Control method and device of electric bicycle, electric bicycle and program product

Abstract

The invention belongs to the field of data processing, and provides a control method and device of an electric bicycle, the electric bicycle and a program product. According to the embodiment of the invention, the sensing data of the target electric bicycle is obtained by responding to the starting of the target electric bicycle, and the sensing data comprises at least one of the actual angular velocity and the power torque data. Based on the sensing data, the target speed, the wheel rotation estimation value and the disturbance estimation value of the target electric bicycle are determined, and the disturbance estimation value comprises at least one of the power torque data, the friction resistance, the gravity component and the wind resistance. Based on the wheel rotation estimation value, the disturbance estimation value and the target bicycle speed, the motor assistance torque is determined, a control instruction is generated and sent to the motor to control rotation of the motor, the actual bicycle speed of the target electric bicycle reaches the target bicycle speed, and therefore power assistance can be provided in a self-adaptive mode when a complex environment is dealt with; and the anti-disturbance capability of the electric bicycle during riding is enhanced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data processing, and particularly relates to a control method and device of an electric bicycle, the electric bicycle and a program product. BACKGROUND

[0002] An electric bicycle is a hybrid power vehicle that is powered by human pedaling and electric power. During the operation of the electric bicycle, the electric bicycle is disturbed by external environmental factors (such as road slope fluctuation, load change and wind resistance disturbance), thereby affecting the driving speed of the electric bicycle.

[0003] The related electric bicycle power-assisted control method is difficult to cope with the changes in complex environments, and has the problems of insufficient power assistance or excessive power assistance, which causes the electric bicycle to have the problems of excessive speed increase or insufficient speed increase, so that the speed of the electric bicycle cannot meet the user's demand, and the user's riding experience is affected. SUMMARY

[0004] Therefore, the embodiments of the present application provide a control method and device of an electric bicycle, the electric bicycle and a program product, which can cope with complex environments and adaptively provide power assistance, thereby enhancing the anti-disturbance ability of the electric bicycle during riding and improving the user's riding experience.

[0005] A first aspect of the embodiments of the present application provides a control method of an electric bicycle, comprising: In response to the start of a target electric bicycle, obtaining sensing data of the target electric bicycle; wherein the sensing data comprises at least one of actual angular velocity and power torque data; Based on the sensing data, determining a target speed of the target electric bicycle; Based on the sensing data, determining a wheel rotation estimation value and a disturbance estimation value of the target electric bicycle; wherein the disturbance estimation value comprises at least one of the power torque data, friction resistance, gravity component and wind resistance; Based on the wheel rotation estimation value, the disturbance estimation value and the target speed, determining a motor power-assisted torque; Based on the motor power-assisted torque, generating a control instruction and sending to the motor; wherein the control instruction is used to control the rotation of the motor, so that the actual speed of the target electric bicycle reaches the target speed.

[0006] A second aspect of the embodiments of the present application provides a control device of an electric bicycle, comprising: The first acquisition module is used to acquire sensor data of the target electric bicycle in response to the start of the target electric bicycle; wherein the sensor data includes at least one of actual angular velocity and power torque data; The first determining module is used to determine the target speed of the target electric bicycle based on the sensing data. The second determining module is used to determine the wheel rotation estimate and disturbance estimate of the target electric bicycle based on the sensing data; wherein the disturbance estimate includes at least one of the power torque data, frictional resistance, gravity component and wind resistance; The third determining module is used to determine the motor assist torque based on the wheel rotation estimate, the disturbance estimate, and the target vehicle speed; The first generation module is used to generate control commands based on the motor's assist torque and send them to the motor; wherein the control commands are used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target speed.

[0007] A third aspect of this application provides an electric bicycle, including a memory, a controller, and a computer program stored in the memory and executable on the controller, wherein the controller executes the computer program to implement the steps of the control method for the electric bicycle provided in the first aspect.

[0008] A fourth aspect of this application provides a computer program product, characterized in that, when the computer program product is running on an electric bicycle, it causes the electric bicycle to execute the steps of the electric bicycle control method provided in the first aspect.

[0009] The electric bicycle control method provided in the first aspect of this application acquires sensor data of the target electric bicycle in response to its start-up. Based on the sensor data, it determines the target speed, wheel rotation estimate, and disturbance estimate fused from multiple complex disturbance factors. This facilitates the determination of the motor assist torque based on the wheel rotation estimate, disturbance estimate, and target speed, and generates control commands to send to the motor to control its rotation, thereby enabling the actual speed of the target electric bicycle to reach the target speed. This allows the method to adapt to complex environments, adaptively provide high-precision electric assistance, enhance the anti-disturbance capability of the electric bicycle during riding, and improve the user's riding experience.

[0010] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram illustrating the application scenario of the electric bicycle provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the control method for an electric bicycle provided in an embodiment of this application; Figure 3 This is a flowchart illustrating step S102 of the electric bicycle control method provided in this application embodiment; Figure 4 This is a schematic diagram of the membership function relationship of vehicle speed provided in an embodiment of this application; Figure 5 This is a schematic diagram of the membership function relationship of the power torque data provided in the embodiments of this application; Figure 6 This is a schematic diagram of the data processing flow provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the control device for an electric bicycle provided in an embodiment of this application; Figure 8 This is one of the structural schematic diagrams of the electric bicycle provided in the embodiments of this application; Figure 9 This is the second structural schematic diagram of the electric bicycle provided in the embodiments of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0015] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0016] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0017] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0019] Figure 1 This is a schematic diagram illustrating an application scenario for an electric bicycle.

