Energy-saving motor control system and method based on magnetic encoder feedback

Abstract

The invention provides an energy-saving motor control system and method based on magnetic encoder feedback, and relates to the technical field of energy-saving motors, and the system comprises a task obtaining module which is used for obtaining a preset driving task of a target electric vehicle; the rotating speed matching module is used for calculating to obtain the average running set speed and the average rotating speed of the motor; a road information acquisition module; the rotating speed adjusting and optimizing module is used for adjusting and optimizing the average rotating speed of the motor to obtain the target rotating speed of the motor; the real-time monitoring module is used for performing real-time monitoring through a magnetic encoder to obtain real-time operation data; and the feedback control module is used for performing real-time feedback regulation on the rotating speed of the motor to obtain a feedback regulation rotating speed. The technical problem that the energy efficiency of the energy-saving motor is low due to the fact that motor control is often performed based on a fixed model or static parameters and the rotating speed of the motor cannot be accurately controlled under the condition that the load and environmental conditions change greatly in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving motors, and particularly to an energy-saving motor control system and method based on magnetic encoder feedback. Background Art

[0002] In the field of energy-saving motors, especially in electric vehicles, the currently common control methods mainly include open-loop control based on the motor characteristic model, closed-loop control based on sensors, and systems based on feedback control. Among them, although open-loop control is simple, its efficiency is low because it fails to respond to external environmental changes in real time, such as load and road conditions. For the closed-loop control method based on sensors, although it can provide more accurate feedback, it usually relies on multiple sensors, such as speed, position, temperature sensors, etc., and the accuracy and response speed of the sensors will affect the control effect of the system. In addition, traditional energy-saving motor control methods are usually based on static models or fixed parameter adjustments, which results in low efficiency in complex and dynamically changing driving environments. Summary of the Invention

[0003] This application provides an energy-saving motor control system and method based on magnetic encoder feedback, aiming to solve the technical problem that existing technologies often control motors based on fixed models or static parameters, and in the case of large changes in load and environmental conditions, the motor speed cannot be accurately controlled, which affects the operating efficiency and response speed of the motor, and further leads to low energy efficiency of energy-saving motors.

[0004] In the first aspect disclosed in this application, an energy-saving motor control system based on magnetic encoder feedback is provided. The system includes: a task acquisition module for acquiring a preset driving task of a target electric vehicle, where the preset driving task is marked with a preset driving time period, a preset driving section, and a preset driving load; a speed matching module for calculating an average driving speed according to the preset driving time period and the preset driving section, and performing motor speed matching in combination with the performance characteristics of the energy-saving motor to obtain an average motor speed; a road information acquisition module for interacting to obtain road information of the preset driving section; a speed adjustment and optimization module for adjusting and optimizing the average motor speed according to the preset driving section, the preset driving load, and the road information within the preset driving time period to obtain a target motor speed; a real-time monitoring module for performing real-time monitoring through a magnetic encoder during the process of controlling the energy-saving motor at the target motor speed to obtain real-time operation data; and a feedback control module for performing real-time feedback regulation of the motor speed according to the real-time operation data to obtain a feedback regulation speed and perform feedback control of the energy-saving motor.

[0005] The second aspect disclosed in this application provides an energy-saving motor control method based on magnetic encoder feedback. The method is implemented by the above-mentioned energy-saving motor control system based on magnetic encoder feedback, and the method includes: obtaining a preset driving task of a target electric vehicle, where the preset driving task is marked with a preset driving time period, a preset driving road section, and a preset driving load; calculating an average driving speed based on the preset driving time period and the preset driving road section, and performing motor speed matching in combination with the performance characteristics of the energy-saving motor to obtain an average motor speed; interacting to obtain road information of the preset driving road section; within the preset driving time period, adjusting and optimizing the average motor speed according to the preset driving road section, the preset driving load, and the road information to obtain a target motor speed; during the control process of the energy-saving motor at the target motor speed, performing real-time monitoring through a magnetic encoder to obtain real-time operation data; according to the real-time operation data, performing real-time feedback adjustment of the motor speed to obtain a feedback adjustment speed, and performing feedback control of the energy-saving motor.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: Calculating the average driving speed according to the preset driving time period, driving road section, and load, and performing motor speed matching in combination with the performance characteristics of the energy-saving motor can ensure that the motor operates at the optimal speed under different driving conditions. This matching process minimizes energy waste, improves the energy efficiency of the motor, and extends the battery usage time; within the preset driving time period, adjusting and optimizing the average speed of the energy-saving motor according to the preset driving road section, load, and road information enables the motor to adapt to different road conditions and load changes, thus ensuring that the vehicle always operates at the best performance and a stable speed under different working conditions, reducing the unstable driving of the vehicle or the reduction of energy efficiency caused by too fast or too slow speeds; real-time monitoring of the motor speed by the magnetic encoder and providing real-time data enable the system to continuously track the actual operating state of the motor. Through real-time feedback data, a feedback adjustment mechanism is used to adjust the motor speed. This real-time adjustment can minimize the error between the motor speed and the target speed, ensuring that the vehicle always maintains the best power output and energy efficiency during operation. Generally speaking, this intelligent adjustment can improve the adaptability of the system, especially under complex environmental conditions and changing driving tasks, ensuring that the energy-saving motor is accurately controlled according to actual needs, thereby improving the overall performance and efficiency of the electric vehicle.

