CN110147578B - IGBT device service life prediction method based on semi-physical simulation platform - Google Patents
IGBT device service life prediction method based on semi-physical simulation platform Download PDFInfo
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Abstract
The invention belongs to the technical field of power electronic devices for rail transit, and particularly relates to a service life prediction method of an IGBT device based on a semi-physical simulation platform, which comprises the following steps: acquiring electrical parameters of the IGBT device through a semi-physical simulation platform by utilizing a real TCU of the locomotive/motor car; calculating the loss value of the IGBT device according to the electrical parameters; acquiring a crust temperature change curve of the IGBT device according to the loss value by using an electric heating network model; performing rain flow counting statistics on the incrustation temperature load of the IGBT device according to a rain flow algorithm; and calculating the service life of the IGBT device by using a damage accumulation model of the IGBT device. The life prediction method provides current and voltage changes closer to actual working conditions by using the real TCU of the motor car/locomotive, is equivalent to the accuracy of field data acquisition, and provides a theoretical basis for determining the replacement time of the IGBT device for rail transit and realizing the whole life cycle management of the IGBT device.
Description
Technical Field
The invention belongs to the technical field of power electronic devices for rail transit, relates to a service life prediction method of an IGBT device, and particularly relates to a service life prediction method of the IGBT device based on a semi-physical simulation platform.
Background
Insulated Gate Bipolar Transistor (IGBT for short) is used as a power switch device, has many advantages of large current-carrying density, reduced saturation voltage, and the like, and is widely applied in the fields of rail transit, smart grid, industrial control, electric vehicles, and the like. The IGBT device has a severe working environment, such as a large current intensity, a high voltage level, and a high switching frequency during operation, which are all prone to cause fatigue aging of the device, and thus, the reliability of the device is hidden. Through long-term research and working experience, about 55% of failure reasons are that the junction temperature of the device is increased due to higher voltage, current and switching frequency when the device works; the aging of the device is accelerated by the increase of the junction temperature, and when the junction temperature reaches a certain height, the fatigue degree of the device presents a geometric progression increasing trend, so that the device fails. The mechanical stress failure of the IGBT device is caused by the fact that different materials bear different thermal stresses due to the difference of thermal expansion coefficients of materials of all layers of the device, and the thermal fatigue failure of the device is caused after long-term accumulation, and finally the overheating failure of the device is caused.
Currently, some major power semiconductor companies develop module-based power consumption and junction temperature simulation software, such as english-flying IPOSIM, smith-control seiel, mitsubishi-MELCOSIM, and fushi simulation software, in order to help users to know the system power consumption and the highest junction temperature in the design process, optimize a heat dissipation system, and select the most appropriate device. However, the temperature time history is complex and randomly changed, real-time monitoring is difficult, and the software cannot simulate junction temperature changes under the condition of working condition change. Therefore, related software such as Matlab/Simulink, PLECS and the like is gradually used for simulation under different operating conditions, and the converter is in rectification and inversion operating modes, so that the junction temperature/shell temperature of the IGBT device is extracted. Some researchers collect voltage and current data of the IGBT device on site for extracting the incrustation temperature.
However, the IGBT crust temperature obtained by the simulation of the power consumption and junction temperature simulation software of the module is different from real data to some extent, and the accuracy is not high. Real-time field data are collected, IGBT junction temperature is calculated in real time through a Matlab/Simulink electric heating model, locomotive real-time data need to be collected on site, the practicability is low, and the investment cost is high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a service life prediction method of an IGBT device based on a semi-physical simulation platform, which provides a theoretical basis for determining the replacement time of the IGBT device for rail transit and realizing the full-life cycle management of the IGBT device.
In order to achieve the purpose, the invention provides the following technical scheme:
the method for predicting the service life of the IGBT device based on the semi-physical simulation platform specifically comprises the following steps:
1) Acquiring electrical parameters of the IGBT device through a semi-physical simulation platform by utilizing a real TCU of the locomotive/motor car;
2) Calculating a power loss value of the IGBT device according to the electrical parameters;
3) Acquiring a crust temperature change curve of the IGBT device according to the loss value by using an electric heating network model;
4) Carrying out rain flow counting statistics on the incrustation temperature load of the IGBT device according to a rain flow algorithm;
5) And calculating the service life of the IGBT device by using a damage accumulation model of the IGBT device.
