Junction temperature-guided traction converter life optimization control method, system and device

Abstract

The present invention provides a junction temperature-guided traction converter life optimization control method, system and device, including S1, using a dual-vector model to predict torque control to track the traction motor stator reference flux and reference torque; S2, adding a dynamic loss factor and a flux control factor g to the cost function of the set corresponding vector according to the state at the next moment k+1. m , magnetic linkage control factor g m Based on operating conditions, S3 selects the optimal dual vector that minimizes the cost function and uses it as the output for the next moment. S4 calculates the power loss of the power devices and uses Miner's theorem to predict the degree of damage to the power devices over different cycles to determine the actual lifespan of the traction converter's power devices. While ensuring good control performance for torque and flux linkage, the two voltage vectors that produce the lowest losses are applied to the next sampling cycle whenever possible, thereby reducing the losses and junction temperature of the six IGBTs / Diodes in the three-phase, two-level inverter.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimization control of key execution components of train operation, and specifically relates to a junction temperature-guided traction converter life optimization control method, system and device. Background Art

[0002] Traction inverters are considered the "heart" of rail transit trains, providing strong power for train operation. However, as the key actuator for the inverter to achieve electrical energy conversion, the insulated gate bipolar transistor (IGBT) module is significantly affected by variable operating conditions and is relatively fragile, posing a significant challenge to the safe operation of trains. Studies have shown that the junction temperature variation amplitude and average junction temperature largely determine the lifespan of the IGBT module. Specifically, the IGBT lifespan is related to the number of temperature cycles under different junction temperature variation amplitudes and average junction temperatures. Therefore, the IGBT lifespan can be expressed by the junction temperature fluctuation value and average junction temperature. Therefore, reducing the average junction temperature and junction temperature fluctuation value of the IGBT is of great value in improving the lifespan of the IGBT module, and thus the lifespan of the traction inverter. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a junction temperature-guided traction converter life optimization control method, system and device to solve at least one of the above problems existing in the prior art.

[0004] Based on the above objectives, one or more embodiments of the present application provide a junction temperature-guided traction converter life optimization control method, which includes the following steps:

[0005] S1. Using a dual-vector model to predict traction motor torque control to track stator reference flux and reference torque; when selecting two vectors, first select a first voltage vector based on the dual-vector model to predict traction motor torque control, and then select a second voltage vector from eight basic voltage vectors; when selecting the second voltage vector, select the voltage vector that is switched at most once compared to the first voltage vector as the second voltage vector;

[0006] S2. According to the state of the next moment k+1, add the dynamic loss factor to the cost function of the corresponding vector to reduce the power device loss and the flux control factor g m To eliminate the voltage vector that makes the flux error larger, the flux control factor g m According to the working conditions, the dynamic loss factor changes as follows:

[0007] Determine whether there is loss in the power devices of the traction converter at the corresponding moment; if there is loss, add the corresponding loss factor to the cost function; if there is no loss, the cost function only needs to include the terms of torque and flux linkage control;

[0008] Magnetic flux control factor g m The changing conditions are:

[0009] When the flux deviation exceeds the set value at the next moment, the corresponding voltage vector is eliminated;

[0010] S3. Calculate the action time of each vector in each sampling period based on the torque deadbeat principle to obtain a composite vector. Based on the composite vector, re-predict the torque and flux linkage values ​​at the next moment. Through iterative cycles, select the optimal dual vector that minimizes the cost function and use the optimal dual vector as the output at the next moment.

[0011] S4. Calculate the power loss of the power devices and use the Foster thermal network model to calculate the junction temperature based on the obtained power loss data. Use the rain flow algorithm to calculate the average junction temperature and junction temperature fluctuation of the power devices within a working cycle. Then use Miner's theorem to predict the degree of damage to the power devices in different cycles to obtain the actual life of the traction converter power devices and adjust the traction converter output accordingly.

[0012] Based on the above technical solution of the present invention, the following improvements can also be made:

[0013] Optionally, when determining whether there is loss in the power device of the traction converter at the corresponding moment in step S2, the loss includes conduction loss and switching loss.

