Iterative self-learning control method for motor braking torque in energy recovery system

Abstract

The application relates to an iterative self-learning control method of motor braking torque in an energy recovery system, comprising the following steps: obtaining an expected motor braking torque and an actual motor braking torque according to a regenerative braking braking force distribution strategy; calculating the difference between the expected motor braking torque and the actual motor braking torque to obtain a closed-loop PI-ILC control deviation of a closed-loop control link; selecting the closed-loop PI-ILC control deviation calculated at the last moment as an open-loop PI-ILC control deviation at the current moment; calculating an open-closed-loop PI-ILC control variable of an open-closed-loop PI-ILC control link according to the closed-loop PI-ILC control deviation and the open-loop PI-ILC control deviation, and continuously iteratively updating the closed-loop PI-ILC control deviation and the open-closed-loop PI-ILC control variable; presetting a storage time, applying open-closed-loop PI-ILC control when the energy recovery time is less than the storage time, and otherwise applying conventional PI control. The method overcomes the error caused by the delay of the motor torque control system in the prior art, reduces the error through open-closed-loop PI iterative learning, and makes the control more accurate.

Description

Technical Field

[0001] This invention relates to the field of energy recovery in new energy vehicles, and in particular to an iterative self-learning control method for motor braking torque in an energy recovery system. Background Technology

[0002] To extend the driving range of electric vehicles, the energy recovery system plays an indispensable role. Energy recovery in electric vehicles refers to the process by which the motor converts a portion of the electric vehicle's kinetic energy into electrical energy during coasting or braking. This electrical energy is then used to power the vehicle's electrical accessories or stored in the battery, thereby improving the vehicle's driving range.

[0003] Traditional solutions typically utilize signals from the ABS system controller to adjust the braking torque to recover braking energy as much as possible. For example, patent application CN201610188178.2, entitled "Method for Regenerative Braking Torque in Electric Vehicles," involves dynamically adjusting the braking torque based on signals from the ABS system controller indicating whether the ABS is currently active, thereby recovering as much braking energy as possible. However, this approach does not address the inherent delay in the motor torque control system, which leads to a reduced energy recovery rate. Summary of the Invention

[0004] The objective of this invention is to provide an iterative self-learning control method for motor braking torque in an energy recovery system, overcoming the error caused by the inherent delay in existing motor torque control systems, which leads to a decrease in energy recovery rate. This invention controls the motor braking torque using an open-loop and closed-loop PI iterative self-learning control method within a preset time, reducing the difference between the desired braking torque input to the motor and the actual braking torque. This improves the PI control's tracking performance, thereby reducing the delay in the motor torque control system. In this way, the energy recovery system can quickly enter regenerative braking, achieving the effect of improving energy recovery efficiency.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an iterative self-learning control method for motor braking torque in an energy recovery system, specifically including the following steps: S1: Obtain the desired motor braking torque T according to the regenerative braking force distribution strategy. des and the actual braking torque T of the motor motor ;

[0006] S2: Calculate the desired motor braking torque T des and the actual braking torque T of the motor modtorThe difference is used to obtain the closed-loop PI-ILC control deviation of the closed-loop control loop;

[0007] S3: Select the closed-loop PI-ILC control deviation calculated at the previous moment as the open-loop PI-ILC control deviation at the current moment.

[0008] S4: Calculate the open-loop and closed-loop PI-ILC control variables of the open-loop and closed-loop PI-ILC control loops based on the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control variables, and continuously iterate and update the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control variables.

[0009] S5: Preset storage time. When the energy recovery time is less than the storage time, open-loop PI-ILC control is applied; otherwise, conventional PI control is applied.

[0010] By adopting the above technical solution, the present invention has the following advantages: by obtaining the desired motor braking torque T des and the actual braking torque T of the motor motor The difference between the two values ​​is calculated to obtain the closed-loop PI-ILC control deviation, which yields the error between the expected and actual values. The calculated closed-loop PI-ILC control deviation from the previous moment is then used as the open-loop PI-ILC control deviation for the current moment. By summing the two values—that is, the sum of the closed-loop PI-ILC control deviation calculated from the previous moment and the current moment—the open-loop and closed-loop PI-ILC control variables are obtained. Iterating through these variables yields open-loop and closed-loop PI-ILC control variables with smaller error values. A storage time is set, and by determining whether the energy recovery time is less than the storage time, different control methods are selected, thereby improving control accuracy.

