WO2024051370A1

WO2024051370A1 - Aging detection method, aging detection device, and computer readable storage medium

Info

Publication number: WO2024051370A1
Application number: PCT/CN2023/109085
Authority: WO
Inventors: 孙永朝; 王凯; 王蓓
Original assignee: 蔚来动力科技(合肥)有限公司
Priority date: 2022-09-09
Filing date: 2023-07-25
Publication date: 2024-03-14
Also published as: CN117723307A

Abstract

An aging detection method for an electric drive system. The electric drive system (100) comprises a motor (110), a motor control device (120), and a cooling mechanism for the motor (110) and the motor control device (120). The method comprises the following steps: S100: on the basis of vehicle state parameters, determining whether a vehicle is in a preset working condition; S200: in response to the fact that the vehicle is in the preset working condition, determining an overall aging degree of the electric drive system on the basis of monitored data of the cooling mechanism in a preset time, the monitored data comprising an inlet temperature and an outlet temperature of a cooling medium at the motor control device, an inlet temperature and an outlet temperature of the cooling medium at the motor, and a cooling medium flow rate; and S300: in response to the fact that the overall aging degree reaches a preset aging level, determining an aging type according to an aging root cause positioning model, the aging type representing location information of the aging related to each sub-component of the motor and the motor control device. Also provided are an aging detection device for an electric drive system, and a computer readable storage medium.

Description

Aging detection method, aging detection device and computer-readable storage medium

Technical field

The present invention relates to an aging detection method for an electric drive system, an aging detection device and a computer-readable storage medium for executing the method.

Background technique

The electric drive system is the main component used to realize the drive control of converting high-voltage electrical energy to mechanical energy and the feedback control of converting mechanical energy to high-voltage electrical energy. During its operation, key components in the electric drive system (such as power modules, busbars, capacitors, shaft teeth, etc.) will carry loads such as high voltage, alternating high current, high speed, high torque or high heat. This is New energy vehicles are prone to weak links that cause fatigue failure.

At the current stage, diagnosis of abnormal status or aging of key components of the electric drive system is either missing, or abnormal diagnosis is performed indirectly through torque monitoring or sensor values such as temperature sensors, current sensors, resolvers, etc. As a result, the vehicle There is insufficient downgrade operation time before the fault stops, and the vehicle driver lacks sufficient warning time to perform emergency avoidance operations on the vehicle. If a vehicle breaks down while driving at high speed, it is extremely dangerous if power is suddenly lost or power is increased unexpectedly. On the other hand, if a fault is diagnosed only after it occurs, the damage is often greater and the repair cost is higher.

If the health status or abnormality of components can be accurately detected, early warning or maintenance can be carried out when the health of the component falls below a certain value or an abnormality is detected, which will help improve the reliability of the product throughout its life cycle.

Currently, there are the following detection methods for fault detection of electric drive systems, that is, by detecting whether the entire electric drive system, especially the cooling system associated with it, has an over-temperature phenomenon. If the detection result is positive, it is determined that there is a fault in the electric drive system. . On the one hand, this fault detection method cannot determine the specific location of the fault in the electric drive system. On the other hand, the impact of individual component failures on the overall temperature rise of the cooling system is relatively insignificant and therefore it is impossible to achieve better performance. Fault detection effect.

Contents of the invention

According to different aspects, the object of the present invention is to provide an improved aging detection method for an electric drive system as well as an aging detection device and a computer-readable storage medium, wherein the detection method can realize its aging position with less effort. position.

In addition, the present invention also aims to solve or alleviate other technical problems existing in the prior art.

The present invention solves the above problems by providing an aging detection method. Specifically, the electric drive system includes a motor, a motor control device, and a cooling mechanism for the motor and the motor control device, which includes the following steps:

S100: Based on vehicle status parameters, determine whether the vehicle is in preset working conditions;

S200: In response to the vehicle being in the preset working condition, determine the overall aging degree of the electric drive system based on the monitoring data of the cooling mechanism within the preset time. The monitoring data includes the inlet of the cooling medium at the motor control device. temperature and outlet temperature, the inlet and outlet temperatures of the cooling medium at the motor and the cooling medium flow rate; and

S300: In response to the overall aging degree reaching a preset aging level, determine an aging type according to the aging root cause positioning model, and the aging type represents the aging position information about each sub-component of the motor and the motor control device.

According to the aging detection method proposed in one aspect of the present invention, in step S100, it is determined whether the vehicle is in the predetermined state based on a KNN clustering model that has been trained in advance with multiple sets of training data including the vehicle status parameters and corresponding labels. Assume that the vehicle status parameters include at least one of a motor output torque value, a motor speed value, a current value, a voltage value, and a cooling medium flow rate; the label represents a preset working condition type.