[0007] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically illustrates the embodiments of this application. Brief Description of the Drawings

[0008] Figure 1 FIG. 1 is a schematic structural diagram of an energy-saving motor control system based on magnetic encoder feedback provided by an embodiment of the present application.

[0009] Figure 2 FIG. 2 is a schematic flow diagram of an energy-saving motor control method based on magnetic encoder feedback provided by an embodiment of the present application.

[0010] Description of the reference numerals: Task acquisition module 10, speed matching module 20, road information acquisition module 30, speed adjustment and optimization module 40, real-time monitoring module 50, feedback control module 60. Detailed Embodiments

[0011] By providing an energy-saving motor control system and method based on magnetic encoder feedback in the embodiments of the present application, the technical problem in the prior art that motor control is often based on a fixed model or static parameters, and in the case of large changes in load and environmental conditions, the motor speed cannot be accurately controlled, affecting the operation efficiency and response speed of the motor, and thus resulting in low energy efficiency of the energy-saving motor is solved.

[0012] After introducing the basic principle of the present application, various non-limiting embodiments of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1 shown, the embodiment of the present application provides an energy-saving motor control system based on magnetic encoder feedback, and the system includes: A task acquisition module 10, configured to acquire a preset driving task of a target electric vehicle, where the preset driving task is marked with a preset driving time period, a preset driving road section, and a preset driving load.

[0014] A speed matching module 20, configured to calculate an average driving speed according to the preset driving time period and the preset driving road section, and perform motor speed matching in combination with the performance characteristics of the energy-saving motor to obtain an average motor speed.

[0015] A road information acquisition module 30, configured to interactively acquire road information of the preset driving road section.

[0016] A speed adjustment and optimization module 40, configured to adjust and optimize the average motor speed according to the preset driving road section, the preset driving load, and the road information within the preset driving time period to obtain a target motor speed.

[0017] The real-time monitoring module 50 is used to perform real-time monitoring through a magnetic encoder during the process of controlling the energy-saving motor at the target motor speed, so as to obtain real-time operation data.

[0018] The feedback control module 60 is used to perform real-time feedback regulation of the motor speed according to the real-time operation data, obtain the feedback regulation speed, and perform feedback control of the energy-saving motor.

[0019] Furthermore, the system further includes a control constraint generation module, including: The networked data calling unit is used to interactively obtain the motor characteristics of the energy-saving motor, and perform networked data calling according to the motor characteristics to obtain an electric vehicle model, wherein the electric vehicle model internally contains a motor model that conforms to the motor characteristics.

[0020] The simulation control unit is used to perform simulation control of the electric vehicle model according to the preset driving load and the road information, and output motor control constraints, wherein the motor control constraints include the maximum motor speed.

[0021] The adjustment and optimization unit is used to adjust and optimize the average motor speed based on the motor control constraints.

[0022] Furthermore, the road information acquisition module includes: The road category determination unit is used to analyze the preset driving section and determine the road category, wherein the division dimensions of the road category include section geometric characteristics, road surface conditions, slope, and traffic conditions.

[0023] The division standard acquisition unit is used to interactively obtain the road condition division standard of the road category.

[0024] The road condition division unit is used to divide the preset driving section according to the road condition division standard to obtain multiple road condition information of multiple driving section intervals, and the multiple road condition information combined with the road category constitutes the road information.

[0025] Furthermore, the speed adjustment and optimization module includes: The first road condition information acquisition unit is used to obtain the first road condition information of the first driving section interval, wherein the first driving section interval is any one of the multiple driving section intervals.

[0026] The speed adjustment unit is used to adjust the average motor speed of the first driving section interval according to the preset driving load and the first road condition information to obtain the first motor adjustment speed.

[0027] The rotational speed adjustment acquisition unit is used to obtain, by analogy, the adjusted rotational speeds of the motors for the multiple driving section intervals.

[0028] The driving time acquisition unit is used to perform speed matching for the electric vehicle based on the adjusted rotational speeds of the motors, and obtain multiple driving times by combining the multiple obtained electric vehicle speeds with the multiple driving section intervals.

[0029] The target rotational speed acquisition unit is used to, when the sum of the multiple driving times exceeds the preset driving period, perform geometric compression on the multiple driving times with the preset driving period as a constraint, optimize the rotational speeds of the adjusted rotational speeds of the motors according to the time compression result, obtain multiple target rotational speeds of the motors, and integrate to obtain the target rotational speed of the motor.

[0030] Furthermore, the rotational speed adjustment unit includes: The first record extraction channel is used to extract the first historical rotational speed adjustment record from the historical rotational speed adjustment records, where the first historical rotational speed adjustment record includes the first historical driving load, the first historical road condition information, and the first historical target rotational speed.

[0031] The supervised learning channel is used to perform supervised learning on the first historical rotational speed adjustment record based on the neural network principle to obtain an intelligent rotational speed analysis model.

[0032] The analysis channel is used to analyze the preset driving load and the first road condition information based on the intelligent rotational speed analysis model, and adjust the average rotational speed of the motor according to the obtained target rotational speed.

[0033] Furthermore, the feedback control module includes: The real-time rotational speed extraction unit is used to extract the real-time rotational speed from the real-time operation data.

[0034] The target rotational speed extraction unit is used to extract a corresponding target rotational speed sequence from the target rotational speed of the motor starting from the current moment according to a preset time window, where the target rotational speed sequence includes the current target rotational speed at the current moment.