Further, the step 1) of obtaining the electrical parameters of the IGBT device through the semi-physical simulation platform by using the real TCU of the locomotive/motor car specifically includes the following steps:
1.1 The simulation of the whole working condition of the locomotive, namely starting, accelerating, uniform speed and braking, is finished by utilizing the real TCU of the locomotive/motor car through a semi-physical simulation platform;
1.2 The data acquisition of the electrical parameters of the IGBT devices of the four-quadrant power module and the inversion power module is completed by using a high-resolution wave recorder.
Further, the step 1.1) of utilizing the real TCU of the locomotive/motor car to complete the whole working condition simulation of starting, accelerating, uniform speed and braking of the locomotive through a semi-physical simulation platform specifically comprises the following steps:
1.1.1 Connecting the semi-physical simulation platform with the real TCU of the bullet train to perform real-time simulation test;
1.1.2 Simulating the working operation condition of the locomotive through a semi-physical simulation platform to simulate the traction state and the braking state;
1.1.3 The power consumption of the locomotive load is set, the four-quadrant module load comprises a traction motor and an auxiliary power module, and the inversion module load only comprises the traction motor, so that the whole working condition simulation of starting, accelerating, uniform speed and braking is completed.
Further, in the step 1.2), a high-resolution wave recorder is used to complete data acquisition of electrical parameters of the IGBT device of the four-quadrant power module/inverter power module, and the method specifically includes the following steps:
1.2.1 In the semi-physical operation process, a high-resolution wave recorder is used for respectively completing the acquisition of eight signals including a four-quadrant pulse signal, a four-quadrant input current, a four-quadrant IGBT current, an inversion IGBT pulse, an inversion current, a direct-current bus voltage, an inversion IGBT current and a motor rotating speed;
1.2.2 Read the waveform of the eight collected signals and convert it into an excel data table.
Further, the power loss value of the IGBT device in step 2) includes power losses of the IGBT and the FRD chip, and the specific calculation steps are as follows:
2.1 The turn-on loss of the IGBT device is calculated using an average loss method based on the switching frequency period: in the negative half cycle of the current, the IGBT is turned off, the FRD operates, and assuming that in the negative half cycle, there are N switching pulse cycles (wave head number), and there are k calculation points in each pulse cycle, and the conduction losses of each pulse cycle are superposed, and can be obtained:
in the formula (1), P cond_Tr Average conduction loss, V, of IGBT chip cesat (T j ,I C(tj) ) Is the saturation voltage drop when the jth sampling point in the ith output period of the IGBT is conducted, I C The current value of the j sampling point is; in the formula (2), P cond_D Is the average conduction loss, V, of the FRD chip F (T k ,I D(tk) ) Is the saturation voltage drop when the kth sampling point in the s output period of the FRD chip is conducted, I D The current value of the j sampling point is;
2.2 The switching losses of the IGBT devices are calculated using equations (3), (4) where,
in the formula (3), P sw,Tr For switching losses of IGBT chips, E on For IGBT turn-on energy, E off For IGBT turn-off energy, ic (i-1) Current value at time i-1, ic (i) Current value at time i, V dc Is an input voltage value, V nom Is a nominal voltage value; in the formula (4), P sw,D Switching losses for FRD chips, E rec,s Turning off energy for the FRD chip;
2.3 Using the formulas (5) and (6) to calculate the total IGBT loss and the total FRD loss in the switching period;
P IGBT ＝P cond_Tr +P sw_Tr (5)
P Diode ＝P cond_D +P sw_D (6)
2.4 The total loss value of the IGBT device during the switching period is calculated using equation (7).
P General assembly ＝P Diode +P IGBT (7)
Further, the step 3) of obtaining a junction temperature change curve of the IGBT device according to the loss value by using an electric heating network model specifically includes the following steps:
3.1 According to the IGBT turn-on and turn-off curves provided in the specification of the IGBT device, converting the IGBT turn-on and turn-off curves into a device parameter database;
3.2 Using a loss calculation model based on a mathematical method, and using a table look-up method to calculate real-time loss corresponding to real-time voltage and current data;
3.3 And) carrying out modeling calculation by using Matlab to obtain the actual junction temperature and shell temperature change of the IGBT device in the working process by adopting a calculation method of continuous working loss and instantaneous junction temperature.
Further, the electric heating network model is an RC (resistance-capacitance) heat network model based on Foster and Cauer.