According to the aging detection method proposed in one aspect of the present invention, step S200 includes the following sub-steps:

S210: Obtain the monitoring data of the cooling mechanism within the preset time and input the monitoring data into the trained preliminary diagnosis model. The trained preliminary diagnosis model is based on the preset working conditions according to the multivariate statistical analysis method. Construct based on health history data;

S220: According to the trained preliminary diagnosis model, obtain the deviation statistical value of the monitoring data and compare it with a preset threshold; and

S230: In response to the deviation statistical value exceeding the preset threshold, determine the overall aging degree of the electric drive system.

According to the aging detection method proposed in one aspect of the present invention, step S300 includes the following sub-steps:

S310: In response to the overall aging degree of the electric drive system reaching the preset aging level, obtain the monitored temperature parameters of each subcomponent within the preset time and/or the monitored cooling temperature of the cooling mechanism at each subcomponent. Parameters; the monitored cooling temperature parameters are the inlet temperature and outlet temperature of the cooling medium at each subcomponent respectively; and

S320: Determine the aging type based on the monitoring temperature parameters and/or monitoring cooling temperature parameters with the help of the aging root cause model, wherein the aging root cause model is based on multiple groups including health monitoring temperature parameters and /Or health monitoring cooling temperature parameter training data to construct.

According to the aging detection method proposed by one aspect of the present invention, in sub-step S310, based on the thermal resistance of the cooling medium contained in the cooling mechanism, the thermal resistance of each subcomponent and the monitored temperature parameters of each subcomponent, the said The cooling mechanism monitors cooling temperature parameters at each sub-component.

According to the aging detection method proposed in one aspect of the present invention, sub-step S320 includes the following steps:

S321: According to the aging root cause model, obtain the monitoring contribution rate of the monitoring temperature parameter and/or the monitoring cooling temperature parameter and the deviation between the monitoring contribution rate and the theoretical contribution rate, wherein the theoretical contribution rate is based on the predetermined Assuming the phase under working conditions The corresponding health monitoring temperature parameters and/or health monitoring cooling temperature parameters are obtained; and

S322: Obtain the maximum value of the deviation between the monitored contribution rate and the theoretical contribution rate, and determine the subcomponent assigned to the maximum value as the aging location information.

According to the aging detection method proposed in one aspect of the present invention, the subcomponents assigned to the motor control device include busbars, capacitors, power modules, vehicle chargers, converters, and high-voltage junction boxes; Subassemblies include the motor stator.

According to another aspect of the present invention, an aging detection device for an electric drive system is provided. The electric drive system includes a motor, a motor control device, and a cooling mechanism for the motor and the motor control device, which includes:

memory;

processor;

A computer program stored on the memory and executable on the processor. The execution of the computer program causes the aging detection method described above to be executed. The aging detection method includes the following steps:

According to the aging detection device proposed by another aspect of the present invention, when performing step S100, it is determined whether the vehicle is in the desired state based on the KNN clustering model that has been trained in advance with multiple sets of training data including the vehicle status parameters and corresponding labels. In the preset operating conditions, the vehicle status parameters include at least one of motor output torque value, motor speed value, current value, voltage value, and cooling medium flow rate; and the label represents the preset operating condition type.

According to the aging detection device proposed by another aspect of the present invention, when executing step S200, the following sub-steps are executed:

According to the aging detection device proposed by another aspect of the present invention, when executing step S300, the following sub-steps are executed:

According to the aging detection device proposed by another aspect of the present invention, when performing sub-step S310, based on the thermal resistance of the cooling medium contained in the cooling mechanism, the thermal resistance of each sub-component and the monitored temperature parameters of each sub-component, obtain The cooling mechanism monitors cooling temperature parameters at each subcomponent.

According to the aging detection device proposed by another aspect of the present invention, when executing sub-step S320, the following steps are executed:

S321: According to the aging root cause model, obtain the monitoring contribution rate of the monitoring temperature parameter and/or the monitoring cooling temperature parameter and the deviation between the monitoring contribution rate and the theoretical contribution rate, wherein the theoretical contribution rate is based on the predetermined Obtain the corresponding health monitoring temperature parameters and/or health monitoring cooling temperature parameters under the operating conditions; and

According to the aging detection device proposed by another aspect of the present invention, the subcomponents assigned to the motor control device include busbars, capacitors, power modules, vehicle chargers, converters, and high-voltage junction boxes; The subassemblies include the motor stator.