[0035] The instruction generation unit is used to generate a feedback adjustment instruction when the rotational speed deviation between the real-time rotational speed and the current target rotational speed reaches a preset deviation threshold.

[0036] The smoothing adjustment unit is used to perform smoothing adjustment on the real-time rotational speed according to the feedback adjustment instruction, with 1 / M of the rotational speed deviation as the rotational speed adjustment coefficient and the target rotational speed sequence as the rotational speed adjustment target, to generate an adjusted rotational speed sequence as the feedback adjustment rotational speed, where M is a dynamic smoothing adjustment parameter.

[0037] Furthermore, the feedback control module further includes: A current road condition retrieval unit for retrieving current road condition information at the current moment.

[0038] An environmental feature acquisition unit for acquiring features of the operating environment of the energy-saving motor to obtain operating environment feature information, where the operating environment feature information includes environmental temperature information, environmental humidity information, environmental pH value, and environmental altitude.

[0039] A fuzzy logic analysis unit for performing fuzzy logic analysis based on the current road condition information and the operating environment feature information, and generating the dynamic smoothing adjustment parameter according to the analysis result.

[0040] Through the subsequent detailed description of the energy-saving motor control method based on magnetic encoder feedback in this specification, those skilled in the art can clearly know the energy-saving motor control system based on magnetic encoder feedback in this embodiment. Since it corresponds to the method disclosed in the embodiment, it is described relatively simply. For related parts, refer to the description in the method part.

[0041] Embodiment 2, based on the same inventive concept as the energy-saving motor control system based on magnetic encoder feedback in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides an energy-saving motor control method based on magnetic encoder feedback, and the method includes: Obtain a preset driving task of the target electric vehicle, where the preset driving task is marked with a preset driving time period, a preset driving section, and a preset driving load.

[0042] The preset driving task is a series of tasks set for the target electric vehicle, including the specified driving time period, driving section, and load condition. Among them, the preset driving time period represents the time range during which the electric vehicle needs to drive. For example, the electric vehicle needs to complete the task between 8:00 and 9:00 in the morning, or a specific time period in the afternoon. According to this time period, the departure time and expected end time of the electric vehicle can be determined; the preset driving section is the specific path of the electric vehicle, which may include multiple different blocks, sections, or even highways. According to the characteristics of the section, a suitable driving route can be planned for the electric vehicle, and the working state of the motor can be adjusted to meet the requirements of different sections; the preset driving load is the weight carried by the electric vehicle during driving, such as the number of passengers and the cargo load. The change in the load will affect the energy consumption and driving efficiency of the electric vehicle. Therefore, it needs to be preset and considered in the calculation.

[0043] According to the preset driving time period and the preset driving section, calculate the average driving speed, and perform motor speed matching in combination with the performance characteristics of the energy-saving motor to obtain the average motor speed.

[0044] The pacing speed refers to the average speed of the electric vehicle during driving. Based on the preset driving section and time period, the average driving pacing speed is obtained through ratio calculation. For example, if the total distance of the preset section is 100 kilometers and the driving time period is 2 hours, then the average pacing speed is 50 kilometers per hour. During actual operation, factors such as traffic flow, traffic signals, and speed limits need to be considered, and these factors will affect the actual driving speed.

[0045] The rotational speed of the motor is closely related to the driving speed of the electric vehicle. Energy-saving motors usually have optimized rotational speed-power output characteristics and can maintain high efficiency under different load conditions. Specifically, there is a certain proportional relationship between the rotational speed of the motor and the vehicle speed. The transmission system of the electric vehicle needs to adjust the rotational speed of the motor according to the target pacing speed. For example, when driving at a low speed, the motor needs a lower rotational speed to maintain high efficiency, while when driving at a high speed, the motor needs a higher rotational speed to provide sufficient power. The most suitable average rotational speed of the motor is selected based on the calculated average driving pacing speed.

[0046] Interactively obtain the road information of the preset driving section.

[0047] In practical applications, the electric vehicle needs to consider the detailed road information during driving, such as road surface conditions, slopes, traffic conditions, traffic signals, etc., in order to ensure the optimization of the rotational speed and energy consumption of the electric vehicle. Exemplarily, different road surface conditions will affect the driving force requirements of the electric vehicle, thereby affecting the working state of the motor. For example, flat urban roads, rugged mountain roads, slippery road surfaces, etc.; the up and down slopes of the road have a great impact on the energy consumption and power requirements of the electric vehicle. When going uphill, the motor needs to provide more power. The road information of the preset driving section can be obtained through online map services and traffic information platforms, etc.

[0048] During the preset driving time period, according to the preset driving section, preset driving load, and the road information, adjust and optimize the average rotational speed of the motor to obtain the target rotational speed of the motor.

[0049] Use the road information to evaluate the slope, traffic conditions, and speed limits of the driving section. These factors will directly affect the driving speed of the vehicle, and thus affect the rotational speed required by the motor. For example, when going uphill, the rotational speed of the motor needs to increase to provide sufficient torque; when going downhill, the rotational speed of the motor needs to decrease, and at the same time, regenerative braking is used to recover energy. Changes in the load will affect the power output required by the motor. For example, if there is more cargo in the vehicle, the motor needs to provide more power to maintain the same speed. Therefore, the rotational speed of the motor needs to be appropriately increased or decreased according to the load size.

