A slope perception-based electric power assisting method, device, equipment and medium

Abstract

The present application relates to the field of vehicle power control, and discloses an electric power assisting method and device based on slope sensing, equipment and medium, wherein the road slope data, vehicle speed data and vehicle load data are acquired; the electric power assisting input parameters are acquired according to the road slope data, vehicle speed data and vehicle load data; and the driving motor power assisting control input is controlled according to the electric power assisting input parameters, so as to realize the adaptive control of the slope, load and resistance of the electric power assisting, and reduce the complexity of the power control.

Description

Technical Field

[0001] This invention relates to the field of vehicle power assist control, and in particular to an electric power assist method, device, equipment and medium based on slope sensing. Background Technology

[0002] In existing e-bike systems, riders primarily rely on pedaling to propel the bicycle on flat surfaces. However, on uphill sections, due to gravity, riders need to exert more effort to overcome resistance, which not only increases fatigue but can also affect riding efficiency and safety. To address this issue, many e-bikes are equipped with an assist system that uses electricity to drive the rear wheel when going uphill, assisting the rider. However, the output power of existing assist systems is usually fixed or requires manual adjustment by the rider, which is neither flexible nor convenient in varying road conditions.

[0003] The existing assistance system has the following problems:

[0004] Unable to automatically adapt to different slopes: The power assist system cannot automatically adjust the output power according to the actual slope, resulting in insufficient power on steep slopes and excessive speed that is difficult to control on gentle slopes.

[0005] Riders need to manually turn the power assist system on or off, which not only distracts them but also poses safety hazards when adjusting the power assist system at high speeds or in emergencies.

[0006] Cyclists often find it difficult to accurately judge the gradient with the naked eye, which makes it impossible to properly control the output power of the power assist system. Summary of the Invention

[0007] In view of this, this application provides an electric power assist method based on slope sensing, which solves the technical problems of existing electric power assist systems being unable to automatically adapt to different slopes and the inconvenience of manual control.

[0008] According to a first aspect of this application, a slope-sensing-based electric power assist method is provided, comprising:

[0009] Acquire road slope data, vehicle speed data, and vehicle load data;

[0010] The electric power assist input parameters are obtained based on road slope data, vehicle speed data, and vehicle load data.

[0011] The drive motor assist control input is controlled based on the electric power assist input parameters.

[0012] According to a second aspect of this application, a slope-sensing-based electric power assist device is provided, comprising:

[0013] A slope-sensing-based electric power assist device, characterized in that it comprises:

[0014] The acquisition module is used to acquire road slope data, vehicle speed data, and vehicle load data.

[0015] The processing module is used to obtain electric power assist input parameters based on road slope data, vehicle speed data, and vehicle load data.

[0016] The control module is used to control the drive motor assist control input according to the electric assist input parameters.

[0017] According to a third aspect of this application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described slope-sensing-based electric power assist method.

[0018] According to a fourth aspect of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described slope-sensing-based electric power assist method.

[0019] By employing the above technical solution, this application provides an electric power assist method, device, equipment, and medium based on slope sensing. This method acquires a video recording command, controls at least one fixedly installed camera to start video recording according to the command, saves the video recorded by at least one camera for a preset duration, performs preprocessing, and sends the preprocessed video content to a mobile terminal. This enables convenient video recording during movement, improves safety and convenience during filming, and allows for quick social sharing and interaction using the mobile terminal's post-editing functions.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This illustration shows an application scenario diagram of an electric power assist method based on slope sensing provided in an embodiment of this application;

[0023] Figure 2 A flowchart of an electric power assist method based on slope sensing provided in Embodiment 1 of this application is shown;

[0024] Figure 3 A schematic diagram of an electric power assist control system based on slope sensing provided in Embodiment 2 of this application is shown;

[0025] Figure 4 A flowchart of another slope-sensing-based electric power assist method provided in Embodiment 2 of this application is shown;

