Method and device for predicting health state of electric compressor and storage medium

Abstract

The invention discloses a health state prediction method and device of an electric compressor and a storage medium. The method comprises the following steps: acquiring characteristic data of the electric compressor running under a target working condition; the characteristic data are input into a compressor efficiency model, the performance index of the electric compressor is obtained, and the performance index is represented through at least one of the mechanical efficiency and the output power of the electric compressor; according to the performance indexes, standard health factors are determined, and the standard health factors are used for evaluating the actual performance of the electric compressor under the target working condition; and determining a degradation model of the electric compressor based on the standardized health factor, and predicting the health state of the electric compressor according to the degradation model. According to the method and the device, the problem that the prediction accuracy is low when the health state of the compressor system part is predicted in the prior art is solved.

Description

Background Art

[0002] For pure electric vehicles, the heat pump air-conditioning system not only meets the comfort requirements of the passenger compartment, but also shoulders the cooling and preheating requirements of the power battery and electric drive system. The electric compressor is the core component of the heat pump air-conditioning system. Fatigue use of the electric compressor will gradually reduce its performance. Therefore, in order to meet the design requirements of the entire vehicle, it is particularly important to predict the health status of the electric compressor.

[0003] However, due to the lack of monitoring and analysis of key system indicators in the relevant technology, there is a serious lag in the prediction of the health status of components, resulting in low prediction accuracy when predicting the health status of compressor system parts.

[0004] With regard to the problem of low prediction accuracy when predicting the health status of compressor system parts in related technologies, no effective solution has been proposed so far. Summary of the invention

[0005] The main purpose of the present application is to provide a method, device and storage medium for predicting the health status of an electric compressor, so as to solve the problem of low prediction accuracy when predicting the health status of compressor system parts in the related art.

[0006] In order to achieve the above-mentioned purpose, according to the first aspect of the present application, a method for predicting the health status of an electric compressor is provided. The method comprises: obtaining characteristic data of the electric compressor operating under target working conditions; inputting the characteristic data into a compressor efficiency model to obtain a performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor; determining a standardized health factor according to the performance index, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working conditions; determining a degradation model of the electric compressor based on the standardized health factor, and predicting the health status of the electric compressor according to the degradation model.

[0007] In the technical solution of the embodiment of the present application, on the one hand, by inputting characteristic data into the compressor efficiency model, the performance index is accurately outputted, and on the other hand, by determining the standardization factor and constructing the degradation model according to the performance index, the prediction accuracy is improved when predicting the health status of the electric compressor.

[0008] Furthermore, the mechanical efficiency of the electric compressor includes: critical mechanical efficiency, theoretical mechanical efficiency, and actual mechanical efficiency, wherein the standardized health factor is determined based on the performance index, including: determining the critical efficiency health factor corresponding to the critical mechanical efficiency of the electric compressor; determining the theoretical efficiency health factor corresponding to the theoretical mechanical efficiency of the electric compressor; and determining the standardized health factor based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor.

[0009] In the technical solution of the embodiment of the present application, by setting the critical efficiency health factor and the theoretical efficiency health factor, and then determining the standardized health factor corresponding to the actual mechanical efficiency, the subsequent accurate prediction of the degradation trend information of the electric compressor can be achieved.

[0010] Furthermore, a standardized health factor is determined based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor, including: performing curve fitting based on the critical efficiency health factor and the theoretical efficiency health factor to obtain a curve function, wherein the curve function is used to characterize the relationship between the health factor and the mechanical efficiency; and determining the standardized health factor based on the curve function and the actual mechanical efficiency.

[0011] In the technical solution of the embodiment of the present application, by setting the critical efficiency health factor and the theoretical efficiency health factor, the curve function is constructed to accurately calculate the standardized health factor, which can achieve subsequent accurate prediction of the degradation trend information of the electric compressor.

[0012] Furthermore, curve fitting is performed according to the critical efficiency health factor and the theoretical efficiency health factor, including: respectively obtaining the factor value of the critical efficiency health factor, the efficiency value of the critical mechanical efficiency, the factor value of the theoretical efficiency health factor, and the efficiency value of the theoretical mechanical efficiency; determining the independent variable data according to the factor value of the critical efficiency health factor and the factor value of the theoretical efficiency health factor; determining the dependent variable data according to the efficiency value of the critical mechanical efficiency and the efficiency value of the theoretical mechanical efficiency; and curve fitting is performed between the independent variable data and the dependent variable data by the least squares method.

[0013] In the technical solution of the embodiment of the present application, two coordinate points can be constructed based on the critical efficiency health factor and the theoretical efficiency health factor. For example, the two coordinate points can be a critical coordinate point and a theoretical coordinate point. The independent variable data and the dependent variable data of the critical coordinate point include the critical mechanical efficiency and the critical efficiency health factor. The independent variable data and the dependent variable data of the theoretical coordinate point include the theoretical mechanical efficiency and the theoretical efficiency health factor. Curve fitting is performed on the independent variable data and the dependent variable data based on the least squares method, so that subsequent accurate prediction of the degradation trend information of the electric compressor can be achieved.

[0014] Furthermore, the output power of the electric compressor includes: critical output power, theoretical output power, and actual output power, wherein the standardized health factor is determined based on the performance indicator, including: determining the critical power health factor corresponding to the critical output power of the electric compressor; determining the theoretical power health factor corresponding to the theoretical output power of the electric compressor; and determining the standardized health factor based on the actual output power, the critical power health factor and the theoretical power health factor.

[0015] Furthermore, there are multiple target operating conditions, and the compressor speed under each target operating condition is different.

[0016] In the technical solution of the embodiment of the present application, the target operating conditions are distinguished by different compressor speeds, and the performance indicators of the electric compressor can be analyzed under different limited operating conditions, further improving the subsequent accurate prediction of the degradation trend information of the electric compressor.

