Permanent magnet motor and energy saving method

Abstract

The invention discloses an energy-saving permanent magnet motor which comprises a permanent magnet motor body, an energy conversion unit, an energy storage device, a communication interface and an intelligent control unit. The invention also discloses a permanent magnet motor energy-saving method, which comprises the following steps: S1, acquiring the original data of the operation state of the permanent magnet motor through the sensor, and preprocessing the original data; s2, extracting characteristic parameters which influence the running state and the energy recovery efficiency of the motor from the preprocessed data; s3, selecting a prediction algorithm according to the extracted feature parameters, and constructing a prediction model; s4, based on an output result of the prediction model, making an optimization strategy of energy recovery and reutilization; s5, converting the optimization strategy into a control instruction, and sending the control instruction to a motor in real time through a communication interface for execution; and S6, continuously collecting new operation data, and carrying out optimization and iteration on the prediction model and the optimization strategy. The permanent magnet motor and the energy-saving method have the advantages of being energy-saving, environment-friendly, safe, reliable and remarkable in effect.

Description

Technical Field

[0001] The invention relates to an electric motor, in particular to a permanent magnet motor and an energy-saving method. Background Art

[0002] Permanent magnet motors use permanent magnet materials to manufacture rotors. They do not require external excitation, so that the rotor magnetic field interacts with the stator coil magnetic field lines, reducing the energy loss of the motor and achieving efficient energy conversion. Permanent magnet motors are widely used in many fields such as wind power generation, industrial automation, and household appliances. Their high efficiency and reliability enable these fields to achieve more efficient and environmentally friendly production and operation methods.

[0003] There is still room for further improvement in the efficiency of permanent magnet motors. This can be achieved by improving the structure of the electrodes, or by recovering and storing the electrical energy generated by the permanent magnet motor during braking for future normal operation. This energy recovery technology can further reduce system energy consumption and improve energy utilization efficiency. In some situations where frequent braking is required, energy recovery technology for permanent magnet motors is particularly important. Summary of the invention

[0004] The present invention improves the magnetic poles of a permanent magnet motor and designs an energy recovery device and an algorithm for the permanent magnet motor. The first object of the present invention is to provide an energy-saving permanent magnet motor, comprising a motor body and:

[0005] 1) An energy conversion unit, which is used to convert the mechanical energy generated by the motor into electrical energy during the deceleration or braking process of the motor;

[0006] 2) Energy storage devices for storing recovered and reused energy;

[0007] 3) Communication interface, used to provide communication connection between the motor and external equipment or system;

[0008] 4) Intelligent control unit, used to monitor the operation of the motor and optimize the energy utilization.

[0009] Preferably, the motor body includes a permanent magnet rotor, and the permanent magnet rotor is a neodymium iron boron manganese aluminum carbon composite permanent magnet rotor, or a samarium cobalt iron nitrogen composite permanent magnet rotor.

[0010] Preferably, the permanent magnet rotor adopts a magnetic pole shape of three-segment circular arc pole shoes and a multi-layer magnet structure.

[0011] Preferably, the energy conversion unit includes a bidirectional inverter and an energy recovery circuit; the energy recovery circuit includes a filter circuit and a rectifier circuit.

[0012] Preferably, the energy storage device may be a capacitor and / or a battery; the battery may be a lithium-ion battery or a lead-acid battery.

[0013] The second object of the present invention is to provide an energy-saving method for a permanent magnet motor. In the aforementioned energy-saving permanent magnet motor, the following steps are adopted:

[0014] S1. Collecting the original data of the permanent magnet motor operating status through sensors and preprocessing the original data;

[0015] S2, extracting characteristic parameters that affect the motor operation state and energy recovery efficiency from the preprocessed data;

[0016] S3. Select a prediction algorithm and build a prediction model based on the extracted feature parameters;

[0017] S4. Develop an optimization strategy for energy recovery and reuse based on the output of the prediction model;

[0018] S5, convert the optimization strategy into control instructions, send them to the motor for execution, and make adjustments based on the feedback results;

[0019] S6. Continue to collect new operating data to optimize and iterate the prediction model and optimization strategy.

[0020] Preferably, the characteristic parameters include motor speed, current and voltage, temperature and load conditions.

