Charging module residual life online evaluation method, system, equipment and medium

Abstract

The invention provides a charging module residual life online evaluation method, system and device and a medium, and relates to the technical field of new energy. According to the charging module residual life online evaluation method provided by the invention, the loss of the power device and the aluminum electrolytic capacitor in the charging module is determined according to the operation data of the charging module, and then the junction temperature data of the power device and the hot spot temperature of the aluminum electrolytic capacitor in the charging module are determined by using an external characteristic equivalent mode; the junction temperature data of the power device and the hot spot temperature of the aluminum electrolytic capacitor are equal to the current task profile of the charging module; the fatigue accumulated damage of the power device and the aluminum electrolytic capacitor is evaluated, the historical fatigue accumulated damage of the power device and the aluminum electrolytic capacitor is integrated, the residual life of the charging module is predicted, it is avoided that only a single working condition is considered when life prediction is conducted on the charging module, and the service life of the charging module is predicted in combination with a task profile of the charging module. And the accuracy of the obtained service life prediction result of the charging module is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy, and in particular to a method, system, device and medium for online evaluation of the remaining life of a charging module. Background Art

[0002] With the large demand and rapid development of electric vehicles, it is necessary to build a wide network of charging infrastructure. The common charging infrastructure for charging electric vehicles in the prior art is DC charging piles, which are used to charge electric vehicles to meet the normal use of electric vehicles. DC charging piles can fail due to the aging of internal electronic components. Among the failures of DC charging equipment of electric vehicles, 27% of the failures are caused by the aging of DC charging modules. Therefore, accurate assessment of the remaining life of the charging module of the DC charging pile has a significant impact on the management of DC charging piles.

[0003] In the prior art, when evaluating the remaining life of a charging module of a DC charging pile, it is assumed that the operating conditions of the charging module are constant, and the service life of the charging module is predicted based on the constant operating conditions set for the charging module, and the remaining life of the charging module is calculated based on the service life.

[0004] Since there is a large gap between the remaining life in the prediction results obtained by the remaining life assessment method disclosed in the prior art and the actual service life, there is an urgent need to make a more accurate remaining life prediction for the DC charging module of the DC charging pile. Summary of the invention

[0005] In order to solve the problem that the prediction result generated by the prior art for predicting the remaining life of a DC charging pile is not accurate enough, the present invention proposes an online evaluation method, system, device and medium for the remaining life of a charging module.

[0006] In the online evaluation method for the remaining life of a charging module proposed in the present invention, the losses of power devices and aluminum electrolytic capacitors in the charging module are confirmed based on the operating data of the charging module, and then the junction temperature data of the power devices and the hotspot temperature of the aluminum electrolytic capacitors in the charging module are determined by using the external characteristic equivalence method, and the junction temperature data of the power devices and the hotspot temperature of the aluminum electrolytic capacitors are equivalent to the current mission profile of the charging module; the fatigue cumulative damage of the power devices and the aluminum electrolytic capacitors are evaluated respectively, and the remaining life of the charging module is predicted by integrating the historical fatigue cumulative damage of the power devices and the aluminum electrolytic capacitors, so as to avoid considering only a single working condition when predicting the life of the charging module, and improve the accuracy of the obtained life prediction result of the charging module in combination with the mission profile of the charging module.

[0007] The present invention proposes an online evaluation method for the remaining life of a charging module, comprising:

[0008] According to the current operation data of the charging module, the power device loss and the aluminum electrolytic capacitor loss in the charging module are obtained;

[0009] Performing external characteristic equivalence on the external environment temperature and the power device loss to determine the junction temperature data of the power device; performing life prediction based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device;

[0010] Performing external characteristic equivalence on the external environment temperature and the loss of the aluminum electrolytic capacitor to determine the hot spot temperature of the aluminum electrolytic capacitor; performing life prediction based on the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor;

[0011] According to the fatigue cumulative damage of the power device and the fatigue cumulative damage of the aluminum electrolytic capacitor, combined with the historical fatigue cumulative damage of the power device and the aluminum electrolytic capacitor, the remaining life of the charging module is evaluated using a linear fatigue cumulative damage criterion.

[0012] Optionally, performing external characteristic equivalence on the external environment temperature and the power device loss to determine the junction temperature data of the power device; performing life prediction based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device includes:

[0013] For the external environment temperature and the power device loss, a local network thermal circuit model is used to perform external characteristic equivalence to determine the junction temperature data of the power device;

[0014] Based on the junction temperature data, a rain flow counting method is used to process and determine the junction temperature mean value and junction temperature fluctuation amplitude of the power device;

[0015] According to the junction temperature mean and junction temperature fluctuation amplitude of the power device, a Lesit life prediction model is used to predict and determine the consumed life of the power device, and the consumed life of the power device is used as the fatigue accumulated damage of the power device.

[0016] Optionally, the local network thermal circuit model satisfies the following expression:

[0017]

[0018] In the formula, Z jc is the thermal impedance from the heat source to the shell of the power device obtained by equivalent external characteristics of the power device in the local network thermal circuit model, which is used to obtain the junction temperature data of the power device; i is the i-th order of the local network thermal circuit model; R th_iis the i-th order thermal resistance in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device, which is determined according to the external environment temperature and the power device loss; C th_i is the i-th order heat capacity in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device; τ i is the thermal time constant of the i-th order in the local network thermal circuit model, which is obtained by fitting the dynamic thermal impedance curve of the power device; e is a natural constant; and t is time.

[0019] Optionally, the junction temperature data of the power device satisfies the following expression:

[0020]

[0021] Where, T j is the junction temperature data of the power device; P z_C is the total power loss of the power device, which is given by; Z jc is the thermal impedance from the heat source of the power device to the housing equivalent to the local network thermal circuit model; T c is the case temperature of the power device; T a is the ambient temperature; Z ch is the thermal impedance from the power device housing to the heat sink equivalent to the local network thermal circuit model; Z ha is the thermal impedance from the heat sink case to the environment.

[0022] Optionally, the lifespan of the power device satisfies the following expression:

[0023]

[0024] Where N f is the lifetime of the power device determined by using the Lesit lifetime prediction model; A is a constant; ΔT j is the input junction temperature fluctuation amplitude of the power device; m is a constant; E a is the activation energy of the power device; K b is the Boltzmann constant, which is 8.617×10 -5 eV / K; T jm is the input average junction temperature of the power device.

[0025] Optionally, the external characteristic equivalence is performed on the external environment temperature and the loss of the aluminum electrolytic capacitor to determine the hot spot temperature of the aluminum electrolytic capacitor; and the life span is predicted according to the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor, including:

[0026] For the external environment temperature and the loss of the aluminum electrolytic capacitor, an aluminum electrolytic capacitor thermal model is used to perform external characteristic equivalence to determine the hot spot temperature of the aluminum electrolytic capacitor;

[0027] According to the hot spot temperature of the aluminum electrolytic capacitor, a life prediction model of the aluminum electrolytic capacitor is used to predict and determine the consumed life of the aluminum electrolytic capacitor, and the consumed life of the aluminum electrolytic capacitor is used as the fatigue accumulated damage of the aluminum electrolytic capacitor.