[0020] like Figure 1 As shown, an electric bicycle is a hybrid vehicle that uses human pedaling to trigger electric assistance. In other words, while the user is pedaling the electric bicycle, the bicycle itself provides electricity to assist in its operation, change the riding speed, and thus reduce the user's riding stress.

[0021] During operation, the speed of an electric bicycle is usually affected by complex external environmental factors (such as slope resistance, load, and wind resistance). For example, the undulation of the slope causes changes in friction and gravity, changes in the load of the user and the goods, and wind resistance disturbances, all of which affect the speed of the electric bicycle.

[0022] However, the existing electric bicycle power assist control methods are difficult to cope with changes in complex environments, and often suffer from insufficient or excessive power assist, which prevents the electric bicycle from reaching the user's speed and affects the user's riding experience.

[0023] To address the aforementioned issues, this application provides a control method, device, electric bicycle, and program product for an electric bicycle. In response to the start-up of a target electric bicycle, it acquires sensor data of the target electric bicycle. Based on this sensor data, it determines the target speed, wheel rotation estimate, and disturbance estimate fused from multiple complex disturbance factors. This facilitates the determination of the motor assist torque based on the wheel rotation estimate, disturbance estimate, and target speed. A control command is then generated and sent to the motor to control its rotation, ensuring the actual speed of the target electric bicycle reaches the target speed. This allows for adaptive high-precision electric assistance in complex environments, enhancing the electric bicycle's resistance to disturbances and improving the user's riding experience.

[0024] Figure 2 This is a flowchart illustrating a control method for an electric bicycle provided in an embodiment of this application.

[0025] like Figure 2 As shown in the embodiments of this application, the control method for an electric bicycle includes the following steps: Step S101: In response to the start of the target electric bicycle, acquire the sensing data of the target electric bicycle; wherein, the sensing data includes at least one of actual angular velocity and power torque data.

[0026] In the application, in response to the starting of the target electric bicycle, sensing data of the target electric bicycle is acquired through sensing devices. These sensing devices include, but are not limited to, at least one of angular velocity sensors and torque sensors. The sensing data includes, but is not limited to, at least one of actual angular velocity and power torque data. The actual angular velocity refers to the angular velocity of the wheels of the target electric bicycle during operation; the power torque data refers to the torque data experienced by the pedals during operation of the target electric bicycle.

[0027] For example, an angular velocity sensor can be installed at the wheel hub of an electric bicycle to collect the angular velocity signal of the wheel rotation in real time; a torque sensor can be installed at the crankshaft to obtain the user's pedaling torque data.

[0028] The execution subject of this application embodiment can be an electric bicycle, a control device for the electric bicycle installed in the electric bicycle, or a processor in the electric bicycle. The control device for the electric bicycle can be implemented by software or by a combination of software and hardware.

[0029] Step S102: Based on the sensor data, determine the target speed of the target electric bicycle.

[0030] In applications, the target speed of the target electric bicycle is determined based on the relationship between the magnitudes of angular velocity and power torque data in the sensor data.

[0031] As an example, and not a limitation, the target vehicle speed can be positively correlated with the power torque data; or, the target vehicle speed can be negatively correlated with the angular velocity data.

[0032] Step S103: Based on the sensing data, determine the wheel rotation estimate and disturbance estimate of the target electric bicycle; wherein the disturbance estimate includes at least one of the power torque data, frictional resistance, gravity component and wind resistance.

[0033] In this application, based on sensor data, the wheel rotation estimates of the target electric bicycle and the disturbance estimates obtained by fusing multiple disturbance data are estimated. The wheel rotation estimates include, but are not limited to, wheel rotation angle estimates and wheel angular velocity estimates. Disturbance data refers to external factors that change over time, including, but not limited to, at least one of the following: power torque data, frictional resistance, gravity components, and wind resistance.

[0034] Among them, frictional resistance can be the frictional force between the target electric bicycle wheel and the road; the gravitational component can be the vertical component of the target electric bicycle's own weight and load on a slope with a certain inclination angle; and wind resistance can be the resistance caused by the wind force on the target electric bicycle.

[0035] Understandably, incorporating power torque data into the disturbance data for estimation allows the target bicycle to adjust the disturbance compensation of the user's power torque, thereby reducing the power demand on the user at the target speed and improving the user's riding experience.

[0036] Step S104: Determine the motor assist torque based on the wheel rotation estimate, the disturbance estimate, and the target vehicle speed.

[0037] In the application, based on the wheel rotation estimate and disturbance estimate, the analysis is performed to determine the motor assist torque required to make the actual speed of the target electric bicycle reach the target speed under the current power torque data.

[0038] Step S105: Generate a control command based on the motor assist torque and send it to the motor; wherein, the control command is used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target speed.

[0039] In the application, control commands are generated based on the motor's assist torque and sent to the motor to control the motor's rotation, thereby providing the motor's assist torque so that the actual speed of the target electric vehicle reaches the target speed.

