A control method and device for a new energy vehicle fan motor

Abstract

The application discloses a control method and device for a new energy automobile fan motor, the method comprises the following steps: online self-adaptive adjustment is conducted on the parameters of an EKF algorithm according to a pre-constructed Sigmoid function; the adjusted EKF algorithm is applied to a permanent magnet synchronous motor position sensorless control system to adjust the motor speed and current; the application continuously adjusts the value of the AEKF algorithm parameters by taking the error speed as feedback, so that the rotor position is accurately identified, the feasibility of the algorithm is verified through simulation, the speed error is reduced by about 60% compared with a traditional control method, the control effect is better, the accuracy is higher, and the stability and robustness of the algorithm are verified on a fan experimental platform.

Description

TECHNICAL FIELD

[0001] The application relates to a control method and device for a new energy automobile fan motor and belongs to the technical field of motor control methods. BACKGROUND

[0002] Permanent magnet synchronous motors are gradually replacing traditional motors in vehicles due to their high power density, large torque, compact structure and other advantages. With the concept of carbon neutralization, especially the rise of new energy vehicles, the development of vehicle-grade PMSMs has been greatly promoted. Traditional new energy automobile fan motors obtain rotor angle information through position sensors. However, the position sensors are affected by the external environment, have low reliability and stability, and increase additional costs.

[0003] EKF control algorithm is a common sensorless control algorithm. EKF can not only work effectively in medium and high speed ranges, but also accurately identify rotor positions at low speeds. EKF can obtain accurate state estimation by correcting the predicted estimation through measurement values. Since the PMSM system is a strong coupling nonlinear control system, the estimation accuracy of EKF will be reduced due to the transformation of model parameters during estimation.

[0004] The accuracy of the EKF algorithm depends on the mathematical model of the motor. The motor body parameters and load torque have a great influence on the estimation accuracy of EKF. In actual application, the motor body parameters will change with the increase of temperature caused by the increase of speed, and the external load will also change with time. These transformed parameters will reduce the estimation efficiency and accuracy of EKF. SUMMARY

[0005] The purpose of the application is to overcome the shortcomings of the prior art and provide a control method and device for a new energy automobile fan motor, which solves the problem of the dependence of the traditional EKF algorithm on the motor model and improves the estimation accuracy of the EKF algorithm and the system stability.

[0006] To achieve the above purpose, the application adopts the following technical solutions:

[0007] In a first aspect, the application provides a control method and device for a new energy automobile fan motor, comprising:

[0008] The parameters of the EKF algorithm are adjusted online and adaptively according to the pre-constructed Sigmoid-like function.

[0009] Based on the adjusted EKF algorithm, the motor speed and current are adjusted in the permanent magnet synchronous motor position sensorless control system.

[0010] Further, the construction of the Sigmoid-like function includes:

[0011] Define the Sigmoid-like function υ(e(k)) and the function ρ(e(k)):

[0012]

[0013]

[0014] Wherein e(k) is the absolute value function of speed error, defined as: e(k) = |ω(k) - ω f |, ω f (k) is the value of given angular velocity, ω f is the value of EKF estimated angular velocity, e is the base of natural logarithm, α, β, γ, λ and μ are real numbers, and the adaptive function is constructed by the absolute value of speed error.

[0015] Further, the online adaptive adjustment of the parameters of the EKF algorithm according to the pre-constructed Sigmoid-like function includes:

[0016] In the EKF algorithm model, the matrix Q and R are set as four-order diagonal matrices, and the matrix R is a two-order diagonal matrix:

[0017]

[0018]

[0019] According to the characteristics of the Sigmoid-like function, the parameters of the EKF algorithm are adjusted online by setting Q3 = υ(e(k)) and R1 = R2 = ρ(e(k)), which satisfies the convergence characteristics in a certain range.

[0020] Further, the adjusted EKF algorithm is applied to the permanent magnet synchronous motor sensorless control system to adjust the motor speed and current, including:

[0021] The SPMSM control system simulation is built by using MATLAB / Simulink, and the adaptive EKF algorithm part is written by using S function;

[0022] The permanent magnet synchronous motor sensorless control system is composed of a current loop and a speed loop, the current loop is the inner loop, and the speed loop is the outer loop, both of which use PI control, ω f (k) is the given angular velocity value, ω α (k) is the angular velocity value estimated by the EKF algorithm, and the inputs of the EKF algorithm are u β , u α , i βThe output quantity is rotor position θ, and the motor speed and current are regulated through feedback control.