Further, the step 4) performs rain flow counting statistics on the incrustation temperature of the IGBT device according to a rain flow algorithm, and specifically includes the following steps:
4.1 Dividing temperature fluctuation appearing in the incrustation temperature change curve into a plurality of equal-difference temperature amplitude levels delta T;
4.2 ) the corresponding cycle number num and average temperature T in each temperature variation amplitude level are counted m ；
4.3 The statistical result of the temperature load change is calculated.
Further, in the step 4.3), the statistical result of the temperature load variation includes temperature variation amplitude, number, average temperature and crusting temperature load spectrum.
Further, the step 5) of calculating the lifetime of the IGBT device by using the damage accumulation model of the IGBT device specifically includes the following steps:
5.1 Substituting the incrustation temperature load spectrum obtained in the step 4.3) into formulas (8) and (9) to obtain the power cycle times and the temperature cycle times under different temperature loads;
in the formulae (8) and (9), N f,j The number of power cycles under junction temperature fluctuation, N f,c The temperature cycle times under the shell temperature fluctuation condition are shown, A, B, alpha and beta are constants for data fitting, K is a Boltzmann constant, and K =1.380 × 10 -23 J/K，ΔT j For variation of junction temperature, Δ T c For shell temperature change, E a Excitation energy of silicon chip, E a ＝9.89×10 -20 J，T jm To average junction temperature, T cm Average shell temperature;
5.2 According to the annual consumption times of the PC in the station and the influence of the day cycles caused by the daily environmental temperature introduced in the ABB life manual on the life, the life damage rate of the PC is calculated by using life damage formulas (10) to (12);
in the formula (10), N PC,station For PC cycle number in station cycle, D PC,station The station cycle is the temperature fluctuation damage rate;
in the formula (11), N PC,daily The number of PC cycles in daily cycle, D PC,daily In order to reduce the temperature fluctuation damage rate in daily circulation,
to average junction temperature, T a Is ambient temperature;
D PC ＝D Pc,station +D Pc,daily (12)
in formula (12), D PC Is the rate of damage caused by junction temperature load changes;
5.3 Based on the station TC annual consumption times, based on the influence of day cycles on the lifetime caused by the daily ambient temperature introduced in the ABB lifetime manual, the TC lifetime damage rate is calculated using the lifetime damage equations (13) to (15), wherein,
in formula (13), N TC,station Is the number of TC cycles in the station cycle, D TC,station The damage rate of shell temperature fluctuation in station circulation is determined;
in the formula (14), N TC,daily Is a daily cycleNumber of TC cycles in the ring, D TC,daily The damage rate of the shell temperature fluctuation in daily circulation,
is the average shell temperature, T a Is ambient temperature;
D Tc ＝D Tc,station +D Tc,daily (15)
in the formula (15), D TC The damage rate caused by the change of the shell temperature load;
5.4 Calculating the total damage rate D of the IGBT device;
in the formula (16), the day cycle is the daily cycle number, the station cycle number, the D total damage rate of the IGBT, and the N PC,station Is the number of PC cycles in the station cycle, N PC,daily Is the number of PC cycles in daily cycle, N TC,station Is the number of TC cycles, N, in the station cycle TC,daily Is the TC cycle number in the daily cycle;
5.5 The lifetime of the IGBT device is calculated.
IGBT lifetime =1/D (17)
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects: the real TCU of the motor car/locomotive is utilized, the electric parameters such as the current, the voltage, the trigger pulse and the like of the IGBT device are obtained through the semi-physical simulation platform, the current and voltage changes which are closer to the actual working condition are provided, and the accuracy which is equivalent to the accuracy of field data acquisition is achieved; calculating the loss value of the IGBT device by using MATLAB programming and obtaining a crusting temperature change curve by building an electric heating network model; and introducing a rain flow algorithm to carry out rain flow counting statistics on the crust temperature load, calculating the service life of the IGBT device by using an IGBT device damage accumulation model, and completing analysis calculation of the service life of the IGBT device for rail transit. The service life prediction method provides a theoretical basis for determining the replacement time of the IGBT device for rail transit and realizing the full-life cycle management of the IGBT device.