[0050] The target speed of the motor refers to the speed that the motor should reach under the current road conditions, load, and other environmental conditions to maintain the best energy efficiency and driving speed. Based on road information and load information, mathematical models or algorithms, such as control algorithms, PID control, etc., are used to calculate and adjust the motor speed in real time. For example, when encountering an uphill section, the motor speed is increased, while on flat or downhill sections, the speed is decreased. In some cases, to save energy, the motor is made to operate within its best energy efficiency range as much as possible. By predicting the characteristics of the route, such as whether there are long uphill or downhill sections, the motor speed can be adjusted in advance to avoid unnecessary energy waste. For example, if the expected section is a long flat road, the motor speed can be reduced to reduce energy consumption.

[0051] During the process of controlling the energy-saving motor at the target speed of the motor, real-time monitoring is carried out through a magnetic encoder to obtain real-time operation data.

[0052] The energy-saving motor is controlled according to the target speed of the motor. During this process, the energy-saving motor is monitored in real time through a magnetic encoder. The magnetic encoder, also known as a magnetic rotary encoder, is a sensor that measures and feedbacks the speed, position, and motion state of the motor through magnetic field changes. It can accurately detect the rotation angle, speed, and direction of the motor shaft. During the motor control process, the magnetic encoder is usually installed on the motor shaft to detect the rotation of the motor in real time and feedback the actual speed and position of the motor.

[0053] Through the magnetic encoder, the current actual speed of the motor and other real-time operation data that may affect the motor performance, such as current, voltage, temperature, etc., are obtained. These real-time data can help judge the actual operation of the motor, evaluate whether it is in the best working state, or whether adjustment is needed.

[0054] Based on the real-time operation data, real-time feedback adjustment of the motor speed is carried out to obtain the feedback adjustment speed, and feedback control of the energy-saving motor is carried out.

[0055] The real-time operating data monitored by the magnetic encoder is used to track the rotational speed change of the motor in real time and determine the deviation from the target speed. If the actual speed of the motor deviates from the target speed, real-time feedback adjustment is performed to correct it, ensuring that the motor always operates in the optimal working state. For example, if the motor speed is too low, the power output of the motor is increased to raise the speed; if the speed is too high, the power output is reduced to lower the speed. During the feedback control process, not only the power output of the motor is adjusted, but also the working mode of the motor, such as the force-increasing mode, energy-saving mode, etc., can be adjusted to ensure the best operating efficiency. The feedback adjustment ensures that the motor always remains consistent with the target speed under different road conditions and loads, avoiding energy waste or performance instability caused by speed deviation.

[0056] Furthermore, the method further includes: Interactively obtain the motor characteristics of the energy-saving motor and perform network data calls according to the motor characteristics to obtain an electric vehicle model, where the electric vehicle model incorporates a motor model that conforms to the motor characteristics; according to the preset driving load and the road information, perform simulation control of the electric vehicle model and output motor control constraints, where the motor control constraints include the maximum rotational speed of the motor; based on the motor control constraints, perform adjustment and optimization of the average rotational speed of the motor.

[0057] Motor characteristics refer to the key performance parameters of the motor, such as rated power, rated speed, efficiency, maximum torque, working voltage range, working temperature, torque-speed characteristic curve, etc. These characteristics are usually provided by the motor manufacturer and vary according to the motor type.

[0058] After obtaining the motor characteristics, interact with the data related to the electric vehicle model through the network. The called electric vehicle model is a virtual model that incorporates the motor behavior corresponding to the motor characteristics. These data include the working efficiency of the motor under different loads, the relationship between speed and power, and the interaction mode between the motor and the battery. The electric vehicle model incorporates a motor model that is consistent with the selected motor characteristics in the system, which means that during the simulation and control process, the characteristics of the motor, such as power output, speed-torque relationship, etc., will be correctly reflected in the model.

[0059] Through simulation control, the actual performance of electric motors and electric vehicles can be simulated in a virtual environment, and comprehensive tests can be performed based on preset driving loads, road information and motor characteristics. The performance of electric motors under various working conditions can be simulated. The output of simulation control includes motor control constraints, which are derived from the simulation process and are used to limit the operating range of the motor. The motor control constraints include the maximum speed of the motor, that is, the maximum speed limit that the motor can withstand under specific load and road conditions. Exceeding this speed may cause damage to the motor or a decrease in efficiency.

[0060] Under the obtained motor control constraints, the average motor speed is adjusted and optimized to ensure that the motor can operate efficiently and stably under different road conditions and load conditions, while complying with the motor control constraints to ensure that the motor can operate efficiently and safely under various road conditions and loads.

[0061] Furthermore, the method for interactively obtaining the road information of the preset driving section includes: Analyze the preset driving section and determine the road category, wherein the road category division dimensions include section geometry, road surface conditions, slope, and traffic conditions; interactively obtain road condition division standards for the road category; divide the preset driving section into road conditions according to the road condition division standards, and obtain multiple road condition information for multiple driving section intervals, wherein the multiple road condition information is combined with the road category to form the road information.