[0014] Optionally, the product of the change in current and voltage during switching is added to the cost function as a factor replacing the switching loss to reduce the switching loss, and the square of the instantaneous value of the current is added to the cost function as a factor replacing the conduction loss to reduce the conduction loss.

[0015] Optionally, step S2 further includes adjusting the flux weight coefficient in the cost function, and using the torque change rate and the flux change rate to replace the torque error and the flux error, and performing dynamic adjustment based on the junction temperature reduction effect of the power device.

[0016] Optionally, the cost function is defined as g, as follows:

[0017]

[0018]

[0019] Among them, g1, g2, and g3 represent the power device loss cost functions of phases A, B, and C respectively, and g m is the magnetic linkage control factor, and denote the reference torque and reference flux respectively, ψ s (k+1) is the traction motor flux at time k+1, Te (k+1) is the traction motor torque at time k+1.

[0020] Optionally, the candidate range of the second voltage vector includes three basic voltage vectors and one zero vector, and the number of iteration cycles is reduced to four.

[0021] Optionally, in step S3, after selecting the optimal dual vector that minimizes the cost function, the optimal dual vector is used as the output at the next moment after comparing the duty cycle with the triangular carrier.

[0022] Optionally, in step S4, the step of using the rain flow algorithm to calculate the average junction temperature and junction temperature fluctuation within the working cycle includes: setting a large working cycle to include N small cycles, and the junction temperature difference corresponding to each small cycle is ΔT jn , calculate the temperature difference of each junction temperature ΔT through the power device life prediction model jn The corresponding number of failure cycles N fn , at this time, the life consumption ratio of the power device in one working cycle is R, and the reciprocal of the life consumption ratio R of the power device in one working cycle is the maximum number of working cycles that the power device can withstand. The actual life of the power device is obtained by multiplying the working cycle time by the maximum number of working cycles it can withstand:

[0023]

[0024] According to a second aspect of the present invention, a junction temperature-guided traction converter life optimization control system is provided, which adopts any one of the above-mentioned junction temperature-guided traction converter life optimization control methods.

[0025] According to a third aspect of the present invention, a junction temperature-guided traction converter life optimization control device is provided, which is characterized in that it includes a storage unit and a processing unit, the storage unit is used to store a computer program, and the processing unit is used to execute the steps of any one of the above-mentioned junction temperature-guided traction converter life optimization control methods through the computer program stored in the storage unit.

[0026] The beneficial effect of the present invention is that the present invention provides a junction temperature-guided traction converter life optimization control method, system and device. In order to improve the life of the two-level traction inverter, with the reduction of the average junction temperature and junction temperature fluctuation of the IGBT / diode as the guide, a dual-vector model predictive torque control method is adopted. By optimizing the alternative vector range of the second voltage vector and dynamically adding the loss factor to the cost function, the IGBT / diode junction temperature and junction temperature fluctuation value are reduced as much as possible while ensuring the traction motor torque and flux performance, the life of the six IGBT / diodes is improved, and thus the life of the traction inverter is improved. Finally, the rain flow counting method and the life prediction model are used to calculate the inverter life under this method. Under the premise of ensuring good control performance such as torque and flux, the two voltage vectors that generate low loss can be applied to the next sampling cycle as much as possible, thereby achieving the purpose of reducing the junction temperature of the six IGBT / diodes of the three-phase two-level inverter. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of an alternative range of a second voltage vector of a junction temperature-guided traction converter life optimization control method, system, and device according to an embodiment of the present invention.

[0028] Figure 2 This is a DVMPTC-II control block diagram of a junction temperature-guided traction converter life optimization control method, system, and device according to an embodiment of the present invention.

[0029] Figure 3 A DVMPTC-II flow chart of a junction temperature-guided traction converter life optimization control method, system, and device according to an embodiment of the present invention.

[0030] Figure 4 This is a schematic diagram of power device switching loss generation in a junction temperature-guided traction converter life optimization control method, system, and apparatus according to an embodiment of the present invention.