[0040] Understandably, with This indicates the conversion coefficient of the motor's assist torque. This represents the conversion factor for pedaling torque. This represents the power torque data (or pedaling torque data), which is provided by the motor to assist the pedaling torque. To achieve the total torque When applied to the target electric bicycle, it causes the actual speed of the target electric bicycle to reach the target speed.

[0041] like Figure 3 As shown, in one embodiment, step S102 includes the following steps: Step S1021: Determine the actual vehicle speed based on the actual angular velocity; Step S1022: Perform fuzzy processing on the actual vehicle speed and the power torque data to obtain the first membership degree of the corresponding vehicle speed fuzzy subset and the second membership degree of the power torque fuzzy subset.

[0042] In the application, the actual vehicle speed is calculated based on the actual angular velocity and wheel radius. The actual vehicle speed is then fuzzified based on the membership relationship of the actual vehicle speed to obtain the first membership of the corresponding fuzzy subset of vehicle speed. The power torque data is then fuzzified based on the membership relationship of the power torque data to obtain the second membership of the corresponding fuzzy subset of power torque.

[0043] Among them, the vehicle speed fuzzy subset and the power torque fuzzy subset include, but are not limited to, the first fuzzy subset, the second fuzzy subset or the third fuzzy subset, wherein the first fuzzy subset is smaller than the second fuzzy subset, and the second fuzzy subset is smaller than the third fuzzy subset.

[0044] Understandably, a fuzzy subset refers to a linguistic value used to describe the degree of change in an output variable (such as a control variable). For example, the first fuzzy subset can indicate zero (ZE); the second fuzzy subset can indicate positive small (PS); and the third fuzzy subset can indicate positive big (PB).

[0045] As an example, and not a limitation, three vehicle speed ranges are predefined, including a first speed range, a second speed range, and a third speed range. The maximum value of the first speed range is greater than or equal to the minimum value of the second speed range, and the maximum value of the second speed range is greater than or equal to the minimum value of the third speed range. Similarly, three power torque ranges are predefined, including a first power torque range, a second power torque range, and a third power torque range. The maximum value of the first power torque range is greater than or equal to the minimum value of the second power torque range, and the maximum value of the second power torque range is greater than or equal to the minimum value of the third power torque range.

[0046] For example, the universe of discourse for vehicle speed is {0, 5, 10, 15, 20, 25, 30}, and the universe of discourse for power torque data is {0, 5, 10, 15, 20, 25, 30, 35}. Correspondingly, the first fuzzy subset of vehicle speed includes (0 km / h, 15 km / h); the second fuzzy subset of vehicle speed includes (10 km / h, 25 km / h); and the third fuzzy subset of vehicle speed includes (20 km / h, 30 km / h). The first fuzzy subset of power torque data includes (0 N·m, 15 N·m); the second fuzzy subset of power torque data includes (10 N·m, 25 N·m); and the third fuzzy subset of power torque data includes (20 N·m, 35 N·m).

[0047] Figure 4 This is a membership function diagram of the actual vehicle speed provided in the embodiments of this application.

[0048] Figure 5 Membership function relationship diagram of the power torque data provided in the embodiments of this application.

[0049] When the actual vehicle speed, calculated based on the actual angular velocity, is 12.5 km / h and the power torque is 20 N·m, based on... Figure 4 The membership relationships shown indicate that the membership degree of the first fuzzy subset of actual vehicle speed is 0.5, the membership degree of the second fuzzy subset is 0.5, and the membership degree of the third fuzzy subset is 0. Specifically, the first membership degree of the vehicle speed fuzzy subset is {0.5, 0.5, 0}. Based on... Figure 5 The membership relationships shown can be obtained as follows: the membership degree of the first fuzzy subset of the power torque data is 0, the membership degree of the second fuzzy subset is 1, the membership degree of the third fuzzy subset is 0, and the membership degree of the second fuzzy subset of the power torque data is specifically {0, 1, 0}, thus completing the fuzzification of the input actual vehicle speed and power torque data.

[0050] Step S1023: Based on the first membership degree of the vehicle speed fuzzy subset and the second membership degree of the power torque fuzzy subset, determine the target vehicle speed fuzzy subset and its corresponding third membership degree; wherein, the target vehicle speed fuzzy subset includes at least one of the first fuzzy subset, the second fuzzy subset, or the third fuzzy subset.

[0051] In application, based on the first membership degree of the vehicle speed fuzzy subset, the second membership degree of the power torque fuzzy subset, and pre-defined fuzzy rules, the target vehicle speed fuzzy subset and its corresponding third membership degree are determined. The target vehicle speed fuzzy subset includes, but is not limited to, at least one of the first, second, and third fuzzy subsets.

[0052] For example, fuzzy rules can be determined based on the relationship between vehicle speed, power torque data, and target vehicle speed.