[0023] In a second aspect, the application provides a control device for a new energy automobile fan motor, comprising:

[0024] An EKF algorithm adjustment unit is configured to perform online adaptive adjustment on parameters of the EKF algorithm according to a pre-constructed Sigmoid-like function.

[0025] A control unit is configured to apply the adjusted EKF algorithm to a position sensorless control system of a permanent magnet synchronous motor to regulate the motor speed and current.

[0026] Further, the EKF algorithm adjustment unit comprises a Sigmoid-like function construction unit, which comprises:

[0027] A Sigmoid-like function υ(e(k)) and a function ρ(e(k)) are defined.

[0028]

[0029]

[0030] wherein e(k) is an absolute value function of a speed error, defined as: e(k) = |ω(k) - ω f |, ω(k) is a value of a given angular velocity, ω f is a value of an EKF estimated angular velocity, e is a base of a natural logarithm, and α, β, γ, λ and μ are real numbers, and an adaptive function is constructed through the absolute value of the speed error.

[0031] Further, the EKF algorithm adjustment unit comprises:

[0032] In the EKF algorithm model, the matrix Q and the matrix R are set as four-order diagonal matrices, and the matrix R is a two-order diagonal matrix.

[0033]

[0034]

[0035] According to the characteristics of the Sigmoid-like function, which satisfies the convergence within a certain range, Q3 = υ(e(k)) and R1 = R2 = ρ(e(k)) are set to perform online adaptive adjustment on the parameters of the EKF algorithm.

[0036] Further, the control unit comprises:

[0037] The SPMSM control system simulation is built by using MATLAB / Simulink, and the adaptive EKF algorithm part is written by using S function.

[0038] The permanent magnet synchronous motor sensorless control system is composed of a current loop and a speed loop, the current loop is an inner loop, the speed loop is an outer loop, the inner loop and the outer loop adopt PI control, ω(k) is a given angular velocity value, ω f is an angular velocity value estimated by the EKF algorithm, the input of the EKF algorithm is u α , u β , i α and i β , and the output is a rotor position θ, through feedback control, the motor speed and current are adjusted.

[0039] In a third aspect, the application provides a control device for a new energy automobile fan motor, comprising a processor and a storage medium;

[0040] The storage medium is used for storing instructions;

[0041] The processor is used for operating according to the instructions to perform the steps of the method according to any one of the above.

[0042] In a fourth aspect, the application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of the method according to any one of the above.

[0043] Compared with the prior art, the application has the following beneficial effects:

[0044] The application provides a control method and device for a new energy automobile fan motor, the value of the AEKF algorithm parameter is continuously adjusted by using the error speed as feedback, the rotor position is accurately identified, the feasibility of the algorithm is verified by simulation, the speed error is reduced by about 60% compared with the traditional control method, the control effect is better, the accuracy is higher, and the stability and robustness of the algorithm are verified on a fan experimental platform. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 is a control system block diagram provided by an embodiment of the application;

[0046] Figure 2 is a speed simulation result graph of the adaptive EKF algorithm provided by an embodiment of the application;

[0047] Figure 3 is a speed error simulation result graph of the adaptive EKF algorithm provided by an embodiment of the application;

[0048] Figure 4is a fan start waveform chart of the host computer test provided by the embodiment of the application;

[0049] Figure 5 is a rotor position observation result chart of the adaptive EKF algorithm under the first load environment provided by the embodiment of the application;

[0050] Figure 6 is a rotor position observation result chart of the adaptive EKF algorithm under the second load environment provided by the embodiment of the application;

[0051] Figure 7 is a rotor position observation result chart of the adaptive EKF algorithm under the third load environment provided by the embodiment of the application. DETAILED DESCRIPTION

[0052] The application will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the application, and cannot be used to limit the protection scope of the application.

[0053] Embodiment 1

[0054] The embodiment introduces a control method of a new energy automobile fan motor, comprising:

[0055] The parameters of the EKF algorithm are adjusted online according to the pre-constructed Sigmoid function;

[0056] Based on the adjusted EKF algorithm, the method is applied to a permanent magnet synchronous motor position sensorless control system to adjust the motor speed and current.

[0057] The application process of the control method of the new energy automobile fan motor provided by the embodiment specifically involves the following steps:

[0058] I. The Sigmoid function is a common S-shaped function, which has the properties of single increase and inverse function single increase, and is often used as an activation function of a neural network. The mathematical form of the Sigmoid function is:

[0059]

[0060] The Sigmoid function is continuous, smooth and strictly monotonic, and is a very good threshold function. In the EKF estimation model, the parameters of the system noise covariance matrix Q, the measurement noise covariance matrix R and the calculation error covariance matrix P have an influence on the estimation accuracy and estimation time of the algorithm. In combination with the advantages of continuous and monotonic convergence of the Sigmoid function, a Sigmoid-like function is defined, which can improve the EKF estimation accuracy and reduce the estimation jitter.