[0021] Preferably, the prediction algorithm is a long short-term memory network algorithm, the input layer of the algorithm includes time series data of motor speed, current, voltage, temperature and load conditions; the output layer of the algorithm is set to predict the energy recovery efficiency of the motor.

[0022] Preferably, the S4 step comprises:

[0023] S41, analyzing the prediction model output of the motor performance and energy consumption under different working conditions;

[0024] S42. Analyze the energy conversion efficiency of the motor at different speeds and loads to evaluate the energy recovery potential;

[0025] S43, formulating an optimization strategy for effective recovery and reuse of energy; the optimization strategy includes:

[0026] 1) Dynamically adjust the control parameters of the motor, wherein the control parameters include speed and torque;

[0027] 2) Design a reasonable energy conversion path to efficiently transfer the recovered energy to the energy storage device;

[0028] 3) Scheduling and allocating recovered energy when multiple motors are controlled in linkage;

[0029] 4) When it is predicted that the motor may fail or be abnormal, take timely measures to intervene.

[0030] The energy-saving permanent magnet motor and energy-saving method of the present invention have the following beneficial effects:

[0031] 1) Improve permanent magnet motors, use composite permanent magnet materials, and optimize the shape and structure of permanent magnets.

[0032] 2) Use deep learning algorithms to optimize the efficiency of motor energy recovery.

[0033] The energy-saving permanent magnet motor and energy-saving method of the present invention are energy-saving, environmentally friendly, safe, reliable and have significant effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a step diagram of a permanent magnet motor energy saving method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to further understand the present invention, preferred embodiments of the present invention are described below in conjunction with examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0036] An embodiment of the present invention provides an energy-saving permanent magnet motor, including a motor body, an energy conversion unit, an energy storage device, a protection circuit, a communication interface and an intelligent control unit. Each part is further described below.

[0037] The permanent magnet motor of an embodiment of the present invention includes a motor body, and the motor body includes a permanent magnet rotor, a stator winding, a bearing and other components. The permanent magnet rotor provides a continuous magnetic field, and the stator winding generates a rotating magnetic field by passing current, which interacts with the magnetic field of the permanent magnet rotor to generate a rotational torque. The reason for selecting a permanent magnet rotor is that the magnetic field it provides does not require an external excitation current, so it has a higher efficiency and power factor, and reduces energy loss. The efficiency of a permanent magnet motor is usually above 90%, and can even reach above 95%. Ordinary motors use electrical excitation as a magnetic field source, which requires electrical energy to generate a magnetic field, so the efficiency is relatively low, generally less than 90%.

[0038] The embodiments of the present invention use new high-performance permanent magnet materials to improve the magnetic energy product and temperature stability of the permanent magnet. These materials can generate a stronger magnetic field, thereby improving the efficiency and energy recovery capability of the motor. The preferred permanent magnet materials used in the embodiments of the present invention are:

[0039] 1) NdFeB-MnAlC composite permanent magnet. This is a rare earth-non-rare earth composite permanent magnet material, which combines the rare earth permanent magnet material NdFeB with the non-rare earth permanent magnet material MnAlC to balance the relationship between high performance and cost. Rare earth permanent magnet materials have high magnetic energy product and excellent magnetic properties, but are more expensive; non-rare earth permanent magnet materials have lower costs but limited magnetic properties. Through compounding, permanent magnets with both high performance and cost-effectiveness can be prepared.

[0040] 2) Samarium cobalt iron nitrogen (SmCo-FeN) composite permanent magnet. This is a multi-phase composite permanent magnet material. A variety of permanent magnet materials with different phase structures are compounded to form a composite permanent magnet with a complex magnetic domain structure and excellent magnetic properties. Multi-phase composite permanent magnet materials can achieve higher remanence, coercive force and magnetic energy product through the interaction of phase interfaces and the fine regulation of magnetic domains. At the same time, the diversity of its microstructure also makes it possible to improve the energy recovery efficiency of the motor.

[0041] The embodiment of the present invention adopts a multi-layer magnet structure, which can improve the magnetic field distribution and reduce magnetic leakage, further improving the energy conversion efficiency. The multi-layer magnet structure can achieve higher torque density and lower magnetic resistance loss by optimizing the arrangement and magnetization direction of each layer of magnets, thereby improving the efficiency and performance of the motor.