[0028] Optionally, the hot spot temperature of the aluminum electrolytic capacitor satisfies the following expression:

[0029]

[0030] Where, T h is the hot spot temperature of the aluminum electrolytic capacitor; R ESR (f j ) is the frequency f after Fourier decomposition of the aluminum electrolytic capacitor j The equivalent series impedance corresponding to the ripple current; f j is the frequency of Fourier decomposition; n is the frequency number of Fourier decomposition of the ripple current of the aluminum electrolytic capacitor; The frequency f is obtained by Fourier decomposition of the ripple current of the aluminum electrolytic capacitor. j The square of the corresponding effective value; is the aluminum electrolytic capacitor loss; R th is the sum of the thermal resistance from the hot spot to the shell and the thermal resistance from the shell to the environment of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; T a is the external environment temperature.

[0031] Optionally, the life of the aluminum electrolytic capacitor satisfies the following expression:

[0032]

[0033] Wherein, L is the life of the aluminum electrolytic capacitor; L 0 is the life of the aluminum electrolytic capacitor under the test conditions; U is the actual operating voltage of the aluminum electrolytic capacitor; U 0 is the voltage of the aluminum electrolytic capacitor under test conditions; n 1 is the proportional factor; T is the ambient temperature of the aluminum electrolytic capacitor; T 0 is the ambient temperature of the aluminum electrolytic capacitor under test conditions; It represents the influence factor of the external environment temperature on the service life of the aluminum electrolytic capacitor.

[0034] Optionally, the current operation data of the charging module includes:

[0035] The operating status of the charging module, output power, power device shell temperature, aluminum electrolytic capacitor shell temperature and external environment temperature;

[0036] Among them, the operating status, output power, and external ambient temperature information are used to obtain power device losses and aluminum electrolytic capacitor losses, the shell temperature of the power device is used to verify the junction temperature data of the power device, and the shell temperature of the aluminum electrolytic capacitor is used to verify the hotspot temperature of the aluminum electrolytic capacitor.

[0037] On the other hand, the present invention proposes an online evaluation system for the remaining life of a charging module of a DC charging pile, the system comprising:

[0038] A device loss acquisition unit, used to obtain the power device loss and aluminum electrolytic capacitor loss in the charging module according to the current operation data of the charging module;

[0039] A first loss prediction unit is used to perform external characteristic equivalence on the external environment temperature and the power device loss to determine the junction temperature data of the power device; perform life prediction based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device;

[0040] A second loss prediction unit is used to perform external characteristic equivalence on the external environment temperature and the aluminum electrolytic capacitor loss to determine the hot spot temperature of the aluminum electrolytic capacitor; perform life prediction based on the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor;

[0041] The remaining life evaluation unit is used to evaluate the remaining life of the charging module according to the fatigue cumulative damage of the power device and the fatigue cumulative damage of the aluminum electrolytic capacitor, combined with the historical fatigue cumulative damage of the power device and the aluminum electrolytic capacitor, using a linear fatigue cumulative damage criterion.

[0042] Optionally, the first loss prediction unit includes:

[0043] A junction temperature data acquisition subunit, used to determine the junction temperature data of the power device by performing external characteristic equivalence on the external environment temperature and the power device loss using a local network thermal circuit model;

[0044] A data processing subunit, configured to determine a junction temperature mean and a junction temperature fluctuation amplitude of the power device by processing the junction temperature data using a rain flow counting method;

[0045] The first loss prediction subunit is used to predict the consumed life of the power device according to the junction temperature mean and junction temperature fluctuation amplitude of the power device using the Lesit life prediction model, and use the consumed life of the power device as the fatigue accumulated damage of the power device.

[0046] Optionally, the local network thermal circuit model of the junction temperature data acquisition subunit satisfies the following expression:

[0047]

[0048] In the formula, Z jc is the thermal impedance from the heat source to the shell of the power device obtained after the external characteristics of the power device in the local network thermal circuit model, which is used to obtain the junction temperature data of the power device; i is the i-th order of the determined local network thermal circuit model; R th_i is the i-th order thermal resistance in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device, wherein the dynamic thermal impedance curve of the power device is determined according to the external environment temperature and the power device loss; C th_i is the i-th order heat capacity in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device; τ i is the thermal time constant of the i-th order in the local network thermal circuit model, which is obtained by fitting the dynamic thermal impedance curve of the power device; e is a natural constant; and t is time.

[0049] Optionally, the junction temperature data acquisition subunit determines the junction temperature data of the power device to satisfy the following expression:

[0050]

[0051] Where, T j is the junction temperature data of the power device; P z_C is the total power loss of the power devices in the charging module, as described above; Z jc is the thermal impedance from the heat source of the power device in the charging module to the housing equivalent to the local network thermal circuit model; T c is the shell temperature of the power device in the charging module; T a is the ambient temperature; Z ch is the thermal impedance from the power device housing to the heat sink in the charging module obtained by using the local network thermal circuit model to perform external characteristic equivalence; Z ha is the thermal impedance from the heat sink case to the environment.

[0052] Optionally, the power device loss prediction subunit determines the lifespan consumed by the power device, satisfying the following expression:

[0053]

[0054] Where N f is the lifetime of the power device predicted by the Lesit lifetime prediction model; A is a constant; ΔT jis the junction temperature fluctuation amplitude of the power device; m is a constant; E a is the activation energy of the power device; K b is the Boltzmann constant, which is 8.617×10 -5 eV / K; T jm is the average junction temperature of the power device.

[0055] Optionally, the second loss prediction unit includes:

[0056] A hot spot temperature acquisition subunit is used to determine the hot spot temperature of the aluminum electrolytic capacitor by performing external characteristic equivalence on the external environment temperature and the aluminum electrolytic capacitor loss using an aluminum electrolytic capacitor thermal model;

[0057] The second loss prediction subunit is used to predict the consumed life of the aluminum electrolytic capacitor according to the hot spot temperature of the aluminum electrolytic capacitor using the aluminum electrolytic capacitor life prediction model, and use the consumed life of the aluminum electrolytic capacitor as the fatigue accumulated damage of the aluminum electrolytic capacitor.