[0061] The class Sigmoid function υ(e(k)) and the function ρ(e(k)) are defined as:

[0062]

[0063]

[0064] where e(k) is the absolute value function of the speed error, defined as: e(k) = |ω(k) - ω f |, ω(k) is the value of the given angular velocity, ω f is the value of the EKF estimated angular velocity, e is the base of the natural logarithm, and α, β, γ, λ and μ are real numbers, which are adaptive functions constructed by the absolute value of the speed error.

[0065] In the EKF algorithm model, the matrix Q and R are set as four-order diagonal matrices, and the matrix R is a two-order diagonal matrix:

[0066]

[0067]

[0068] According to the characteristics of the class Sigmoid function, which satisfies the convergence within a certain range, let Q3 = υ(e(k)) and R1 = R2 = ρ(e(k)), and the parameters of the EKF algorithm are adjusted online.

[0069] Three, MATLAB / Simulink is used to build the SPMSM control system simulation, and the adaptive EKF algorithm part is written using S function. The permanent magnet synchronous motor sensorless control system is composed of current loop and speed loop, the current loop is the inner loop and the speed loop is the outer loop, both of which use PI control. ω(k) is the given angular velocity value, ω f is the estimated angular velocity value of the EKF algorithm, and the input of the EKF algorithm is u α , u β , i α and i β , and the output is the rotor position θ. Through feedback control, the motor speed and current are adjusted.

[0070] As Figure 2 and Figure 3 represent the estimated speed, actual speed and speed error of the model output when the given speed is 800 rad / s;

[0071] The adaptive EKF algorithm using the Sigmoid function maintains the speed error within ±2 rad / s when Q3=υ(e(k)), R1=R2=ρ(e(k)), which is about 60% smaller than the error of the traditional fixed parameter algorithm. Meanwhile, the error at the starting moment is controlled within 20 rad / s. Therefore, the adaptive EKF algorithm based on the Sigmoid function can better respond to the change of speed and more accurately track the rotor position.

[0072] Figure 4 The fan starting waveform diagram of the adaptive EKF algorithm using the Sigmoid function shows that the speed estimation accuracy of the algorithm is higher, and the estimation error is smaller. The fan speed error is about ±3 rad / s, Figure 5 、 Figure 6 、 Figure 7 The rotor angle information and phase current information of the fan under different load environments are obtained from Figures 5 to 7 It can be seen that the new EKF algorithm has good robustness to load changes and accurately estimates the rotor angle information under different load environments.

[0073] Under the working condition of the surface-mounted permanent magnet synchronous fan, the traditional EKF control algorithm is affected by its fixed parameters, and the rotor position recognition will produce a large error when the speed or load changes. An improved Sigmoid adaptive Kalman filter algorithm is proposed. This method is an adaptive control strategy that adjusts the value of the AEKF algorithm parameters through the error speed as feedback to accurately identify the rotor position. The simulation verifies the feasibility of the algorithm, which reduces the speed error by about 60% compared with the traditional control method, has better control effect and higher accuracy, and verifies the stability and robustness of the algorithm on the fan experimental platform.

[0074] Embodiment 2

[0075] The embodiment provides a control device of a new energy automobile fan motor, which comprises:

[0076] An EKF algorithm adjusting unit is configured to perform online adaptive adjustment on parameters of the EKF algorithm according to a pre-constructed Sigmoid function.

[0077] A control unit is configured to apply the adjusted EKF algorithm to a permanent magnet synchronous motor position sensorless control system to adjust the motor speed and current.

[0078] Further, the EKF algorithm adjusting unit comprises a Sigmoid function constructing unit, and the Sigmoid function constructing unit comprises:

[0079] Define the class Sigmoid function υ(e(k)) and function ρ(e(k)):

[0080]

[0081]

[0082] Where e(k) is the absolute value function of velocity error, defined as: e(k)=|ω(k)-ω f |,ω(k) is a given angular velocity value, ω f The angular velocity is estimated for EKF, where e is the base of the natural logarithm, and α, β, γ, λ, and μ are real numbers. An adaptive function is constructed using the absolute value of the velocity error.

[0083] Furthermore, the EKF algorithm adjustment unit includes:

[0084] In the EKF algorithm model, matrices Q and R are set as fourth-order diagonal matrices, and matrix R is a second-order diagonal matrix:

[0085]

[0086]

[0087] Based on the characteristics of the Sigmoid function, which satisfies the convergence property within a certain range, let Q3 = υ(e(k)) and R1 = R2 = ρ(e(k)) to adaptively adjust the parameters of the EKF algorithm online.