[0011] Furthermore, before obtaining the desired motor braking torque and the actual motor braking torque according to the regenerative braking force distribution strategy in S1: the braking forces of the front and rear axles are distributed according to the following formula 1:

[0012]

[0013] Where z is the braking intensity, F f For the total braking force of the front wheels, F r G is the total braking force of the rear wheels, a is the total weight of the vehicle, b is the distance from the vehicle's center of gravity to the front axle, and h is the distance from the vehicle's center of gravity to the rear axle. g The height is the vehicle's center of gravity. Formula 1 is used to distribute the braking force between the front and rear axles, yielding the total rear wheel braking force required for subsequent judgments.

[0014] Furthermore, the regenerative braking force distribution strategy includes: obtaining the maximum allowable braking torque T of the motor at the current moment by looking up a table based on the motor's speed and torque. Motor_max When the maximum allowable braking torque T of the motor at the current moment Motor_max ≤F r At that time, the desired motor braking torque T des =T Motor_max When the maximum allowable braking torque T of the motor at the current moment Motor_max >F r At that time, the desired motor braking torque T des =F r The actual braking torque T of the motor motor Obtained via a torque sensor. By comparing T... Motor_max and F r The magnitude of the value can yield different desired motor braking torques under different conditions, making the obtained desired motor braking torque more reasonable.

[0015] Further, S2: Calculating the difference between the desired motor braking torque and the actual motor braking torque to obtain the closed-loop PI-ILC control deviation of the closed-loop control loop includes: calculating the closed-loop PI-ILC control deviation according to the following formula 2:

[0016] e j+1 =T des -T motor Formula 2

[0017] Among them, e j+1 For closed-loop PI-ILC control deviation, T des To achieve the desired motor braking torque, T motor This represents the actual braking torque of the motor. By calculating the difference between the desired motor braking torque and the actual motor braking torque, an accurate value for the actual error can be obtained.

[0018] Furthermore, the step of calculating the open-loop and closed-loop PI-ILC control variables of the open-loop and closed-loop PI-ILC control loops based on the closed-loop PI-ILC control deviation and the open-loop PI-ILC control deviation includes: calculating the open-loop and closed-loop PI-ILC control variables according to the following formula 3:

[0019] U j+1 =U j +k p，o e j +k io ∫e j dt+k p，c e j+1 +k i，c ∫e j+1 Formula 3 for dt

[0020] Among them, U j+1 For open-loop and closed-loop PI-ILC control variables, U j U stored for the previous braking process j+1 k p，o k is the proportional coefficient for open-loop PI control. i，o Let k be the integral coefficient of the open-loop circuit. p，c k is the proportional coefficient of the closed-loop PI control. i，o e represents the integral coefficient of the closed loop. j e represents the control deviation of the open-loop PI-ILC at the previous moment. j+1 This represents the control deviation at the current moment for both the open-loop and closed-loop PI-ILC control. By calculating the sum of the closed-loop and open-loop PI-ILC control deviations, the responsiveness of PI control can be improved.

[0021] Furthermore, the closed-loop PI-ILC control deviation and open-loop and closed-loop PI-ILC control variables are continuously iterated and updated, including: a preset number of iterations K, until the number of iterations reaches the preset number of iterations K, at which point the iteration stops. Through continuous iteration, the closed-loop PI-ILC control deviation and open-loop and closed-loop PI-ILC control variables with continuously reduced errors are obtained, further improving the responsiveness of PI control.

[0022] Furthermore, conventional PI control is applied, including calculating the conventional PI according to the following formula 4:

[0023] U Normal =k p e+k i Formula 4: ∫e dt

[0024] Among them, U Normal For a regular PI, k p k is the proportional coefficient for PI control. i Here, is the integral coefficient, and e is the control deviation at the current moment. By using conventional PI control when the energy recovery time has exceeded the limit, energy recovery from the motor's braking torque can be performed more efficiently.

[0025] Furthermore, the preset storage time includes calculating the storage time based on the array length m after calibration on the actual vehicle and the data sampling period. By setting the storage time in advance, different storage times are used for different vehicle models, thereby avoiding the problem of large control errors caused by the different motor braking torque control performance of different vehicle models.

