met, perform gear ratio self-learning based on the current riding state of the electric bicycle. Assign the current gear ratio based on the self-learned gear ratio. If the self-learning conditions are not met, set the current gear ratio to the historical gear ratio.

[0059] In this embodiment, the central shaft torque sensor generates N1 pulses per revolution, with the time (period) between two pulses being T1, and the central shaft angular velocity being ω1; the rear wheel speed sensor generates N2 pulses per revolution, with the time (period) between two pulses being T2, and the rear wheel angular velocity being ω2; the gear ratio is G.

[0060] Before performing gear ratio self-learning, the current state is first determined to see if the driving wheel is driving the driven wheel. This logic is determined by detecting torque information. When the torque information collected by the torque sensor is greater than the preset torque threshold, it is considered that the driving wheel is driving the driven wheel in the current state, and the gear ratio self-learning function is activated; otherwise, the stored historical gear ratio is read for power assist control.

[0061] In this embodiment, the historical gear ratio is stored in a power-down non-volatile unit, such as an EEPROM, FLASH, or MRAM memory. The power-down non-volatile unit has a preset gear ratio, which serves as the original historical gear ratio. After self-learning is complete, the gear ratio in the power-down non-volatile unit is updated to the self-learned gear ratio, which becomes the updated historical gear ratio.

[0062] To improve the accuracy of self-learning condition judgment, this embodiment filters the torque information after it is collected to avoid high-frequency interference. This embodiment monitors torque information in real time to ensure timely activation of the gear ratio self-learning function, quickly adjusts the power assist control, and enhances the user experience.

[0063] During gear ratio self-learning, the period between two adjacent pulses from the cadence sensor installed on the bottom bracket of the electric bicycle is obtained as T1, and the period between two adjacent pulses from the wheel speed sensor installed on the rear wheel is obtained as T2. The periods T1 and T2 between two pulses from the bottom bracket torque sensor and the wheel speed sensor can be calculated using external interrupts or external input capture and timing functions. The gear ratio is calculated as follows:

[0064]

[0065]

[0066]

[0067] After calculating the gear ratio, the current gear ratio can be directly set to the self-learned gear ratio for subsequent assist control. Alternatively, to prevent high-frequency interference, the calculated gear ratio can be low-pass filtered, and subsequent assist control can be performed based on the filtering result. Specifically, the self-learned gear ratio can be low-pass filtered; if the self-learned gear ratio is not filtered, the current gear ratio is set to the self-learned gear ratio; if the self-learned gear ratio is filtered, the current gear ratio is set to the historical gear ratio.

[0068] It is easy to understand that this embodiment mainly introduces a gear ratio self-learning strategy for assisted control. There are no strict restrictions on how to use the gear ratio obtained by direct self-learning. In addition to the methods mentioned above, historical gear ratios can also be introduced to assist the gear ratio obtained by self-learning in assigning values ​​to the current gear ratio.

[0069] Step 2: Control the motor output of the power-assisted bicycle according to the current gear ratio.

[0070] The total output power of the vehicle, Pout_total, consists of two parts: human power, Pout_man, and motor power, Pout_motor, as shown in the following formula:

[0071] Pout_total=Pout_man+Pout_motor

[0072] The relationship between the central axis rotational angular velocity ω1 and the rotational speed n satisfies the following formula:

[0073] ω1=2πn

[0074] Human power can be calculated from the torque Torque obtained by the center axis torque sensor and the center axis angular velocity ω1 obtained by the center axis pedal frequency sensor:

[0075]

[0076] The output power of a three-phase motor can be obtained by multiplying the input bus voltage Ubus, the bus current Ibus, and the efficiency η; or by the phase voltage U p Phase current I p , impedance angle cosine value The product is obtained, in the formula This is the phase difference angle between the phase voltage and the phase current, i.e., the impedance angle. The formula is as follows:

[0077]

[0078] When the model of an electric bicycle changes, the gear ratio also changes. The purpose of the self-adjustment strategy is to automatically adjust the control parameters according to the gear ratio, thereby automatically adjusting the motor torque to achieve the same assist effect and improve the user's riding experience.

[0079] The conditions for maintaining the same boosting effect are:

[0080] ① The total output power Pout_total of the vehicle remains the same before and after.

[0081] ② The pedaling torque remains the same before and after the Torque.

[0082] ③ The vehicles reach the same wheel speed, that is, the rear wheel angular velocity ω2 is the same.