[0031] Figure 5 Schematic diagram of a Foster thermal network model of a junction temperature-guided traction converter life optimization control method, system and device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0033] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in one or more embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0034] like Figure 1 As shown, one or more embodiments of the present application provide a junction temperature-guided traction converter life optimization control method, the method comprising the following steps:

[0035] S1. Considering that the single-vector MPTC only acts on one voltage vector in one sampling cycle, the control performance will deteriorate sharply for the high-power, low-switching-frequency rail transit traction system. Therefore, a dual-vector model is used to predict torque control. While ensuring good traction control performance, methods are studied to reduce the junction temperature of power devices in the traction converter, thereby improving the life of the traction converter.

[0036] Specifically, a dual-vector model is used to predict traction motor torque control to track the stator reference flux and reference torque. When selecting the two vectors, the first voltage vector is first selected based on the dual-vector model, and then the second voltage vector is selected from eight basic voltage vectors. When selecting the second voltage vector, the voltage vector that switches at most once compared to the first voltage vector is selected as the second voltage vector. First, for a three-phase two-level inverter, since both voltage vectors of the DVMPTC-I are selected from eight basic voltage vectors, to ensure effective torque and flux control, the second voltage vector switches more frequently than the first voltage vector, resulting in increased IGBT switching losses within a sampling cycle, significantly increasing the IGBT junction temperature, and having unpredictable consequences for the inverter life. If the second voltage vector is selected only within the two valid vectors adjacent to the first voltage vector and the zero vector, the second voltage vector switches only once compared to the first voltage vector, which helps reduce power device switching losses and computational complexity. Based on this, this embodiment considers that the second voltage vector may not switch compared to the first voltage vector from the perspective of reducing switching losses, that is, the selection of the second voltage vector can be based on switching at most once compared to the first voltage vector, that is, the second voltage vector does not switch or switches only once compared to the first voltage vector. In this case, the selection range of the second voltage vector is reduced from 8 voltage vectors to 4 voltage vectors, including three basic voltage vectors and 1 zero vector. The number of iterative cycles for selecting the second voltage vector is also reduced to 4 times. Compared with the traditional DVMPTC, the calculation amount and the switching loss of the power device are reduced. The alternative range of the second voltage vector is as follows: Figure 1 shown.

[0037] S2. According to the state of the next moment k+1, add the dynamic loss factor to the cost function of the corresponding vector to reduce the power device loss and the flux control factor g m To eliminate the voltage vector that makes the flux error larger, the flux control factor g m is a dynamic change value, and the dynamic loss factor change condition is:

[0038] Determine whether there is loss in the power devices of the traction converter at the corresponding moment; if there is loss, add the corresponding loss factor to the cost function; if there is no loss, the cost function only needs to include the terms of torque and flux linkage control;

[0039] Magnetic flux control factor g m The changing conditions are:

[0040] When the flux deviation at the next moment is greater than the set value, the corresponding voltage vector is eliminated;

[0041] It is understandable that since the selection range of the second voltage vector at this time is 4 basic voltage vectors, and these 4 voltage vectors can only reduce switching losses, it is still impossible to guarantee that the selected vector is optimal in reducing losses while satisfying the magnetic flux and torque control effects. Therefore, the control of the total loss of the IGBT module (the sum of switching loss and conduction loss) is considered in the cost function of both vectors.

[0042] The product of the change in current and voltage during turn-on and turn-off is added to the cost function as a factor to replace the switching loss to reduce the switching loss:

[0043]

[0044] Since the conduction loss can be expressed as:

[0045]

[0046] Therefore, when the on-state resistance of the IGBT / Diode is 0, it can be seen that the conduction loss is related to the voltage drop across the collector and emitter when conducting and the instantaneous value of the current flowing through the IGBT / Diode. It can be converted into the product of an assumed resistance and the square of the instantaneous value of the current, as shown in the following formula:

[0047]

[0048] It can be seen that the conduction loss is proportional to the square of the instantaneous value of the current. As a factor to replace the conduction loss, a cost function is added to reduce the IGBT / Diode conduction loss. The cost function at this time is as follows:

[0049]

[0050] Among them, g1, g2, and g3 represent the power device loss cost functions of phases A, B, and C, respectively.