[0042] The magnetic pole shapes that can be used in the embodiments of the present invention include V-shaped, crescent-shaped, three-segment arc pole shoes, etc. Preferably, a magnetic pole shape with three-segment arc pole shoes is used. This magnetic pole consists of three arc segments, including a largest main arc and two symmetrical small arcs. The main arc is concentric with the inner diameter of the stator to form the main air gap, and the two small arcs are located on both sides of the main arc to further adjust and optimize the magnetic field distribution. The air gap design of the magnetic pole can be a uniform air gap or a non-uniform air gap.

[0043] The embodiment of the present invention optimizes the permanent magnet rotor, improves the distribution of the magnetic field, makes the magnetic lines of force more uniform, reduces harmonics and leakage magnetic flux, and thus improves the power factor and efficiency of the motor. It also helps to reduce the vibration and noise of the motor, improve the stability and reliability of operation, and lay the foundation for motor energy recovery. The motor with improved permanent magnets, combined with energy recovery technology, can further improve the energy efficiency level of the entire system. In addition, the composite permanent magnet motor has better dynamic response characteristics and control accuracy. Combined with energy recovery technology, it can further optimize the control strategy of the system and improve the stability and reliability of the system.

[0044] The energy conversion unit of the permanent magnet motor of the embodiment of the present invention is used to convert the mechanical energy generated by the motor into electrical energy during the deceleration or braking process of the motor. This can be achieved through the generator mode of the permanent magnet motor, that is, converting the rotational mechanical energy into electrical energy output. The energy conversion unit can use high-efficiency power electronic devices, such as IGBT modules, combined with optimized control strategies to ensure high efficiency in the energy conversion process.

[0045] The energy conversion unit can convert the AC power output by the motor into DC power, or directly process and convert electrical energy. It includes:

[0046] 1) Bidirectional inverter. The inverter is designed to be bidirectional and reversible. It can convert DC power into AC power to drive the motor, and can also convert AC power into DC power for recycling when the motor is used as a generator.

[0047] 2) Energy recovery circuit. Responsible for directing the recovered electric energy to the energy storage device or other forms of reuse. Including filtering and rectification circuits to ensure the stability and quality of the recovered electric energy. The rectification circuit is used to convert the AC power output by the motor into DC power to provide a suitable form of electric energy for the subsequent energy storage device. The filtering circuit is set after the rectification circuit and the inverter to filter out harmonics and clutter in the electric energy and improve the quality of the electric energy.

[0048] The energy conversion unit realizes the effective recovery and reuse of energy during the deceleration, braking or shutdown of the motor. The performance and efficiency of the energy conversion unit can be further improved by introducing high-efficiency rectification technology, intelligent control strategies and energy recovery optimization algorithms.

[0049] The energy storage device of the permanent magnet motor includes an energy storage element and an energy management system. The energy storage element is connected to the output end of the energy conversion unit to store the converted electrical energy. The energy storage device can be a capacitor, a battery or a combination thereof, with fast charging and discharging capabilities and high energy density. The battery can be a lithium-ion battery or a lead-acid battery to store the recovered energy. The capacitor is an energy storage element with fast charging and discharging capabilities, suitable for occasions requiring fast response.

[0050] The energy management system is responsible for monitoring the status of the energy storage device, including parameters such as storage energy, voltage, and current, to ensure the safe operation of the energy storage device. The energy management system dispatches the energy in the energy storage device according to the operating status and energy demand of the motor to achieve reasonable distribution and utilization of energy.

[0051] The protection circuit of the permanent magnet motor is used to automatically cut off or limit the energy conversion and storage process when abnormal conditions such as overvoltage, overcurrent, overheating, etc. are detected to protect the system from damage.

[0052] The communication interface of the permanent magnet motor is used to provide communication connection with external devices or systems, for data transmission and command reception, and to realize remote monitoring and centralized management of the system. Users can view the operating status and parameters of the motor in real time through mobile phones, computers and other terminal devices, and perform remote control and fault diagnosis. At the same time, the cloud platform can also be used to centrally manage and analyze large amounts of data to further improve the intelligence level of the system.