[0083] Assuming the vehicle gear ratio is G0 under standard reference conditions, and the motor controller applies a phase current of I... p0 Therefore, taking the reference gear ratio under standard reference conditions and the reference phase current applied by the motor controller, the reference total output power model of the electric bicycle is established as follows:

[0084]

[0085] In the formula, Pout_total_con is the reference total output power model, Pout_man_con is the reference human power, Pout_motor_con is the reference motor power, Torque is the human pedaling torque, ω2 is the rear wheel angular velocity, G0 is the reference gear ratio, and U p The phase voltage applied to the motor controller, I p0 The reference phase current applied to the motor controller, It is the impedance angle.

[0086] Assuming the actual gear ratio of the vehicle is G1 after gear ratio self-learning, and the applied phase current of the motor is I... p1 To achieve the same assist effect, the total output power Pout_total, pedaling torque Torque, and rear wheel angular velocity ω2 remain constant, while ignoring changes in impedance angle. Using the current gear ratio under the current driving condition and the current phase current applied by the motor controller, a model of the current total output power of the electric bicycle is established.

[0087]

[0088] In the formula, Pout_total_rea is the current total output power model, Pout_man_rea is the current human power, Pout_motor_rea is the current motor power, Torque is the human pedaling torque, ω2 is the rear wheel angular velocity, G1 is the current gear ratio, and U p The phase voltage applied to the motor controller, I p1 The current phase current applied to the motor controller with the solution. It is the impedance angle.

[0089] To maintain the same assist effect, a relationship model is established between the reference total output power model and the current total output power model. In this embodiment, to simplify calculations, the relationship model is established as follows: The reference total output power model equals the current total output power model. The formula is as follows:

[0090]

[0091] Solving the relational model directly based on the equation, the current phase current applied by the motor controller is obtained as follows:

[0092]

[0093] The motor output of the power-assisted bicycle can be controlled based on the current phase current applied by the motor controller obtained from the solution. In this embodiment, the applied motor phase current is continuously and automatically adjusted to control and adjust the motor torque, so as to maintain the same assist effect after changing the gear ratio.

[0094] This embodiment performs cyclical data monitoring, self-learns the gear ratio based on conditions, and provides real-time power assist control. It combines preset gear ratios with self-learned gear ratios using historical gear ratios. After obtaining a new gear ratio through self-learning, the historical gear ratio is updated. Since the current gear ratio is always the latest one, the historical gear ratio can be updated to the current gear ratio after one cycle. Alternatively, the historical gear ratio can be updated and stored in other stages, such as after obtaining the filtered and retained self-learned gear ratio.

[0095] In another embodiment, a power-assisted bicycle control system based on gear ratio self-learning is also provided, wherein the power-assisted bicycle control method based on gear ratio self-learning includes:

[0096] The self-learning module is used to read historical gear ratios, determine if the self-learning conditions are met, perform gear ratio self-learning based on the current riding state of the electric bicycle, and set the current gear ratio to the self-learned gear ratio. If the self-learning conditions are not met, the current gear ratio is set to the historical gear ratio.

[0097] The self-adjusting control module is used to control the motor output of the power-assisted bicycle according to the current gear ratio;

[0098] The parameter update module is used to update the historical gear ratio to the current gear ratio.

[0099] For the limitations of a power-assisted bicycle control system based on gear ratio self-learning, please refer to the foregoing limitations of a power-assisted bicycle control method based on gear ratio self-learning; this embodiment will not repeat them.

[0100] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A control method for a power-assisted bicycle based on gear ratio self-learning, characterized in that, The power-assisted bicycle control method based on gear ratio self-learning includes: After reading historical gear ratios and determining that the self-learning conditions are met, gear ratio self-learning is performed based on the current riding state of the electric bicycle. The gear ratio obtained through self-learning is then assigned to the current gear ratio. If the self-learning conditions are not met, the current gear ratio is set to the historical gear ratio. The step of performing gear ratio self-learning based on the current riding state of the electric bicycle includes: Based on the cadence sensor installed on the bottom bracket of the electric bicycle, the period between two adjacent pulses from the cadence sensor is obtained as follows: Based on the wheel speed sensor installed on the rear wheel of the electric bicycle, the period between two adjacent pulses from the wheel speed sensor is obtained as follows: ; The angular velocities of the center axle and rear wheels are calculated as follows: In the formula, The angular velocity is the central axis. This represents the number of pulses generated per revolution of the cadence sensor. The angular velocity of the rear wheel. This represents the number of pulses generated by the wheel speed sensor per revolution. The calculation of gears is as follows: In the formula, The gear ratio is obtained through self-learning; Controlling the motor output of the electric bicycle according to the current gear ratio and updating the historical gear ratio to the current gear ratio; the control of the motor output of the electric bicycle according to the current gear ratio includes: By taking the reference gear ratio and the reference phase current applied by the motor controller under standard reference conditions, a reference total output power model for the electric bicycle is established. Take the current gear ratio under the current driving state and set the current phase current applied by the motor controller to establish the current total output power model of the electric bicycle; Based on the condition of maintaining the same assist effect, establish a relationship model between the reference total output power model and the current total output power model; Solve the relationship model to obtain the current phase current applied by the motor controller, and control the motor output of the power-assisted bicycle according to the current phase current applied by the motor controller.