[0051] Furthermore, since the operating speed range of the traction motor is relatively large, if the weight coefficient of the magnetic flux in the cost function remains unchanged, it will be difficult to balance the torque and magnetic flux control effects when running in the full speed range. Dynamically adjusting the weight coefficient according to the speed range will make the debugging process more complicated, so it is necessary to use a method without a weight coefficient to eliminate the magnetic flux weight coefficient. The dimensions of magnetic flux and torque are different, so in this embodiment, the torque change rate and magnetic flux change rate are used instead of the torque error and magnetic flux error. In order to make the torque and magnetic flux have a better following effect than the reference torque and reference magnetic flux, the torque change rate and magnetic flux change rate are squared. In addition, in order to ensure the magnetic flux performance of the traction motor in the high-speed range, g is added to the cost function. mThe cost function is shown as follows:

[0052]

[0053]

[0054] In MPTC (hereinafter referred to as DVMPTC-II), since the main goal is torque and flux control, it is necessary to determine whether conduction loss and switching loss exist at the corresponding moment when adding the loss reduction factor to the cost function. If there is loss, the corresponding loss factor is added to the cost function. If no loss occurs, the cost function only needs to include the terms of torque and flux control. It should be noted that when the loss factor is directly added to the cost function without judgment, since the loss factor also has a corresponding weight, the loss will be considered at the moment when the loss does not need to be reduced, thereby reducing the torque and flux control effect. Because the loss cost functions of the three phases are consistent in meaning, when the A phase switching signal S a When the value changes between 0 and 1, the description is as follows:

[0055] ①i a >0 o'clock:

[0056] aS a From 1 to 0: IGBT1 switching loss + Diode4 conduction loss, at this time:

[0057]

[0058] s a From 0 to 1: IGBT1 switching loss + IGBT1 conduction loss + Diode4 switching loss, at this time:

[0059]

[0060] cS a From 0 to 0: Diode4 conduction loss, at this time:

[0061]

[0062] dS a From 1 to 1: IGBT1 conduction loss; at this time:

[0063]

[0064] ②i a <0 o'clock

[0065] aS aFrom 1 to 0: IGBT4 switching loss + IGBT4 conduction loss + Diode1 switching loss, at this time:

[0066]

[0067] s a From 0 to 1: IGBT4 switching loss + Diode1 conduction loss, at this time:

[0068]

[0069] cS a From 0 to 0: IGBT4 conduction loss, at this time:

[0070]

[0071] dS a From 1 to 1: Diode1 conduction loss; at this time:

[0072]

[0073] Among them, Δi a It represents the transient change of phase A current when the switch is in operation, Δv represents the voltage change when the switch is in operation, i a Indicates the current of phase A; λ I1 Represents the IGBT switching loss weight coefficient, λ I2 Represents the IGBT conduction loss weight coefficient, λ D1 Represents the reverse recovery loss weight coefficient of the anti-parallel diode, λ D2 Represents the conduction loss weight coefficient of the anti-parallel diode. Their function is to adjust the importance of the terms with weight coefficients and other control objectives without weight coefficients, or the weight in the entire cost function. The weight coefficients should be dynamically adjusted based on the actual effect of reducing the junction temperature of the six IGBTs / Diodes in the three-phase two-level inverter. Among them, the conclusions for phases B and C are the same as those for phase A. The control principle block diagram is shown in Figure 2 As shown in the flow chart, Figure 3 shown.

[0074] S3. Calculate the action time of each vector in each sampling period based on the torque deadbeat principle to obtain a composite vector. Based on the composite vector, re-predict the torque and flux linkage values ​​at the next moment. Through iterative cycles, select the optimal dual vector that minimizes the cost function and use the optimal dual vector as the output at the next moment.