[0062] According to the different characteristics of the preset driving section, the road category to which the section belongs is analyzed and determined. The classification of road categories is multi-dimensional, covering the geometric characteristics of the section, road conditions, slope, and traffic conditions. Among them, geometric characteristics refer to the morphological characteristics of the road, such as lane width, road curve radius, number of road curves, number of intersections, etc. These characteristics directly affect the driving stability and controllability of electric vehicles; road conditions include factors such as road material, degree of wear, and flatness. These factors directly affect the friction between the tire and the road, thereby affecting the acceleration, braking performance and driving stability of the vehicle; slope refers to the vertical undulation of the road, usually represented by the average slope or maximum slope of the road. Uphill sections have higher power requirements for electric vehicles, while downhill sections may involve energy recovery. Therefore, the slope will affect the speed and power output of the motor; traffic conditions refer to the impact of traffic flow, signal control, traffic accidents or other obstacles on the road on vehicle driving. Sections with good traffic conditions usually allow vehicles to maintain a higher speed, while congested roads may require frequent acceleration and deceleration.

[0063] Combined with actual road data, such as map data, sensor data, historical traffic data, etc., a detailed analysis is performed on the preset driving sections to determine the geometric characteristics, road conditions, slopes and traffic conditions of the sections, and a suitable road category is assigned to each section based on this information.

[0064] Obtain relevant road condition classification standards, which are usually formulated based on actual road characteristics and road design specifications, including road condition standards, slope standards, traffic flow standards, and geometric characteristic standards. By interacting with traffic management systems, map services, or relevant standard databases, obtain specific road category classification standards. These standards can be hard regulations (such as speed limits, load limits, etc.) or recommended suggestions (such as optimizing driving speed, control strategies, etc.).

[0065] According to the length, characteristics and division standards of the preset driving section, the entire section is divided into multiple smaller sections. For example, a 100-kilometer highway can be divided into multiple different sections, each with different slopes, traffic conditions or road surface characteristics. Each divided section will have independent road condition information, including the geometric characteristics of the section, road surface conditions, slope information, and traffic conditions. All road condition information is combined with the road category to form complete road information. Each section will have a complete condition description, including the road category, section characteristics, traffic conditions, etc. of the section.

[0066] Furthermore, the average speed of the motor is adjusted and optimized to obtain a target speed of the motor, and the method includes: Obtain first road condition information of a first driving section interval, wherein the first driving section interval is any one of the multiple driving section intervals; adjust the average speed of the electric motor in the first driving section interval according to the preset driving load and the first road condition information to obtain a first motor adjusted speed; and so on, obtain multiple motor adjusted speeds in the multiple driving section intervals; perform electric vehicle speed matching based on the multiple motor adjusted speeds, and obtain multiple driving times based on the obtained multiple electric vehicle speeds and the multiple driving section intervals; when the sum of the multiple driving times exceeds the preset driving time period, perform geometric compression on the multiple driving times with the preset driving time period as a constraint, optimize the speed of the multiple motor adjusted speeds according to the time compression result, obtain multiple motor target speeds, and integrate to obtain the motor target speed.

[0067] The above steps have divided the entire preset driving section into multiple driving section intervals, and each interval has specific road condition information. Now, any one of these multiple intervals is selected as the first driving section interval as the current analysis object.

[0068] The preset driving load refers to the weight that the electric vehicle is expected to carry during driving, such as the vehicle's own weight, passengers, cargo, etc. The greater the load, the greater the power and torque the motor needs to provide, which affects the speed adjustment. According to the road conditions of the first driving section, such as slope, road conditions, traffic flow, etc., the required motor speed is determined. For example, the motor speed needs to be increased uphill to overcome gravity and maintain a constant speed; the motor speed needs to be reduced downhill and some energy needs to be recovered through regenerative braking; sections with high traffic flow require frequent acceleration and deceleration, so the motor speed needs to be adjusted dynamically. The required motor speed is calculated through optimization algorithms, such as linear models, PID control, etc., to obtain the first motor adjustment speed.

[0069] Each driving section is processed one by one, and the corresponding motor adjustment speed is calculated in the same way to obtain multiple motor adjustment speeds for multiple sections. These adjusted speeds will constitute a complete speed adjustment strategy to ensure that the electric vehicle can achieve the best operating efficiency and power performance in each section.

[0070] The motor speed and electric vehicle speed are closely related. The higher the motor speed, the faster the electric vehicle speed is usually. The relationship between the motor adjustment speed and the electric vehicle speed is calculated to ensure that the motor adjustment speed in each driving range corresponds to the appropriate electric vehicle speed. Specifically, the adjusted speed is converted into the electric vehicle speed through the known motor performance curve (the relationship between speed and torque).

[0071] After obtaining the speed of the electric vehicle in each driving interval, the driving time of each driving interval is calculated according to the ratio of the length of the interval and the speed, and multiple driving times are obtained. Each driving interval has a corresponding driving time, and these driving times reflect the time required for the electric vehicle to complete each section.

[0072] Calculate the total driving time of all driving sections. If the total driving time exceeds the preset driving period, the driving time needs to be adjusted. For example, if the preset driving period is 2 hours and the calculated total driving time is 2.5 hours, the total time exceeds the preset period.

[0073] In order to make the total driving time conform to the preset time period, the geometric compression method is used to adjust multiple driving times. Geometric compression means reducing the driving time of each section according to a certain proportion so that their sum is exactly equal to the preset driving period. After the driving time is compressed, the adjustment speed of the motor is re-optimized according to the new driving time. The compressed time means that the electric vehicle needs to travel at a higher speed, so the speed of the motor needs to increase accordingly. According to the adjusted driving time and speed requirements, the adjustment speed of the motor is optimized to ensure that the electric vehicle can complete the driving task of each section within the compressed time.