[0058] Optionally, the hot spot temperature acquisition subunit determines the hot spot temperature of the aluminum electrolytic capacitor, satisfying the following expression:

[0059]

[0060] Where, T h is the hot spot temperature of the aluminum electrolytic capacitor; R ESR (f j ) is the frequency f after Fourier decomposition of the aluminum electrolytic capacitor j The equivalent series impedance corresponding to the ripple current; f j is the frequency of Fourier decomposition; n is the frequency number of Fourier decomposition of the ripple current of the aluminum electrolytic capacitor; The frequency f is obtained by Fourier decomposition of the ripple current of the aluminum electrolytic capacitor. j The square of the corresponding effective value; is the aluminum electrolytic capacitor loss; R th is the sum of the thermal resistance from the hot spot to the shell and the thermal resistance from the shell to the environment of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; T a is the external environment temperature.

[0061] Optionally, the second loss prediction subunit determines the life of the aluminum electrolytic capacitor, satisfying the following expression:

[0062]

[0063] Wherein, L is the life of the aluminum electrolytic capacitor; L 0is the life of the aluminum electrolytic capacitor under test conditions; U is the actual operating voltage of the aluminum electrolytic capacitor, which is determined by fitting according to the hot spot temperature of the aluminum electrolytic capacitor; U 0 is the voltage of the aluminum electrolytic capacitor under test conditions; n 1 is the proportional factor; T is the ambient temperature of the aluminum electrolytic capacitor; T 0 Test Kelvin temperature for capacitors; It represents the influence factor of the external environment temperature on the service life of the aluminum electrolytic capacitor.

[0064] Optionally, the current operation data of the charging module acquired by the device loss acquisition unit includes:

[0065] The operating status of the charging module, output power, power device shell temperature, aluminum electrolytic capacitor shell temperature and external environment temperature;

[0066] Among them, the operating status, output power, and external ambient temperature information of the charging module are used to calculate the power device loss and the aluminum electrolytic capacitor loss, the shell temperature of the power device is used to verify the junction temperature data of the power device, and the shell temperature of the aluminum electrolytic capacitor is used to verify the hot spot temperature of the aluminum electrolytic capacitor.

[0067] In another aspect, the present invention provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0068] The memory is used to store one or more programs;

[0069] When the one or more programs are executed by the at least one processor, an online evaluation method for the remaining life of a charging module of a DC charging pile as described in the above solution is implemented.

[0070] On the other hand, the present invention further proposes a readable storage medium, characterized in that an execution program is stored thereon, and when the execution program is executed, an online evaluation method for the remaining life of a charging module of a DC charging pile as described in the above scheme is implemented.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] In an online evaluation method for the remaining life of a charging module provided by the present invention, the losses of power devices and aluminum electrolytic capacitors in the charging module are confirmed based on the operating data of the charging module, and then the junction temperature data of the power devices and the hotspot temperature of the aluminum electrolytic capacitors in the charging module are determined by using the external characteristic equivalent method, and the junction temperature data of the power devices and the hotspot temperature of the aluminum electrolytic capacitors are equivalent to the current mission profile of the charging module; the fatigue cumulative damage of the power devices and the aluminum electrolytic capacitors are evaluated respectively, and the historical fatigue cumulative damage of the power devices and the aluminum electrolytic capacitors is integrated to predict the remaining life of the charging module, so as to avoid considering only a single working condition when predicting the life of the charging module, and improve the accuracy of the obtained life prediction result of the charging module in combination with the mission profile of the charging module. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a flow chart of a method for online evaluation of the remaining life of a charging module proposed by the present invention;

[0074] Figure 2 This is a flowchart of an implementation of an online evaluation method for the remaining life of a charging module proposed by the present invention;

[0075] Figure 3 A flow chart of obtaining junction temperature data of power devices in an online evaluation method for remaining life of a charging module proposed by the present invention;

[0076] Figure 4 A schematic diagram of the structure of a local network thermal circuit model of a power device in an online evaluation method for the remaining life of a charging module proposed by the present invention;

[0077] Figure 5 A flowchart of peak-valley value detection using a rain flow counting method in an online evaluation method for the remaining life of a charging module proposed by the present invention;

[0078] Figure 6 A schematic diagram of the structure of a thermal network model of an aluminum electrolytic capacitor in an online evaluation method for the remaining life of a charging module proposed by the present invention;

[0079] Figure 7 A schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0080] Embodiment 1:

[0081] like Figure 1 and Figure 2 As shown, the present invention provides a method for online evaluation of the remaining life of a charging module, comprising:

[0082] S1, obtaining power device loss and aluminum electrolytic capacitor loss in the charging module according to current operation data of the charging module;

[0083] S2, performing external characteristic equivalence on the external environment temperature and the power device loss to determine the junction temperature data of the power device; performing life prediction based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device;

[0084] S3, performing external characteristic equivalence on the external environment temperature and the loss of the aluminum electrolytic capacitor to determine the hot spot temperature of the aluminum electrolytic capacitor; performing life prediction based on the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor;

[0085] S4, according to the fatigue cumulative damage of the power device and the fatigue cumulative damage of the aluminum electrolytic capacitor, combined with the historical fatigue cumulative damage of the power device and the aluminum electrolytic capacitor, using a linear fatigue cumulative damage criterion, the remaining life of the charging module is evaluated.

[0086] Currently, when the remaining life of the charging module is predicted, it is assumed that the operating condition of the charging module is constant, and an offline life prediction is performed for the charging module.

[0087] In the above-mentioned remaining life prediction of the charging module, the default constant operating condition of the charging module is inconsistent with the actual operating condition of the charging module, and is not combined with the task profile of the charging pile where the charging module is located, making it difficult to obtain an accurate life prediction result; at the same time, the offline life prediction of the charging module causes the obtained life prediction result to have a delay defect.

[0088] The present invention obtains the current operating condition of the charging module in real time, thereby solving the problem of considering only a single operating condition when predicting the life of the charging module, and making the result of the life prediction more meaningful for reference; when evaluating the remaining life of the charging module, external factors are combined with the task profile of the charging module, thereby solving the problem of inaccurate life prediction results caused by considering environmental factors alone when predicting the life of the DC charging module; through online evaluation of the remaining life of the charging module, the predicted remaining life of the charging module can be obtained conveniently and timely.

[0089] The charging module is implemented as a DC charging module of a DC charging pile. The current operation data of the charging module in S1 is the operation data generated by the charging module during normal operation, including the operation status, output power, power device shell temperature, aluminum electrolytic capacitor shell temperature and external environment temperature of the charging module. Among them, the operation status, output power and external environment temperature information of the charging module are used to calculate the power device loss and aluminum electrolytic capacitor loss respectively, the shell temperature of the power device is used to verify the junction temperature data of the power device, and the shell temperature of the aluminum electrolytic capacitor is used to verify the hot spot temperature of the aluminum electrolytic capacitor.

[0090] The power device loss consists of conduction loss and switching loss. The conduction loss and switching loss of the power device are first obtained separately, and then the conduction loss and switching loss are obtained comprehensively to obtain the power device loss used to evaluate the cumulative fatigue damage of the power device.