[0053] The intelligent control unit of the permanent magnet motor is connected to the energy conversion unit and the energy storage device to monitor the motor's operating status and load requirements, and intelligently adjust the energy recovery process based on the monitoring results. The intelligent control unit includes sensors, processors, and control algorithms, which can calculate the optimal energy recovery strategy in real time to ensure efficient and stable operation of the system.

[0054] The intelligent control unit realizes the following functions:

[0055] 1) Realize the coordinated control of the motor part, rectifier circuit and filter circuit.

[0056] 2) According to the real-time operating status and energy demand of the motor, dynamically adjust the working mode and parameters of the energy conversion unit to achieve optimal energy recovery and reuse effects.

[0057] 3) Accurately control the energy recovery process of the motor during deceleration, braking or shutdown.

[0058] The intelligent control unit achieves maximum energy recovery and efficient utilization through algorithm optimization, while reducing loss and waste in the energy conversion process. The intelligent control unit performs intelligent prediction and optimization, such as Figure 1 As shown, it includes the following steps:

[0059] S1. Collect the original data of the operating status through sensors and pre-process the original data.

[0060] Through the sensors integrated in the motor system, including speed sensors, current sensors, temperature sensors, etc., the motor's operating status data, including speed, current, voltage, temperature, etc., are collected in real time.

[0061] Preprocess the collected raw data, including:

[0062] 1) Data cleaning, removing noise, outliers, etc.

[0063] 2) Data conversion, converting analog signals into digital signals.

[0064] 3) Data standardization and normalization to facilitate subsequent model processing.

[0065] S2. Extract characteristic parameters that affect the motor operating state and energy recovery efficiency from the preprocessed data.

[0066] Using feature selection technology, the most representative features are screened out to improve the accuracy and efficiency of subsequent prediction and optimization. Parameters that have a significant impact on the motor's operating status and energy recovery efficiency are extracted, including:

[0067] 1) Motor speed: Speed ​​is one of the key parameters that reflects the operating status of the motor.

[0068] 2) Current and voltage: The current and voltage data of the motor can directly reflect the load condition and energy consumption of the motor.

[0069] 3) Temperature: The temperature data of the motor is very important for evaluating the thermal state of the motor and predicting the life of the motor. Excessive temperature may cause the motor performance to deteriorate or even damage, so temperature is one of the characteristics that the intelligent control unit must pay attention to.

[0070] 4) Load conditions: The load conditions of the motor directly affect its energy consumption and recovery efficiency. By monitoring load changes, the intelligent control unit can adjust the control strategy in time to optimize energy recovery and utilization.

[0071] S3. According to the extracted feature parameters, select the appropriate prediction algorithm and build a prediction model.

[0072] According to the extracted characteristic parameters, select the appropriate prediction algorithm to build the prediction model. Use historical data to train the prediction model and adjust the model parameters to optimize the prediction performance. Evaluate the accuracy and generalization ability of the prediction model to ensure that the model can accurately predict the operating status and energy recovery efficiency of the motor under different working conditions.

[0073] Prediction algorithms can be used:

[0074] 1) Machine learning algorithms: including support vector machine (SVM), random forest and gradient boosted tree (GBDT), and K-nearest neighbor (KNN).

[0075] 2) Deep learning algorithms: including recurrent neural networks (RNN), long short-term memory networks (LSTM), and convolutional neural networks (CNN).

[0076] The embodiment of the present invention preferably uses LSTM network, which is particularly suitable for processing time series data and can capture the time dependency and long-term memory effect in the data. In the prediction of motor energy recovery and reuse, since it is necessary to process time series data, such as data that changes over time such as motor speed, current, voltage, etc., and consider the historical operating status and future change trend of the motor, the LSTM network algorithm has obvious advantages.

[0077] In the embodiment of the present invention, the input layer of the LSTM network includes time series data such as motor speed, current, voltage, temperature and load conditions. The output layer is set to predict the energy recovery efficiency of the motor. The hidden layer can select the appropriate number of LSTM units according to the size and complexity of the data set. More units can capture more complex patterns, but it will also increase the amount of calculation and the risk of overfitting. The number of hidden layers can be designed as a single-layer or multi-layer LSTM structure. It is preferred to use a multi-layer LSTM structure, but it will also increase the complexity of the model and the training time. Configure the LSTM model parameters, including:

[0078] 1) Learning rate: controls the step size of model weight update and is adjusted based on test results.

