A pre-winding deviation correction control method based on fuzzy PID lithium ion battery high-speed winding machine

Abstract

The application discloses a kind of before winding rectification control methods of lithium ion battery high-speed winding machine based on fuzzy PID, comprising the following steps: the action principle of rectification control mechanism before winding, screw feed principle, sensor detection principle, AC servo motor principle are analyzed, and the equivalent transfer function of each link is obtained;According to the above analysis, the rectification control model before winding related to winding deviation and the input voltage of motor armature two ends is obtained;The winding deviation and offset change are obtained by edge position sensor;According to the simulation model of rectification control system model before winding and relevant environmental variables, the model is designed fuzzy PID control algorithm, the corresponding membership function is designed and given fuzzy control rule, to improve the rectification control speed and control precision before winding as target, so as to reach the optimization of rectification control effect before winding.The present application can effectively improve the adjustment speed of rectification before winding compared with traditional PID control, and guarantee the safety and stability of high-speed winding.

Description

Technical Field

[0001] This invention relates to the field of lithium battery production control, and in particular to a fuzzy PID-based method for front-winding correction control of high-speed lithium-ion battery winding machines. Background Technology

[0002] Lithium-ion batteries, as a green and environmentally friendly energy storage element, have advantages such as high operating voltage, wide operating temperature range, high specific energy, good cycle stability, and excellent comprehensive chemical performance. They are gradually replacing nickel-cadmium and nickel-metal hydride batteries and have become the primary choice for power batteries. They are widely used in electronic devices, office equipment, medical devices, and aerospace applications.

[0003] As people increasingly value a low-carbon future and sustainable development, the demand for lithium-ion batteries in electric vehicles, hybrid vehicles, and grid power supply is constantly increasing, leading to rapid growth in the lithium-ion battery industry.

[0004] With the increasing market demand for lithium-ion batteries, manufacturers are placing higher demands on the efficiency and quality of lithium battery production equipment. The cell winding process is a crucial step in lithium-ion battery production, and pre-winding correction is a particularly important step. If correction is not timely during winding, it increases the probability of short circuits between the positive and negative electrodes, leading to battery burnout. These problems are more likely to occur during high-speed winding. Therefore, it is necessary to study control methods to improve the timely response of pre-winding correction.

[0005] For the pre-winding correction control of lithium-ion battery winding machines, a standalone fuzzy controller has good stability and robustness. However, since it is a discrete rule control, it is prone to small-amplitude oscillations near the operating point, resulting in a large steady-state error of the controlled system.

[0006] While PID control alone has poor stability and robustness, its integral action can effectively eliminate the steady-state error of the system and has a good regulating effect in a small range near the operating point.

[0007] Therefore, designing a fast and high-precision front-roll correction control method is of great research and application value. Summary of the Invention

[0008] The purpose of this invention is to solve the problem of untimely response in the pre-winding correction control of high-speed lithium-ion battery winding machines, and to provide a pre-winding correction control method based on fuzzy PID for high-speed lithium-ion battery winding machines. This invention adjusts the motor in real time based on the real-time winding offset and its change, enabling fast and precise active control according to the magnitude of the offset.

[0009] This invention is achieved through the following technical solution:

[0010] A method for front-winding correction control of a high-speed lithium-ion battery winding machine based on fuzzy PID includes the following steps:

[0011] S1: Analyze the operating principle, lead screw feeding principle, sensor detection principle, and AC servo motor principle of the front winding correction control mechanism, and obtain the equivalent transfer function of each link;

[0012] S2: Obtain the winding offset and the input voltage at both ends of the motor armature related to the winding correction control model;

[0013] S3: Obtain the offset and offset change through the edge position sensor;

[0014] S4: Design a fuzzy PID controller for the model. Given a fuzzy subset and fuzzy control rules, design a membership function to improve the speed and accuracy of the pre-roll correction control, thereby optimizing the pre-roll correction control effect.

[0015] S5: Build a simulation model based on the fuzzy PID controller designed in the model.

[0016] Step S1 involves obtaining the equivalent transfer functions of each component of the pre-coil correction control, including: the motion model of the pre-coil correction mechanism.

[0017] The winding offset and the correction amount of the correction mechanism are expressed by equation (1):

[0018]

[0019] x d This is the offset of the winding.

[0020] x o The amount of correction by the correction agency;

[0021] V1 is the winding speed;

[0022] L1 is the distance between the guide roller and the correction roller;

[0023] L2 is the distance between the guide roller and the sensor.

[0024] Step S1 involves obtaining the equivalent transfer functions of each stage of the pre-coil correction control, including: the leadscrew feed model:

[0025] The motor output angle and the correction amount of the correction mechanism are expressed by equation (2):

[0026]

[0027] P h For the lead screw;

[0028] θ mThis refers to the motor's output rotation angle.