[0017] In order to achieve the above-mentioned purpose, according to the second aspect of the present application, a method for predicting the health status of an electric compressor is provided. The method comprises: obtaining a standardized health factor, wherein the standardized health factor is obtained by obtaining characteristic data of the electric compressor operating under target working conditions, inputting the characteristic data into a compressor efficiency model, obtaining a performance index of the electric compressor, and obtaining the performance index according to the performance index, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor, wherein the standardized health factor is used to evaluate the actual performance index of the electric compressor under the target working conditions; determining a degradation model of the electric compressor based on the standardized health factor; and predicting the health status of the electric compressor according to the degradation model.

[0018] In the technical solution of another embodiment of the present application, by acquiring the standardized health factor and then determining the degradation model according to the standardized health factor, when the health status of the electric compressor is predicted according to the degradation model, the prediction accuracy is improved.

[0019] Furthermore, the standardized health factor includes at least one of the following: a standardized factor of a first operating condition, a standardized factor of a second operating condition, and a standardized factor of a third operating condition, wherein the speed of the electric compressor under the first operating condition is greater than the speed of the electric compressor under the second operating condition, and the speed of the electric compressor under the second operating condition is greater than the speed of the electric compressor under the third operating condition. Determining the degradation model of the electric compressor based on the standardized health factor includes: selecting the minimum factor among the standardized factor of the first operating condition, the standardized factor of the second operating condition, and the standardized factor of the third operating condition; and determining the degradation model according to the minimum factor.

[0020] In the technical solution of another embodiment of the present application, by determining the degradation model through the minimum factor, it is possible to accurately predict the health trend of the electric compressor while ensuring that the electric compressor operates normally with the lowest mechanical efficiency.

[0021] Furthermore, determining a degradation model based on the minimum factor includes: determining the mileage of a vehicle when it runs at performance parameters corresponding to the minimum factor, wherein the performance parameters include at least one of the following: mechanical efficiency, output power; determining the mileage time corresponding to the mileage; calculating the degradation rate of the minimum factor based on the minimum factor, mileage and mileage time; and constructing a degradation model based on the degradation rate.

[0022] In the technical solution of another embodiment of the present application, by calculating the degradation rate of the minimum factor and constructing a curve function for representing the degradation model based on the tangent points corresponding to different degradation rates, it is possible to accurately predict the health trend of the electric compressor while ensuring that the electric compressor operates normally with the lowest mechanical efficiency.

[0023] Furthermore, the health status of the electric compressor is predicted based on the degradation model, including: obtaining historical degradation data of the vehicle through a big data algorithm; training the degradation model by fitting the historical degradation data of the vehicle through a least squares method to obtain a target degradation model; and predicting the health status of the electric compressor through the target degradation model.

[0024] In the technical solution of another embodiment of the present application, the degradation model is optimally fitted by the least squares method to obtain a target degradation model. Based on the target degradation model, the current remaining service life of the compressor equipment is accurately predicted according to the moment when the predicted value exceeds the failure threshold.

[0025] In order to achieve the above-mentioned purpose, according to the third aspect of the present application, a device for predicting the health status of an electric compressor is provided. The device comprises: a first acquisition unit, used to acquire characteristic data of the electric compressor operating under target working conditions; a second acquisition unit, used to input the characteristic data into a compressor efficiency model to obtain a performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor; a first determination unit, used to determine a standardized health factor according to the performance index, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under target working conditions; a second determination unit, used to determine a degradation model of the electric compressor based on the standardized health factor, and predict the health status of the electric compressor according to the degradation model.

[0026] Furthermore, the mechanical efficiency of the electric compressor includes: critical mechanical efficiency, theoretical mechanical efficiency, and actual mechanical efficiency, wherein the first determination unit includes: a first determination subunit, used to determine the critical efficiency health factor corresponding to the critical mechanical efficiency of the electric compressor; a second determination subunit, used to determine the theoretical efficiency health factor corresponding to the theoretical mechanical efficiency of the electric compressor; and a third determination subunit, used to determine the standardized health factor based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor.

[0027] Furthermore, the third determination subunit includes: a fitting module, used to perform curve fitting according to the critical efficiency health factor and the theoretical efficiency health factor to obtain a curve function, wherein the curve function is used to characterize the relationship between the health factor and the mechanical efficiency; a first determination module, used to determine the standardized health factor according to the curve function and the actual mechanical efficiency.

[0028] Furthermore, the fitting module includes: an acquisition submodule, used to respectively acquire the factor value of the critical efficiency health factor, the efficiency value of the critical mechanical efficiency, the factor value of the theoretical efficiency health factor, and the efficiency value of the theoretical mechanical efficiency; a first determination submodule, used to determine the independent variable data according to the factor value of the critical efficiency health factor and the factor value of the theoretical efficiency health factor; a second determination submodule, used to determine the dependent variable data according to the efficiency value of the critical mechanical efficiency and the efficiency value of the theoretical mechanical efficiency; and a fitting submodule, used to perform curve fitting of the independent variable data and the dependent variable data by the least squares method.

[0029] Furthermore, the output power of the electric compressor includes: critical output power, theoretical output power, and actual output power, wherein the first determination unit includes: a fourth determination subunit, used to determine a critical power health factor corresponding to the critical output power of the electric compressor; a fifth determination subunit, used to determine a theoretical power health factor corresponding to the theoretical output power of the electric compressor; and a sixth determination subunit, used to determine a standardized health factor based on the actual output power, the critical power health factor, and the theoretical power health factor.

[0030] Furthermore, there are multiple target operating conditions, and the compressor speed under each target operating condition is different.