[0026] Figure 5 A flowchart of another slope-sensing-based electric power assist method provided in Embodiment 3 of this application is shown;

[0027] Figure 6 A schematic diagram of a slope-sensing-based electric power assist device provided in an embodiment of this application is shown. Detailed Implementation

[0028] The specific implementation of this application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0029] The present invention provides a slope-sensing-based electric power assist method, which can be applied to, for example... Figure 1 In the hardware system, to adapt to changes in slope, load, and resistance, sensors collect road slope data, vehicle speed data, and vehicle load data. The processor controls the torque of the drive motor to provide electric assist, thereby ensuring smooth speed. The processor executes a slope-sensing-based electric assist method provided in this embodiment of the invention, acquiring road slope data, vehicle speed data, and vehicle load data; based on these data, it obtains electric assist input parameters; and based on these parameters, it controls the drive motor assist control input. The physical quantities of the motor assist control input can take various forms, such as motor drive power, torque magnitude, and motor speed, depending on the specific control scenario.

[0030] The present invention will now be described in detail through specific embodiments.

[0031] Example 1

[0032] like Figure 2 As shown, an electric power assist method based on slope sensing is provided in an embodiment of the present invention, comprising:

[0033] Step 201: Obtain road slope data, vehicle speed data, and vehicle load data;

[0034] Specifically, road slope data is acquired through cameras or gyroscopes; vehicle speed data is acquired through Hall effect sensors, GPS modules, or inertial measurement units; and vehicle load data is acquired through pressure sensors.

[0035] Step 202: Obtain the electric power assist input parameters based on road slope data, vehicle speed data, and vehicle load data;

[0036] Among them, the physical quantities input for motor-assisted control can take many forms, such as motor drive power, torque, and motor speed, depending on the specific control scenario.

[0037] Step 203: Control the drive motor assist control input according to the electric assist input parameters.

[0038] The slope-sensing-based electric power assist method provided in Embodiment 1 of this invention acquires road slope data, vehicle speed data, and vehicle load data; obtains electric power assist input parameters based on the road slope data, vehicle speed data, and vehicle load data; and controls the drive motor assist control input based on the electric power assist input parameters, thereby achieving adaptive control of the slope, load, and resistance of the electric power assist and reducing the complexity of the assist control.

[0039] Example 2

[0040] like Figure 3 The diagram shows a schematic of an electric power assist control system. The disturbances in the diagram are mostly unpredictable resistances such as air resistance, internal mechanical resistance of the motor, external load, and ground friction. These disturbances often lead to a decrease in the accuracy of adaptive control of the electric power assist input parameters. Embodiment 2 of this invention introduces an interference detector to perform feedforward compensation at the control input end for the observed unknown disturbances (in electric power assist systems, disturbances are mostly unpredictable resistances such as air resistance, internal mechanical resistance of the motor, external load, and ground friction), thereby reducing or eliminating the impact of unknown disturbances on the drive motor control. The observer is used to estimate and actively compensate for various uncertainties, effectively improving the accuracy of adaptive adjustment of the electric power assist input parameters. Combined with... Figure 3 The description of a slope-sensing-based electric power assist method is as follows: Figure 4 As shown, it includes:

[0041] Step 401: Obtain road slope data, vehicle speed data, and vehicle load data;

[0042] Step 402: Construct an interference detector model;

[0043] The interference detector model is as follows: T mThe driving torque generated by the motor is ω, the angular velocity of the motor is I, and the inertia of the vehicle is I = Mr. 2 +I w1 +I w2 M is the total mass of the vehicle, r is the wheel radius, and I w1 I is the moment of inertia of the front wheel. w2 Let ΔT be the moment of inertia of the rear wheel and ΔT be the disturbance torque.

[0044] Step 403: Generate interference error of drive motor assist control input through interference detector;

[0045] Among them, the derivative of the interference error of the drive motor assist control input is calculated. K D The observer gain is a normal value; based on the derivative of the interference error, the interference error of the drive motor assist control input is generated in real time.