[0088] Furthermore, the control unit includes:

[0089] A simulation of an SPMSM control system was built using MATLAB / Simulink, and the adaptive EKF algorithm was written using S-functions.

[0090] The sensorless control system for the permanent magnet synchronous motor consists of a current loop and a speed loop. The current loop is the inner loop, and the speed loop is the outer loop. Both the inner and outer loops use PI control, where ω(k) is the given angular velocity value. f This is the angular velocity value estimated by the EKF algorithm. The input to the EKF algorithm is u. α u β i α and i β The output is the rotor position θ, which is used to adjust the motor speed and current through feedback control.

[0091] Example 3

[0092] This embodiment provides a control device for a fan motor in a new energy vehicle, including a processor and a storage medium;

[0093] The storage medium is configured to store instructions.

[0094] The processor is configured to operate according to the instructions to perform the steps of the method according to any one of embodiments 1.

[0095] Embodiment 4

[0096] The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any one of embodiments 1.

[0097] The above merely describes the preferred embodiments of the present application, and it should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present application, and these improvements and modifications should also be considered as falling within the scope of protection of the present application.

Claims

1. A control method of a new energy vehicle fan motor, characterized in that, Comprise: The parameters of the EKF algorithm are adjusted online and adaptively according to a pre-constructed Sigmoid function, comprising: Defining a class Sigmoid function and a function : ； ； wherein is the absolute value function of velocity error, defined as: , is the value of given angular velocity, is the value of EKF estimated angular velocity, e is the base of natural logarithm, 、 、 、 and are real numbers, and the adaptive function is constructed by the absolute value of velocity error; In the EKF algorithm model, the matrix Q and P are set as four-order diagonal matrices, and the matrix R is a two-order diagonal matrix: ； ； Based on the characteristics of sigmoid-like functions, satisfying the property of convergence within a certain range, let... , The parameters of the EKF algorithm are adaptively adjusted online. Based on the adjusted EKF algorithm, it is applied to the permanent magnet synchronous motor position sensorless control system to regulate the motor speed and current, comprising: The SPMSM control system simulation is built by MATLAB / Simulink, and the adaptive EKF algorithm is written by S function; The permanent magnet synchronous motor position sensorless control system is composed of a current loop and a speed loop, the current loop is an inner loop, and the speed loop is an outer loop, both the inner loop and the outer loop adopt PI control, is a given angular velocity value, is an angular velocity value estimated by an EKF algorithm, the input of the EKF algorithm is , , and , and the output is a rotor position , through feedback control, the motor speed and current are adjusted.

2. A control device of a new energy vehicle fan motor, characterized in that, Comprise: The EKF algorithm adjustment unit is used for adjusting the parameters of the EKF algorithm online and adaptively according to a pre-constructed Sigmoid function; The EKF algorithm adjustment unit comprises a Sigmoid function construction unit, which comprises: Defining a class Sigmoid function and function : ； ； wherein is the absolute value of the velocity error function defined as: , is the value of the given angular velocity, is the value of the EKF estimated angular velocity, e is the base of the natural logarithm, , , , and are real numbers, the adaptive function is constructed by the absolute value of the velocity error. The EKF algorithm adjustment unit comprises: In the EKF algorithm model, the matrix Q and P are set as four-order diagonal matrices, and the matrix R is a two-order diagonal matrix: ； ； According to the characteristics of the Sigmoid function, the convergence in a certain range is met, and , , the parameters of the EKF algorithm are adjusted online. The control unit is used for adjusting the motor speed and current based on the adjusted EKF algorithm, which is applied to the permanent magnet synchronous motor position sensorless control system, comprising: The SPMSM control system simulation is built by MATLAB / Simulink, and the adaptive EKF algorithm is written by S function; The permanent magnet synchronous motor position sensorless control system is composed of a current loop and a speed loop, the current loop is an inner loop, and the speed loop is an outer loop, both the inner loop and the outer loop adopt PI control, is a given angular velocity value, is an angular velocity value estimated by an EKF algorithm, the input of the EKF algorithm is , , and , and the output is a rotor position , through feedback control, the motor speed and current are adjusted.

3. A control device of a new energy vehicle fan motor, characterized in that: Comprise a processor and a storage medium; The storage medium is used for storing instructions; The processor is used for operating according to the instructions to execute the steps of the method according to claim 1.

4. A computer readable storage medium having stored thereon a computer program, characterized in that: The program is executed by the processor to realize the steps of the method of claim 1.

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