[0031] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present application, a device for predicting the health status of an electric compressor is provided. The device comprises: a third acquisition unit, used to obtain a standardized health factor, wherein the standardized health factor is obtained by obtaining characteristic data of the electric compressor operating under the target working condition, inputting the characteristic data into the compressor efficiency model, obtaining the performance index of the electric compressor, and obtaining it according to the performance index, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor, wherein the standardized health factor is used to evaluate the actual performance index of the electric compressor under the target working condition; a third determination unit, used to determine the degradation model of the electric compressor based on the standardized health factor; and a prediction unit, used to predict the health status of the electric compressor according to the degradation model.

[0032] Furthermore, the standardized health factor includes at least one of the following: a standardized factor of a first operating condition, a standardized factor of a second operating condition, and a standardized factor of a third operating condition, wherein the speed of the electric compressor under the first operating condition is greater than the speed of the electric compressor under the second operating condition, and the speed of the electric compressor under the second operating condition is greater than the speed of the electric compressor under the third operating condition. The third determination unit includes: a selection subunit for selecting the minimum factor among the standardized factor of the first operating condition, the standardized factor of the second operating condition, and the standardized factor of the third operating condition; and a seventh determination subunit for determining the degradation model according to the minimum factor.

[0033] Furthermore, the seventh determination subunit includes: a second determination module, used to determine the mileage of the vehicle when it runs with the performance parameters corresponding to the minimum factor, wherein the performance parameters include at least one of the following: mechanical efficiency, output power; a third determination module, used to determine the mileage time corresponding to the mileage; a calculation module, used to calculate the degradation rate of the minimum factor based on the minimum factor, mileage and mileage time; a construction module, used to construct a degradation model based on the degradation rate.

[0034] Furthermore, the prediction unit includes: an acquisition subunit, used to acquire the historical degradation data of the vehicle through a big data algorithm; a training subunit, used to train the degradation model by fitting the historical degradation data of the vehicle through the least squares method to obtain a target degradation model; and a prediction subunit, used to predict the health status of the electric compressor through the target degradation model.

[0035] According to a fifth aspect of an embodiment of the present application, an electronic device is provided, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned methods for predicting the health status of an electric compressor is implemented.

[0036] According to a sixth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for predicting the health status of an electric compressor according to any one of the above items is implemented.

[0037] Through this application, the following steps are adopted: obtaining characteristic data of the electric compressor operating under the target working condition; inputting the characteristic data into the compressor efficiency model to obtain the performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor; determining the standardized health factor according to the performance index, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working condition; determining the degradation model of the electric compressor based on the standardized health factor, and predicting the health status of the electric compressor according to the degradation model. Through this application, the problem of low prediction accuracy when predicting the health status of the compressor system parts in the related art is solved. By determining the standardized factor and constructing the degradation model, the effect of improving the prediction accuracy when predicting the health status of the electric compressor is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0039] Figure 1 is a flow chart of a method for predicting the health status of an electric compressor provided in an embodiment of the present application;

[0040] Figure 2 is a schematic diagram of a standardized health factor generation process provided according to an embodiment of the present application;

[0041] Figure 3 is a schematic diagram of the association between mechanical efficiency and health factor provided according to an embodiment of the present application;

[0042] Figure 4 is a schematic diagram of the association between output power and health factors provided according to an embodiment of the present application;

[0043] Figure 5 is a flow chart of a method for predicting the health status of an electric compressor according to another embodiment of the present application;

[0044] Figure 6 is a schematic diagram of cloud server algorithm processing provided according to an embodiment of the present application;

[0045] Figure 7 is a curve diagram of a degradation model function provided according to an embodiment of the present application;

[0046] Figure 8 is a schematic diagram of vehicle-cloud interaction provided according to an embodiment of the present application;

[0047] Fig. 9 is a curve diagram of a target degradation model function provided according to an embodiment of the present application;

[0048] Fig.10 is a schematic diagram of a device for predicting the health status of an electric compressor according to an embodiment of the present application;

[0049] Fig.11 is a schematic diagram of a device for predicting the health status of an electric compressor according to another embodiment of the present application;

[0050] Fig.12 It is a schematic diagram of the network architecture of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0054] At present, for pure electric vehicles, the electric compressor is the core component of the heat pump air-conditioning system. Its performance degradation directly reduces the electric vehicle's range and affects the comfort of the passenger compartment. However, as the number of times the electric compressor runs and the length of its working time increase, the fatigue use of the system components will gradually reduce its performance until failure occurs, making the thermal management system unable to meet the design requirements of the entire vehicle.

[0055] For example, the following problems exist in the full life cycle monitoring of key indicators such as the aging status, performance indicators, and real-time diagnostic maintenance information of electric compressors:

[0056] The operating conditions and external environment of the electric vehicle heat pump air-conditioning system are constantly changing. The thermal management system itself involves multiple components (electric compressor, condenser, expansion valve and evaporator). The degradation of the overall system performance lacks key monitoring and analysis, resulting in serious lags in the prediction of the health status of the components, which further leads to low efficiency in the operation and maintenance of the thermal management system.

[0057] In order to solve the problem of low prediction accuracy when predicting the health status of compressor system parts, the health status prediction method of the electric compressor disclosed in the present application monitors and analyzes the key indicators of the compressor system from the vehicle side and the cloud server side respectively, and performs health analysis on the performance indicators through the standardized health factor generation algorithm on the vehicle side; the degradation model determined by the cloud server side based on the standardized health factor effectively improves the prediction accuracy of the health status of the electric compressor.

[0058] According to an embodiment of the present application, a method for predicting the health status of an electric compressor is provided.

[0059] Figure 1 FIG. 1 is a flow chart of a method for predicting the health status of an electric compressor according to an embodiment of the present application. Figure 1 As shown, the method is applied to the vehicle side or the server. This embodiment is described in detail by taking the vehicle side as an example, and mainly includes the following steps:

[0060] Step S101, acquiring characteristic data of the electric compressor operating under a target operating condition.