[0046] Step 404: Based on the interference error of the drive motor assist control input, correct the drive motor assist control input to obtain the electric assist input parameters.

[0047] Step 405: Control the drive motor assist control input according to the electric assist input parameters.

[0048] Embodiment 2 of this invention presents a load and road gradient adaptive control method based on an interference observer. This method estimates the road gradient and bicycle load by processing speed and torque signals. Accordingly, it adjusts the electric assist input parameters in real time to achieve optimal assist power adjustment, thereby providing the rider with a better riding experience.

[0049] Example 3

[0050] Due to the complexity and uncertainty of the external environment, Embodiment 3 of this invention introduces a fuzzy control strategy to simulate the human decision-making process when facing uncertainty and fuzziness. This technology, based on fuzzy set theory, fuzzy linguistic variables, and fuzzy logic reasoning, achieves nonlinear control. Furthermore, by proposing an update mechanism for fuzzy control rules, it realizes the updating of load and road slope based on changes in load and road slope, improving the accuracy and environmental adaptability of fuzzy control. To clearly explain the application process of Embodiment 3 of this invention combined with fuzzy control theory, the design steps of the fuzzy controller are first introduced as follows:

[0051] (1) Select vehicle speed data as the observation and electric power assist input parameter as the control quantity;

[0052] (2) Fuzzification of input and output quantities: The speed linguistic values ​​are “Negative Medium (NM), Negative Low (NL), Zero (Z), Positive Low (PL), Positive Medium (PM), Positive High (PH), Positive Very High (PVH)”, respectively represented by “NM, NL, Z, PL, PM, PH, PVH”. The electric assist input linguistic values ​​are “Very Low (VL), Lower (RL), Low (L), High (H), Higher (RH), Very High (VH)”, and the motor assist control input is represented by “VL, RL, L, H, RH, VH”, respectively. These two variables are represented by seven and six membership degrees, respectively. Therefore, there are a total of 42 rules corresponding to the output membership function;

[0053] (3) Formulating fuzzy rules: The quality of control performance is largely determined by fuzzy rules, which can be derived from experimental data or human experience. In this paper, we adopt a heuristic rule generation method based on fundamental principles, namely, pedal power and speed error are proportional to motor output power. For example, a higher speed error means the vehicle speed is slower, requiring an increase in motor output; conversely, when the speed error remains constant, a higher pedal power indicates uphill riding, requiring an increase in motor output to reduce the rider's burden. Currently, rules are mainly formulated based on experience, but can also be learned by combining neural networks. The specific rule formulation methods are existing technologies and will not be elaborated here. For example, if PL corresponds to H, the membership degree is 0.5.

[0054] (4) Solve the fuzzy relationship according to the fuzzy rules and perform fuzzy reasoning (the three embodiments of the present invention need to update the corresponding fuzzy rules for different loads and road slopes. These fuzzy rules can be obtained by pre-training or by continuously accumulating scenario cases in actual use).

[0055] (5) Defuzzification of control variables: interpreting control variables as specific behaviors in reality, i.e., defuzzification operation. Defuzzification methods include maximum membership method, centroid method, weighted average method, etc.

[0056] Based on the above description, a slope-sensing-based electric assist method is as follows: Figure 5 As shown, including

[0057] Step 501: Obtain road slope data, vehicle speed data, and vehicle load data;

[0058] Step 502: Obtain the desired vehicle speed;

[0059] Step 503: Calculate the speed error based on the vehicle speed data and the vehicle's desired speed;

[0060] Step 504: Based on the speed error, obtain the adjustment amount of the motor assist control input language value;

[0061] Among them, the speed error v is determined. err Is it greater than the preset threshold?