Drawings
FIG. 1 is a schematic flow chart of a life prediction method of an IGBT device for track traffic based on a simulation platform, provided by the invention;
FIG. 2 is a diagram of a semi-physical simulation acquisition waveform provided by the present invention;
FIG. 3 is a waveform diagram of a switching pulse period within an output frequency period according to the average loss method based on the switching frequency period provided by the present invention;
FIG. 4 is a schematic diagram of an electrothermal model of an IGBT device provided by the present invention;
FIG. 5 is a semi-physical simulation waveform of the new eight-axle locomotive provided by the present invention;
fig. 6 (a) and 6 (b) are a junction temperature load change graph and a shell temperature load change graph obtained by rain flow algorithm processing provided by the present invention, respectively.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods consistent with certain aspects of the invention, as detailed in the appended claims.
In order to make those skilled in the art better understand the technical solution of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings and examples.
Example 1
Referring to fig. 1, the invention provides a method for predicting the service life of an IGBT device based on a semi-physical simulation platform, which specifically includes the following steps:
1) Acquiring electrical parameters of the IGBT device through a semi-physical simulation platform by utilizing a real TCU of the locomotive/motor car;
2) Calculating the loss value of the IGBT device according to the electrical parameters;
3) Acquiring a crusting temperature change curve of the IGBT device according to the loss value by using an electric heating network model;
4) Performing rain flow counting statistics on the incrustation temperature load of the IGBT device according to a rain flow algorithm;
5) And calculating the service life of the IGBT device by using a damage accumulation model of the IGBT device.
Further, the step 1) of acquiring the electrical parameters of the IGBT device through the semi-physical simulation platform by using the real TCU of the locomotive/motor car in combination with the semi-physical simulation acquisition oscillogram shown in fig. 2 specifically includes the following steps:
1.1 The method comprises the following steps of) utilizing a real TCU of the locomotive/motor car to complete the whole working condition simulation of starting, accelerating, uniform speed and braking of the locomotive through a semi-physical simulation platform, and specifically comprising the following steps of:
1.1.1 Connecting the semi-physical simulation platform with a real TCU of the bullet train to perform real-time simulation test;
1.1.2 Simulating the working operation condition of the locomotive through a semi-physical simulation platform to simulate a traction state and a braking state;
1.1.3 According to the power consumption of the locomotive load, the four-quadrant module load comprises a traction motor and an auxiliary power module, and the inversion module load only comprises the traction motor, so that the whole working condition simulation of starting, accelerating, uniform speed and braking is completed;
1.2 The method comprises the following steps of) completing data acquisition of electrical parameters of IGBT devices of a four-quadrant power module and an inversion power module by using a high-resolution wave recorder, and specifically comprising the following steps of:
1.2.1 A high-resolution wave recorder is used for respectively completing the acquisition of eight paths of signals including a four-quadrant pulse signal, a four-quadrant input current, a four-quadrant IGBT current, an inversion IGBT pulse, an inversion current, a direct-current bus voltage, an inversion IGBT current and a motor rotating speed in a semi-physical operation process;
1.2.2 The waveforms of the acquired eight-way signals are read by using Xviewer software and converted into an excel data sheet.
Further, the electrical parameters in step 1.2) include numerical information such as current, voltage, trigger pulse, and motor speed.
The semi-physical simulation platform simulates a controlled object by using a mathematical model, is connected with a real controller unit (TCU), and performs real-time simulation test. The semi-physical simulation platform of the traction system consists of four groups of traction cabinets with the same configuration and a group of dynamic cabinets. The traction cabinet comprises a HiGale simulator, an adaptive box, a conditioning box, a photoelectric conversion box, a high-speed data acquisition box and the like, and is mainly used for controlling and operating a real-time simulation model of a main loop of a pantograph-catenary current collector, a transformer, a rectifier, an inverter, a traction motor and the like.
Further, the loss value of the IGBT device in step 2) is calculated by an average loss method based on the switching frequency period, and a schematic diagram thereof is shown in fig. 3, which specifically includes the following steps:
2.1 The turn-on loss of the IGBT device is calculated using an average loss method based on the switching frequency period: in the negative half cycle of the current, the IGBT is turned off, the FRD operates, and assuming that in the negative half cycle, there are N switching pulse cycles (wave head number), and there are k calculation points in each pulse cycle, and the conduction losses of each pulse cycle are superposed, and can be obtained:
in the formula (1), P cond_Tr Average conduction loss, V, of IGBT chip cesat (T j ,I C(tj) ) Is the saturation voltage drop when the jth sampling point in the ith output period of the IGBT is conducted, I C The current value of the j sampling point is; in formula (2), P cond_D Is the average conduction loss, V, of the FRD chip F (T k ,I D(tk) ) Is the saturation voltage drop when the kth sampling point in the s output period of the FRD chip is conducted, I D The current value of the j sampling point is;
2.2 The switching losses of the IGBT devices are calculated using the equations (3), (4) where,
in formula (3), P sw,Tr For switching losses of IGBT chips, E on For IGBT turn-on energy, E off For IGBT turn-off energy, ic (i-1) The current value at time i-1, ic (i) Current value at time i, V dc Is the value of an input voltage, V nom Is a nominal voltage value; in formula (4), P sw,D Switching losses for FRD chips, E rec,s Turning off energy for the FRD chip;
2.3 Calculating the total IGBT loss and the total FRD loss in the switching period by using the formulas (5) and (6);
P IGBT ＝P cond_Tr +P sw_Tr (5)
P Diode ＝P cond_D +P sw_D (6)