[0091] Exemplarily, the conduction loss and switching loss satisfy the following expression:

[0092]

[0093] Where P con , P sw are the conduction loss and switching loss of the power device respectively; i d (t) is the current flowing through the power device at time t, which can be calculated from the output power of the charging module collected; v ds (t) is the on-state voltage drop at time t, which can be obtained by looking up the switching characteristic curve of the power device; σ(t) is the duty cycle at time t, which is determined by the circuit structure of the power device itself; E on 、E off are the energy required to turn on and off the power device, which can also be obtained by consulting the power device data sheet; f sw is the switching frequency of the power device circuit, which is determined by the circuit characteristics of the power device itself; t is time.

[0094] For example, the aluminum electrolytic capacitor loss can be obtained by the following formula:

[0095]

[0096] Where P C,loss is the power loss of the aluminum electrolytic capacitor; The frequency f after Fourier decomposition of the ripple current of the aluminum electrolytic capacitor i The corresponding square of the effective value of the ripple current; R ESR (f i ) is the frequency f after Fourier decomposition i The equivalent series resistance corresponding to the ripple current can be obtained from the data sheet of the aluminum electrolytic capacitor; i is the series corresponding to the Fourier decomposition of the ripple current of the aluminum electrolytic capacitor.

[0097] In this embodiment, the current operation data of the charging module can be obtained through a controller area network (CAN, a serial communication network that can implement distributed real-time control). At the same time, the time interval for obtaining the current operation data of the charging module can be implemented as 10s.

[0098] The operating status of the charging module, output power, shell temperature of the power device and aluminum electrolytic capacitor, and external ambient temperature information within a collection time interval are obtained through the controller local area network, so as to calculate the power loss of the power device and the aluminum electrolytic capacitor. At the same time, the influence of ripple current of different frequencies is considered when calculating the power loss of the aluminum electrolytic capacitor, which facilitates the accuracy of the life prediction of the aluminum electrolytic capacitor.

[0099] Reference Figure 3 In some embodiments, S2 provided by the present invention includes:

[0100] S201, using a local network thermal circuit model to perform external characteristic equivalence on the external environment temperature and the power device loss, and determining the junction temperature data of the power device;

[0101] S202, based on the junction temperature data, using a rain flow counting method to process and determine a junction temperature mean and a junction temperature fluctuation amplitude of the power device;

[0102] S203, predicting using a Lesit life prediction model according to the junction temperature mean and junction temperature fluctuation amplitude of the power device, determining the consumed life of the power device, and using the consumed life of the power device as the fatigue accumulated damage of the power device.

[0103] Since the local network thermal circuit model (Foster thermal network model) can be regarded as a "black box" for the external characteristic equivalent process of the power device, the parameters of the local network thermal circuit model no longer correspond to the actual material layer of the power device, and the interference of the parameters of the power device and the actual physical meaning of the nodes on the external characteristic equivalent process is also weakened. However, the parameters of the local network thermal circuit model can be obtained by fitting the transient thermal impedance curve of the power device. Therefore, the local network thermal circuit model can achieve better external characteristic equivalent effect in the external characteristic equivalent process.

[0104] Reference Figure 4 In this embodiment, by comprehensively considering the accuracy and speed of calculation, the present invention selects a fourth-order local network thermal circuit model as the equivalent thermal network model of the power device.

[0105] The fourth-order local network thermal circuit model selected by the present invention satisfies the following expression:

[0106]

[0107] In the formula, Z jc is the thermal impedance from the heat source to the shell of the power device obtained by equivalent external characteristics of the power device in the local network thermal circuit model, which is used to obtain the junction temperature data of the power device; i is the i-th order of the local network thermal circuit model; R th_iis the i-th order thermal resistance in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device, which is determined according to the external environment temperature and the power device loss; C th_i is the i-th order heat capacity in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device; τ i is the thermal time constant of the i-th order in the local network thermal circuit model, which is obtained by fitting the dynamic thermal impedance curve of the power device; e is a natural constant; and t is time.

[0108] The total power loss of the power device and the operating ambient temperature are input into the local network thermal circuit model to obtain the junction temperature data during the power period.

[0109] Exemplarily, the junction temperature data of the power device obtained by the present invention satisfies the following expression:

[0110]

[0111] Where, T j is the junction temperature data of the power device; P z_C is the total power loss of the power device, which is given by; Z jc is the thermal impedance from the heat source of the power device to the housing equivalent to the local network thermal circuit model; T c is the case temperature of the power device; T a is the ambient temperature; Z ch is the thermal impedance from the power device housing to the heat sink equivalent to the local network thermal circuit model; Z ha is the thermal impedance from the heat sink case to the environment.

[0112] Since the junction temperature mean and junction temperature fluctuation of power devices have the characteristics of random changes, it is very difficult to directly use the junction temperature data of power devices to calculate fatigue cumulative damage, and it is difficult to evaluate the consumed life of power devices. Therefore, in order to evaluate the consumed life of power devices, it is necessary to first perform statistical processing on the junction temperature data of power devices.

[0113] In this embodiment, the method for statistically processing the junction temperature data of the power device is the rain flow counting method, which counts the average junction temperature T of the power device within a period of time. jm and junction temperature fluctuation amplitude ΔT j The corresponding number of fluctuations N(T jm ,ΔT j ), and then evaluate the service life of the power device.

[0114] The process of rain flow counting method includes: peak and valley value detection and cycle number extraction. The steps of peak and valley value detection are as follows: Figure 5As shown in the figure, T(t) is the junction temperature data of the power device to be statistically processed; T(i-1), T(i) and T(i+1) are the junction temperature data of the power device at the previous moment, the current moment and the next moment respectively; T(j) is the peak and valley value of the output junction temperature data of the power device; N is the cycle termination condition; and the process of extracting the number of cycles is similar to the phenomenon of raindrops falling. Raindrops start to flow downward from the peak and valley values, and stop flowing when encountering any of the following situations, so as to calculate the number of half-cycles of the cycle.

[0115] ① The rain flow stops at the end of the load time history;

[0116] ② When the rain flow encounters the rain flow flowing from an earlier peak, or when the rain flow of the opposite peak has a larger amplitude, it stops flowing.

[0117] By using the rain flow counting method to perform statistical processing on the junction temperature data of the power device, the junction temperature mean and junction temperature fluctuation of the power device can be obtained, and the lifespan of the power device can be further predicted.

[0118] In this embodiment, the service life prediction model of the power device adopts the Lesit life prediction model (a life prediction model of the power device), and the Lesit life prediction model takes into account the average junction temperature T jm and junction temperature fluctuation ΔT j The impact on the remaining life of power devices is simple to express and is currently the most widely used life prediction model.