According to yet another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. The computer program, when executed by a processor, can implement such an aging detection method for an electric drive system.

By combining a rough preliminary diagnosis with specific aging root cause location under certain preset operating conditions, the aging detection method according to the present invention can more accurately identify aging components of the electric drive system with less calculation effort. .

Description of the drawings

The above and other features of the present invention will become apparent with reference to the accompanying drawings, wherein:

Figure 1 shows the structure of a common electric drive system in a block diagram;

Figure 2 schematically shows the main steps of the aging detection method according to the present invention;

Figure 3 schematically shows the main sub-steps of the working condition judgment step of the aging detection method according to the present invention;

Figure 4 schematically shows the main sub-steps of the preliminary diagnosis step of the aging detection method according to the present invention;

Figure 5 schematically shows the sub-steps of the aging root cause determination step of the aging detection method according to the present invention;

Figure 6 schematically shows the relevant sub-steps derived from the aging root cause determination step of Figure 4;

Figure 7 schematically shows an aging detection device according to the present invention.

Detailed ways

It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, those of ordinary skill in the art can propose various structural methods and implementation methods that can be replaced with each other. Therefore, the following specific embodiments and drawings are only illustrative descriptions of the technical solutions of the present invention, and should not be regarded as the entirety of the present invention or as limitations or restrictions on the technical solutions of the present invention.

Orientation terms such as upper, lower, left, right, front, back, front, back, top, and bottom mentioned or may be mentioned in this specification are defined relative to the structure shown in each drawing, and they are It is a relative concept, so it may change accordingly according to its different locations and different uses. Therefore, these or other directional terms should not be construed as restrictive terms. In addition, the terms “first”, “second”, “third”, etc. or similar expressions are only used for description and differentiation purposes, and shall not be understood as indicating or implying the relative importance of the corresponding components.

First, the structure of the electric drive system of new energy vehicles is briefly explained. Referring to FIG. 1 , an embodiment of a common electric drive system 100 is shown, which overall has a motor 110 , a motor control device 120 , a gearbox 130 and an associated cooling mechanism. The motor control device includes a motor controller in the usual sense and additional control components. The motor controller includes a busbar/capacitor 121 and a power module 122; the additional control components involve an on-board charger 123 and a converter 124 (such as DC/DC converter), high voltage junction box 125. The listed additional control elements can be arranged integrally in the housing of the motor controller, as shown in FIG. 1 ; or the listed additional control elements can be arranged in the motor controller (in this case corresponding to outside the housing of the motor control device), that is, arranged separately from the motor controller. Generally, each sub-component within the motor control device 120 is arranged in series, so that the cooling medium of the cooling mechanism flows through each sub-component in sequence.

Here, the cooling medium contained in the cooling mechanism can involve cooling liquid (eg water, oil), air cooling or natural cooling. For the sake of hierarchical clarity, in the drawings only straight lines with arrows are used to show the course of the cooling medium or an exemplary cooling sequence of each sub-component, which can be understood as a cooling mechanism in an extended manner. For example, for a motor controller including a busbar/capacitor 121 and a power module 122, its cooling mechanism may be implemented as a cooling plate on which cooling ducts containing cooling medium are arranged. The cooling mechanisms for the motor control device 120 and the motor 110 can be configured identically, for example, the cooling ducts of the two are connected or involve the same cooling medium; however, it is also possible that the cooling mechanisms of the two are relatively independent or The two involve different cooling media, for example one involves water cooling and the other involves oil cooling.

In another possible embodiment that is not shown, a heat exchanger can also be provided along the direction of the cooling pipe, which will not be described again.

It should be noted that the arrangement of the components of the electric drive system, especially the sub-components of the motor control device, is not limited to the examples shown in the drawings, and can be arranged in any order according to the available vehicle space. In addition, the aging or failure of the gearbox is mainly reflected in the mechanical wear of the teeth or bearings, and the aging detection process can be implemented with reference to the method according to the present disclosure or it can also be performed in other ways.

For the above-mentioned type of electric drive system, the aging detection method according to the present invention mainly includes the following steps as shown in Figure 2:

S100 (working condition determination step): Based on the vehicle status parameters, determine whether the vehicle is in the preset working condition;

S200 (i.e. preliminary diagnosis step): In response to the vehicle being in the preset working condition, determine the overall aging degree of the electric drive system based on the monitoring data of the cooling mechanism within the preset time. The monitoring data includes the cooling medium in the preset working condition. The inlet and outlet temperatures at the motor control device, the inlet and outlet temperatures of the cooling medium at the motor, and the cooling medium flow rate; and

S300 (i.e., aging root cause positioning step): In response to the overall aging degree reaching the preset aging level, determine the aging type according to the aging root cause positioning model. The aging type represents the aging components of the motor and the motor control device. The location information of the part.