[0026] Furthermore, this includes the stabilization process, calibrating parameters P1 and P2, and calculating the control variable U of the open-loop and closed-loop PI-ILC at the previous moment. j+1，k and the control variable U of the open-loop and closed-loop PI-ILC at the current moment. j+1，k+1 , when P1≤|Uj+1，k+1 -U j+1，k When |≤P2, output the control variable U of the open-loop PI-ILC at the previous moment. j+1，k For the motor controller, when |U j+1，k+1 -U j+1，k |<P1 or|U j+1 -U j When |>P2, output the control variable U of the open-loop and closed-loop PI-ILC at the current time. j+1，k+1 The motor controller is configured to reduce vehicle vibration caused by the constant addition and subtraction of negative torque by the motor under different conditions, thus making the vehicle more stable.

[0027] Furthermore, when a motor malfunctions or the MCU fails to execute the motor braking torque command, mechanical braking torque compensation is used; otherwise, a motor braking torque command is sent. By comparing the motor braking torque command with the actual motor braking torque response, it is determined whether a motor malfunction has occurred or the MCU has failed to execute the motor braking torque command. Corresponding measures are then taken promptly to compensate, preventing dangerous situations and improving system safety.

[0028] As can be seen from the above technical solutions, the present invention also has the following advantages: by setting a preset number of iterations, the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control deviation are continuously calculated and the data is updated so that the data obtained at the current moment has a smaller error than the data obtained at the previous moment. The value with the smallest error obtained after the final iteration is input into the system. Combined with the preset storage time, the control can play its maximum role within the energy recovery time. By inputting the value with the smallest error and adding an anti-shake element, the stability is improved. At the same time, the consistency of control between different vehicles of the same model is improved, and the calibration work is reduced. Thus, when the system re-enters regenerative braking, it can quickly enter a stable control state and improve control accuracy, ensuring safety and comfort. Attached Figure Description

[0029] Figure 1 This is a flowchart of the open-loop and closed-loop PI-ILC control method according to an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the open-loop and closed-loop PI-ILC control principle of an embodiment of the present invention;

[0031] Figure 3 This is an overall flowchart of an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0034] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0035] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, "and / or B" can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0036] The technical solution of the present invention will be described in detail below with reference to specific embodiments. Embodiments may be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0037] Please see Figure 1 , Figure 2 and Figure 3 , Figure 1 This is a flowchart of the open-loop and closed-loop PI-ILC control method according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the open-loop and closed-loop PI-ILC control principle according to an embodiment of the present invention. Figure 3 This is an overall flowchart of an embodiment of the present invention.

[0038] This invention provides an iterative self-learning control method for motor braking torque in an energy recovery system, which specifically includes the following steps:

[0039] S1: Based on the regenerative braking force distribution strategy, obtain the desired motor braking torque and the actual motor braking torque;

[0040] In this embodiment of the invention, the braking forces of the front and rear axles are first distributed according to the following formula 1:

[0041]

[0042] Where z is the braking intensity, F f For the total braking force of the front wheels, F r G is the total braking force of the rear wheels, G is the total weight of the vehicle, a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle (the rear axle is the drive axle), and h is the total braking force of the rear wheels. g The height of the car's center of gravity.

[0043] In this embodiment of the invention, under conditions where regenerative braking is possible, regenerative braking torque is preferentially used to achieve the driver's desired braking torque. When the motor cannot meet the driver's desired braking torque, mechanical braking torque is used for compensation. Desired motor braking torque T des There are two different values ​​under different conditions. Based on the lookup table of motor speed and torque, and taking into full account the influence of factors such as motor temperature, maximum battery charging current, and maximum braking force that the rear axle can withstand, the maximum allowable braking torque T of the motor at the current moment is obtained. Motor_max When the maximum allowable braking torque T of the motor at the current moment Motor_max ≤F r At that time, the desired motor braking torque T des =T Motor_max When the maximum allowable braking torque T of the motor at the current moment Motor_max >F r At that time, the desired motor braking torque T des =F r The actual braking torque T of the motor motor This information is obtained through a torque sensor.

[0044] S2: Calculate the difference between the desired motor braking torque and the actual motor braking torque to obtain the closed-loop PI-ILC control deviation of the closed-loop control loop.

[0045] In this embodiment of the invention, the control deviation of the proportional-integral closed-loop control is obtained according to the following formula 2:

[0046] e j+1 =T des -T motor Formula 2

[0047] Among them, e j+1 For closed-loop PI-ILC control deviation, T des To achieve the desired motor braking torque, Tmotor This represents the actual braking torque of the motor.