[0119] Exemplarily, the lifetime consumed by the power device satisfies the following expression:

[0120]

[0121] Where N f is the lifetime of the power device determined by using the Lesit lifetime prediction model; A is a constant; ΔT j is the input junction temperature fluctuation amplitude of the power device; m is a constant; E a is the activation energy of the power device; K b is the Boltzmann constant, which is 8.617×10 -5 eV / K; T jm is the input average junction temperature of the power device.

[0122] In the process of calculating the life of power devices, the impact of aging of power devices on the life of power devices must be considered. Aging of power devices will cause an increase in thermal resistance of power devices. According to the failure criteria of power devices, there is a linear relationship between the aging thermal resistance of power devices and the cumulative fatigue damage. When the increase in thermal resistance of a power device exceeds the normal value by 20%, the device is considered to have failed.

[0123] Therefore, according to the principle of linear increase, the aging process of power devices is divided into several stages. Theoretically, the more segments the aging process is divided into, the closer the life prediction result is to the actual life, but it will also lead to complex calculations.

[0124] In this embodiment, the aging process of the power device is divided into five stages, and the corresponding thermal resistance parameters and damage degrees are shown in Table 1 below.

[0125] Table 1 Thermal resistance parameters and damage degree of power devices during aging process

[0126]

[0127] Furthermore, the linear cumulative damage criterion (Palmgren-Miner linear cumulative damage criterion, referred to as Miner theory) is also required for life prediction of power devices. According to the linear fatigue cumulative damage criterion, when the stress level is constant, the cumulative damage rate of fatigue damage of power devices is fixed and has nothing to do with the load history experienced by the power devices. The total fatigue cumulative damage suffered by the power device is equal to the sum of the damage corresponding to each stress.

[0128] For example, considering the average junction temperature T jm and junction temperature fluctuation ΔT j In the case of , the damage degree corresponding to the thermal stress level is:

[0129]

[0130] In the formula, c i,j is the total fatigue cumulative damage corresponding to the thermal stress level; n i,j is the number of cycles of this thermal stress level, obtained by the rain flow method; (N f ) i,j is the number of fatigue limit cycles corresponding to the current thermal stress level.

[0131] During the life of evaluating the consumption of the power device, if the calculated total damage accumulation value c is close to 1, it is determined that the corresponding power device has failed.

[0132] By fully considering the impact of period aging when establishing the thermal network model of the power device, the mission profile of the charging model operation and the ambient temperature are fully combined, thereby improving the accuracy of the calculated junction temperature data of the power device.

[0133] In some embodiments, S3 provided by the present invention includes:

[0134] S301, using a thermal model of an aluminum electrolytic capacitor to perform external characteristic equivalence on the external environment temperature and the loss of the aluminum electrolytic capacitor, and determining the hot spot temperature of the aluminum electrolytic capacitor;

[0135] S302, predicting the consumed life of the aluminum electrolytic capacitor using an aluminum electrolytic capacitor life prediction model according to the hot spot temperature of the aluminum electrolytic capacitor, and using the consumed life of the aluminum electrolytic capacitor as the fatigue accumulated damage of the aluminum electrolytic capacitor.

[0136] Reference Figure 6 ,The thermal model of aluminum electrolytic capacitor can simulate the heat flow inside the aluminum electrolytic capacitor, so as to obtain the temperature of each node inside the aluminum electrolytic capacitor in real time. Figure 6 In the figure, C is the equivalent capacitance; R thhc is the thermal resistance from the hot spot to the shell of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; R thca is the thermal resistance from the shell to the environment of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; P C,loss is the power loss caused by the equivalent series impedance in the thermal model of aluminum electrolytic capacitor; T h is the hot spot temperature of the aluminum electrolytic capacitor; T a is the external environment temperature; T c is the case temperature of the aluminum electrolytic capacitor.

[0137] When the aluminum electrolytic capacitor is working, the power loss generated by the equivalent series resistance (ESR) in the thermal model of the aluminum electrolytic capacitor is used as the excitation input thermal model, and heat is generated on the equivalent series resistance, causing the hot spot temperature of the aluminum electrolytic capacitor to rise. The evaporation rate of the electrolyte of the aluminum electrolytic capacitor is positively correlated with its hot spot temperature, that is, the evaporation rate of the electrolyte increases with the increase of the hot spot temperature, which in turn causes the aluminum electrolytic capacitor to age faster.

[0138] Exemplarily, the hot spot temperature of an aluminum electrolytic capacitor satisfies the following expression:

[0139]

[0140] Where, T h is the hot spot temperature of the aluminum electrolytic capacitor; R ESR (f j ) is the frequency f after Fourier decomposition of the aluminum electrolytic capacitor j The equivalent series impedance corresponding to the ripple current; f j is the frequency of Fourier decomposition; n is the frequency number of Fourier decomposition of the ripple current of the aluminum electrolytic capacitor; The frequency f is obtained by Fourier decomposition of the ripple current of the aluminum electrolytic capacitor. j The square of the corresponding effective value; is the aluminum electrolytic capacitor loss; Rth is the sum of the thermal resistance from the hot spot to the shell and the thermal resistance from the shell to the environment of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; T a is the external environment temperature.

[0141] The life of the aluminum electrolytic capacitor obtained by the aluminum electrolytic capacitor life prediction model satisfies the following expression:

[0142]

[0143] Wherein, L is the life of the aluminum electrolytic capacitor; L 0 is the life of the aluminum electrolytic capacitor under the test conditions; U is the actual operating voltage of the aluminum electrolytic capacitor; U 0 is the voltage of the aluminum electrolytic capacitor under test conditions; n 1 is the proportional factor; T is the ambient temperature of the aluminum electrolytic capacitor; T 0 is the ambient temperature of the aluminum electrolytic capacitor under test conditions; It represents the influence factor of the external environment temperature on the service life of the aluminum electrolytic capacitor.

[0144] By constructing respective life prediction models for the power devices and aluminum electrolytic capacitors in the charging module, the consumed life in the current data collection interval is calculated. Combined with the historical fatigue cumulative damage of the power devices and aluminum electrolytic capacitors, the linear fatigue cumulative damage criterion is conveniently used. The fatigue cumulative damage rate in the current data interval is used as the standard, thereby realizing real-time prediction of the remaining life of the charging module.

[0145] In some embodiments, S4 provided by the present invention is specifically implemented as follows: after respectively calculating the fatigue cumulative damage of the power device and the electrolytic capacitor within the sampling time interval, the cumulative damage is superimposed with the historical fatigue cumulative damage of the power device and the aluminum electrolytic capacitor to obtain the fatigue cumulative damage rate of the power device and the aluminum electrolytic capacitor within the period of time, and then predicting the life of the power device and the electrolytic capacitor, and finally predicting the remaining life of the entire charging module.