2.4 The total loss value of the IGBT device during the switching period is calculated using equation (7).
P General assembly ＝P Diode +P IGBT (7)
Further, step 3) obtains a junction temperature change curve of the IGBT device according to the loss value by using an electrothermal network model, and specifically includes the following steps:
3.1 According to the IGBT turn-on and turn-off curves provided in the specification of the IGBT device, converting the IGBT turn-on and turn-off curves into a device parameter database;
3.2 Using a loss calculation model based on a mathematical method, and using a table look-up method to calculate real-time loss corresponding to real-time voltage and current data;
3.3 By adopting a continuous working loss and instantaneous junction temperature calculation method, and utilizing Matlab to carry out modeling calculation to obtain the actual junction temperature and shell temperature change of the IGBT device in the working process.
Furthermore, the electric heating network model is an RC heat network model based on Foster and Cauer, is centralized and equivalent to the actual heat transfer process of the IGBT device, is easily realized in simulation software in a circuit network form, and is a junction temperature calculation method of the IGBT device commonly used in engineering. The Foster model is formed by connecting RC loops formed by connecting thermal resistance R and heat capacity C in parallel in series, and the water-cooling radiator adopts a Cauer RC heat network model (formed by the thermal resistance R and the heat capacity C) based on a real physical layer for heat dissipation. Power consumption data are input into a Foster thermal network model of the device through a controllable current source and then are transmitted to a Cauer thermal network model of a radiator, output voltage can be analogized to output temperature, a temperature reference point is water inlet temperature, accordingly, an electric heating model of the IGBT device for five vehicles is built by Matlab/Simulink software to realize electric heating combined simulation, and the model is shown in figure 4.
Further, step 4) carries out rain flow counting statistics on the incrustation temperature of the IGBT device according to a rain flow algorithm, and specifically comprises the following steps:
4.1 Dividing temperature fluctuation appearing in the incrustation temperature change curve into a plurality of temperature amplitude levels delta T with equal difference;
4.2 ) the corresponding cycle number num and average temperature T in each temperature variation amplitude level are counted m ；
4.3 Calculating to obtain a statistical result of the temperature load change; wherein, the statistical result of the temperature load change comprises the temperature change amplitude, the number, the average temperature and the crusting temperature load spectrum.
Further, step 5) calculates the lifetime of the IGBT device using the damage accumulation model of the IGBT device, and specifically includes the following steps:
5.1 Substituting the incrustation temperature load spectrum obtained in the step 4.3) into formulas (8) and (9) to obtain the power cycle times and the temperature cycle times under different temperature loads;
in the formulae (8) and (9), N f,j The number of power cycles under junction temperature fluctuation, N f,c The temperature cycle times under the shell temperature fluctuation condition are shown, A, B, alpha and beta are constants for data fitting, K is a Boltzmann constant, and K =1.380 × 10 -23 J/K，ΔT j Delta T for junction temperature variation c For shell temperature change, E a Excitation energy of silicon chip, E a ＝9.89×10 -20 J，T jm To average junction temperature, T cm Is the average shell temperature;
5.2 According to the annual consumption times of the PC in the station and the influence of the day cycles caused by the daily environmental temperature introduced in the ABB life manual on the life, the life damage rate of the PC is calculated by using life damage formulas (10) to (12);
in the formula (10), N PC,station For PC cycle number in station cycle, D PC,station The station cycle is the temperature fluctuation damage rate;
in the formula (11), N PC,daily The number of PC cycles in daily cycle, D PC,daily In order to reduce the temperature fluctuation damage rate in daily circulation,
to average junction temperature, T a Is ambient temperature;
D PC ＝D Pc,station +D Pc,daily (12)
5.3 Based on the station TC annual consumption times, based on the influence of day cycles on the lifetime caused by the daily ambient temperature introduced in the ABB lifetime manual, the TC lifetime damage rate is calculated using the lifetime damage equations (13) to (15), wherein,
in formula (13), N TC,station Is the number of TC cycles in the station cycle, D TC , station The shell temperature fluctuation damage rate in station circulation is shown;
in formula (14), N TC,daily The number of TC cycles in daily cycle, D TC,daily The damage rate of the shell temperature fluctuation in daily circulation,
is the average shell temperature, T a Is ambient temperature;
D Tc ＝D Tc,station +D Tc,daily (15)
5.4 Calculating the total damage rate D of the IGBT device;
in the formula (16), the day cycle is the daily cycle number, the station cycle number, the D total damage rate of the IGBT, and the N PC,station Is the number of PC cycles in the station cycle, N PC,daily PC cycle number in daily cycle, N TC,station Is the number of TC cycles, N, in the station cycle TC,daily Is the number of TC cycles in the daily cycle;