[0053] For example: 1. If the vehicle speed fuzzy value is the first fuzzy subset and the power torque fuzzy value is the first fuzzy subset, then the target vehicle speed fuzzy value is the second fuzzy subset; 2. If the vehicle speed fuzzy value is the first fuzzy subset and the power torque fuzzy value is the second fuzzy subset, then the target vehicle speed fuzzy value is the second fuzzy subset; 3. If the vehicle speed fuzzy value is the first fuzzy subset and the power torque fuzzy value is the third fuzzy subset, then the target vehicle speed fuzzy value is the third fuzzy subset; 4. If the vehicle speed fuzzy value is the second fuzzy subset and the power torque fuzzy value is the first fuzzy subset, then the target vehicle speed fuzzy value is the first fuzzy subset; 5. If the vehicle speed fuzzy value is the second fuzzy subset and the power torque fuzzy value is the second fuzzy subset, then the target vehicle speed fuzzy value is the second fuzzy subset. 6. If the vehicle speed fuzzy value is the second fuzzy subset and the power torque fuzzy value is the third fuzzy subset, then the target vehicle speed fuzzy value is the second fuzzy subset. 7. If the vehicle speed fuzzy value is the third fuzzy subset and the power torque fuzzy value is the first fuzzy subset, then the target vehicle speed fuzzy value is the first fuzzy subset. 8. If the vehicle speed fuzzy value is the third fuzzy subset and the power torque fuzzy value is the second fuzzy subset, then the target vehicle speed fuzzy value is the first fuzzy subset. 9. If the vehicle speed fuzzy value is the third fuzzy subset and the power torque fuzzy value is the third fuzzy subset, then the target vehicle speed fuzzy value is the first fuzzy subset.

[0054] Step S1024: Determine the target vehicle speed based on the third membership degree of the target vehicle speed fuzzy subset and the preset speed range.

[0055] In the application, a preset speed range is set in advance, including at least three preset speed sub-ranges. The preset speed sub-range corresponding to the fuzzy subset of the target vehicle speed is determined as the target vehicle speed range. The target vehicle speed is calculated based on the third membership degree of the fuzzy subset of the target vehicle speed and the preset speed range.

[0056] In one embodiment, the preset speed range includes a first preset sub-range, a second preset sub-range, and a third preset sub-range.

[0057] In application, the preset speed range includes, but is not limited to, a first preset sub-range, a second preset sub-range, and a third preset sub-range. The maximum value of the first preset sub-range is greater than or equal to the minimum value of the second preset sub-range, and the maximum value of the second preset sub-range is greater than or equal to the minimum value of the third preset sub-range.

[0058] Optionally, the upper and lower limits of a sub-interval within a preset speed range can be equal, and the corresponding sub-interval includes only one speed value, which can also be called a preset speed. For example, the preset speed range includes a first preset speed, a second preset speed, and a third preset speed.

[0059] Understandably, the preset speed range can be specifically set according to the actual operating parameters of the target electric bicycle. For example, the maximum value of the preset speed range can be set to the maximum operating speed of the target electric bicycle, and the minimum value of the preset speed range can be set to the minimum operating speed of the target electric bicycle (for example, a natural number greater than 0).

[0060] In one embodiment, step S1023 includes the following steps: Based on the fuzzy subset of the target vehicle speed, the corresponding target speed interval is determined in the first preset sub-interval, the second preset sub-interval, and the third preset sub-interval; The target speed is calculated based on the target speed range and the third membership degree.

[0061] In application, when the target vehicle speed fuzzy subset is the first fuzzy subset, the corresponding first preset sub-interval is determined as the target vehicle speed interval; when the target vehicle speed fuzzy subset is the second fuzzy subset, the corresponding second preset sub-interval is determined as the target vehicle speed interval; and when the target vehicle speed fuzzy subset is the third fuzzy subset, the corresponding third preset sub-interval is determined as the target vehicle speed interval.

[0062] In the application, the third membership degree is defined as the centroid parameter of the target speed range, and the centroid parameters of other preset speed ranges are set to zero. Based on the target speed range, the centroid parameter of the target speed range, and the centroid parameters of other preset speed ranges, the target speed is calculated.

[0063] For example, the preset speed ranges include (0km / h, 11km / h], (10km / h, 21km / h], and (20km / h, 30km / h). When the target vehicle speed fuzzy subset is the second fuzzy subset, the second preset sub-range (10km / h, 21km / h) is determined as the target vehicle speed range, the third membership degree is determined as the centroid parameter of the second preset sub-range, and the centroid parameters of the first and third preset sub-ranges are set to zero; or, when the target vehicle speed fuzzy subset is the third fuzzy subset, the third preset sub-range (21km / h, 30km / h) is determined as the target vehicle speed range, the third membership degree is determined as the centroid parameter of the third preset sub-range, and the centroid parameters of the first and second preset sub-ranges are set to zero.

[0064] As an example and not a limitation, when calculating the target speed based on a preset speed range, the maximum value in the preset speed range can be used for calculation; or, the average value of the preset speed range can be used for calculation. The embodiments of this application do not make specific limitations here.