[0146] In an online evaluation method for the remaining life of a charging module provided by the present invention, the losses of power devices and aluminum electrolytic capacitors in the charging module are confirmed based on the operating data of the charging module, and then the junction temperature data of the power devices and the hotspot temperature of the aluminum electrolytic capacitors in the charging module are determined by using the external characteristic equivalent method, and the junction temperature data of the power devices and the hotspot temperature of the aluminum electrolytic capacitors are equivalent to the current mission profile of the charging module; the fatigue cumulative damage of the power devices and the aluminum electrolytic capacitors are evaluated respectively, and the historical fatigue cumulative damage of the power devices and the aluminum electrolytic capacitors is integrated to predict the remaining life of the charging module, so as to avoid considering only a single working condition when predicting the life of the charging module, and improve the accuracy of the obtained life prediction result of the charging module in combination with the mission profile of the charging module.

[0147] The present invention can be used by operators to perform reasonable maintenance on charging modules, prevent accidents, and make them operate more safely through real-time prediction of the remaining life of charging modules. At the same time, corresponding testing standards can be formed according to the evaluation method, which can serve as an important reference for charging and discharging facility manufacturers and third-party testing agencies, and can obtain data related to the remaining life of charging modules. By analyzing and mining these data, business insights such as the usage of DC charging pile facilities and user behavior can be obtained, providing decision support and market forecasts for related companies and institutions.

[0148] Embodiment 2:

[0149] The present invention based on the same inventive concept also provides an online evaluation system for the remaining life of a charging module, the system comprising:

[0150] A device loss acquisition unit, used to obtain the power device loss and aluminum electrolytic capacitor loss in the charging module according to the current operation data of the charging module;

[0151] A first loss prediction unit is used to perform external characteristic equivalence on the external environment temperature and the power device loss to determine the junction temperature data of the power device; perform life prediction based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device;

[0152] A second loss prediction unit is used to perform external characteristic equivalence on the external environment temperature and the aluminum electrolytic capacitor loss to determine the hot spot temperature of the aluminum electrolytic capacitor; perform life prediction based on the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor;

[0153] The remaining life evaluation unit is used to evaluate the remaining life of the charging module according to the fatigue cumulative damage of the power device and the fatigue cumulative damage of the aluminum electrolytic capacitor, combined with the historical fatigue cumulative damage of the power device and the aluminum electrolytic capacitor, using a linear fatigue cumulative damage criterion.

[0154] Optionally, the first loss prediction unit includes:

[0155] A junction temperature data acquisition subunit, used to determine the junction temperature data of the power device by performing external characteristic equivalence on the external environment temperature and the power device loss using a local network thermal circuit model;

[0156] A data processing subunit, configured to determine a junction temperature mean and a junction temperature fluctuation amplitude of the power device by processing the junction temperature data using a rain flow counting method;

[0157] The first loss prediction subunit is used to predict the consumed life of the power device according to the junction temperature mean and junction temperature fluctuation amplitude of the power device using the Lesit life prediction model, and use the consumed life of the power device as the fatigue accumulated damage of the power device.

[0158] Optionally, the local network thermal circuit model of the junction temperature data acquisition subunit satisfies the following expression:

[0159]

[0160] In the formula, Z jc is the thermal impedance from the heat source to the shell of the power device obtained after the external characteristics of the power device in the local network thermal circuit model, which is used to obtain the junction temperature data of the power device; i is the i-th order of the determined local network thermal circuit model; R th_i is the i-th order thermal resistance in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device, wherein the dynamic thermal impedance curve of the power device is determined according to the external environment temperature and the power device loss; C th_i is the i-th order heat capacity in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device; τ i is the thermal time constant of the i-th order in the local network thermal circuit model, which is obtained by fitting the dynamic thermal impedance curve of the power device; e is a natural constant; and t is time.

[0161] Optionally, the junction temperature data acquisition subunit determines the junction temperature data of the power device to satisfy the following expression:

[0162]

[0163] Where, T j is the junction temperature data of the power device; P z_C is the total power loss of the power devices in the charging module, as described above; Z jc is the thermal impedance from the heat source of the power device in the charging module to the housing equivalent to the local network thermal circuit model; T cis the shell temperature of the power device in the charging module; T a is the ambient temperature; Z ch is the thermal impedance from the power device housing to the heat sink in the charging module obtained by using the local network thermal circuit model to perform external characteristic equivalence; Z ha is the thermal impedance from the heat sink case to the environment.

[0164] Optionally, the power device loss prediction subunit determines the lifespan consumed by the power device, satisfying the following expression:

[0165]

[0166] Where N f is the lifetime of the power device predicted by the Lesit lifetime prediction model; A is a constant; ΔT j is the junction temperature fluctuation amplitude of the power device; m is a constant; E a is the activation energy of the power device; K b is the Boltzmann constant, which is 8.617×10 -5 eV / K; T jm is the average junction temperature of the power device.

[0167] Optionally, the second loss prediction unit includes:

[0168] A hot spot temperature acquisition subunit is used to determine the hot spot temperature of the aluminum electrolytic capacitor by performing external characteristic equivalence on the external environment temperature and the aluminum electrolytic capacitor loss using an aluminum electrolytic capacitor thermal model;

[0169] The second loss prediction subunit is used to predict the consumed life of the aluminum electrolytic capacitor according to the hot spot temperature of the aluminum electrolytic capacitor using the aluminum electrolytic capacitor life prediction model, and use the consumed life of the aluminum electrolytic capacitor as the fatigue accumulated damage of the aluminum electrolytic capacitor.

[0170] Optionally, the hot spot temperature acquisition subunit determines the hot spot temperature of the aluminum electrolytic capacitor, satisfying the following expression:

[0171]

[0172] Where, T h is the hot spot temperature of the aluminum electrolytic capacitor; R ESR (f j ) is the frequency f after Fourier decomposition of the aluminum electrolytic capacitor j The equivalent series impedance corresponding to the ripple current; f j is the frequency of Fourier decomposition; n is the frequency number of Fourier decomposition of the ripple current of the aluminum electrolytic capacitor; The frequency f is obtained by Fourier decomposition of the ripple current of the aluminum electrolytic capacitor. j The square of the corresponding effective value; is the aluminum electrolytic capacitor loss; R th is the sum of the thermal resistance from the hot spot to the shell and the thermal resistance from the shell to the environment of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; T a is the external environment temperature.

[0173] Optionally, the second loss prediction subunit determines the life of the aluminum electrolytic capacitor, satisfying the following expression:

[0174]

[0175] Wherein, L is the life of the aluminum electrolytic capacitor; L 0 is the life of the aluminum electrolytic capacitor under test conditions; U is the actual operating voltage of the aluminum electrolytic capacitor, which is determined by fitting according to the hot spot temperature of the aluminum electrolytic capacitor; U 0 is the voltage of the aluminum electrolytic capacitor under test conditions; n 1 is the proportional factor; T is the ambient temperature of the aluminum electrolytic capacitor; T 0 Test Kelvin temperature for capacitors; It represents the influence factor of the external environment temperature on the service life of the aluminum electrolytic capacitor.