[0029] Step S1 involves obtaining the equivalent transfer functions of each stage of the pre-coil correction control, including: sensor detection model:

[0030] The winding offset and the input voltage at both ends of the motor armature are expressed by equation (3):

[0031]

[0032] k j This represents the sensor amplification factor.

[0033] Step S1 involves obtaining the equivalent transfer functions of each stage of the pre-coil correction control, including: AC servo motor model:

[0034] The motor output angle and the input voltage across the motor armature are expressed by equation (4):

[0035]

[0036] K i This is the motor rotation coefficient;

[0037] J em This is the equivalent rotational inertia of the motor shaft.

[0038] B em The coefficient of viscous friction of the motor shaft;

[0039] R a Armature resistance;

[0040] L a It is armature inductance;

[0041] K b It is the back electromotive force coefficient.

[0042] The pre-roll correction control model in step S2 is as follows:

[0043]

[0044] In step S3, the offset and offset change are obtained by the edge position sensor as the real-time measured winding offset.

[0045] In step S4, the fuzzy PID control design method is as follows:

[0046] It adopts a "dual-input, three-output" mode, selecting the winding offset E and the winding offset change EC as input control variables, and based on E and EC and the three PID control parameters K... P K i K d Fuzzy relationship, output parameter K P K iK d Increment ΔK P ΔK i ΔK d To the PID controller; the PID controller determines the initial parameter K. P ′、K i ′、K d and parameter increment ΔK P ΔK i ΔK d Obtain real-time parameter K P K i K d The calculation formula is as follows:

[0047] K P =K P ′+ΔK P

[0048] K i =K i ′+ΔK i

[0049] K d =K d ′+ΔK d

[0050] The control system then derives the control quantity u, which in turn changes the voltage of the front-end correction device, thereby adjusting the correction amount.

[0051] In step S5, the basic universe of discourse for the winding offset E is defined as [-2, 2], the basic universe of discourse for the offset change EC is defined as [-0.5, 0.5], the fuzzy universe of discourse for the control and output quantities is defined as [-6, 6], and the quantization levels for both input parameters and the output are divided into [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]. The preset fuzzy subsets are: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, which are abbreviated as {NB, NM, NS, ZO, PS, PM, PB}.

[0052] Compared with the prior art, the present invention has the following advantages and effects:

[0053] This invention employs a fuzzy adaptive PID control algorithm and establishes a simulation model of fuzzy PID control combined with pre-winding correction in a high-speed lithium-ion battery winding device. Compared with traditional PID control, the fuzzy adaptive PID control method demonstrates faster adjustment speed and higher control accuracy, proving that this invention has significant advantages in ensuring the high efficiency and stability of the high-speed lithium-ion battery winding device. Attached Figure Description

[0054] Figure 1 This is a flowchart of the present invention;

[0055] Figure 2 This is a schematic diagram of the front-roll correction mechanism of the present invention;

[0056] Figure 3 This is a schematic diagram of the fuzzy PID structure of the present invention;

[0057] Figure 4 This is a schematic diagram of the membership function curves of E and EC in this invention;

[0058] Figure 5 For the present invention, ΔK P ΔK i ΔK d A schematic diagram of the membership function curve;

[0059] Figure 6 This invention provides a fuzzy PID control and a simulation model for PID control.

[0060] Figure 7 This is a schematic diagram of the simulation curve of the present invention.

[0061] Figure 2 Explanation of reference numerals in the attached diagram: 1. Central axis; 2. Servo motor; 3. Connecting rod; 4. Slider; 5. Guide rail; 6. Lower correction roller; 7. Upper correction roller; 8. Cylinder; 9. Third connecting plate; 10. Second connecting plate; 11. First connecting plate; 12. Slider seat; 13. Fixing plate; 14. Ball screw; X direction indicated by the arrow in the diagram. Detailed Implementation

[0062] The present invention will now be described in further detail with reference to specific embodiments.

[0063] This invention discloses a method for front-winding correction control of a high-speed lithium-ion battery winding machine based on fuzzy PID, such as... Figure 1 As shown, it includes the following steps:

[0064] Step S1: Analyze the operating principle, lead screw feeding principle, sensor detection principle, and AC servo motor principle of the winding front correction control mechanism, and obtain the equivalent transfer function of each link.