[0065] Taking an actual angular velocity of 12.5 km / h, a power torque of 20 N·m, a membership degree of the actual vehicle speed of {0.5, 0.5, 0}, and a membership degree of the power torque data of {0, 1, 0} as an example, when the universe of discourse of the target speed is {10, 20, 30}, the first preset sub-interval can be determined as (0 km / h, 10 km / h], the second preset sub-interval as (10 km / h, 20 km / h], and the third preset sub-interval as (20 km / h, 30 km / h). Based on the membership degrees of the actual vehicle speed and the power torque data, the fuzzy rules 2 and 5 above are satisfied. Therefore, the fuzzy value of the target vehicle speed can be determined as the second fuzzy subset, and the third membership degree is calculated as follows: The membership degrees of the first and third fuzzy subsets are set to 0, resulting in the membership degree of the target vehicle speed being {0, 0.5, 0}. The target speed range is determined as the second preset sub-range [10km / h, 20km / h], and the target vehicle speed is calculated using the center of gravity method. for:

[0066] In this way, the actual vehicle speed can be input. Power and torque data The system performs fuzzification using Fuzzy Logic Control (FLC) and inference based on fuzzy rules to output the target vehicle speed. .

[0067] In one embodiment, step S103 includes the following steps: Based on the actual angular velocity, determine the wheel rotation angle; Based on the wheel rotation angle, the actual angular velocity, and the structural parameters, a state-space relationship is constructed between the motor assist torque, the wheel rotation angle, and the disturbance estimate; wherein, the structural parameters include at least one of rotational torque, motor torque conversion coefficient, wheel radius, and friction coefficient. The actual angular velocity is processed based on the state-space relationship to obtain the wheel rotation estimate and the disturbance estimate.

[0068] In the application, the actual angular velocity of the target electric bicycle is integrated to obtain the wheel rotation angle. Based on the calculated relationship between the wheel rotation angle and the actual angular velocity, as well as the structural parameters of the target electric bicycle, a dynamic model including power torque data is constructed. Based on the dynamic model, the state-space relationship between the motor assist torque, wheel rotation speed, and disturbance estimate is constructed. The actual angular velocity is then processed based on the state-space relationship to obtain the wheel rotation estimate and disturbance estimate.

[0069] Among them, structural parameters refer to the static structural data of the target electric bicycle that do not change over time, including but not limited to at least one of the following: rotational torque, motor torque conversion coefficient, wheel radius, and friction coefficient.

[0070] For example, Figure 6 This is a schematic diagram of a data processing flow provided in an embodiment of this application.

[0071] See Figure 6 Based on Newton's second law, a longitudinal dynamics model of the electric bicycle, incorporating perturbation data, can be constructed. The model input data is the motor's assist torque. and power torque data The output is the actual speed of the bicycle. The model expression is as follows: (1); in, This represents the wheel rotation torque data. Represents the angular velocity of the wheel. This represents the conversion factor for power torque data. The conversion coefficient that describes the motor's assist torque. Indicates the wheel radius. Indicates the driving force of a bicycle. This indicates the total mass of the target electric bicycle, including its own weight and the load it carries. Indicates the coefficient of friction. Represents gravitational acceleration. Indicates the road slope. Indicates air density, This indicates the frontal area of ​​the target electric bicycle.

[0072] Based on the above dynamic model, a state-space model can be further constructed. The state variable is defined as the wheel rotation angle. Wheel angular velocity The motor's assist torque is used as the control input. The data includes power torque, frictional resistance, gravity component, and wind resistance as total disturbance data. The state space can then be represented as: (2).

[0073] Based on the aforementioned state-space model, an Extended State Observer (ESO) is designed to determine the wheel rotation estimates and disturbance estimates. The actual vehicle speed is then used. Converted to wheel rotation angle Input data includes wheel rotation angles. and motor assist torque The output data includes estimated wheel rotation angles. Wheel angular velocity estimation value Total disturbance estimate The extended state observer expression is: (3); in, The observer gain coefficient can be determined using the polar coordinate method. .

[0074] Based on the longitudinal dynamics model of an electric bicycle, a state-space model with disturbance terms is constructed. A fuzzy controller, using current speed and user pedal torque data based on an established fuzzy rule base, dynamically adjusts the target speed to adapt to different riding states and road conditions. Furthermore, a cascaded extended state observer is integrated to achieve intelligent adjustment and disturbance compensation of the electric bicycle motor's assist torque, improving the adjustment accuracy and thus enhancing the user's riding experience.

[0075] In one embodiment, step S104 includes the following steps: Based on the target vehicle speed, determine the target rotational angular velocity; The estimated wheel rotation value, the estimated disturbance value, and the target rotation angular velocity are processed by sliding mode control to obtain the motor assist torque.

[0076] In the application, the target rotational angular velocity is determined based on the target speed and wheel radius of the target electric bicycle. Sliding mode control is then applied to the wheel rotation estimate, disturbance estimate, and target rotational angular velocity to obtain the motor assist torque.

[0077] Optionally, the target vehicle speed Converted into target wheel rotation angle , as input data for sliding mode control.

[0078] For example, the motor assist torque is determined using sliding mode control (SMC). The input data is the target wheel rotation speed. Estimated value of wheel rotation angle Wheel angular velocity estimation value and total disturbance estimate The obtained output data is the motor assist torque. .

[0079] Alternatively, the sliding surface function can be expressed as: (4); in, , Indicates the parameters of the sliding surface, for example, It is 50 or 60.

[0080] Differentiating the sliding surface function yields: (5); The control law is set as follows: (6); Where k represents the control rate.