[0075] Specifically, in this embodiment, the dual-vector MPTC acts on two voltage vectors within one sampling period. First, the first voltage vector u is selected according to the principle of selecting voltage vectors in traditional MPTC. opt1, then select the second voltage vector u from the eight basic voltage vectors opt2 Then, according to the torque deadbeat principle, the action time of each vector in each sampling period is calculated to obtain the synthetic vector. Then, based on the synthetic vector, the torque and flux values ​​at the next moment are re-predicted. Through iterative cycles, the optimal dual vector that minimizes the cost function is selected. Finally, the dual vector is used as the output at the next moment by comparing the duty cycle with the triangular carrier.

[0076] According to the asynchronous motor flux equation and voltage equation, the torque change rate when two voltage vectors act can be obtained:

[0077]

[0078]

[0079] According to the torque deadbeat principle, the torque at the next moment is controlled to reach a given value. Combining the above two equations, we can get:

[0080]

[0081] So the action time of the first voltage vector t can be solved opt1 As follows:

[0082]

[0083] According to the volt-second balance principle, the synthetic vector acting in the next sampling period can be obtained as follows:

[0084] u s (k)=(t opt1 ·u opt1 +(T s -t opt1 )·u j ) / T s

[0085] Finally, the calculated synthetic voltage vector re-predicts the torque and flux at the next moment, and the vector with the minimum cost function is used as the second voltage vector, i.e. u opt2 Output.

[0086] S4. Calculate the power loss of power devices and calculate the junction temperature using the Foster thermal network model. Use the rain flow algorithm to calculate the average junction temperature and junction temperature fluctuation of power devices within a working cycle. Then, use Miner's theorem to predict the damage degree of power devices in different cycles to obtain the actual lifespan of traction converter power devices and adjust the traction converter output accordingly.

[0087] Specifically, the power loss of IGBT is mainly divided into two parts: switching loss and conduction loss. The calculation is based on the table lookup method. During the turn-on and turn-off process of the power device IGBT, the voltage and current waveforms overlap in a very short period of time, which will cause switching loss. Figure 4 As shown in Figure 2, the loss of the anti-parallel diode during the turn-on process is very small and can generally be ignored, but reverse recovery loss will occur during the turn-off process. Due to the initial saturation voltage drop and on-resistance, the power device will generate conduction loss during the conduction process. The switching loss and conduction loss calculations are shown in the following two equations:

[0088] Switching losses:

[0089] Conduction loss: P cond =v ce (t)(v f (t))×i c (t)(i f (t));

[0090] Among them, P on / off represents switching loss; t on / off Indicates the switching time; v ce Indicates the IGBT collector-emitter instantaneous voltage; i c Indicates the instantaneous current of the IGBT collector; P cond Represents conduction loss; v f Indicates the voltage drop across the diode when it is conducting; i f Represents the instantaneous current flowing through the diode;

[0091] The junction temperature of the power device is calculated based on its loss and its internal impedance thermal network model. The Foster thermal network model used in the embodiment is as follows: Figure 5 As shown, the thermal impedance expression is:

[0092]

[0093] Among them, Z th(j-c) Represents the equivalent thermal impedance of the power device from junction to case; R i represents thermal resistance; τ i The time constant is the product of thermal resistance and thermal capacity. The losses of the IGBT and its anti-parallel diode are calculated. The power loss of the traction inverter is used to calculate the junction temperature T j . The power loss is fed into Figure 5 The Foster thermal network model shown in the figure is used to obtain the IGBT / diode junction temperature. j Indicates the junction temperature of the power device; P loss Represents power loss; Z j-c Represents the equivalent thermal impedance from junction to case; Zc-h Represents the equivalent thermal impedance from the shell to the heat sink; Z h-a It represents the equivalent thermal impedance from the heat sink to the environment; T a Indicates the ambient temperature.

[0094] T j =P loss (Z j-c +Z c-h +Z h-a )+T a .