2. The power-assisted bicycle control method based on gear ratio self-learning as described in claim 1, characterized in that, The self-learning condition is that the torque information collected by the center axis torque sensor is greater than the preset torque threshold, and the center axis torque sensor is installed on the center axis of the electric bicycle.

3. The power-assisted bicycle control method based on gear ratio self-learning as described in claim 1, characterized in that, The step of assigning a value to the current gear ratio based on the gear ratio obtained through self-learning includes: The gear ratio obtained through self-learning is low-pass filtered. If the gear ratio obtained through self-learning is not filtered, the current gear ratio is set to the gear ratio obtained through self-learning; if the gear ratio obtained through self-learning is filtered, the current gear ratio is set to the historical gear ratio.

4. The power-assisted bicycle control method based on gear ratio self-learning as described in claim 1, characterized in that, The reference gear ratio and reference phase current applied by the motor controller under the standard reference condition are used to establish a reference total output power model for the electric bicycle, including: In the formula, For reference to the total output power model, For reference of manpower efficiency, For reference motor power, The torque of stepping on someone, The angular velocity of the rear wheel. For reference gear ratio, The phase voltage applied to the motor controller, The reference phase current applied to the motor controller, It is the impedance angle.

5. The power-assisted bicycle control method based on gear ratio self-learning as described in claim 1, characterized in that, The relationship model between the reference total output power model and the current total output power model, established under the condition of maintaining the same assist effect, includes: In the formula, For the current total output power model, Based on current manpower capacity, This represents the current motor power. The torque of stepping on someone, The angular velocity of the rear wheel. The current gear ratio, The phase voltage applied to the motor controller, The current phase current applied to the motor controller with the solution. It is the impedance angle.

6. The power-assisted bicycle control method based on gear ratio self-learning as described in claim 1, characterized in that, The conditions for maintaining the same power assist effect are: the total output power remains the same, the pedaling torque remains the same, and the rear wheel angular velocity remains the same when the same wheel speed is reached.

7. The power-assisted bicycle control method based on gear ratio self-learning as described in claim 1, characterized in that, The relationship model is that the reference total output power model is equal to the current total output power model.

8. A power-assisted bicycle control system based on gear ratio self-learning, characterized in that, The power-assisted bicycle control system based on gear ratio self-learning includes: The self-learning module is used to read historical gear ratios, determine if the self-learning conditions are met, perform gear ratio self-learning based on the current riding state of the electric bicycle, and set the current gear ratio to the self-learned gear ratio. If the self-learning conditions are not met, the current gear ratio is set to the historical gear ratio. The step of performing gear ratio self-learning based on the current riding state of the electric bicycle includes: Based on the cadence sensor installed on the bottom bracket of the electric bicycle, the period between two adjacent pulses from the cadence sensor is obtained as follows: Based on the wheel speed sensor installed on the rear wheel of the electric bicycle, the period between two adjacent pulses from the wheel speed sensor is obtained as follows: ; The angular velocities of the center axle and rear wheels are calculated as follows: In the formula, The angular velocity is the central axis. This represents the number of pulses generated per revolution of the cadence sensor. The angular velocity of the rear wheel. This represents the number of pulses generated by the wheel speed sensor per revolution. The calculation of gears is as follows: In the formula, The gear ratio is obtained through self-learning; The self-adjusting control module is used to control the motor output of the power-assisted bicycle according to the current gear ratio, including: By taking the reference gear ratio and the reference phase current applied by the motor controller under standard reference conditions, a reference total output power model for the electric bicycle is established. Take the current gear ratio under the current driving state and set the current phase current applied by the motor controller to establish the current total output power model of the electric bicycle; Based on the condition of maintaining the same assist effect, establish a relationship model between the reference total output power model and the current total output power model; Solve the relationship model to obtain the current phase current applied by the motor controller, and control the motor output of the power-assisted bicycle according to the current phase current applied by the motor controller; The parameter update module is used to update the historical gear ratio to the current gear ratio.

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