5.5 The lifetime of the IGBT device is calculated.
IGBT lifetime =1/D (17)
Example 2
Based on the embodiment 1, the service life prediction scheme of the English flying company comprehensively considers delta T j (junction temperature variation), Δ T c (case temperature change) and the effect of device switching frequency on lifetime. And calculating the power cycle life/thermal cycle life of the device by using a Weibull failure formula, and finally, integrating the power cycle life and the thermal cycle life to obtain a final life predicted value of the device.
Taking a new eight-axis locomotive as an example for illustration, a task curve diagram 5 is obtained through semi-physical simulation, the temperature load change (the temperature change amplitude, the number and the average temperature) after rain flow counting is shown in fig. 6, fig. 6 (a) is a junction temperature load change diagram after rain flow treatment, and fig. 6 (b) is a temperature load change diagram of a rear shell after rain flow treatment:
wherein, CH1: performing tube pulse on a four-quadrant U-phase; CH2: a four quadrant Uab voltage; CH3: the IGBT current of the tube is arranged on the four-quadrant U-phase; CH4: conducting tube pulse on a U-phase of the inverter; CH5: the rotating speed of the motor; CH6: inverter U-phase tube IGBT current.
Substituting the incrustation temperature load spectrum obtained by the rain flow algorithm into a formula (8) and a formula (9) according to a service life and service life estimation method in a reliability manual in the application of IGBT modules published by England flying by adopting a linear damage accumulation theory to obtain the power cycle times N under different temperature loads f,j And number of temperature cycles N f,c And then, calculating by using the formulas (10) - (17) to obtain the service life of the IGBT device.
In conclusion, the method for predicting the service life of the IGBT device based on the semi-physical simulation platform provided by the invention utilizes the real TCU of the motor car/locomotive to obtain the electric parameters such as the current, the voltage, the trigger pulse and the like of the IGBT device through the semi-physical simulation platform, thereby providing the accuracy which is closer to the current and the voltage change under the actual working condition and is equivalent to the on-site data acquisition, and providing a theoretical basis for determining the replacement time of the IGBT device for rail transit and realizing the full-service-life cycle management of the IGBT device.
The above description is merely illustrative of particular embodiments of the invention that enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention.
It will be understood that the invention is not limited to what has been described above and that various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform is characterized by comprising the following steps:
1) Acquiring electrical parameters of the IGBT device through a semi-physical simulation platform by utilizing a real TCU of the locomotive/motor car;
2) Calculating a power loss value of the IGBT device according to the electrical parameters;
3) Acquiring a crust temperature change curve of the IGBT device according to the loss value by using an electric heating network model;
4) Performing rain flow counting statistics on the incrustation temperature load of the IGBT device according to a rain flow algorithm;
5) And calculating the service life of the IGBT device by using a damage accumulation model of the IGBT device.
2. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform according to claim 1, wherein the step 1) of obtaining the electrical parameters of the IGBT device through the semi-physical simulation platform by using the real TCU of the locomotive/motor car comprises the following steps:
1.1 The real TCU of the locomotive/motor car is utilized to complete the whole working condition simulation of starting, accelerating, uniform speed and braking of the locomotive through a semi-physical simulation platform;
1.2 The data acquisition of the electrical parameters of the IGBT devices of the four-quadrant power module and the inversion power module is completed by using a high-resolution wave recorder.
3. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform according to claim 2, wherein the step 1.1) is to complete the whole working condition simulation of starting, accelerating, uniform speed and braking of the locomotive through the semi-physical simulation platform by utilizing the real TCU of the locomotive/motor car, and specifically comprises the following steps:
1.1.1 Connecting the semi-physical simulation platform with the real TCU of the bullet train to perform real-time simulation test;
1.1.2 Simulating the working operation condition of the locomotive through a semi-physical simulation platform to simulate the traction state and the braking state;
1.1.3 The power consumption of the locomotive load is set, the four-quadrant module load comprises a traction motor and an auxiliary power module, and the inversion module load only comprises the traction motor, so that the whole working condition simulation of starting, accelerating, uniform speed and braking is completed.
4. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform according to claim 2, wherein the step 1.2) completes data acquisition of electrical parameters of the IGBT device of the four-quadrant power module and the inversion power module by using a high-resolution wave recorder, and specifically comprises the following steps:
1.2.1 In the semi-physical operation process, a high-resolution wave recorder is used for respectively completing the acquisition of eight signals including a four-quadrant pulse signal, a four-quadrant input current, a four-quadrant IGBT current, an inversion IGBT pulse, an inversion current, a direct-current bus voltage, an inversion IGBT current and a motor rotating speed;
1.2.2 Read the waveform of the eight collected signals and convert it into an excel data table.
5. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform according to claim 1, wherein the power loss value of the IGBT device in the step 2) comprises the power loss of the IGBT and the FRD chip, and the specific calculation steps are as follows:
2.1 The turn-on loss of the IGBT device is calculated by using an average loss method based on the switching frequency period: in the negative half cycle of the current, the IGBT is turned off, the FRD operates, and assuming that in the negative half cycle, there are N switching pulse cycles (wave head number), and there are k calculation points in each pulse cycle, and the conduction losses of each pulse cycle are superposed, and can be obtained:
in formula (1), P cond_Tr Average conduction loss of IGBT chipConsumption, V cesat (T j ,I C(tj) ) Is the saturation voltage drop when the jth sampling point in the ith output period of the IGBT is conducted, I C The current value of the j sampling point is; in formula (2), P cond_D Is the average conduction loss, V, of the FRD chip F (T k ,I D(tk) ) Is the saturation voltage drop when the kth sampling point in the s output period of the FRD chip is conducted, I D The current value of the j sampling point;
2.2 The switching losses of the IGBT devices are calculated using equations (3), (4) where,
in formula (3), P sw,Tr For switching losses of IGBT chips, E on For IGBT turn-on energy, E off For IGBT turn-off energy, ic (i-1) The current value at time i-1, ic (i) Current value at time i, V dc Is an input voltage value, V nom Is a nominal voltage value; in the formula (4), P sw,D Switching losses for FRD chips, E rec,s Turning off energy for the FRD chip;
2.3 Calculating the total IGBT loss and the total FRD loss in the switching period by using the formulas (5) and (6);
P IGBT ＝P cond_Tr +P sw_Tr (5)
P Diode ＝P cond_D +P sw_D (6)
2.4 Calculating the total loss value of the IGBT device in the switching period by using a formula (7);
P general (1) ＝P Diode +P IGBT (7) 。
6. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform according to claim 1, wherein the step 3) of obtaining a crusting temperature change curve of the IGBT device according to the loss value by using an electric heating network model specifically comprises the following steps:
3.1 According to the IGBT turn-on and turn-off curve provided in the specification of the IGBT device, converting the curve into a device parameter database;
3.2 Using a loss calculation model based on a mathematical method, and using a table look-up method to calculate real-time loss corresponding to real-time voltage and current data;
3.3 And) carrying out modeling calculation by using Matlab to obtain the actual junction temperature and shell temperature change of the IGBT device in the working process by adopting a calculation method of continuous working loss and instantaneous junction temperature.
7. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform as claimed in claim 1 or 6, wherein the electric heating network model is an RC thermal network model based on Foster and Cauer.
8. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform according to claim 1, wherein the step 4) performs rain flow counting statistics on the junction temperature of the IGBT device according to a rain flow algorithm, and specifically comprises the following steps:
4.1 Dividing temperature fluctuation appearing in the incrustation temperature change curve into a plurality of temperature amplitude levels delta T with equal difference;
4.2 ) the corresponding cycle number num and average temperature T in each temperature variation amplitude level are counted m ；
4.3 The statistical result of the temperature load change is calculated.
9. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform as claimed in claim 8, wherein in the step 4.3), the statistical results of the temperature load variation comprise temperature variation amplitude, quantity, average temperature and incrustation temperature load spectrum.