[0061] Among them, the electric compressor can be a scroll compressor or other types of compressors, and this application is not limited thereto. The target working condition can be a limited working condition for the compressor refrigeration capacity test. The specific working condition data can be shown in Table 1 below:

[0062] Table 1

[0063]

[0064] For another example, the characteristic data may be evaporation temperature data, condensation temperature data, suction temperature data, subcooling data, and compressor speed data under different operating conditions.

[0065] That is, there may be multiple target operating conditions mentioned in the present application, and the compressor speed under each target operating condition is different.

[0066] Step S102: inputting the characteristic data into a compressor efficiency model to obtain a performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and the output power of the electric compressor.

[0067] Among them, Figure 2 As shown, feature extraction is performed on the data in the electric compressor, and the extracted feature data is input into the compressor efficiency model to extract degradation parameters and perform regression fitting to obtain a health factor generation algorithm, wherein, depending on the target operating conditions, there may be multiple compressor efficiency models, for example, the compressor efficiency model may include: efficiency model 1, efficiency model 2, and efficiency model 3.

[0068] Among them, the efficiency model design of the characteristic working condition of the refrigeration performance analysis of the electric compressor can be shown in the following Table 2:

[0069] Table 2

[0070]

[0071]

[0072] Step S103, determining a standardized health factor according to the performance indicator, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working condition.

[0073] The normalization factor may be a value for evaluating the actual performance. For example, the normalization factor may be at least one of the values ​​0-100, such as Figure 2 As shown, the degradation parameter may be a performance index parameter of the electric compressor, such as the mechanical efficiency of the electric compressor and the output power of the electric compressor. The standardized health factor is determined based on the mechanical efficiency of the electric compressor or the output power of the electric compressor.

[0074] Specifically, the mechanical efficiency of the electric compressor includes: critical mechanical efficiency, theoretical mechanical efficiency, and actual mechanical efficiency. Among them, determining the standardized health factor based on the performance index can be achieved through the following steps: determining the critical efficiency health factor corresponding to the critical mechanical efficiency of the electric compressor; determining the theoretical efficiency health factor corresponding to the theoretical mechanical efficiency of the electric compressor; determining the standardized health factor based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor.

[0075] For example, Figure 3As shown, under the preset working condition x, the critical mechanical efficiency of the electric compressor is set to 0.5, and the corresponding critical efficiency health factor is 0; the theoretical mechanical efficiency of the electric compressor is set to 0.9, and the corresponding theoretical efficiency health factor is 100. Among them, a reduction in mechanical efficiency under any working condition will lead to a reduction in performance in the actual use scenario. The present application sets the critical efficiency health factor and the theoretical efficiency health factor, and then determines the standardized health factor corresponding to the actual mechanical efficiency, which can realize the subsequent accurate prediction of the degradation trend information of the electric compressor.

[0076] Specifically, the standardized health factor can be determined based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor. This can be achieved through the following steps: curve fitting is performed based on the critical efficiency health factor and the theoretical efficiency health factor to obtain a curve function, wherein the curve function is used to characterize the relationship between the health factor and the mechanical efficiency; and the standardized health factor is determined based on the curve function and the actual mechanical efficiency.

[0077] For example, Figure 3 As shown, two coordinate points can be constructed based on the critical efficiency health factor and the theoretical efficiency health factor. For example, the two coordinate points can be a critical coordinate point and a theoretical coordinate point. The critical coordinate point includes the critical mechanical efficiency and the critical efficiency health factor, and the theoretical coordinate point includes the theoretical mechanical efficiency and the theoretical efficiency health factor. The following is determined based on the two coordinate points: Figure 3 The curve function shown represents the relationship between the health factor and the mechanical efficiency. After the curve function is determined, the actual mechanical efficiency is calculated, and the efficiency value of the actual mechanical efficiency is substituted into the curve function to obtain the health factor corresponding to the efficiency value of the actual mechanical efficiency, which is the standardized health factor in this application, and further improves the subsequent accurate prediction of the degradation trend information of the electric compressor.

[0078] Furthermore, curve fitting based on the critical efficiency health factor and the theoretical efficiency health factor can be achieved through the following steps: respectively obtain the factor value of the critical efficiency health factor, the efficiency value of the critical mechanical efficiency, the factor value of the theoretical efficiency health factor, and the efficiency value of the theoretical mechanical efficiency; determine the independent variable data based on the factor value of the critical efficiency health factor and the factor value of the theoretical efficiency health factor; determine the dependent variable data based on the efficiency value of the critical mechanical efficiency and the efficiency value of the theoretical mechanical efficiency; and perform curve fitting between the independent variable data and the dependent variable data by the least squares method.

[0079] For example, Figure 3As shown, the factor value of the critical efficiency health factor is 0, the factor value of the theoretical efficiency health factor is 100, the efficiency value of the critical mechanical efficiency is 0.5, and the efficiency value of the theoretical mechanical efficiency is 0.9. The efficiency value and the factor value are used as the independent variable data and the dependent variable data respectively, and the two data are curve-fitted by the least squares method to obtain a curve function. Then, the efficiency value of the actual mechanical efficiency is substituted into the curve function to obtain the health factor corresponding to the efficiency value of the actual mechanical efficiency. That is, the present application uses the least squares method to use variable data to achieve accurate fitting of the curve function, and further improves the subsequent accurate prediction of the degradation trend information of the electric compressor.

[0080] Specifically, the output power of the electric compressor includes: critical output power, theoretical output power, and actual output power, wherein the standardized health factor is determined according to the performance index and can be obtained through the following steps: determining the critical power health factor corresponding to the critical output power of the electric compressor; determining the theoretical power health factor corresponding to the theoretical output power of the electric compressor; determining the standardized health factor according to the actual output power, the critical power health factor and the theoretical power health factor.