It should be noted that the step names mentioned above (and those mentioned below) are only used to distinguish between steps and facilitate the reference of steps, and do not represent the sequential relationship between steps, including the flow of the drawings. The figure is also just an example of how to implement this method. Steps can be performed in various orders or simultaneously without apparent conflict.

In the working condition determination step S100, aging detection is performed on the electric drive system only when the vehicle is in the preset working condition. Considering that different driving conditions of the vehicle will cause differences in the temperature rise of the electric drive system, especially the cooling mechanism assigned to it, the differences in each driving condition can be reflected in the motor torque, motor speed value, current value, Differences in voltage values and cooling conditions, such as cooling medium flow rates, by purposefully triggering the aging detection can eliminate interference factors caused by problems other than the electric drive system itself and thereby improve the accuracy of the aging detection.

In addition, when the preliminary diagnosis step determines that the electric drive system has reached a certain aging level, that is, when the overall aging level reaches the preset aging level, the aging detection of each sub-component is triggered. This is compared to the real-time aging detection of all sub-components. This method can reduce the computational load of the vehicle controller to a certain extent. On the other hand, through the combination of the preliminary diagnosis step and the aging root cause location step, a comprehensive aging diagnosis of the electric drive system can be achieved with less calculation.

Optionally, the working condition judgment step is implemented based on the KNN clustering model. Specifically, during the driving process of the vehicle, the corresponding vehicle status parameters are input into the trained KNN clustering model and it is directly determined whether the vehicle is in In preset operating conditions. The trained KNN clustering model is pre-trained with multiple sets of training data including relevant vehicle state parameters and corresponding labels. The vehicle status parameters are selected from the following group, which includes: motor output torque value, motor speed value, current value, voltage value, cooling medium flow rate, and the label is used to mark the type of the preset working condition.

Referring to Figure 3, it shows the specific flow of the model building process in the working condition judgment step S100. Specifically, it includes the following sub-steps:

Sub-step S110: Determine vehicle status parameters that can be used to characterize different working conditions;

Sub-step S120: Collect vehicle status parameters under all working conditions;

Sub-step S130: Determine the preset working conditions that can be used for aging detection of the electric drive system and assign corresponding labels to the preset working conditions;

Sub-step S140: Construct a KNN clustering model based on the collected vehicle status parameters and corresponding labels.

Here, in sub-step S110, one or more of the motor torque value, the current value, the voltage value (for example, the DC bus voltage value measured by the motor controller), the motor speed, and the cooling medium flow rate are exemplarily selected. To distinguish working conditions, these parameters can be read or called directly from the CAN line.

In sub-step S130, one or more typical working conditions that can be used for aging detection in all working conditions are determined as preset working conditions, which may involve common urban working conditions (for example, the motor speed is 4800 r/min, and the motor torque value is 100Nm), commonly used high-speed working conditions (for example, the motor speed is 7500r/min, the motor torque value is 50Nm), and commonly used high-speed sliding conditions (for example, the motor speed is 7500r/min, the motor torque value is 0Nm). When there are n preset working conditions, the data corresponding to the n preset working conditions are assigned corresponding labels 1 to n, and the data that does not conform to these preset working conditions are assigned labels (n+1 ), this process can also be called marking.

During the driving process of the vehicle, for example, the relevant vehicle parameters collected directly from the CAN line are transmitted to the KNN clustering model trained by the above sub-steps, and based on this, it is judged whether the vehicle is in the preset working condition. If the judgment result is positive, a preliminary aging diagnosis and subsequent determination of the specific aging type will be carried out.

The preliminary diagnosis step of the aging detection method according to the present invention can be implemented, for example, based on a multivariate statistical analysis model, which can involve but is not limited to a principal component analysis model and an independent component model. Hereinafter, the principal component analysis model (PCA for short) model) to illustrate. Specifically, the preliminary diagnosis step includes the following sub-steps as shown in Figure 4:

S210: Obtain the monitoring data of the cooling mechanism within the preset time and input it into the trained preliminary diagnosis model. The trained preliminary diagnosis model is based on the health history under the preset working conditions according to the multivariate analysis method. The data is constructed; S220: According to the trained preliminary diagnosis model, obtain the deviation statistical value of the monitoring data and compare it with the preset threshold; and