[0065] For the pre-winding correction control of a high-speed lithium-ion battery winding machine, which exhibits nonlinear dynamic characteristics, the change in winding offset during operation is relatively small, so the system dynamics can be modeled using a linearization method. Therefore, this invention simplifies the mathematical model of each part of the pre-winding correction control system using reasonable assumptions, and represents the simplified linear model in the form of a transfer function. The simplified mathematical model is described as follows:

[0066] (1) Action model of the front correction mechanism

[0067] like Figure 2 The pre-winding correction mechanism shown in the figure operates by using the driving torque of a servo motor to rotate a ball screw. The ball screw pair converts the rotational motion into linear motion between the slider and the slider seat. The fixed plate is connected to the slider seat via a connecting rod. The slider seat is mounted on the slider, causing it to move along the guide rail in the X direction. The slider seat is connected to the upper and lower correction rollers via connecting plates 1, 2, and 3, thereby driving the upper and lower correction rollers to perform lateral correction. Simultaneously, the cylinder pushes the lower and upper correction rollers to clamp the electrode sheet, coordinating with the movement of the slider seat to complete the lateral correction of the electrode sheet. Therefore, the winding offset and the correction amount of the correction mechanism can be expressed by equation (1):

[0068]

[0069] Where: x i x is the offset of the winding. o V1 is the correction amount of the correction mechanism; L1 is the winding speed; L1 is the distance between the guide roller and the correction roller; L2 is the distance between the guide roller and the sensor.

[0070] (2) Lead screw feed model

[0071] Based on the screw feed principle, the relationship between the motor output angle and the correction amount of the correction mechanism can be obtained, and its transfer function is shown in equation (2):

[0072]

[0073] Where: P h For the lead screw, θ m This refers to the motor's output rotation angle.

[0074] (3) Sensor detection model

[0075] The transfer function of the sensor detection model is shown in equation (3):

[0076]

[0077] Where: k j This represents the sensor amplification factor.

[0078] (4) AC servo motor model

[0079] The transfer function of the AC servo motor is shown in equation (4):

[0080]

[0081] Where: K i J is the motor rotation coefficient. em B is the equivalent rotational inertia of the motor shaft. em R is the coefficient of viscous friction of the motor shaft. a Armature resistance, La It is armature inductance, K b It is the back electromotive force coefficient.

[0082] Step S2: Based on the above analysis, the transfer function of the pre-roll correction control is obtained as shown in equation (5):

[0083]

[0084] Step S3: Obtain the offset and offset change through the edge position sensor. The obtained value is the real-time measured winding offset.

[0085] Step S4: Design a fuzzy PID controller for the model. Given a fuzzy subset and fuzzy control rules, design a membership function to improve the speed and accuracy of the pre-roll correction control, thereby optimizing the pre-roll correction control effect.

[0086] Figure 3 The diagram illustrates a fuzzy PID controller, employing a "dual-input, three-output" mode. The winding offset E and the change in winding offset EC are selected as the input control variables. The controller operates based on E and EC in conjunction with the three PID control parameters K. P K i K d Fuzzy relationship, output parameter K P K i K d Increment ΔK P ΔK i ΔK d The PID controller then operates based on the initial parameter K. P ′、K i ′、K d and parameter increment ΔK P ΔK i ΔK d Obtain real-time parameter K P K i K d The calculation formula is as follows:

[0087] K P =K P ′+ΔK P

[0088] K i =K i ′+ΔK i

[0089] K d =K d ′+ΔK d

[0090] The control system then derives the control quantity u, which in turn changes the voltage of the front-end correction device, thereby adjusting the correction amount.

[0091] Step S5: Build a simulation model based on the model and the designed fuzzy PID controller.

[0092] The fundamental universe of discourse for the winding offset E is defined as [-2, 2], the fundamental universe of discourse for the offset change EC is defined as [-0.5, 0.5], and the fuzzy universe of discourse for the control and output quantities is defined as [-6, 6]. The quantization levels for both input parameters and the output are divided into [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]. The preset fuzzy subsets are: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, ZO, PS, PM, PB}. Establish K... P K i K d The fuzzy control rule table is shown below.

[0093]

[0094]

[0095]

[0096] Common fuzzy membership functions include triangular, Gaussian, and trapezoidal forms. In this study, E and EC use a membership function combining triangular and Gaussian forms, as shown below. Figure 4 As shown, ΔK P ΔK i ΔK d The membership function is a triangle, such as Figure 5 As shown.

[0097] Establish as Figure 6 The simulation system model shown can be run after the simulation model is built to obtain a comparison of the effects of fuzzy PID control and PID control on the front-roll correction control using the technology of this invention.

[0098] Simulation results are as follows Figure 7 As shown in the simulation results, in the control system, the fuzzy PID control algorithm reduces the overshoot of the system compared with the PID control algorithm in terms of front-roll correction, thereby improving the control speed of the system.

[0099] As described above, the present invention can be implemented well.