[0095] Furthermore, the continuous action of the alternating electrical, thermal, and mechanical stress fields in the IGBT module is the primary cause of IGBT module failure. From the perspective of the electric field, an increase in junction temperature will increase the on-state voltage drop and leakage current, thereby increasing the total loss. From the perspective of the stress field, the thermal stress between the various layers of the IGBT material will increase with increasing junction temperature, causing cracks and deformation in the material, leading to damage to the IGBT module. For every 10°C increase in the junction temperature of the IGBT module, the failure rate will double. This embodiment uses the Lesit model to predict the life of the IGBT module. The model expression is as follows:

[0096]

[0097] Among them, N f Indicates the number of cycles the power device will take to fail when the junction temperature fluctuation is the current value; ΔT j Indicates the junction temperature fluctuation range of the power device, that is, the junction temperature fluctuation; T m Represents the average junction temperature of the power device; m and n are two constants related to the internal material characteristics and properties of the power device, which can reflect the cyclic fatigue degree of the device and the strain capacity of its material. Their approximate values ​​are generally obtained through cyclic experiments and numerical simulation methods; E a Indicates the activation energy of the material inside the power device. Specific materials should be treated specifically. For the failure mechanism of a certain material, when the temperature is greater than 500K, E a It changes with temperature. When the temperature is less than 500K, E a = represents a constant; k represents the air constant, which is generally taken as 8.314 J / mol / K. Since the IGBT module used in this paper is produced by Infineon and has a power level of 3300V / 1500A, m=10 is set in this embodiment. 15 , E a =-440, n=5.16.

[0098] When the train is actually running, the junction temperature variation of the power devices in the traction converter is complex, and it is not easy to extract the accurate average junction temperature and junction temperature fluctuation parameters. Therefore, it is necessary to use the cycle counting method to calculate the ΔT under each power cycle from the simulation data. j and T m , and then calculate the IGBT damage degree in each power cycle.

[0099] In this embodiment, the rain flow algorithm is used to calculate the average junction temperature and junction temperature fluctuation within a working cycle.

[0100] Specifically, after calculating the maximum junction temperature and junction temperature fluctuation within a working cycle, the Miner theorem is used to predict the degree of damage within different cycles. Assume that a large working cycle includes N small cycles, and the junction temperature difference corresponding to each small cycle is ΔT jn , then the IGBT life model can be used to calculate the lifespan of each ΔT jn The corresponding number of failure cycles N fn , the IGBT life consumption ratio R within one working cycle can be calculated. And because the reciprocal of R is the maximum number of working cycles that the IGBT can withstand, the actual life of the IGBT can be obtained by multiplying the working cycle time and the maximum number of working cycles that can be tolerated:

[0101]

[0102] The IGBT / diode in the embodiment is an example of a power device, and based on the actual life of the IGBT obtained, the actual life of the traction inverter can be known, and then this data can be used as the basis for output control to adjust the output so that it can play the maximum role within the life range.

[0103] In another embodiment, a junction temperature-guided traction converter life optimization control system is provided, which adopts any one of the above-mentioned junction temperature-guided traction converter life optimization control methods.

[0104] In another embodiment, a junction temperature-guided traction converter life optimization control device is provided, which is characterized in that it includes a storage unit and a processing unit, the storage unit is used to store a computer program, and the processing unit is used to execute the steps of any one of the above-mentioned junction temperature-guided traction converter life optimization control methods through the computer program stored in the storage unit.