When building a preliminary diagnostic model based on the PCA model, first obtain the corresponding health history data of a new car or an electric drive system that has not aged. The parameters contained in the health history data are consistent with the monitoring data collected during the aging detection process. Same kind. For example, the health history data and corresponding detection data can relate to, but are not limited to, the inlet and outlet temperatures of the cooling medium of the cooling mechanism at the motor control device, the cooling The inlet temperature and outlet temperature of the medium in the motor and the flow rate of the cooling medium. The location information of these temperatures is marked with prism symbols in Figure 1, and the cooling medium temperature and flow rate can be obtained from the pump used to control the cooling medium. Called in the controller. If the motor controller and the additional control element are not arranged integrally, the monitoring data and the health history data may relate to the inlet and outlet temperatures of the cooling medium at the motor controller, the additional control element and the motor respectively as a whole.

The preliminary diagnosis process will be explained in more detail below, taking the vehicle driving in the first preset working condition as an example.

When building a preliminary diagnosis model, first, make the vehicle (or new vehicle) in a healthy state run for a preset time under the first preset working condition and collect relevant monitoring data within the preset time as health history data; secondly, , perform data derivation on the obtained health history data, and build a PCA model based on the derived and directly collected health history data. After constructing the PCA model, it is necessary to make a statistical judgment on the health history data, that is, to obtain its deviation statistical value and set a corresponding preset threshold. Here, the deviation statistic may relate to the T ² statistic, which is obtained by calculating the Mahalanobis distance of the score vector of the pivot in space. Of course, the deviation statistical value may also involve the Q statistic, which will not be described again.

If it is determined that the vehicle has traveled for a preset time in a preset working condition, the monitoring data within the preset time are called and the above-mentioned defined deviation statistical value (for clarity, it is called is the actual deviation statistical value). If the actual deviation statistical value exceeds the preset threshold, it is determined that the electric drive system has reached a certain aging level and the subsequent aging root cause determination step is activated. For data input into the model, it is also feasible to perform data derivation on the monitoring data and input it into the PCA model together with the original monitoring data. Here, on the one hand, it is feasible to only determine the overall aging degree of the electric drive system as a whole based on the PCA model; on the other hand, it is also feasible to directly determine a certain component of the electric drive system, such as The motor controller or the overall aging of the motor, both of which can be achieved by changing the model calculation method.

If the various sub-components of the electric drive system do not age, the temperature rise of each sub-component and the temperature rise of the cooling medium in each sub-component will have a certain changing trend under a given driving condition. Based on this, the above aging root cause determination step can be implemented based on the relevant temperature parameters based on the trained model. In an optional embodiment, the aging root cause determination step can also be implemented based on a multivariate analysis method. Specifically, as shown in Figure 5, the aging root cause determination step S300 Includes the following sub-steps:

S310: In response to the overall aging degree of the electric drive system reaching the preset aging level, obtain the monitoring temperature parameters of each subcomponent within the preset time and/or the monitoring cooling temperature parameters of the cooling mechanism at each subcomponent; the monitoring cooling The temperature parameters are the inlet temperature and outlet temperature of the cooling medium at each subcomponent respectively; and

S320: Determine the aging type based on the monitoring temperature parameters and/or monitoring cooling temperature parameters with the help of an aging root cause model, wherein the aging root cause model is based on multiple groups including health monitoring temperature parameters and/or health based on a multivariate analysis method. Training data for monitoring cooling temperature parameters are constructed.

Here, the concept of "aging type" relates to the name of each sub-component of the electric drive system. For example, the aging type can be expressed as converter aging, power module aging, etc. "Monitoring temperature parameters" and "monitoring cooling temperature parameters" relate to the actual parameters of the electric drive system under preset operating conditions and within an earlier preset time; in contrast, "health monitoring temperature parameters" and "health monitoring parameters" "Monitoring cooling temperature parameters" involves relevant parameters of unaged electric drive systems or new vehicles under the same operating conditions. The measuring points involved in monitoring the cooling temperature parameter of the cooling medium are shown as circles in FIG. 1 .

In sub-step S310, relevant temperature parameters can be measured using a temperature sensor already equipped in the electric drive system itself, such as an NTC temperature sensor, and the corresponding detected temperature parameters can optionally be input into the aging root cause model via a CAN line. However, considering that there is a certain difficulty in measuring the temperature of the cooling medium at each subcomponent, the detected cooling temperature parameter can be based on the temperature of the associated subcomponent itself (ie, the monitored temperature parameter) and the temperature of another subcomponent upstream of the subcomponent. The temperature of the cooling mechanism at a subassembly is calculated. Below, in the arrangement shown in Figure 1, taking the busbar/capacitor as an example, the outlet temperature of the cooling medium at the busbar/capacitor is calculated according to the following formula:

Among them, T _Cap is the temperature of the busbar/capacitor itself;

T _{Coolant_Cap_In} is the inlet temperature of the cooling medium at the busbar/capacitor;

P _Cap is the power loss of the busbar/capacitor;

R _Cap is the equivalent thermal resistance of the busbar/capacitor;

T _{Coolant_Cap_Out} is the outlet temperature of the cooling medium at the busbar/capacitor;

R _Coolant is the equivalent thermal resistance of the cooling medium.