[0081] Therefore, we get: (7); (8); Replace the wheel rotation angle x1 with the estimated wheel rotation angle. Replace wheel angular velocity x2 with the estimated wheel angular velocity. Total disturbance data Replace with total disturbance estimate Substituting into the above formula, we obtain the motor assist torque. , can be represented as: (9); Based on this, the motor assist torque is provided by controlling the motor. This makes the total torque data = It is applied to the target electric bicycle to adjust the actual speed so that it reaches the target speed, thus achieving the riding assistance function.

[0082] Optionally, the sliding mode control (SMC) can be updated to other controllers, such as proportional-integral-derivative control or model predictive control; the fuzzy logic control (FLC) can be updated to a neural network or adaptive fuzzy control according to actual needs; and the extended state observer (ESO) can be replaced with a Kalman filter for state and disturbance estimation.

[0083] Understandably, fuzzy logic control primarily achieves target vehicle speed control through fuzzy rules and empirical data, rather than relying on precise mathematical models. For example, when controlling motor speed, fuzzy logic control can adjust vehicle speed using fuzzy concepts such as "slightly reduce" or "significantly reduce" fuzzy subsets, ensuring the controller gain always matches the current operating conditions. Sliding mode control can ignore external disturbances and uncertain parameters, weakening the source of chattering and achieving speed control. By extending the state observer, the uncertainties of the system model and external disturbances can be uniformly treated as "total disturbance data" and estimated in real time, providing a basis for feedforward compensation. This achieves a closed-loop causal chain of "disturbance estimation - feedforward compensation - parameter adaptation," improving the motor assist response speed. In complex driving conditions, it can provide adaptive motor assist torque according to the user's intentions, exhibiting strong robustness and smoothness.

[0084] In one embodiment, the following step is further included after step S105: Obtain the actual speed of the target electric bicycle; Determine the difference between the actual vehicle speed and the target vehicle speed; When the difference is greater than or equal to a preset threshold, the process returns to the steps of responding to the start of the target electric bicycle, acquiring the sensor data of the target electric bicycle, and so on, until the difference is detected to be less than the preset threshold.

[0085] In the application, the actual speed of the target electric bicycle is acquired in real time, and the difference between the actual speed and the target speed is calculated. When the difference is greater than or equal to a preset threshold, the process returns to the steps described above, including responding to the start of the target electric bicycle, acquiring the sensor data of the target electric bicycle, and subsequent steps, until the difference between the actual speed and the target speed is detected to be less than or equal to the preset threshold.

[0086] The preset threshold can be set according to the actual situation. For example, the preset threshold can be set to 2km / h or 3km / h.

[0087] As an example rather than a limitation, the target electric bicycle can adjust the motor's assist torque in real time based on actual vehicle speed and power torque data, thereby adapting to external disturbance data that changes over time.

[0088] Optionally, when the difference between the actual vehicle speed and the target vehicle speed is less than a preset threshold, a timer can be started. Once the timer reaches the target duration, the process returns to responding to the target electric bicycle's start-up, acquiring its sensor data, and proceeding with subsequent steps. This allows for pausing the estimation of disturbance data and determination of motor assist torque when the actual and target speeds are relatively close, reducing power consumption. Restarting the estimation of disturbance data and determination of motor assist torque upon reaching the target time allows for dynamic adjustment of the motor assist torque value, thereby improving the user's riding experience.

[0089] The target duration can be set according to the actual situation. For example, the target duration can be set to 10 minutes or 15 minutes.

[0090] In one embodiment, the following step is further included after step S105: When the power torque data is updated to zero, a second control command is generated based on the target vehicle speed and sent to the motor; wherein, the second control command is used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target vehicle speed.

[0091] In the application, power torque data is acquired in real time. When the power torque data is updated to zero, a new motor assist torque is determined based on the target vehicle speed, and a second control command is generated and sent to the motor to control the motor to rotate based on the aforementioned motor assist torque, so that the actual speed of the target electric bicycle reaches the target speed.

[0092] The electric bicycle control method provided in this application acquires sensor data of the target electric bicycle in response to its start-up. Based on the sensor data, it determines the target speed, wheel rotation estimate, and disturbance estimate fused from multiple complex disturbance factors. This facilitates the determination of the motor assist torque based on the wheel rotation estimate, disturbance estimate, and target speed, and generates control commands to send to the motor to control its rotation, ensuring that the actual speed of the target electric bicycle reaches the target speed. This allows the method to adapt to complex environments, adaptively provide high-precision electric assistance, enhance the anti-disturbance capability of the electric bicycle during riding, and improve the user's riding experience.

[0093] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] This application also provides a control device for an electric bicycle, used to execute the steps in the above method embodiments. The control device for the electric bicycle can be a virtual appliance within the electric bicycle, run by the electric bicycle's processor, or it can be the electric bicycle itself. Figure 7 As shown, the electric bicycle control device 100 provided in this application embodiment includes: The first acquisition module 101 is used to acquire the sensor data of the target electric bicycle in response to the start of the target electric bicycle; wherein the sensor data includes at least one of actual angular velocity and power torque data; The first determining module 102 is used to determine the target speed of the target electric bicycle based on the sensing data. The second determining module 103 is used to determine the wheel rotation estimate and disturbance estimate of the target electric bicycle based on the sensing data; wherein the disturbance estimate includes at least one of the power torque data, frictional resistance, gravity component and wind resistance; The third determining module 104 is used to determine the motor assist torque based on the wheel rotation estimate, the disturbance estimate and the target vehicle speed; The first generation module 105 is used to generate control commands based on the motor assist torque and send them to the motor; wherein the control commands are used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target speed.