[0074] Through the above optimization process, a new target speed for each driving section is obtained, that is, the target speed of the motor. These target speeds will ensure that the electric vehicle completes the task within the specified driving period and optimizes energy efficiency as much as possible.

[0075] Furthermore, the method of adjusting the average speed of the motor in the first driving section according to the preset driving load and the first road condition information to obtain the first motor adjustment speed includes: Extracting a first historical speed adjustment record from the historical speed adjustment records, wherein the first historical speed adjustment record includes a first historical driving load, a first historical road condition information, and a first historical target speed; performing supervised learning on the first historical speed adjustment record based on the neural network principle to obtain an intelligent speed analysis model; analyzing the preset driving load and the first road condition information based on the intelligent speed analysis model, and adjusting the average speed of the motor according to the obtained target speed.

[0076] The historical speed adjustment records are the motor speed adjustment situations recorded during the previous driving process. These records contain the adjustment process of the motor speed under different road conditions, loads and other conditions. As a reference for future optimization and prediction, the first historical speed adjustment record is screened and extracted from the historical database as the basic data for model training and future optimization, including: the first historical driving load, which corresponds to the vehicle load in the driving task, such as the load in the vehicle, the number of passengers, etc.; the first historical road condition information, which corresponds to the road conditions during driving, including road conditions, slope, traffic conditions, etc.; the first historical target speed, which is the target motor speed adjusted based on the historical data, usually the optimal speed obtained based on the road conditions and load requirements at the time.

[0077] Supervised learning is a machine learning method in which the training data contains known inputs and corresponding target outputs. In this case, the inputs are historical driving load and road condition information, and the target output is the historical target speed. The neural network will learn from these known input-output pairs how to predict the most appropriate motor speed from a given load and road condition. Through training, the neural network is able to capture the complex relationship between load, road conditions and motor speed.

[0078] The first historical speed adjustment record is input into the neural network as training data. The input layer of the neural network includes historical driving load and historical road condition information, and the output layer is the corresponding target speed. The neural network adjusts the internal weights and biases through the back propagation algorithm so that the output of the network gradually approaches the actual target speed. This process will be iterated until the network converges and can accurately predict the speed from the load and road condition information. After training, an intelligent speed analysis model is obtained, which can predict the most suitable motor speed based on the new driving load and road condition information.

[0079] The preset driving load and the first road condition information are input as input data to the trained intelligent speed analysis model, and the predicted target speed is output, which is calculated based on the optimal relationship between the load and road conditions and the motor speed found in the historical data. According to the target speed output by the model, the average speed of the motor is adjusted to adapt to the new driving load and road conditions. This adjustment process may include precise adjustment of the control parameters of the motor so that the motor can run at the optimal speed under the current conditions to ensure driving efficiency and stability.

[0080] Furthermore, the method of performing real-time feedback regulation of the motor speed according to the real-time operation data to obtain the feedback regulated speed includes: Extract the real-time speed from the real-time operation data; extract the corresponding target speed sequence from the motor target speed starting from the current moment according to the preset time window, wherein the target speed sequence includes the current target speed at the current moment; when the speed deviation between the real-time speed and the current target speed reaches a preset deviation threshold, generate a feedback adjustment instruction; according to the feedback adjustment instruction, take 1 / M of the speed deviation as the speed adjustment coefficient, take the target speed sequence as the speed adjustment target, perform smooth adjustment of the real-time speed, and generate an adjustment speed sequence as the feedback adjustment speed, wherein M is a dynamic smooth adjustment parameter.

[0081] The real-time speed at the current moment is extracted from the real-time operation data of the motor. The real-time speed reflects the current state of the motor in actual operation, and the subsequent speed adjustment will be performed based on this data.

[0082] The preset time window is a fixed time interval, usually in seconds. This time window is used to evaluate the performance and behavior of the motor in a short period of time. The size of the time window will affect the response speed and regulation accuracy of the system. For example, a smaller time window makes the system respond faster to speed changes, but it may also lead to unnecessary frequent adjustments; a larger time window helps to smooth the system response.

[0083] According to the preset time window, the target speed sequence is extracted starting from the current moment. The target speed sequence contains the target speed that the motor should reach in the next few time steps. The target speed sequence includes the current target speed at the current moment, reflecting the optimal operating state that the motor should reach at that moment.

[0084] The speed deviation refers to the difference between the current real-time speed and the current target speed. The speed deviation is compared with the preset deviation threshold. If the speed deviation exceeds the threshold, it is considered that the motor needs to be adjusted to reach the target speed. The deviation threshold can be set according to the actual operation of the electric vehicle to avoid excessive adjustment caused by too small a deviation. When the speed deviation reaches the preset threshold, a feedback adjustment instruction is generated to require the motor to adjust. The instruction includes the direction of adjustment, such as increasing or decreasing the speed, and the magnitude of the adjustment.

[0085] The speed of the motor is smoothly adjusted based on the feedback adjustment command to ensure that the speed adjustment process is smooth and not excessive. Specifically, the speed adjustment coefficient is used to control the amplitude of the speed adjustment. In this step, the adjustment coefficient is 1 / M of the speed deviation. M is a dynamic smooth adjustment parameter used to control the amplitude of the smooth adjustment. A smaller M value will make the adjustment faster, while a larger M value will make the adjustment process smoother. By dynamically adjusting M to adapt to different operating conditions, for example, when the electric vehicle load is large or the road conditions are more complex, a larger M value will be selected to avoid instability caused by over-adjustment.