[0176] Optionally, the current operation data of the charging module acquired by the device loss acquisition unit includes:

[0177] The operating status of the charging module, output power, power device shell temperature, aluminum electrolytic capacitor shell temperature and external environment temperature;

[0178] Among them, the operating status, output power, and external ambient temperature information of the charging module are used to calculate the power device loss and the aluminum electrolytic capacitor loss, the shell temperature of the power device is used to verify the junction temperature data of the power device, and the shell temperature of the aluminum electrolytic capacitor is used to verify the hot spot temperature of the aluminum electrolytic capacitor.

[0179] Embodiment 3:

[0180] like Figure 7As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0181] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of an online evaluation method for the remaining life of a charging module in the above-mentioned embodiment.

[0182] Embodiment 4:

[0183] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the storage medium here may include both built-in storage media in an electronic device and, of course, extended storage media supported by the electronic device. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more execution programs (including program codes). It should be noted that the storage medium here may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of an online evaluation method for the remaining life of a charging module in the above-mentioned embodiment.

[0184] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0185] 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 processor, 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0188] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for online evaluation of the remaining life of a charging module, characterized in that: include: According to the current operation data of the charging module, the power device loss and the aluminum electrolytic capacitor loss in the charging module are obtained; Performing external characteristic equivalence on the external environment temperature and the power device loss to determine the junction temperature data of the power device; performing life prediction based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device; Performing external characteristic equivalence on the external environment temperature and the loss of the aluminum electrolytic capacitor to determine the hot spot temperature of the aluminum electrolytic capacitor; performing life prediction based on the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor; According to the fatigue cumulative damage of the power device and the fatigue cumulative damage of the aluminum electrolytic capacitor, combined with the historical fatigue cumulative damage of the power device and the aluminum electrolytic capacitor, the remaining life of the charging module is evaluated using a linear fatigue cumulative damage criterion.

2. The method according to claim 1, characterized in that The external characteristic equivalence is performed on the external environment temperature and the power device loss to determine the junction temperature data of the power device; life prediction is performed based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device, including: For the external environment temperature and the power device loss, a local network thermal circuit model is used to perform external characteristic equivalence to determine the junction temperature data of the power device; Based on the junction temperature data, a rain flow counting method is used to process and determine the junction temperature mean value and junction temperature fluctuation amplitude of the power device; According to the junction temperature mean and junction temperature fluctuation amplitude of the power device, a Lesit life prediction model is used to predict and determine the consumed life of the power device, and the consumed life of the power device is used as the fatigue accumulated damage of the power device.

3. The method according to claim 2, characterized in that The local network thermal circuit model satisfies the following expression: In the formula, Z jc is the thermal impedance from the heat source to the shell of the power device obtained by equivalent external characteristics of the power device in the local network thermal circuit model, which is used to obtain the junction temperature data of the power device; i is the i-th order of the local network thermal circuit model; R th_i is the i-th order thermal resistance in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device, which is determined according to the external environment temperature and the power device loss; C th_i is the i-th order heat capacity in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device; τ i is the thermal time constant of the i-th order in the local network thermal circuit model, which is obtained by fitting the dynamic thermal impedance curve of the power device; e is a natural constant; and t is time.

4. The method according to claim 3, characterized in that The junction temperature data of the power device satisfies the following expression: Where, T j is the junction temperature data of the power device; P z_C is the total power loss of the power device, which is given by; Z jc The thermal impedance from the heat source of the power device to the housing equivalent to the local network thermal circuit model; T c is the case temperature of the power device; T a is the ambient temperature; Z ch The thermal impedance from the power device housing to the heat sink equivalent to the local network thermal circuit model; Z ha is the thermal impedance from the heat sink case to the environment.

5. The method according to claim 2, characterized in that The life of the power device satisfies the following expression: Where N f is the lifetime of the power device determined by using the Lesit lifetime prediction model; A is a constant; ΔT j is the input junction temperature fluctuation amplitude of the power device; m is a constant; E a is the activation energy of the power device; K b is the Boltzmann constant, which is 8.617×10 -5 eV / K; T jm is the input average junction temperature of the power device.

6. The method according to claim 1, characterized in that The external characteristic equivalence is performed on the external environment temperature and the loss of the aluminum electrolytic capacitor to determine the hot spot temperature of the aluminum electrolytic capacitor; life prediction is performed according to the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor, including: For the external environment temperature and the loss of the aluminum electrolytic capacitor, an aluminum electrolytic capacitor thermal model is used to perform external characteristic equivalence to determine the hot spot temperature of the aluminum electrolytic capacitor; According to the hot spot temperature of the aluminum electrolytic capacitor, a life prediction model of the aluminum electrolytic capacitor is used to predict and determine the consumed life of the aluminum electrolytic capacitor, and the consumed life of the aluminum electrolytic capacitor is used as the fatigue accumulated damage of the aluminum electrolytic capacitor.

7. The method according to claim 6, characterized in that The hot spot temperature of the aluminum electrolytic capacitor satisfies the following expression: Where, T h is the hot spot temperature of the aluminum electrolytic capacitor; R ESR (f j ) is the frequency f after Fourier decomposition of the aluminum electrolytic capacitor j The equivalent series impedance corresponding to the ripple current; f j is the frequency of Fourier decomposition; n is the frequency number of Fourier decomposition of the ripple current of the aluminum electrolytic capacitor; The frequency f is obtained by Fourier decomposition of the ripple current of the aluminum electrolytic capacitor. j The square of the corresponding effective value; is the aluminum electrolytic capacitor loss; R th is the sum of the thermal resistance from the hot spot to the shell and the thermal resistance from the shell to the environment of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; T a is the external environment temperature.

8. The method according to claim 6, characterized in that The life of the aluminum electrolytic capacitor satisfies the following expression: Wherein, L is the life of the aluminum electrolytic capacitor; L0 is the life of the aluminum electrolytic capacitor under the test conditions; U is the actual operating voltage of the aluminum electrolytic capacitor; U0 is the voltage of the aluminum electrolytic capacitor under the test conditions; n1 is the proportional factor; T is the external ambient temperature of the aluminum electrolytic capacitor; T0 is the external ambient temperature of the aluminum electrolytic capacitor under the test conditions; It represents the influence factor of the external environment temperature on the service life of the aluminum electrolytic capacitor.

9. The method according to claim 1, characterized in that The current operation data of the charging module includes: The operating status of the charging module, output power, power device shell temperature, aluminum electrolytic capacitor shell temperature and external environment temperature; Among them, the operating status, output power, and external ambient temperature information are used to obtain power device losses and aluminum electrolytic capacitor losses, the shell temperature of the power device is used to verify the junction temperature data of the power device, and the shell temperature of the aluminum electrolytic capacitor is used to verify the hot spot temperature of the aluminum electrolytic capacitor.