[0048] S3: Select the closed-loop PI-ILC control deviation calculated at the previous moment as the open-loop PI-ILC control deviation at the current moment.

[0049] In this embodiment of the invention, variable e is set. j+1 The data length m (m is used as the calibration value) is used when the system just allows regenerative braking and the braking intensity has not decreased significantly. j+1 Store it.

[0050] S4: Calculate the open-loop and closed-loop PI-ILC control variables of the open-loop and closed-loop PI-ILC control loops based on the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control variables, and continuously iterate and update the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control variables.

[0051] In this embodiment of the invention, the open-loop and closed-loop PI-ILC control variables are calculated according to the following formula 3:

[0052] U j+1 =U j +k p，o e j +k i，o ∫e j dt+k p，c e j+1 +k i，c ∫e j+1 Formula 3 for dt

[0053] Among them, U j+1 For open-loop and closed-loop PI-ILC control variables, U j U stored for the previous braking process j+1 k p，o k is the proportional coefficient for open-loop PI control. i，o Let k be the integral coefficient of the open-loop circuit. p，c k is the proportional coefficient of the closed-loop PI control. i，c e represents the integral coefficient of the closed loop. j e represents the control deviation of the open-loop PI-ILC at the previous moment. j+1 The current control deviation of the open-loop PI-ILC is given.

[0054] In this embodiment of the invention, variable U is set. j+1 The data length m, when the system just allows regenerative braking and the braking intensity has not decreased significantly, is used to... j+1 Store it.

[0055] In this embodiment of the invention, a preset number of iterations K is used until the number of iterations reaches the preset number of iterations K. Then, the iteration stops and the control variables of the control deviation and open-loop PI-ILC are no longer stored. Until the current vehicle power-off, the last stored sum is used, where K is a calibrable parameter.

[0056] S5: Preset storage time. When the energy recovery time is less than the storage time, open-loop PI-ILC control is applied; otherwise, conventional PI control is applied.

[0057] In this embodiment of the invention, the storage time is calculated based on the array length m after vehicle calibration and the data sampling period. During system operation, when the time for entering energy recovery exceeds the preset storage time, the system uses conventional PI control after that time, and calculates the conventional PI according to the following formula 4:

[0058] U Normal =k p e+k i Formula 4: ∫e dt

[0059] Among them, U Normal For a regular PI, k p k is the proportional coefficient for PI control. i is the integral coefficient, and e is the control deviation at the current time.

[0060] This also includes the addition of image stabilization;

[0061] In this embodiment of the invention, before the system reaches the equilibrium point, the system sometimes continuously adjusts the motor braking torque command. This constant adjustment of the motor's negative torque within a certain range causes vehicle vibration, thus requiring an additional vibration damping mechanism. Calibration parameters P1 and P2 are used to calculate the control variable U of the open-loop and closed-loop PI-ILC at the previous moment. j+1，k and the control variable U of the open-loop and closed-loop PI-ILC at the current moment. j+1，k+1 , when P1≤|U j+1，k+1 -U j+1，k When |≤P2, output the control variable U of the open-loop PI-ILC at the previous moment. j+1，k For the motor controller, when |U j+1，k+1 -U j+1，k |<P1 or|U j+1 -U j When |>P2, output the control variable U of the open-loop and closed-loop PI-ILC at the current time. j+1，k+1 For the motor controller.

[0062] In this embodiment of the invention, for U Normal The same image stabilization measures as described above.

[0063] It also includes the use of mechanical braking torque compensation;

[0064] In this embodiment of the invention, mechanical braking torque compensation is used when encountering motor failure or when the MCU does not respond to the braking torque command requested by the regenerative braking controller under special operating conditions.

[0065] Specifically, the system determines whether a motor malfunction has occurred or the MCU is not executing the motor braking torque command based on the motor braking torque command and the actual motor torque response. If a motor malfunction occurs or the MCU is not executing the motor braking torque command, mechanical braking torque is used to compensate for the expected motor braking torque. If no motor malfunction occurs or the MCU is not executing the motor braking torque command, a motor braking torque command is output to the motor controller.

[0066] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0067] In the embodiments provided in this application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another structure, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between structures or units, and may be electrical, mechanical, or other forms.