[0081] For example, Figure 4 As shown, when the critical output power of the electric compressor is 2600w, the critical power health factor is set to 0, and when the theoretical output power of the electric compressor is 4700w, the theoretical power health factor is set to 100. A curve function of the relationship between the output power and the normalization factor is constructed based on the critical power health factor and the theoretical power health factor. The actual output power is substituted into the curve function to obtain the normalized health factor of the actual output power. Similarly, the calculation method of the normalized health factor corresponding to the minimum output power and the maximum output power of the electric compressor is similar to the above, and will not be described in detail here.

[0082] This application can effectively improve the subsequent accurate prediction of the health status of the electric compressor by determining the standardized health factor based on the performance indicators.

[0083] It should be noted that the calculation relationship between the performance indicators of the scroll compressor can include: Or approximately Among them, η 机械 is the mechanical efficiency of the electric compressor, W c is the actual compression power of the electric compressor, P 输出 is the output power of the electric compressor, P 0 is the input power of the electric compressor.

[0084] Among them, W c The calculation formula is as follows:

[0085] Wc =m com (h dis -h suc ) / f Q ;

[0086] Among them, W c is the actual effective power consumption of the compressor, kW; m com is the compressor mass flow rate, kg / s; h dis is the exhaust enthalpy, kJ / kg; h suc is the suction enthalpy, kJ / kg; f Q is the heat loss coefficient (which can be regarded as a constant 1, generally between 0.9 and 1.0). com =η v V th r com / γ suc ; Among them, m com is the compressor mass flow rate, kg / s; η v is the volumetric efficiency; V th is the theoretical exhaust volume, m 3 / r;r com is the compressor speed, r / s; γ suc is the inspiratory specific volume, m 3 / kg.

[0087] Among them, the motor output power: P_output = n*Tq / 9550; n is the motor speed; Tq is the motor torque.

[0088] After obtaining the performance index of the electric compressor according to the above algorithm, the present application evaluates the actual performance of the electric compressor under the target working conditions based on the performance index, and can achieve accurate prediction of the health status of the electric compressor.

[0089] Step S104: determining a degradation model of the electric compressor based on the standardized health factor, and predicting the health state of the electric compressor according to the degradation model.

[0090] Exemplarily, the vehicle side can determine the minimum factor of the standardized health factor through a local data processing algorithm, and determine the degradation model based on the minimum factor. Specifically, the degradation model is constructed by selecting the minimum factor under the standardized factor under different working conditions, and determining the degradation rate based on the minimum factor, so as to describe the health status of the electric compressor.

[0091] In some embodiments, the vehicle side can also feed back the standardized health factor determined by the actual mechanical efficiency and output power data of the compressor under each selected working condition to the cloud server in real time, so that the cloud big data model can perform statistical analysis on the usage data of the electric compressor, and thus achieve accurate prediction of the health status of the electric compressor.

[0092] In summary, the embodiments of the present application: by acquiring characteristic data of the electric compressor operating under target conditions; inputting the characteristic data into the compressor efficiency model to obtain the performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor; determining a standardized health factor according to the performance index, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target conditions; determining the degradation model of the electric compressor based on the standardized health factor, and predicting the health status of the electric compressor according to the degradation model. Through this application, the problem of low prediction accuracy when predicting the health status of compressor system parts in the related art is solved. By determining the standardized factor and constructing the degradation model, the effect of improving the prediction accuracy when predicting the health status of the electric compressor is achieved.

[0093] According to an embodiment of the present application, a method for predicting the health status of an electric compressor is also provided.

[0094] Figure 5 FIG. 1 is a flow chart of a method for predicting the health status of an electric compressor according to an embodiment of the present application. Figure 5 As shown, the method is applied to the vehicle side or the server. This embodiment takes the server side as an example for detailed description, and includes the following steps:

[0095] S501, obtaining a standardized health factor, wherein the standardized health factor is obtained by obtaining characteristic data of the electric compressor operating under target conditions, inputting the characteristic data into a compressor efficiency model, obtaining a performance indicator of the electric compressor, and obtaining the performance indicator based on the performance indicator, wherein the performance indicator is characterized by at least one of the mechanical efficiency and output power of the electric compressor, wherein the standardized health factor is used to evaluate the actual performance indicator of the electric compressor under the target conditions.

[0096] Specifically, Figure 6 As shown, on the one hand, at the vehicle end, the data in the electric compressor is feature extracted, and then the feature data is respectively input into the efficiency model 1, the efficiency model 2, and the efficiency model 3 to obtain multiple performance indicators of the electric compressor. The degradation parameters of the degradation model are determined by the multiple performance indicator data in the later stage, and then the degradation parameters are regressed and fitted by the least squares method to obtain the standardized health factor. On the other hand, the cloud-based big data database obtains the feature data of multiple vehicles from the vehicle end, and can also obtain the single historical prior data of a single vehicle. The degradation model of the standardized health factor is determined by the single vehicle data, and the degradation model is extrapolated and predicted by the feature data of multiple vehicles, thereby realizing the prediction of the life of the electric compressor.

[0097] Among them, the relevant performance indicators of the compressor are shown in Table 3 below:

[0098] Table 3

[0099]

[0100] S503: predicting the health status of the electric compressor according to the degradation model.

[0101] The degradation model is constructed by selecting the minimum factor under the standardized factors under different working conditions and determining the degradation rate according to the minimum factor to describe the health status of the electric compressor.