In the arrangement shown in FIG. 1 , the cooling medium first flows through the busbar/capacitor. The inlet temperature T _{Coolant_Cap_In} of the cooling medium at the busbar/capacitor can be obtained directly from the vehicle controller and can be regarded as known. The temperature of the busbar/capacitor itself can be directly obtained through the existing temperature sensor on it. The calculated outlet temperature of the cooling medium at the busbar/capacitor can be regarded as a detected cooling temperature parameter assigned to the power module directly downstream of it (i.e. the cooling medium inlet temperature at the power module).

The above formula is used to calculate the outlet temperature of the cooling medium in each subcomponent, which ensures the accuracy of aging detection in a cost-effective manner. Here, it should be noted that for the calculation of the inlet temperature and outlet temperature of the cooling medium at other sub-components, reference can be made to the above description of the busbar/capacitor, which will not be described again.

The monitored temperature parameters for the power module can be determined by means of one or more NTC temperature sensors, which can be arranged, for example, on the base plate of the power module and, in the case of a plurality of such temperature sensors, be input to the root cause location. The monitored temperature parameter in the model is the average or maximum value of multiple detected temperatures. Correspondingly, the monitoring temperature parameters used for the vehicle charger may involve its key chip temperature, current change module temperature or MOS tube temperature. In addition, the monitored temperature parameters of the converter may involve its key chip temperature, transformer module parameters or its MOS tube temperature. For the motor or motor stator, its temperature can be obtained by the NTC temperature sensor arranged on the stator.

For sub-step S320, it includes the following steps as shown in Figure 6:

S321: According to the aging root cause model, obtain the monitoring contribution rate of the monitoring temperature parameter and/or the monitoring cooling temperature parameter and the deviation between the monitoring contribution rate and the theoretical contribution rate, wherein the theoretical contribution rate is based on the preset working time. The corresponding health monitoring temperature parameters and/or health monitoring cooling temperature parameters are obtained under the condition; and

Next, the aging root cause determination process is described in more detail, which can be divided into the early model construction and the subsequent positioning process. First, an aging root cause model is constructed based on the health parameters of the unaged electric drive system or new vehicle (including health monitoring temperature parameters of each subcomponent and health monitoring cooling temperature parameters of the cooling medium) and their corresponding derived data and calculated in The theoretical contribution rate of each parameter in a healthy state. Secondly, the constructed aging root cause model can be deployed on the cloud and/or on the vehicle side. During the driving process of the vehicle, if the aging root cause determination step is activated, the corresponding parameters described above are collected and input into the aging root cause model. The actual contribution rate of each parameter in the current state is calculated through the aging root cause model. Next, the theoretical contribution rate and the actual contribution rate are compared, and the subcomponent assigned to the parameter with the largest deviation between the two is determined as the aging position. Here, the name of the aging component can be directly transmitted to the vehicle controller in the form of a signal, so that the vehicle controller can then use this information to make torque redistribution, degradation operation, or instrument display based on the aging degree of each subcomponent. Warning, so that maintenance personnel can carry out targeted maintenance on the electric drive system.

It should be noted that the above is only an exemplary implementation for realizing aging root cause determination, and it can also be implemented in any other manner. For example, it is also possible to simply compare the difference between the inlet temperature and outlet temperature of the cooling medium in each subcomponent, and compare the difference with the theoretical difference under the same preset working condition. If the deviation between the two is too large, it can be determined that the subcomponent is abnormal.

To sum up, by combining a rough preliminary diagnosis with specific aging root cause location under certain preset working conditions, the aging detection method according to the present invention can more accurately identify electrical faults with less calculation and more accurate. Aging parts of the drive system. In an optional embodiment, by performing the above steps with the help of a multivariate analysis model, the efficiency of aging detection can be significantly improved. In another optional embodiment, instead of directly measuring the temperature of the cooling medium in each subcomponent, which is technically difficult to achieve, the aging detection method according to the present invention provides a simple and easier to implement calculation method.