[0095] In one embodiment, the first determining module includes: The first determining unit is used to determine the actual vehicle speed based on the actual angular velocity; The fuzzy processing unit is used to perform fuzzy processing on the actual vehicle speed and the power torque data to obtain the first membership degree of the corresponding vehicle speed fuzzy subset and the second membership degree of the power torque fuzzy subset; The second determining unit is used to determine a target vehicle speed fuzzy subset and its corresponding third membership degree based on the first membership degree of the vehicle speed fuzzy subset and the second membership degree of the power torque fuzzy subset; wherein the target vehicle speed fuzzy subset includes at least one of the first fuzzy subset, the second fuzzy subset, or the third fuzzy subset; The third determining unit is used to determine the target vehicle speed based on the third membership degree of the fuzzy subset of the target vehicle speed and the preset speed range.

[0096] In one embodiment, the preset speed range includes a first preset sub-range, a second preset sub-range, and a third preset sub-range.

[0097] In one embodiment, the third determining unit is specifically used for: Based on the fuzzy subset of the target vehicle speed, the corresponding target speed interval is determined in the first preset sub-interval, the second preset sub-interval, and the third preset sub-interval; The target speed is calculated based on the target speed range and the third membership degree.

[0098] In one embodiment, the second determining module includes: The fourth determining unit is used to determine the wheel rotation angle based on the actual angular velocity; The relationship construction unit is used to construct a state-space relationship between the motor assist torque, the wheel rotation angle, and the disturbance estimate based on the wheel rotation angle, the actual angular velocity, and structural parameters; wherein the structural parameters include at least one of rotational torque, motor torque conversion coefficient, wheel radius, and friction coefficient. The disturbance estimation unit is used to process the actual angular velocity based on the state space relationship to obtain the wheel rotation estimate and the disturbance estimate.

[0099] In one embodiment, the third determining module includes: The fifth determining unit is used to determine the target rotational angular velocity based on the target vehicle speed; The data processing unit is used to perform sliding mode control processing on the wheel rotation estimate, the disturbance estimate, and the target rotation angular velocity to obtain the motor assist torque.

[0100] In one embodiment, the control device 100 for the electric bicycle further includes: The second acquisition module is used to acquire the actual speed of the target electric bicycle; The fourth determining module is used to determine the difference between the actual vehicle speed and the target vehicle speed; The loop module is used to return to the steps of responding to the start of the target electric bicycle, acquiring the sensor data of the target electric bicycle, and subsequent steps when the difference is greater than or equal to a preset threshold, until the difference is detected to be less than the preset threshold.

[0101] In one embodiment, the control device 100 for the electric bicycle further includes: The second generation module is used to generate a second control command based on the target vehicle speed when the power torque data is updated to zero, and send it to the motor; wherein, the second control command is used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target vehicle speed.

[0102] In applications, the modules in the control device of an electric bicycle can be software program modules, or they can be implemented through different logic circuits integrated in a processor, or they can be implemented through multiple distributed processors.

[0103] like Figure 8 As shown, this application embodiment also provides an electric bicycle 200, including: at least one processor 201 ( Figure 8 The diagram shows only one processor, memory 202, and computer program 203 stored in memory 202 and executable on at least one processor 201. When processor 201 executes computer program 203, it implements the steps in any of the above method embodiments.

[0104] In applications, electric bicycles may include, but are not limited to, processors and memory. Those skilled in the art will understand that... Figure 8 This is merely an example of an electric bicycle and does not constitute a limitation on electric bicycles. It may include more or fewer parts than shown in the illustration, or a combination of certain parts, or different parts, such as input / output devices, network access devices, etc.

[0105] Figure 9 An exemplary structural diagram of another electric bicycle is provided.

[0106] See Figure 9 The electric bicycle 200 also includes a torque sensor 204, a wheel speed sensor 205, a motor controller 206, and a motor 207.

[0107] Among them, torque sensor 204 is used to collect the power torque data of the target electric bicycle, and angular velocity sensor 205 is used to collect the actual angular velocity of the target electric bicycle.

[0108] In applications, both torque sensor 204 and wheel speed sensor 205 can communicate with processor 201 via CAN bus.

[0109] The motor controller 206 is used to receive control commands (e.g., control commands including pulse width modulation (PWM) control signals) sent by the processor 201, and to control the rotation of the motor 207 based on the control commands. The motor 207 is used to rotate under the control of the motor controller 206.

[0110] In applications, the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0111] For example, processor 201 includes a 32-bit microcontroller.

[0112] In applications, the memory may be an internal storage unit of the electric bicycle in some embodiments, such as the electric bicycle's hard drive or RAM. In other embodiments, the memory may be an external storage device of the electric bicycle, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the electric bicycle. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of computer programs. The memory can also be used to temporarily store data that has been output or will be output.

[0113] It should be noted that the information interaction and execution process between the above-mentioned devices / modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The functional modules in the embodiments can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules can be implemented in hardware or as software functional modules. Furthermore, the specific names of the functional modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the modules in the above-described device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0115] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0116] This application provides a computer program product that, when run on an electric bicycle, enables the implementation of the steps described in the various method embodiments above.