[0079] 2) Optimizer: Adam (Adaptive Moment Estimation) or RMSprop (Root Mean Square Propagation) can be used to optimize the model training process.

[0080] 3) Loss function: The mean square error (MSE) and root mean square error (RMSE) can be used to evaluate the difference between the model prediction value and the actual value.

[0081] 4) Batch size: The number of samples input into the model during each training, which affects the training speed and stability of the model.

[0082] 5) Number of iterations: The number of times the entire dataset is traversed, that is, the training cycle.

[0083] The model is trained using the divided training set data. During the training process, the model will continuously adjust its parameters and weights according to the error between the input data and the expected output to reduce the error and improve the prediction accuracy. Some techniques can also be used during the training process, such as learning rate adjustment, batch processing, regularization, etc., to prevent overfitting and improve training efficiency.

[0084] Use the test set data to evaluate the trained model to verify its performance in practical applications. Evaluation indicators can include accuracy, recall, F1 score, etc. Make necessary adjustments and optimizations to the model based on the evaluation results to improve the model's prediction accuracy and generalization ability.

[0085] The optimized model is deployed in the intelligent control unit and integrated with other system components, such as energy conversion units, energy storage devices, etc. The model will make predictions based on the real-time collected motor operation data and formulate optimization strategies to achieve effective energy recovery and reuse.

[0086] S4. Forecasting and optimization strategy formulation.

[0087] Based on the output of the prediction model, an optimization strategy for energy recovery and reuse is formulated. According to the real-time operating status of the motor and the prediction results, the control parameters of the motor, such as speed and torque, are dynamically adjusted to maximize the energy recovery efficiency. At the same time, the safety and stability of the motor are considered to ensure that the performance and life of the motor are not damaged while optimizing energy recovery. The following sub-steps are included:

[0088] S41. Analyze the prediction model output.

[0089] The intelligent control unit receives the prediction model's prediction results on the motor's future operating state, including speed, current, voltage, etc. These prediction results reflect the motor's performance and energy consumption under different operating conditions.

[0090] S42. Evaluate energy recovery potential.

[0091] Based on the output of the prediction model, the intelligent control unit evaluates the energy that the motor may release during deceleration, braking or stopping. This involves analyzing the energy conversion efficiency of the motor at different speeds and loads.

[0092] S43. Develop optimization strategies.

[0093] After evaluating the energy recovery potential, the intelligent control unit will develop specific optimization strategies to achieve effective energy recovery and reuse. These strategies include:

[0094] 1) Dynamically adjust control parameters. Based on the prediction results, the intelligent control unit can dynamically adjust the motor's control parameters, such as speed and torque, to optimize the motor's operating state and reduce unnecessary energy loss. For example, during deceleration, the braking energy can be recovered to the maximum extent by precisely controlling the motor's braking torque.

[0095] 2) Optimize the energy conversion path. Design a reasonable energy conversion path to efficiently transfer the recovered energy to energy storage devices, such as batteries and capacitors. Consider the efficiency loss in the energy conversion process and select the optimal conversion method and conversion equipment.

[0096] 3) Intelligent scheduling and allocation: If there are multiple motor linkage controls, the recovered energy can be intelligently scheduled and allocated between multiple motors or systems to maximize energy utilization.

[0097] 4) Fault warning and protection. Combined with the output results of the prediction model, the intelligent control unit can also realize the fault warning and protection functions of the motor. When it is predicted that the motor may fail or be abnormal, timely measures are taken to intervene to prevent the fault from expanding and protect the motor from damage.

[0098] Implement the developed optimization strategy into the motor control system and monitor the motor's operating status and energy recovery effect in real time. Make necessary adjustments and optimizations to the optimization strategy based on the feedback results to ensure its effectiveness and reliability in practical applications.

[0099] When formulating the optimization strategy for energy recovery and reuse, the intelligent control unit needs to comprehensively consider the motor's operating status, energy recovery potential, and the overall system requirements and constraints. Effective energy recovery and reuse can be achieved through dynamic adjustment of control parameters, optimization of energy conversion paths, intelligent scheduling and allocation, and fault warning and protection.