[0102] Specifically, the standardized health factor includes at least one of the following: a standardized factor of a first operating condition, a standardized factor of a second operating condition, and a standardized factor of a third operating condition, wherein the speed of the electric compressor under the first operating condition is greater than the speed of the electric compressor under the second operating condition, and the speed of the electric compressor under the second operating condition is greater than the speed of the electric compressor under the third operating condition. The degradation model of the electric compressor based on the standardized health factor can be obtained by the following steps: selecting the minimum factor among the standardized factor of the first operating condition, the standardized factor of the second operating condition, and the standardized factor of the third operating condition; and determining the degradation model according to the minimum factor.

[0103] Exemplarily, the speed of the electric compressor under the first working condition is 5000, the speed of the electric compressor under the second working condition is 4000, and the speed of the electric compressor under the third working condition is 3000. For example, a reduction in mechanical efficiency under any working condition will lead to a reduction in performance under the actual usage scenario. The standardized health factors of the actual mechanical efficiency of the compressor calculated under each selected working condition are comprehensively considered, and the minimum value is taken as the final minimum factor, that is, the minimum factor = minimum value {standardized health factor 1 of the actual mechanical efficiency of the compressor under the first working condition, standardized health factor 2 of the actual mechanical efficiency of the compressor under the second working condition, standardized health factor 3 of the actual mechanical efficiency of the compressor under the third working condition}.

[0104] It should be noted that the minimum factor determined according to the normalization factor of the actual output power of the electric compressor under any working condition is similar to the above calculation method and will not be described in detail here.

[0105] The present application determines the degradation model through the minimum factor, and can achieve accurate prediction of the health trend of the electric compressor while ensuring that the electric compressor operates normally with the lowest mechanical efficiency.

[0106] Specifically, determining the degradation model according to the minimum factor can be achieved through the following steps: determining the mileage of the vehicle when it runs with the performance parameters corresponding to the minimum factor, wherein the performance parameters include at least one of the following: mechanical efficiency, output power; determining the mileage time corresponding to the mileage; calculating the degradation rate of the minimum factor according to the minimum factor, mileage and mileage time; and constructing the degradation model based on the degradation rate.

[0107] For example, if the vehicle is running at the mechanical efficiency corresponding to the minimum factor fh, the mileage S and the time T required to travel the mileage are counted, and the time taken to travel a unit mileage is calculated as t, where t=T / S, and the degradation rate v is calculated based on t, where v can be calculated by the following formula:

[0108] v = Δfh / Δt;

[0109] Among them, Δfh is the change value of the minimum factor, and Δt is the change value of the time taken to travel a unit mileage.

[0110] The present application can construct a degradation model according to the degradation rate. Specifically, the degradation model can be as follows: Figure 7 As shown in the curve function, based on the tangent points corresponding to different degradation rates, a curve function used to represent the degradation model is constructed, which can accurately predict the health trend of the electric compressor while ensuring that the electric compressor operates normally with the lowest mechanical efficiency.

[0111] It should be noted that if the vehicle runs at the output power corresponding to the minimum factor fh, the construction method of the degradation model is similar to the above and will not be described in detail here.

[0112] S503: predicting the health status of the electric compressor according to the degradation model.

[0113] Specifically, the health status of the electric compressor is predicted according to the degradation model, which is achieved through the following steps: obtaining the historical degradation data of the vehicle through a big data algorithm; training the degradation model by fitting the historical degradation data of the vehicle through the least squares method to obtain a target degradation model; and predicting the health status of the electric compressor through the target degradation model.

[0114] Exemplarily, the historical degradation data may be health factor data of the same components of various types of vehicles when in operation, for example, Figure 8 The data of Car 1, Car 2, and Car 3 in the diagram are used to obtain the historical degradation data of different vehicles using big data algorithm services based on car-to-cloud communication, and the least squares method is used to extrapolate and fit the degradation model. Specifically, the degradation model can be calibrated based on the overall degradation trend of the same components (operating environment, mileage) to obtain the target degradation model.

[0115] Among them, the curve function of the target degradation model can be obtained by Fig. 9 To express, according to Fig. 9 The time of complete failure of the vehicle during its entire life can be accurately predicted. Specifically, the degradation model is optimally fitted through the least squares method to obtain the target degradation model. Based on the target degradation model, the current remaining service life of the compressor equipment is accurately predicted according to the moment when the predicted value exceeds the failure threshold. Fig. 9 The dots on both sides of the curve represent the sampling data of the historical degradation data of different vehicles. The sampling data is fitted by the least squares method to obtain the following: Fig. 9 The curve shown, where Fig. 9 The star icon in the figure is used to identify the functional failure threshold of the electric compressor calculated based on the efficiency mechanism, and the curve derivative after the star icon is used to predict the full life of the electric compressor.

[0116] The method for predicting the health status of an electric compressor provided in an embodiment of the present application obtains a standardized health factor, wherein the standardized health factor is obtained by obtaining characteristic data of the electric compressor operating under target conditions, inputting the characteristic data into a compressor efficiency model, obtaining a performance index of the electric compressor, and obtaining the performance index based on the performance index, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor, wherein the standardized health factor is used to evaluate the actual performance index of the electric compressor under the target conditions; determine the degradation model of the electric compressor based on the standardized health factor; and predict the health status of the electric compressor based on the degradation model. Through the present application, the problem of low prediction accuracy when predicting the health status of compressor system parts in the related art is solved. By determining the standardized factor and constructing the degradation model, the effect of improving the prediction accuracy when predicting the health status of the electric compressor is achieved.

[0117] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0118] The embodiment of the present application provides a health status prediction device for an electric compressor. It should be noted that the health status prediction device for an electric compressor in the embodiment of the present application can be used to execute the health status prediction method for an electric compressor provided in the embodiment of the present application. The health status prediction device for an electric compressor provided in the embodiment of the present application is introduced below.