[0086] The real-time speed is smoothly adjusted according to the adjustment coefficient. The adjustment is gradual and fine-tuned according to the target speed sequence to ensure that the speed does not change drastically and avoid unnecessary pressure or energy loss on the motor. Through the smooth adjustment process, a new adjustment speed sequence is generated, which represents the target speed of the motor at the next few moments. These adjustment speeds will be used as new feedback adjustment speeds for subsequent motor control to ensure that the speed is gradually adjusted to the target value.

[0087] Furthermore, the method further comprises: Retrieve the current road condition information at the current moment; collect characteristics of the operating environment of the energy-saving motor to obtain operating environment characteristic information, wherein the operating environment characteristic information includes ambient temperature information, ambient humidity information, ambient pH value, and ambient altitude; perform fuzzy logic analysis based on the current road condition information and the operating environment characteristic information, and generate the dynamic smoothing adjustment parameter based on the analysis result.

[0088] Retrieve the current road condition information, including road surface condition, slope, traffic flow, road type, etc., as input for subsequent control and analysis.

[0089] Collect characteristic data of the current operating environment of the motor so as to adjust the operating state of the motor according to environmental conditions, including ambient temperature information, ambient humidity information, ambient pH value, and ambient altitude. Among them, the ambient temperature directly affects the working efficiency and energy efficiency of the motor. High temperature may cause the motor to overheat, while low temperature may reduce the efficiency of the battery. The loss and efficiency of the motor will vary at different temperatures. The ambient humidity may cause corrosion or short circuit of the motor and its control system, and low humidity may cause static electricity accumulation or circuit failure. Humidity has a certain impact on the electrical performance of the motor. The ambient pH value will affect the corrosion of the motor. A lower pH value may accelerate the aging or corrosion of the motor and its components. The increase in the ambient altitude will cause the air to become thinner, thereby affecting the cooling effect of the motor. The air density in high altitude areas is lower, which may lead to poor heat dissipation of the motor.

[0090] Fuzzy logic is a control method based on fuzzy set theory. It can handle uncertainty and ambiguity. Specifically, the current road condition information and environmental characteristic information are used as input variables, respectively expressed as fuzzy sets, and a rule base is used to define the relationship between the input variables. For example, if the temperature is high and the road is uphill, the smoothing adjustment parameter is small; if the temperature is low and the road is flat, the smoothing adjustment parameter is large. Fuzzy reasoning is performed based on the input variables and the rule base to calculate a fuzzy output, namely the dynamic smoothing adjustment parameter. This parameter is used to control the speed adjustment strategy of the motor. According to different road conditions and environmental characteristics, the dynamic smoothing adjustment parameter can adjust the response degree of the motor speed to ensure smooth and efficient operation.

[0091] In summary, the energy-saving motor control method based on magnetic encoder feedback provided in the embodiment of the present application has the following technical effects: The average speed of the vehicle is calculated according to the preset driving period, driving section and load, and the motor speed is matched in combination with the performance characteristics of the energy-saving motor, which can ensure that the motor runs at the optimal speed under different driving conditions. This matching process minimizes energy waste, improves the energy efficiency of the motor, and prolongs the battery life; within the preset driving period, the average speed of the energy-saving motor is adjusted and optimized according to the preset driving section, load and road information. Through the adjustment, the motor can adapt to different road conditions and load changes, thereby ensuring that the vehicle always runs at the best performance and stable speed under different working conditions, reducing the unstable driving of the vehicle or the reduction of energy efficiency caused by too fast or too slow speed; the motor speed is monitored in real time by the magnetic encoder and provides real-time data, so that the system can continuously track the actual operating status of the motor, and the motor speed is adjusted by the feedback adjustment mechanism through real-time feedback data. This real-time adjustment can ensure that the error between the motor speed and the target speed is minimized, ensuring that the vehicle always maintains the best power output and energy efficiency during operation. In general, this intelligent adjustment can improve the adaptability of the system, especially under complex environmental conditions and variable driving tasks, ensuring that the energy-saving motor is accurately controlled according to actual needs, thereby improving the overall performance and efficiency of electric vehicles.

[0092] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy-saving motor control system based on magnetic encoder feedback, characterized in that: The system comprises: A task acquisition module, used to acquire a preset driving task of a target electric vehicle, wherein the preset driving task identifier includes a preset driving period, a preset driving section, and a preset driving load; A speed matching module, used to calculate and obtain an average driving speed according to the preset driving period and the preset driving section, and to perform motor speed matching in combination with the performance characteristics of the energy-saving motor to obtain an average motor speed; A road information acquisition module, used to interactively obtain road information of the preset driving section; A speed adjustment and optimization module, used to adjust and optimize the average speed of the motor according to the preset driving section, the preset driving load, and the road information within the preset driving period to obtain a target speed of the motor; A real-time monitoring module, used for performing real-time monitoring through a magnetic encoder to obtain real-time operation data during the process of controlling the energy-saving motor at the target speed of the motor; The feedback control module is used to perform real-time feedback adjustment of the motor speed according to the real-time operation data, obtain the feedback-adjusted speed, and perform feedback control of the energy-saving motor.