[0100] S5. Real-time control and feedback adjustment. Convert the optimization strategy into specific control instructions and send them to the motor for execution in real time through the intelligent control unit. Monitor the actual response and operating status of the motor and compare and analyze them with the predicted results. Adjust and optimize the prediction model and optimization strategy in real time based on the feedback results to adapt to the uncertainties and changes in the motor operation process.

[0101] S6. Continuous optimization and iteration. With the continuous accumulation of motor operation data and the continuous improvement of the prediction model, the intelligent control unit will continue to iterate and optimize the optimization strategy. By continuously learning and adapting to the operating characteristics of the motor, the efficiency and accuracy of energy recovery and reuse can be improved.

[0102] Through the above steps, the intelligent control unit can realize intelligent prediction and optimization of the motor operating status, thereby realizing effective energy recovery and reuse during deceleration, braking or shutdown to achieve energy saving.

[0103] The embodiments of the present invention improve the efficiency of the motor to the maximum extent by improving the magnetic poles and optimizing the energy recovery function, so as to achieve the purposes of environmental protection, energy saving, stable operation, safety and reliability.

[0104] The above embodiments are only used to help understand the method and core idea of ​​the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. An energy-saving permanent magnet motor, comprising a motor body, characterized in that: Also includes: 1) An energy conversion unit, which is used to convert the mechanical energy generated by the motor into electrical energy during the deceleration or braking process of the motor; 2) Energy storage devices for storing recovered and reused energy; 3) Communication interface, used to provide communication connection between the motor and external equipment or system; 4) Intelligent control unit, used to monitor the operation of the motor and optimize the energy utilization.

2. An energy-saving permanent magnet motor according to claim 1, characterized in that: The motor body comprises a permanent magnet rotor, which is a NdFeB-MnAl-C composite permanent magnet rotor or a SmCoFeN composite permanent magnet rotor.

3. An energy-saving permanent magnet motor according to claim 2, characterized in that: The permanent magnet rotor adopts a magnetic pole shape of three-segment circular arc pole shoes and a multi-layer magnet structure.

4. The energy-saving permanent magnet motor according to claim 1, characterized in that: The energy conversion unit includes a bidirectional inverter and an energy recovery circuit; the energy recovery circuit includes a filter circuit and a rectifier circuit.

5. An energy-saving permanent magnet motor according to any one of claims 1 to 4, characterized in that: The energy storage device may be a capacitor and / or a battery; the battery may be a lithium-ion battery or a lead-acid battery.

6. A method for energy saving of a permanent magnet motor, in an energy saving permanent magnet motor according to any one of claims 1 to 5, adopts the following steps: S1. Collecting the original data of the permanent magnet motor operating status through sensors and preprocessing the original data; S2, extracting characteristic parameters that affect the motor operation state and energy recovery efficiency from the preprocessed data; S3. Select a prediction algorithm and build a prediction model based on the extracted feature parameters; S4. Develop an optimization strategy for energy recovery and reuse based on the output of the prediction model; S5, convert the optimization strategy into control instructions, send them to the motor for execution, and make adjustments based on the feedback results; S6. Continue to collect new operating data to optimize and iterate the prediction model and optimization strategy.

7. The energy-saving method of a permanent magnet motor according to claim 6, characterized in that: The characteristic parameters include motor speed, current and voltage, temperature and load conditions.

8. The energy-saving method of a permanent magnet motor according to claim 6, characterized in that: The prediction algorithm is a long short-term memory network algorithm, the input layer of the algorithm includes time series data of motor speed, current, voltage, temperature and load conditions; the output layer of the algorithm is set to predict the energy recovery efficiency of the motor.

9. A permanent magnet motor energy saving method according to any one of claims 6 to 8, characterized in that: The S4 step includes: S41, analyzing the prediction model output of the motor performance and energy consumption under different working conditions; S42. Analyze the energy conversion efficiency of the motor at different speeds and loads to evaluate the energy recovery potential; S43, formulating an optimization strategy for effective recovery and reuse of energy; the optimization strategy includes: 1) Dynamically adjust the control parameters of the motor, wherein the control parameters include speed and torque; 2) Design a reasonable energy conversion path to efficiently transfer the recovered energy to the energy storage device; 3) Scheduling and allocating recovered energy when multiple motors are controlled in linkage; 4) When it is predicted that the motor may fail or be abnormal, take timely measures to intervene.

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