[0068] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Furthermore, in the embodiments of this application, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An iterative self-learning control method for motor braking torque in an energy recovery system, characterized in that, Specifically, the following steps are included: S1: Obtain the desired motor braking torque based on the regenerative braking force distribution strategy. and the actual braking torque of the motor ; S2: Calculate the desired motor braking torque and the actual braking torque of the motor The difference is used to obtain the closed-loop PI-ILC control deviation of the closed-loop control loop; S3: Select the closed-loop PI-ILC control deviation calculated at the previous moment as the open-loop PI-ILC control deviation at the current moment. S4: Calculate the open-loop and closed-loop PI-ILC control variables of the open-loop and closed-loop PI-ILC control loops based on the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control variables, and continuously iterate and update the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control variables. S5: Preset storage time. When the energy recovery time is less than the storage time, open-loop PI-ILC control is applied; otherwise, conventional PI control is applied. In step S1: Based on the regenerative braking force distribution strategy, the desired motor braking torque is obtained. and the actual braking torque of the motor Previously: The braking force between the front and rear axles was distributed according to the following formula 1: Official 1 in, For braking intensity, For the total braking force of the front wheels, For the total braking force of the rear wheels, The total weight of the car. The distance from the car's center of gravity to the front axle. This is the distance from the car's center of gravity to the rear axle. The height of the car's center of gravity; S2: Calculate the desired motor braking torque and the actual braking torque of the motor The difference is used to obtain the closed-loop PI-ILC control deviation of the closed-loop control loop, including: calculating the closed-loop PI-ILC control deviation according to the following formula 2: Official 2 in, This refers to the deviation of the closed-loop PI-ILC control. To achieve the desired motor braking torque, This represents the actual braking torque of the motor. The step of calculating the open-loop and closed-loop PI-ILC control variables based on the closed-loop PI-ILC control deviation and the open-loop PI-ILC control deviation includes: calculating the open-loop and closed-loop PI-ILC control variables according to the following formula 3: Official 3 in, For open-loop and closed-loop PI-ILC control variables. Stored for the previous braking process , The proportional gain for open-loop PI control. Let be the integral coefficient of the open-loop circuit. This is the proportional coefficient for closed-loop PI control. For the integral coefficients of the closed loop, The control deviation of the open-loop PI-ILC at the previous moment. The current control deviation of the open-loop PI-ILC is given.

2. The iterative self-learning control method for motor braking torque in an energy recovery system according to claim 1, characterized in that, The regenerative braking force distribution strategy includes: obtaining the maximum allowable braking torque of the motor at the current moment by looking up a table based on the motor's speed and torque. When the maximum allowable braking torque of the motor at the current moment At that time, the desired motor braking torque When the maximum allowable braking torque of the motor at the current moment At that time, the desired motor braking torque = The actual braking torque of the motor This information is obtained through a torque sensor.

3. The iterative self-learning control method for motor braking torque in an energy recovery system according to claim 1, characterized in that, The process of continuously iterating and updating the closed-loop PI-ILC control deviation and the open-loop and closed-loop PI-ILC control variables includes: a preset number of iterations K, until the number of iterations reaches the preset number of iterations K, at which point the iteration stops.

4. The iterative self-learning control method for motor braking torque in an energy recovery system according to claim 1, characterized in that, The application of conventional PI control includes: calculating the conventional PI according to the following formula 4: Official 4 in, For conventional PI, The proportional coefficient for PI control. The integral coefficient is... This represents the control deviation at the current moment.

5. The iterative self-learning control method for motor braking torque in an energy recovery system according to claim 1, characterized in that, The preset storage time includes: calculating the storage time based on the array length m after the actual vehicle calibration and the data sampling period.

6. The iterative self-learning control method for motor braking torque in an energy recovery system according to claim 1, characterized in that, It also includes image stabilization and calibration parameters. and Calculate the control variables of the open-loop and closed-loop PI-ILC at the previous moment. and the control variables of the open-loop and closed-loop PI-ILC at the current moment. ,when At that time, the control variables of the open-loop and closed-loop PI-ILC at the previous moment are output. For the motor controller, when or At that time, output the control variables of the open-loop and closed-loop PI-ILC at the current moment. For the motor controller.

7. The iterative self-learning control method for motor braking torque in an energy recovery system according to claim 1, characterized in that, When the motor fails or the MCU does not execute the motor braking torque command, mechanical braking torque compensation is used; otherwise, a motor braking torque command is sent.

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