[0062] When the speed error v err Greater than the preset threshold v threshold At that time, the adjustment amount of the motor assist control input language value is equal to ρ×v err , where ρ is an adjustment coefficient used to control the intensity of rule adjustment;

[0063] When the speed error v err Less than the preset threshold v threshold At that time, the adjustment amount of the motor assist control input language value is equal to 0.

[0064] Step 505: Adjust the fuzzy control rules according to the input language value of the motor assist control, and store the updated fuzzy control rules in correspondence with the road slope data and vehicle load data;

[0065] Specifically, the rules in the fuzzy rule base are dynamically adjusted based on the real-time speed error, and the fuzzy control rules are updated according to the adjustment amount based on the input language value of the motor assist control.

[0066] L (i) (n+1)=L (i) (n)+ΔL (i) (n), where i is the rule identifier, L (i) (n+1) is the language value at time n+1, L (i) (n) represents the language value at time n, L (i) (n) represents the language value adjustment amount;

[0067] Store the updated fuzzy control rule identifier i in relation to the current road slope data and vehicle load data.

[0068] Step 506: Perform fuzzy inference based on the updated fuzzy control rules to obtain the fuzzy control quantity of the motor assist control input;

[0069] Step 507: Defuzzify the fuzzy control quantity to obtain the electric power assist input parameters.

[0070] Step 508: Control the drive motor assist control input according to the electric assist input parameters.

[0071] Combination Figure 5The process is illustrated using the adaptive adjustment of motor power as the input for motor assist control in a practical application of an electric bicycle. During riding, the system records the bicycle speed and uses the speed error (verr) as the basis for adjusting the fuzzy rules. When the speed error is within a tolerable range, the corresponding rule remains unchanged. However, if the error value exceeds the range, the rule is adjusted, with the range set to km / h. L represents the adjustment amount of the motor power linguistic value for the i-th rule at the current time n, and p is the learning weight determined experimentally. Then, the updated linguistic value of the motor power (used to adjust the corresponding i-th rule in the fuzzy rule base) is applied. The rule adjustment module proposed in this embodiment adjusts the linguistic value of the motor power in the used fuzzy rules. For example, if the rider's pedal power is low and the speed error is zero, the motor power output is low as long as the speed error is within a tolerable range. However, if the rider encounters unexpected resistance, such as a sudden gust of wind, causing the error to exceed the range, the linguistic value of the motor power may be adjusted to high to provide more assistance to the rider.

[0072] Furthermore, as Figure 2 In a specific implementation of the method, this embodiment of the invention provides an electric power assist device based on slope sensing, such as... Figure 6 As shown, the device includes:

[0073] The acquisition module 610 is used to acquire road slope data, vehicle speed data, and vehicle load data.

[0074] The processing module 620 is used to obtain electric power assist input parameters based on road slope data, vehicle speed data, and vehicle load data.

[0075] The control module 630 is used to control the drive motor assist control input according to the electric assist input parameters.

[0076] This invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a slope-sensing-based electric assist method, including:

[0077] Acquire road slope data, vehicle speed data, and vehicle load data;

[0078] The electric power assist input parameters are obtained based on road slope data, vehicle speed data, and vehicle load data.

[0079] The drive motor assist control input is controlled based on the electric power assist input parameters.

[0080] This invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following steps:

[0081] Acquire road slope data, vehicle speed data, and vehicle load data;

[0082] The electric power assist input parameters are obtained based on road slope data, vehicle speed data, and vehicle load data.

[0083] The drive motor assist control input is controlled based on the electric power assist input parameters.