10. The method for predicting the service life of the IGBT device based on the semi-physical simulation platform according to claim 1, wherein the step 5) of calculating the service life of the IGBT device by using a damage accumulation model of the IGBT device specifically comprises the following steps:
5.1 Substituting the incrustation temperature load spectrum obtained in the step 4.3) into formulas (8) and (9) to obtain the power cycle times and the temperature cycle times under different temperature loads;
in the formulae (8) and (9), N f,j The number of power cycles under junction temperature fluctuation, N f,c The temperature cycle times under the shell temperature fluctuation condition are shown, A, B, alpha and beta are constants for data fitting, K is a Boltzmann constant, and K =1.380 × 10 -23 J/K，ΔT j For variation of junction temperature, Δ T c For shell temperature change, E a Excitation energy of silicon chip, E a ＝9.89×10 -20 J，T jm To average junction temperature, T cm Is the average shell temperature;
5.2 According to the annual consumption times of the PC in the station and the influence of the day cycles caused by the daily environmental temperature introduced in the ABB life manual on the life, the life damage rate of the PC is calculated by using life damage formulas (10) to (12);
in formula (10), N PC,station For PC cycle number in station cycle, D PC,stattion The station cycle is the temperature fluctuation damage rate;
in formula (11), N PC,daily The number of PC cycles in daily cycle, D PC,daily In order to reduce the temperature fluctuation damage rate in daily circulation,
to average junction temperature, T a Is ambient temperature;
D PC ＝D Pc,station +D Pc,daily (12)
in formula (12), D PC Is the rate of damage caused by junction temperature load changes;
5.3 Based on the station TC annual consumption times, based on the influence of day cycles on the lifetime caused by the daily ambient temperature introduced in the ABB lifetime manual, the TC lifetime damage rate is calculated using the lifetime damage equations (13) to (15), wherein,
in formula (13), N TC,station For the number of TC cycles in the station cycle, D TC,station The shell temperature fluctuation damage rate in station circulation is shown;
in the formula (14), N TC,daily The number of TC cycles in daily cycle, D TC,daily The damage rate of the shell temperature fluctuation in daily circulation,
is the average shell temperature, T a Is ambient temperature;
D Tc ＝D Tc,station +D Tc,daily (15)
in formula (15), D TC The damage rate caused by the change of the shell temperature load;
5.4 Calculating the total damage rate D of the IGBT device;
in the formula (16), the day cycle is the daily cycle number, the station cycle is the station cycle number, D is the total damage rate of the IGBT, and N is PC,station Is the number of PC cycles in the station cycle, N PC,daily Is the number of PC cycles in daily cycle, N TC,station Is the number of TC cycles, N, in the station cycle TC,daily Is the number of TC cycles in the daily cycle;
5.5 Calculating the lifetime of the IGBT device;
IGBT lifetime =1/D (17).
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106528987A (en) * | 2016-11-03 | 2017-03-22 | 河北工业大学 | Method for accumulated damage computation and life prediction of IGBT module used for electric car |
| CN107341326A (en) * | 2017-08-29 | 2017-11-10 | 中国南方电网有限责任公司电网技术研究中心 | Modularization multi-level converter lifetime estimation method |
| WO2019063000A1 (en) * | 2017-12-06 | 2019-04-04 | 南京埃斯顿自动化股份有限公司 | Real-time online prediction method for semiconductor power device dynamic junction temperature |
-
2019
- 2019-04-19 CN CN201910316828.0A patent/CN110147578B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106528987A (en) * | 2016-11-03 | 2017-03-22 | 河北工业大学 | Method for accumulated damage computation and life prediction of IGBT module used for electric car |
| CN107341326A (en) * | 2017-08-29 | 2017-11-10 | 中国南方电网有限责任公司电网技术研究中心 | Modularization multi-level converter lifetime estimation method |
| WO2019063000A1 (en) * | 2017-12-06 | 2019-04-04 | 南京埃斯顿自动化股份有限公司 | Real-time online prediction method for semiconductor power device dynamic junction temperature |
Non-Patent Citations (1)
| Title |
|---|
| 逆变器中IGBT功率模块的电热联合仿真模型;姚芳等;《半导体技术》;20160603(第06期);全文 * |
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