[0100] The implementation of the present invention is not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for pre-winding correction control of a high-speed lithium-ion battery winding machine based on fuzzy PID, characterized in that, Includes the following steps: S1: Analyze the operating principle, lead screw feeding principle, sensor detection principle, and AC servo motor principle of the front winding correction control mechanism, and obtain the equivalent transfer function of each link; S2: Obtain the winding offset and the input voltage at both ends of the motor armature related to the winding correction control model; S3: Obtain the offset and offset change through the edge position sensor; S4: Design a fuzzy PID controller for the model. Given a fuzzy subset and fuzzy control rules, design a membership function to improve the speed and accuracy of the pre-roll correction control, thereby optimizing the pre-roll correction control effect. S5: Build a simulation model based on the fuzzy PID controller model and design; Step S1 involves obtaining the equivalent transfer functions of each component of the pre-coil correction control, including: the motion model of the pre-coil correction mechanism. The winding offset and the correction amount of the correction mechanism are expressed by equation (1): x i This is the offset of the winding. x o The amount of correction by the correction agency; V1 is the winding speed; L1 is the distance between the guide roller and the correction roller; L2 is the distance between the guide roller and the sensor; S represents the conversion from the time domain to frequency.

2. The method for pre-winding correction control of high-speed lithium-ion battery winding machines based on fuzzy PID as described in claim 1, characterized in that, Step S1 involves obtaining the equivalent transfer functions of each stage of the pre-coil correction control, including: the leadscrew feed model: The motor output angle and the correction amount of the correction mechanism are expressed by equation (2): P h For the lead screw; θ m This refers to the motor's output rotation angle.

3. The method for pre-winding correction control of high-speed lithium-ion battery winding machines based on fuzzy PID as described in claim 1, characterized in that, Step S1 involves obtaining the equivalent transfer functions of each stage of the pre-coil correction control, including: sensor detection model: The winding offset and the input voltage at both ends of the motor armature are expressed by equation (3): k j This refers to the sensor amplification factor; E a This is the input voltage of the motor.

4. The method for pre-winding correction control of high-speed lithium-ion battery winding machines based on fuzzy PID as described in claim 1, characterized in that, Step S1 involves obtaining the equivalent transfer functions of each stage of the pre-coil correction control, including: AC servo motor model: The motor output angle and the input voltage across the motor armature are expressed by equation (4): K i This is the motor rotation coefficient; J em This is the equivalent rotational inertia of the motor shaft. B em The coefficient of viscous friction of the motor shaft; R a Armature resistance; L a It is armature inductance; K b It is the back electromotive force coefficient; θ m This refers to the motor's output rotation angle. E a This is the input voltage of the motor.

5. The method for pre-winding correction control of high-speed lithium-ion battery winding machines based on fuzzy PID as described in claim 1, characterized in that, The pre-roll correction control model in step S2 is as follows: x i This is the offset of the winding. x o The amount of correction by the correction agency; V1 is the winding speed; L1 is the distance between the guide roller and the correction roller; L2 is the distance between the guide roller and the sensor; P h For the lead screw; K i This is the motor rotation coefficient; J em This is the equivalent rotational inertia of the motor shaft. B em The coefficient of viscous friction of the motor shaft; R a Armature resistance; L a It is armature inductance; K b It is the back electromotive force coefficient; θ m This refers to the motor's output rotation angle. E a This is the input voltage of the motor.

6. The method for pre-winding correction control of high-speed lithium-ion battery winding machines based on fuzzy PID as described in claim 1, characterized in that, In step S3, the offset and offset change are obtained by the edge position sensor as the real-time measured winding offset.

7. The method for pre-winding correction control of a high-speed lithium-ion battery winding machine based on fuzzy PID as described in claim 1, characterized in that, In step S4, the fuzzy PID control design method is as follows: It adopts a "dual-input, three-output" mode, selecting the winding offset E and the winding offset change EC as input control variables, and based on E and EC and the three PID control parameters K... P K i K d Fuzzy relationship, output parameter K P K i K d Increment ΔK P ΔK i ΔK d To the PID controller; the PID controller determines the initial parameter K. P '、K i '、K d 'and parameter increment ΔK P ΔK i ΔK d Obtain real-time parameter K P K i K d The calculation formula is as follows: K P =K P '+ΔK P K i =K i '+ΔK i K d =K d '+ΔK d The control system then derives the control quantity u, which in turn changes the voltage of the front-end correction device, thereby adjusting the correction amount.

8. The method for pre-winding correction control of high-speed lithium-ion battery winding machines based on fuzzy PID as described in claim 1, characterized in that, In step S5, the basic universe of discourse for the winding offset E is defined as [-2, 2], the basic universe of discourse for the offset change EC is defined as [-0.5, 0.5], the fuzzy universe of discourse for the control and output quantities is defined as [-6, 6], and the quantization levels for both input parameters and the output are divided into [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]. The preset fuzzy subsets are: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, which are abbreviated as {NB, NM, NS, ZO, PS, PM, PB}.

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