[0084] It should be noted that the above embodiments only use electric bicycles to illustrate the principles and implementation steps of the embodiments of the present invention, and do not specifically limit the actual means of transportation. For example, the technical solution of the present invention can also be applied to electric bicycles, motorcycles, cars, etc. Regarding the functions or steps that can be implemented by computer-readable storage media or computer devices, please refer to the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0085] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0087] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A slope-sensing-based electric power assist method, characterized in that, include: The system acquires road slope data, vehicle speed data, and vehicle load data. Specifically, the road slope data is acquired via a camera or gyroscope, the vehicle speed data is acquired via a Hall effect sensor, GPS module, or inertial measurement unit, and the vehicle load data is acquired via a pressure sensor. Based on the road slope data, vehicle speed data, and vehicle load data, electric power assist input parameters are obtained. This obtaining of electric power assist input parameters includes: constructing an interference detector model, wherein the interference detector model is... ,in, The driving torque generated by the motor, The angular velocity of the motor. For the vehicle's inertia, , The total mass of the vehicle. For the wheel radius, The moment of inertia of the front wheels, For the moment of inertia of the rear wheel, The disturbance torque is used; the disturbance error of the drive motor assist control input is generated by an disturbance detector, wherein the derivative of the disturbance error of the drive motor assist control input is calculated. , The observer gain is set to a normal value; based on the interference error, the drive motor assist control input is corrected to obtain the electric assist input parameters; The drive motor assist control input is controlled according to the electric power assist input parameters.

2. The electric power assist method based on slope sensing according to claim 1, characterized in that, The step of obtaining electric power assist input parameters based on road slope data, vehicle speed data, and vehicle load data includes: Obtain the vehicle's desired speed; Calculate the speed error based on vehicle speed data and the vehicle's desired speed; Based on the speed error, the adjustment amount of the motor assist control input language value is obtained; Based on the adjustment amount of the motor assist control input language value, update the fuzzy control rules, and store the updated fuzzy control rules in correspondence with road slope data and vehicle load data. Based on the updated fuzzy control rules, fuzzy inference is performed to obtain the fuzzy control quantity of the motor assist control input; The fuzzy control quantity is defuzzified to obtain the electric power assist input parameters.

3. The electric power assist method based on slope sensing according to claim 2, characterized in that, The step of obtaining the motor assist control input language value adjustment amount based on the speed error includes: Determine the speed error Is it greater than the preset threshold? When the speed error Greater than the preset threshold At that time, the adjustment amount of the motor assist control input language value is equal to ,in, For adjustment coefficients; When the speed error Less than the preset threshold At that time, the adjustment amount of the motor assist control input language value is equal to 0.

4. The slope-sensing-based electric power assist method according to claim 3, characterized in that, The steps include: adjusting the fuzzy control rules based on the input language value of the motor assist control, updating the fuzzy control rules, and storing the updated fuzzy control rules in relation to road slope data and vehicle load data; The fuzzy control rules are updated based on the adjustment amount of the motor assist control input language value. Where i is the rule identifier. The language value at time n+1. The language value at time n. Adjustment amount for language values; Store the updated fuzzy control rule identifier i in relation to the current road slope data and vehicle load data.

5. An electric power assist device based on slope sensing, characterized in that, include: The acquisition module is used to acquire road slope data, vehicle speed data, and vehicle load data. Specifically, the road slope data is acquired through a camera or gyroscope, the vehicle speed data is acquired through a Hall effect sensor, GPS module, or inertial measurement unit, and the vehicle load data is acquired through a pressure sensor. The processing module is used to obtain electric assist input parameters based on the road slope data, the vehicle speed data, and the vehicle load data. Specifically, the processing module is used to: construct an interference detector model, wherein the interference detector model is... ,in, The driving torque generated by the motor, The angular velocity of the motor. For the vehicle's inertia, , The total mass of the vehicle. For the wheel radius, The moment of inertia of the front wheels, For the moment of inertia of the rear wheel, The disturbance torque is used; the disturbance error of the drive motor assist control input is generated by an disturbance detector, wherein the derivative of the disturbance error of the drive motor assist control input is calculated. , The observer gain is set to a normal value; the drive motor assist control input is corrected according to the interference error to obtain the electric assist input parameters; The control module is used to control the drive motor assist control input according to the electric assist input parameters.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the slope-sensing electric power assist method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the slope-sensing electric power assist method as described in any one of claims 1 to 4.

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