[0119] Fig.10 Schematic diagram of a device for predicting the health status of an electric compressor according to an embodiment of the present application. Fig.10As shown, the device includes: a first acquisition unit 1001, a second acquisition unit 1002, a first determination unit 1003, and a second determination unit 1004.

[0120] Specifically, the first acquisition unit 1001 is used to acquire characteristic data of the electric compressor operating under the target working condition;

[0121] A second acquisition unit 1002 is used to input the characteristic data into the compressor efficiency model to obtain a performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and the output power of the electric compressor;

[0122] A first determining unit 1003 is used to determine a standardized health factor according to the performance indicator, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working condition;

[0123] The second determining unit 1004 is used to determine a degradation model of the electric compressor based on the standardized health factor, and predict the health state of the electric compressor according to the degradation model.

[0124] In summary, the health status prediction device of the electric compressor provided in the embodiment of the present application obtains the characteristic data of the electric compressor operating under the target working condition through the first acquisition unit 1001; the second acquisition unit 1002 inputs the characteristic data into the compressor efficiency model to obtain the performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor; the first determination unit 1003 determines the standardized health factor according to the performance index, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working condition; the second determination unit 1004 determines the degradation model of the electric compressor based on the standardized health factor, and predicts the health status of the electric compressor according to the degradation model. Through this application, the problem of low prediction accuracy when predicting the health status of the compressor system parts in the related art is solved. By determining the standardized factor and constructing the degradation model, the effect of improving the prediction accuracy when predicting the health status of the electric compressor is achieved.

[0125] Optionally, in the health status prediction device of the electric compressor provided in the embodiment of the present application, the mechanical efficiency of the electric compressor includes: critical mechanical efficiency, theoretical mechanical efficiency, and actual mechanical efficiency, wherein the first determination unit includes: a first determination subunit, used to determine the critical efficiency health factor corresponding to the critical mechanical efficiency of the electric compressor; a second determination subunit, used to determine the theoretical efficiency health factor corresponding to the theoretical mechanical efficiency of the electric compressor; and a third determination subunit, used to determine the standardized health factor based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor.

[0126] Optionally, in the health status prediction device of the electric compressor provided in the embodiment of the present application, the third determination subunit includes: a fitting module, used to perform curve fitting according to the critical efficiency health factor and the theoretical efficiency health factor to obtain a curve function, wherein the curve function is used to characterize the relationship between the health factor and the mechanical efficiency; a first determination module, used to determine the standardized health factor based on the curve function and the actual mechanical efficiency.

[0127] Optionally, in the health status prediction device of the electric compressor provided in the embodiment of the present application, the fitting module includes: an acquisition submodule, used to respectively acquire the factor value of the critical efficiency health factor, the efficiency value of the critical mechanical efficiency, the factor value of the theoretical efficiency health factor, and the efficiency value of the theoretical mechanical efficiency; a first determination submodule, used to determine the independent variable data according to the factor value of the critical efficiency health factor and the factor value of the theoretical efficiency health factor; a second determination submodule, used to determine the dependent variable data according to the efficiency value of the critical mechanical efficiency and the efficiency value of the theoretical mechanical efficiency; and a fitting submodule, used to perform curve fitting of the independent variable data and the dependent variable data by the least squares method.

[0128] Optionally, in the health status prediction device of the electric compressor provided in an embodiment of the present application, the output power of the electric compressor includes: critical output power, theoretical output power, and actual output power, wherein the first determination unit includes: a fourth determination subunit, used to determine the critical power health factor corresponding to the critical output power of the electric compressor; a fifth determination subunit, used to determine the theoretical power health factor corresponding to the theoretical output power of the electric compressor; and a sixth determination subunit, used to determine the standardized health factor based on the actual output power, the critical power health factor and the theoretical power health factor.

[0129] Optionally, in the health status prediction device for the electric compressor provided in the embodiment of the present application, there are multiple target operating conditions, and the compressor speed under each target operating condition is different.

[0130] The embodiment of the present application also provides a health status prediction device for an electric compressor. It should be noted that the health status prediction device for an electric compressor in the embodiment of the present application can be used to execute the health status prediction method for an electric compressor provided in the embodiment of the present application. The health status prediction device for an electric compressor provided in the embodiment of the present application is introduced below.

[0131] Fig.11 Schematic diagram of a device for predicting the health status of an electric compressor according to an embodiment of the present application. Fig.11 As shown, the device includes: a third acquisition unit 1101, a third determination unit 1102, and a prediction unit 1103.

[0132] Specifically, the third acquisition unit 1101 is used to acquire a standardized health factor, wherein the standardized health factor is obtained by acquiring characteristic data of the electric compressor operating under the target working condition, inputting the characteristic data into the compressor efficiency model, obtaining a performance index of the electric compressor, and obtaining it according to the performance index, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor, wherein the standardized health factor is used to evaluate the actual performance index of the electric compressor under the target working condition;

[0133] A third determining unit 1102 is used to determine a degradation model of the electric compressor based on the normalized health factor;

[0134] The prediction unit 1103 is used to predict the health status of the electric compressor according to the degradation model.

[0135] In summary, the health status prediction device of the electric compressor provided in the embodiment of the present application obtains the standardized health factor through the third acquisition unit 1101, wherein the standardized health factor is obtained by obtaining the characteristic data of the electric compressor running under the target working condition, inputting the characteristic data into the compressor efficiency model, obtaining the performance index of the electric compressor, and obtaining it according to the performance index, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor, wherein the standardized health factor is used to evaluate the actual performance index of the electric compressor under the target working condition; the third determination unit 1102 determines the degradation model of the electric compressor based on the standardized health factor; the prediction unit 1103 predicts the health status of the electric compressor according to the degradation model, and through this application, the problem of low prediction accuracy when predicting the health status of the compressor system parts in the related art is solved. By determining the standardized factor and constructing the degradation model, the effect of improving the prediction accuracy when predicting the health status of the electric compressor is achieved.