Referring to FIG. 6 , a schematic diagram of an aging detection device 200 according to another aspect of the present invention is shown, which includes a memory 210 (eg, non-volatile memory such as flash memory, ROM, hard drive, magnetic disk, optical disk, etc.) , a processor 220 and a computer program 230 stored on the memory 210 and executable on the processor 220. The operation of the computer program implements the method for electric drive according to one or more embodiments of the present invention. Systematic aging detection method. For a description of this device, please refer to the above description of the aging detection method, which will not be described again.

Optionally, the aging detection device 200 can be a cloud computing device. For example, the memory 210 and the processor 220 as cloud computing resources can not only be located in the same physical device (eg, the same server), but also can be located at different physical devices (eg, different servers). In addition, the aging detection device can also be integrated in the vehicle controller or form a part of the vehicle controller; or it can also be integrated in the motor controller or form a part thereof.

Furthermore, the present invention also relates to a computer-readable storage medium for implementing an aging detection method for an electric drive system according to one or more embodiments of the present invention. The computer-readable storage media mentioned herein includes various types of computer storage media and can be any available media that can be accessed by a general-purpose or special-purpose computer. By way of example, computer-readable storage media may include RAM, ROM, EPROM, E2PROM, registers, hard disks, removable disks, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or may be used for portability or storage. Any other transitory or non-transitory medium having desired program code units in the form of instructions or data structures and capable of being accessed by a general or special purpose computer, or a general or special purpose processor. For the description of the computer-readable storage medium according to the present invention, reference can be made to the explanation of the method according to the present invention, which will not be described again.

It should be understood that all the above preferred embodiments are illustrative rather than restrictive, and those skilled in the art should make various modifications or variations to the specific embodiments described above under the concept of the present invention. within the scope of legal protection of this invention.

Claims

An aging detection method for an electric drive system. The electric drive system includes a motor, a motor control device, and a cooling mechanism for the motor and the motor control device. It is characterized by including the following steps:

S100: Based on vehicle status parameters, determine whether the vehicle is in preset working conditions;

S200: In response to the vehicle being in the preset working condition, determine the overall aging degree of the electric drive system based on the monitoring data of the cooling mechanism within the preset time. The monitoring data includes the inlet of the cooling medium at the motor control device. temperature and outlet temperature, the inlet and outlet temperatures of the cooling medium at the motor and the cooling medium flow rate; and

S300: In response to the overall aging degree reaching a preset aging level, determine an aging type according to the aging root cause positioning model, and the aging type represents the aging position information about each sub-component of the motor and the motor control device.
The aging detection method according to claim 1, characterized in that, in step S100, it is judged whether the vehicle is in a certain state according to a KNN clustering model that has been trained in advance with multiple sets of training data including the vehicle status parameters and corresponding labels. In the preset operating conditions, the vehicle status parameters include at least one of motor output torque value, motor speed value, current value, voltage value, and cooling medium flow rate; and the label represents the preset operating condition type.
The aging detection method according to claim 2, characterized in that step S200 includes the following sub-steps:

S210: Obtain the monitoring data of the cooling mechanism within the preset time and input the monitoring data into the trained preliminary diagnosis model. The trained preliminary diagnosis model is based on the preset working conditions according to the multivariate statistical analysis method. Construct based on health history data;

S220: According to the trained preliminary diagnosis model, obtain the deviation statistical value of the monitoring data and compare it with a preset threshold; and

S230: In response to the deviation statistical value exceeding the preset threshold, determine the overall aging degree of the electric drive system.
The aging detection method according to any one of claims 1 to 3, characterized in that step S300 includes the following sub-steps:

S310: In response to the overall aging degree of the electric drive system reaching the preset aging level, obtain the monitored temperature parameters of each subcomponent within the preset time and/or the monitored cooling temperature of the cooling mechanism at each subcomponent. Parameters; the monitored cooling temperature parameters are the inlet temperature and outlet temperature of the cooling medium at each subcomponent respectively; and

S320: Determine the aging type based on the monitoring temperature parameters and/or monitoring cooling temperature parameters with the help of the aging root cause model, wherein the aging root cause model is based on multiple groups including health monitoring temperature parameters and /Or health monitoring cooling temperature parameter training data to construct.
The aging detection method according to claim 4, characterized in that, in sub-step S310, based on the thermal resistance of the cooling medium contained in the cooling mechanism, the thermal resistance of each sub-component and the monitored temperature parameters of each sub-component, the The cooling mechanism monitors cooling temperature parameters at each subcomponent.
The aging detection method according to claim 5, characterized in that sub-step S320 includes the following steps:

S321: According to the aging root cause model, obtain the monitoring contribution rate of the monitoring temperature parameter and/or the monitoring cooling temperature parameter and the deviation between the monitoring contribution rate and the theoretical contribution rate, wherein the theoretical contribution rate is based on the predetermined Obtain the corresponding health monitoring temperature parameters and/or health monitoring cooling temperature parameters under the operating conditions; and

S322: Obtain the maximum value of the deviation between the monitored contribution rate and the theoretical contribution rate, and determine the subcomponent assigned to the maximum value as the aging location information.
The aging detection method according to claim 1, characterized in that, the sub-components assigned to the motor control device include busbars, capacitors, power modules, vehicle chargers, converters, and high-voltage junction boxes; Subcomponents of the motor include the motor stator.
An aging detection device for an electric drive system. The electric drive system includes a motor, a motor control device, and a cooling mechanism for the motor and the motor control device. It is characterized in that it includes:

memory;

processor;

A computer program stored on the memory and executable on the processor, the execution of which causes the aging detection method according to any one of claims 1 to 7 to be executed, the aging detection method Includes the following steps:

S100: Based on vehicle status parameters, determine whether the vehicle is in preset working conditions;

S200: In response to the vehicle being in the preset working condition, determine the overall aging degree of the electric drive system based on the monitoring data of the cooling mechanism within the preset time. The monitoring data includes the inlet of the cooling medium at the motor control device. temperature and outlet temperature, the inlet and outlet temperatures of the cooling medium at the motor and the cooling medium flow rate; and

S300: In response to the overall aging degree reaching a preset aging level, determine an aging type according to the aging root cause positioning model, and the aging type represents the aging position information about each sub-component of the motor and the motor control device.
The aging detection device according to claim 8, characterized in that when executing step S100, it is determined whether the vehicle is in the In the preset working condition, the vehicle status parameters include at least one of motor output torque value, motor speed value, current value, voltage value, and cooling medium flow rate; and the label represents the preset working condition type.
The aging detection device according to claim 9, characterized in that when performing step S200, the following sub-steps are performed:

S210: Obtain the monitoring data of the cooling mechanism within the preset time and input the monitoring data into the trained preliminary diagnosis model. The trained preliminary diagnosis model is based on the preset working conditions according to the multivariate statistical analysis method. Construct based on health history data;

S220: According to the trained preliminary diagnosis model, obtain the deviation statistical value of the monitoring data and compare it with a preset threshold; and

S230: In response to the deviation statistical value exceeding the preset threshold, determine the overall aging degree of the electric drive system.
The aging detection device according to any one of claims 8 to 10, characterized in that when executing step S300, the following sub-steps are executed:

S310: In response to the overall aging degree of the electric drive system reaching the preset aging level, obtain the monitored temperature parameters of each subcomponent within the preset time and/or the monitored cooling temperature of the cooling mechanism at each subcomponent. Parameters; the monitored cooling temperature parameters are the inlet temperature and outlet temperature of the cooling medium at each subcomponent respectively; and

S320: Determine the aging type based on the monitoring temperature parameters and/or monitoring cooling temperature parameters with the help of the aging root cause model, wherein the aging root cause model is based on multiple groups including health monitoring temperature parameters and /Or health monitoring cooling temperature parameter training data to construct.
The aging detection device according to claim 11, characterized in that when performing sub-step S310, based on the thermal resistance of the cooling medium contained in the cooling mechanism, the thermal resistance of each sub-component and the monitored temperature parameters of each sub-component, Obtain the monitored cooling temperature parameters of the cooling mechanism at each subcomponent.
The aging detection device according to claim 12, characterized in that when executing sub-step S320, the following steps are executed:

S321: According to the aging root cause model, obtain the monitoring contribution rate of the monitoring temperature parameter and/or the monitoring cooling temperature parameter and the deviation between the monitoring contribution rate and the theoretical contribution rate, wherein the theoretical contribution rate is based on the predetermined Obtain the corresponding health monitoring temperature parameters and/or health monitoring cooling temperature parameters under the operating conditions; and

S322: Obtain the maximum value of the deviation between the monitored contribution rate and the theoretical contribution rate, and determine the subcomponent assigned to the maximum value as the aging location information.
The aging detection device according to claim 8, characterized in that, the sub-components assigned to the motor control device include busbars, capacitors, power modules, vehicle chargers, converters, and high-voltage junction boxes; Subcomponents of the motor include the motor stator.
A computer-readable storage medium, a computer program is stored on the computer-readable storage medium, characterized in that when the computer program is executed by a processor, the use according to any one of claims 1 to 7 is realized. Aging detection method for electric drive system.