10. A DC charging pile charging module remaining life online evaluation system, characterized in that: The system comprises: A device loss acquisition unit, used to obtain the power device loss and aluminum electrolytic capacitor loss in the charging module according to the current operation data of the charging module; A first loss prediction unit is used to perform external characteristic equivalence on the external environment temperature and the power device loss to determine the junction temperature data of the power device; perform life prediction based on the junction temperature data of the power device to determine the fatigue cumulative damage of the power device; A second loss prediction unit is used to perform external characteristic equivalence on the external environment temperature and the aluminum electrolytic capacitor loss to determine the hot spot temperature of the aluminum electrolytic capacitor; perform life prediction based on the hot spot temperature of the aluminum electrolytic capacitor to determine the fatigue cumulative damage of the aluminum electrolytic capacitor; The remaining life evaluation unit is used to evaluate the remaining life of the charging module according to the fatigue cumulative damage of the power device and the fatigue cumulative damage of the aluminum electrolytic capacitor, combined with the historical fatigue cumulative damage of the power device and the aluminum electrolytic capacitor, using a linear fatigue cumulative damage criterion.

11. The system according to claim 10, characterized in that The first loss prediction unit comprises: A junction temperature data acquisition subunit, used to determine the junction temperature data of the power device by performing external characteristic equivalence on the external environment temperature and the power device loss using a local network thermal circuit model; A data processing subunit, configured to determine a junction temperature mean and a junction temperature fluctuation amplitude of the power device by processing the junction temperature data using a rain flow counting method; The first loss prediction subunit is used to predict the consumed life of the power device according to the junction temperature mean and junction temperature fluctuation amplitude of the power device using the Lesit life prediction model, and use the consumed life of the power device as the fatigue accumulated damage of the power device.

12. The system according to claim 11, characterized in that The local network thermal circuit model of the junction temperature data acquisition subunit satisfies the following expression: In the formula, Z jc is the thermal impedance from the heat source to the shell of the power device obtained after the external characteristics of the power device in the local network thermal circuit model, which is used to obtain the junction temperature data of the power device; i is the i-th order of the determined local network thermal circuit model; R th_i is the i-th order thermal resistance in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device, wherein the dynamic thermal impedance curve of the power device is determined according to the external environment temperature and the power device loss; C th_i is the i-th order heat capacity in the local network thermal circuit model, obtained by fitting the dynamic thermal impedance curve of the power device; τ i is the thermal time constant of the i-th order in the local network thermal circuit model, which is obtained by fitting the dynamic thermal impedance curve of the power device; e is a natural constant; and t is time.

13. The system according to claim 11, characterized in that The junction temperature data acquisition subunit determines the junction temperature data of the power device, which satisfies the following expression: Where, T j is the junction temperature data of the power device; P z_C is the total power loss of the power devices in the charging module, as described above; Z jc The thermal impedance from the heat source of the power device in the charging module to the housing is equivalent to the local network thermal circuit model; T c is the shell temperature of the power device in the charging module; T a is the ambient temperature; Z ch The thermal impedance from the housing of the power device to the heat sink in the charging module is obtained by using a local network thermal circuit model to perform external characteristic equivalence; Z ha is the thermal impedance from the heat sink case to the environment.

14. The system of claim 13, wherein: The power device loss prediction subunit determines the consumed life of the power device, satisfying the following expression: Where N f is the lifetime of the power device predicted by the Lesit lifetime prediction model; A is a constant; ΔT j is the junction temperature fluctuation amplitude of the power device; m is a constant; E a is the activation energy of the power device; K b is the Boltzmann constant, which is 8.617×10 -5 eV / K; T jm is the average junction temperature of the power device.

15. The system of claim 10, wherein: The second loss prediction unit comprises: A hot spot temperature acquisition subunit is used to determine the hot spot temperature of the aluminum electrolytic capacitor by performing external characteristic equivalence on the external environment temperature and the aluminum electrolytic capacitor loss using an aluminum electrolytic capacitor thermal model; The second loss prediction subunit is used to predict the consumed life of the aluminum electrolytic capacitor according to the hot spot temperature of the aluminum electrolytic capacitor using the aluminum electrolytic capacitor life prediction model, and use the consumed life of the aluminum electrolytic capacitor as the fatigue accumulated damage of the aluminum electrolytic capacitor.

16. The system of claim 15, wherein: The hot spot temperature acquisition subunit determines the hot spot temperature of the aluminum electrolytic capacitor, which satisfies the following expression: Where, T h is the hot spot temperature of the aluminum electrolytic capacitor; R ESR (f j ) is the frequency f after Fourier decomposition of the aluminum electrolytic capacitor j The equivalent series impedance corresponding to the ripple current; f j is the frequency of Fourier decomposition; n is the frequency number of Fourier decomposition of the ripple current of the aluminum electrolytic capacitor; The frequency f is obtained by Fourier decomposition of the ripple current of the aluminum electrolytic capacitor. j The square of the corresponding effective value; is the aluminum electrolytic capacitor loss; R th is the sum of the thermal resistance from the hot spot to the shell and the thermal resistance from the shell to the environment of the aluminum electrolytic capacitor obtained by using the thermal model of the aluminum electrolytic capacitor to perform external characteristic equivalence; T a is the external environment temperature.

17. The system of claim 15, wherein: The second loss prediction subunit determines the consumed life of the aluminum electrolytic capacitor, satisfying the following expression: Wherein, L is the life of the aluminum electrolytic capacitor; L0 is the life of the aluminum electrolytic capacitor under test conditions; U is the actual operating voltage of the aluminum electrolytic capacitor, which is determined by fitting according to the hot spot temperature of the aluminum electrolytic capacitor; U0 is the voltage of the aluminum electrolytic capacitor under test conditions; n1 is the proportional factor; T is the ambient temperature of the aluminum electrolytic capacitor; T0 is the Kelvin temperature of the capacitor test; It represents the influence factor of the external environment temperature on the service life of the aluminum electrolytic capacitor.

18. The system of claim 10, wherein: The current operation data of the charging module acquired by the device loss acquisition unit includes: The operating status of the charging module, output power, power device shell temperature, aluminum electrolytic capacitor shell temperature and external environment temperature; Among them, the operating status, output power, and external ambient temperature information of the charging module are used to calculate the power device loss and the aluminum electrolytic capacitor loss, the shell temperature of the power device is used to verify the junction temperature data of the power device, and the shell temperature of the aluminum electrolytic capacitor is used to verify the hot spot temperature of the aluminum electrolytic capacitor.

19. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an online evaluation method for the remaining life of a charging module of a DC charging pile as described in any one of claims 1 to 9 is implemented.

20. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, an online evaluation method for the remaining life of a charging module of a DC charging pile as described in any one of claims 1 to 9 is implemented.

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