[0117] If an integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to the device / electric bicycle, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0118] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0119] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0120] In the embodiments provided in this application, it should be understood that the disclosed apparatus / device and method can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or modules may be electrical, mechanical, or other forms.

[0121] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0122] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A control method for an electric bicycle, characterized in that, include: In response to the start of the target electric bicycle, sensor data of the target electric bicycle is acquired; wherein, the sensor data includes at least one of actual angular velocity and power torque data; Based on the sensor data, the target speed of the target electric bicycle is determined; Based on the sensor data, the wheel rotation estimate and disturbance estimate of the target electric bicycle are determined; wherein, the disturbance estimate includes at least one of the power torque data, frictional resistance, gravity component and wind resistance; Based on the wheel rotation estimate, the disturbance estimate, and the target vehicle speed, the motor assist torque is determined; A control command is generated based on the motor's assist torque and sent to the motor; wherein the control command is used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target speed.

2. The control method for an electric bicycle as described in claim 1, characterized in that, Determining the target speed of the target electric bicycle based on the sensor data includes: The actual vehicle speed is determined based on the actual angular velocity. The actual vehicle speed and the power torque data are fuzzy processed to obtain the first membership degree of the corresponding vehicle speed fuzzy subset and the second membership degree of the power torque fuzzy subset; Based on the first membership degree of the vehicle speed fuzzy subset and the second membership degree of the power torque fuzzy subset, a target vehicle speed fuzzy subset and its corresponding third membership degree are determined; wherein, the target vehicle speed fuzzy subset includes at least one of the first fuzzy subset, the second fuzzy subset, or the third fuzzy subset; The target vehicle speed is determined based on the third membership degree of the fuzzy subset of the target vehicle speed and the preset speed range.

3. The control method for an electric bicycle as described in claim 2, characterized in that, The preset speed range includes a first preset sub-range, a second preset sub-range, and a third preset sub-range; Determining the target vehicle speed based on the third membership degree of the fuzzy subset of the target vehicle speed and the preset speed range includes: Based on the fuzzy subset of the target vehicle speed, the corresponding target speed interval is determined in the first preset sub-interval, the second preset sub-interval, and the third preset sub-interval; The target speed is calculated based on the target speed range and the third membership degree.

4. The control method for an electric bicycle as described in claim 1, characterized in that, The step of determining the wheel rotation estimate and disturbance estimate of the target electric bicycle based on the sensing data includes: Based on the actual angular velocity, determine the wheel rotation angle; Based on the wheel rotation angle, the actual angular velocity, and the structural parameters, a state-space relationship is constructed between the motor assist torque, the wheel rotation angle, and the disturbance estimate; wherein, the structural parameters include at least one of rotational torque, motor torque conversion coefficient, wheel radius, and friction coefficient. The actual angular velocity is processed based on the state-space relationship to obtain the wheel rotation estimate and the disturbance estimate.

5. The control method for an electric bicycle as described in claim 1, characterized in that, Determining the motor assist torque based on the wheel rotation estimate, the disturbance estimate, and the target vehicle speed includes: Based on the target vehicle speed, determine the target rotational angular velocity; The estimated wheel rotation value, the estimated disturbance value, and the target rotation angular velocity are processed by sliding mode control to obtain the motor assist torque.

6. The control method for an electric bicycle as described in any one of claims 1 to 5, characterized in that, After generating control commands based on the motor's assist torque and sending them to the motor, the process includes: Obtain the actual speed of the target electric bicycle; Determine the difference between the actual vehicle speed and the target vehicle speed; When the difference is greater than or equal to a preset threshold, the process returns to the steps of responding to the start of the target electric bicycle, acquiring the sensor data of the target electric bicycle, and so on, until the difference is detected to be less than the preset threshold.

7. The control method for an electric bicycle as described in any one of claims 1 to 5, characterized in that, After generating and sending the control command based on the motor assist torque to the motor, the method further includes: When the power torque data is updated to zero, a second control command is generated based on the target vehicle speed and sent to the motor; wherein, the second control command is used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target vehicle speed.

8. A control device for an electric bicycle, characterized in that, include: The first acquisition module is used to acquire sensor data of the target electric bicycle in response to the start of the target electric bicycle; wherein the sensor data includes at least one of actual angular velocity and power torque data; The first determining module is used to determine the target speed of the target electric bicycle based on the sensing data. The second determining module is used to determine the wheel rotation estimate and disturbance estimate of the target electric bicycle based on the sensing data; wherein the disturbance estimate includes at least one of the power torque data, frictional resistance, gravity component and wind resistance; The third determining module is used to determine the motor assist torque based on the wheel rotation estimate, the disturbance estimate, and the target vehicle speed; The first generation module is used to generate control commands based on the motor's assist torque and send them to the motor; wherein the control commands are used to control the motor to rotate so that the actual speed of the target electric bicycle reaches the target speed.

9. An electric bicycle, characterized in that, The electric bicycle includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the control method for the electric bicycle according to any one of claims 1 to 7.

10. A computer program product, characterized in that, When the computer program product is run on an electric bicycle, it causes the electric bicycle to perform the steps of the control method for the electric bicycle according to any one of claims 1 to 7.

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