[0105] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0107] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0109] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0110] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A junction temperature-guided traction converter life optimization control method, characterized by: It includes the following steps: S1. Using a dual-vector model predictive torque control to track the stator reference flux and reference torque of the traction motor; when selecting two vectors, first select a first voltage vector based on the dual-vector model predictive torque control, and then select a second voltage vector from eight basic voltage vectors; when selecting the second voltage vector, the voltage vector that switches at most once compared to the first voltage vector is selected as the second voltage vector; S2, according to the state of the next moment k+1, add the dynamic loss factor to the cost function of the corresponding vector to reduce the power device loss and add the flux control factor g m To eliminate the voltage vector that makes the flux error larger, the flux control factor g m It changes according to the working conditions, among which the changing conditions of the dynamic loss factor are: Determine whether there is loss in the power devices of the traction converter at the corresponding moment; if there is loss, add the corresponding loss factor to the cost function; if there is no loss, the cost function only needs to include the terms of torque and flux linkage control; The cost function is defined as g, as follows: Among them, g1, g2, and g3 represent the power device loss cost functions of phases A, B, and C respectively, and g m is the magnetic linkage control factor, and denote the reference torque and reference flux respectively, ψ s (k+1) is the traction motor flux at time k+1, T e (k+1) is the traction motor torque at time k+1; Magnetic flux control factor g m The changing conditions are: If the flux deviation at the next moment exceeds the set value, the voltage vector will be eliminated; S3. Calculate the action time of each vector in each sampling period based on the torque deadbeat principle to obtain a composite vector. Based on the composite vector, re-predict the torque and flux linkage values ​​at the next moment. Through iterative cycles, select the optimal dual vector that minimizes the cost function and use the optimal dual vector as the output at the next moment. S4. Calculate the power loss of the power devices and use the Foster thermal network model to calculate the junction temperature based on the obtained power loss data. Use the rain flow algorithm to calculate the average junction temperature and junction temperature fluctuation of the power devices within a working cycle. Then use Miner's theorem to predict the degree of damage to the power devices in different cycles to obtain the actual life of the traction converter power devices and adjust the traction converter output accordingly.

2. The junction temperature-guided traction converter life optimization control method according to claim 1, characterized in that: When determining whether there is loss in the power device of the traction converter at the corresponding moment in step S2, the loss includes conduction loss and switching loss.

3. The junction temperature-guided traction converter life optimization control method according to claim 2, characterized in that: The product of the change in current and voltage during switching on and off is added to the cost function as a factor to replace the switching loss to reduce the switching loss, and the square of the instantaneous current is added to the cost function as a factor to replace the conduction loss to reduce the conduction loss.

4. The junction temperature-guided traction converter life optimization control method according to claim 3, characterized in that: The step S2 further includes adjusting the flux weight coefficient in the cost function, and using the torque change rate and the flux change rate to replace the torque error and the flux error, and performing dynamic adjustment based on the junction temperature reduction effect of the power device.

5. The junction temperature-guided traction converter life optimization control method according to claim 1, wherein: The candidate range of the second voltage vector includes three basic voltage vectors and one zero vector, and the number of iteration cycles is reduced to four.

6. The junction temperature-guided traction converter life optimization control method according to claim 1, wherein: In step S3, after the optimal dual vector that minimizes the cost function is selected, the optimal dual vector is compared with the triangular carrier through the duty cycle and is used as the output at the next moment.

7. The junction temperature-guided traction converter life optimization control method according to claim 1, wherein: In step S4, the step of using the rain flow algorithm to calculate the average junction temperature and junction temperature fluctuation within the working cycle includes: setting a large working cycle to include N small cycles, and the junction temperature difference corresponding to each small cycle is ΔT jn , calculate the temperature difference of each junction temperature ΔT through the power device life prediction model jn The corresponding number of failure cycles N fn , at this time, the life consumption ratio of the power device in one working cycle is R, and the reciprocal of the life consumption ratio R of the power device in one working cycle is the maximum number of working cycles that the power device can withstand. The actual life of the power device is obtained by multiplying the working cycle time by the maximum number of working cycles it can withstand:

8. A junction temperature-guided traction converter life optimization control system, characterized by: It adopts the junction temperature-guided traction converter life optimization control method described in any one of claims 1 to 7.

9. A junction temperature-guided traction converter life optimization control device, characterized in that: The invention comprises a storage unit and a processing unit, wherein the storage unit is used to store a computer program, and the processing unit is used to execute the steps of the junction temperature-guided traction converter life optimization control method according to any one of claims 1 to 7 through the computer program stored in the storage unit.

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