2. The energy-saving motor control system based on magnetic encoder feedback according to claim 1, characterized in that: The system also includes a control constraint generation module, including: A networked data calling unit, used for interactively obtaining the motor characteristics of the energy-saving motor, and performing networked data calling according to the motor characteristics to obtain an electric vehicle model, wherein the electric vehicle model has a built-in motor model that meets the motor characteristics; A simulation control unit, configured to perform simulation control of the electric vehicle model according to the preset driving load and the road information, and output motor control constraints, wherein the motor control constraints include a maximum motor speed; The adjustment and optimization unit is used to adjust and optimize the average speed of the motor based on the motor control constraint.

3. The energy-saving motor control system based on magnetic encoder feedback according to claim 1, characterized in that: The road information acquisition module comprises: A road category determination unit, used for analyzing the preset driving section and determining the road category, wherein the road category is divided into the following dimensions: the geometric characteristics of the section, the road surface condition, the slope, and the traffic condition; A classification standard acquisition unit, used for interactively acquiring a road condition classification standard for the road category; The road condition division unit is used to divide the road condition of the preset driving section according to the road condition division standard, obtain multiple road condition information of multiple driving section intervals, and the multiple road condition information is combined with the road category to form the road information.

4. The energy-saving motor control system based on magnetic encoder feedback as claimed in claim 3, characterized in that: The speed adjustment optimization module includes: A first road condition information acquisition unit, configured to obtain first road condition information of a first driving section interval, wherein the first driving section interval is any one of the multiple driving section intervals; A speed adjustment unit, configured to adjust the average speed of the motor in the first driving section according to the preset driving load and the first road condition information, and obtain an adjusted speed of the first motor; An adjusted speed acquisition unit, used to obtain, by analogy, a plurality of motor adjusted speeds in the plurality of driving sections; A driving time acquisition unit, configured to perform electric vehicle speed matching based on the rotation speed adjustment of the plurality of electric motors, and to acquire a plurality of driving times according to the acquired plurality of electric vehicle speeds in combination with the plurality of driving road sections; The target speed acquisition unit is used to compress the multiple driving times proportionally based on the preset driving period when the sum of the multiple driving times exceeds the preset driving period, optimize the speed of the multiple motors according to the time compression result, obtain multiple motor target speeds, and integrate to obtain the motor target speed.

5. The energy-saving motor control system based on magnetic encoder feedback as claimed in claim 4, characterized in that: The speed adjustment unit comprises: a first record extraction channel, used to extract a first historical speed adjustment record from the historical speed adjustment records, wherein the first historical speed adjustment record includes a first historical driving load, first historical road condition information, and a first historical target speed; A supervised learning channel, used for performing supervised learning on the first historical speed adjustment record based on the neural network principle to obtain an intelligent speed analysis model; An analysis channel is used to analyze the preset driving load and the first road condition information based on the intelligent speed analysis model, and adjust the average speed of the motor according to the obtained target speed.

6. The energy-saving motor control system based on magnetic encoder feedback according to claim 1, characterized in that: The feedback control module comprises: A real-time rotation speed extraction unit, used to extract the real-time rotation speed from the real-time operation data; A target speed extraction unit, configured to extract a corresponding target speed sequence from the motor target speed starting from a current moment according to a preset time window, wherein the target speed sequence includes a current target speed at a current moment; An instruction generating unit, configured to generate a feedback adjustment instruction when a speed deviation between the real-time speed and the current target speed reaches a preset deviation threshold; A smoothing adjustment unit is used to perform smooth adjustment of the real-time speed according to the feedback adjustment instruction, with 1 / M of the speed deviation as the speed adjustment coefficient and the target speed sequence as the speed adjustment target, to generate an adjustment speed sequence as the feedback adjustment speed, wherein M is a dynamic smoothing adjustment parameter.

7. The energy-saving motor control system based on magnetic encoder feedback according to claim 6, characterized in that: The feedback control module also includes: A current road condition retrieving unit, used to retrieve current road condition information at the current moment; An environmental characteristic acquisition unit, used to acquire characteristics of the operating environment of the energy-saving motor to obtain operating environment characteristic information, wherein the operating environment characteristic information includes environmental temperature information, environmental humidity information, environmental pH value, and environmental altitude; The fuzzy logic analysis unit is used to perform fuzzy logic analysis according to the current road condition information and the operating environment characteristic information, and generate the dynamic smoothing adjustment parameter according to the analysis result.

8. An energy-saving motor control method based on magnetic encoder feedback, characterized in that: Based on the implementation of the energy-saving motor control system based on magnetic encoder feedback according to any one of claims 1 to 7, the method comprises: Acquire a preset driving task of the target electric vehicle, wherein the preset driving task identifier includes a preset driving time period, a preset driving section, and a preset driving load; According to the preset driving period and the preset driving section, an average driving speed is calculated, and based on the average driving speed, the motor speed is matched according to the performance characteristics of the energy-saving motor to obtain an average motor speed; interactively obtaining road information of the preset driving section; During the preset driving period, the average speed of the motor is adjusted and optimized according to the preset driving section, the preset driving load, and the road information to obtain a target speed of the motor; During the process of controlling the energy-saving motor at the target speed of the motor, real-time monitoring of the energy-saving motor is performed by a magnetic encoder to obtain real-time operation data; According to the real-time operation data, the motor speed is feedback-adjusted in real time to obtain the feedback-adjusted speed, and the energy-saving motor is feedback-controlled according to the feedback-adjusted speed.

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