[0136] Optionally, in the health status prediction device of the electric compressor provided in the embodiment of the present application, the standardized health factor includes at least one of the following: a standardized factor of a first operating condition, a standardized factor of a second operating condition, and a standardized factor of a third operating condition, wherein the speed of the electric compressor under the first operating condition is greater than the speed of the electric compressor under the second operating condition, and the speed of the electric compressor under the second operating condition is greater than the speed of the electric compressor under the third operating condition, and the third determination unit includes: a selection subunit for selecting the minimum factor among the standardized factor of the first operating condition, the standardized factor of the second operating condition, and the standardized factor of the third operating condition; and a seventh determination subunit for determining a degradation model based on the minimum factor.

[0137] Optionally, in the health status prediction device of the electric compressor provided in the embodiment of the present application, the seventh determination subunit includes: a second determination module, used to determine the mileage of the vehicle when it runs with the performance parameters corresponding to the minimum factor, wherein the performance parameters include at least one of the following: mechanical efficiency, output power; a third determination module, used to determine the mileage time corresponding to the mileage; a calculation module, used to calculate the degradation rate of the minimum factor based on the minimum factor, mileage and mileage time; a construction module, used to construct a degradation model based on the degradation rate.

[0138] Optionally, in the health status prediction device for the electric compressor provided in the embodiment of the present application, the prediction unit includes: an acquisition subunit, used to acquire the vehicle's historical degradation data through a big data algorithm; a training subunit, used to train the degradation model by fitting the vehicle's historical degradation data through the least squares method to obtain a target degradation model; and a prediction subunit, used to predict the health status of the electric compressor through the target degradation model.

[0139] The health status prediction device of the electric compressor includes a processor and a memory. The above-mentioned first acquisition unit 1001, the second acquisition unit 1002, the first determination unit 1003, the second determination unit 1004, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0140] The processor includes a kernel, and the kernel calls the corresponding program unit from the memory. One or more kernels can be set, and the health status of the electric compressor is predicted by adjusting kernel parameters.

[0141] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0142] In the exemplary embodiment of the present application, a computer storage medium capable of implementing the above method is also provided. A program product capable of implementing the above method of the present specification is stored thereon. In some possible embodiments, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present application described in the above "Exemplary Method" section of this specification, such as the following steps can be performed: obtain characteristic data of the electric compressor operating under the target working condition; input the characteristic data into the compressor efficiency model to obtain the performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor; determine the standardized health factor according to the performance index, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working condition; determine the degradation model of the electric compressor based on the standardized health factor, and predict the health state of the electric compressor according to the degradation model.

[0143] In an optional embodiment: determining a critical efficiency health factor corresponding to the critical mechanical efficiency of the electric compressor; determining a theoretical efficiency health factor corresponding to the theoretical mechanical efficiency of the electric compressor; determining a standardized health factor based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor.

[0144] In an optional embodiment: curve fitting is performed based on the critical efficiency health factor and the theoretical efficiency health factor to obtain a curve function, wherein the curve function is used to characterize the relationship between the health factor and the mechanical efficiency; and a standardized health factor is determined based on the curve function and the actual mechanical efficiency.

[0145] In an optional implementation: respectively obtain the factor value of the critical efficiency health factor, the efficiency value of the critical mechanical efficiency, the factor value of the theoretical efficiency health factor, and the efficiency value of the theoretical mechanical efficiency; determine the independent variable data according to the factor value of the critical efficiency health factor and the factor value of the theoretical efficiency health factor; determine the dependent variable data according to the efficiency value of the critical mechanical efficiency and the efficiency value of the theoretical mechanical efficiency; and perform curve fitting between the independent variable data and the dependent variable data by the least squares method.

[0146] In an optional embodiment: determining a critical power health factor corresponding to the critical output power of the electric compressor; determining a theoretical power health factor corresponding to the theoretical output power of the electric compressor; determining a standardized health factor based on the actual output power, the critical power health factor and the theoretical power health factor.

[0147] In an optional implementation, there are multiple target operating conditions, and the compressor speed under each target operating condition is different.

[0148] In an optional embodiment, the embodiment of the present application may also include a program product for implementing the above method, which may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0149] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0150] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0151] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0152] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0153] In addition, in an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0154] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0155] Refer to the following Fig.12 1200 according to this embodiment of the present application is described. Fig.12 The electronic device 1200 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0156] like Fig.12 As shown, the electronic device 1200 is in the form of a general computing device. The components of the electronic device 1200 may include, but are not limited to: the at least one processing unit 1210, the at least one storage unit 1220, a bus 1230 connecting different system components (including the storage unit 1220 and the processing unit 1210), and a display unit 1240.

[0157] The storage unit stores program codes, which can be executed by the processing unit 1210, so that the processing unit 1210 executes the steps described in the "Exemplary Method" section of the present specification according to various exemplary embodiments of the present application. For example, the processing unit 1210 can execute the following steps: obtaining characteristic data of the electric compressor operating under the target working condition; inputting the characteristic data into the compressor efficiency model to obtain the performance index of the electric compressor, wherein the performance index is characterized by at least one of the mechanical efficiency and output power of the electric compressor; determining a standardized health factor according to the performance index, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working condition; determining a degradation model of the electric compressor based on the standardized health factor, and predicting the health status of the electric compressor according to the degradation model.

[0158] In an optional embodiment: determining a critical efficiency health factor corresponding to the critical mechanical efficiency of the electric compressor; determining a theoretical efficiency health factor corresponding to the theoretical mechanical efficiency of the electric compressor; determining a standardized health factor based on the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor.

[0159] In an optional embodiment: curve fitting is performed based on the critical efficiency health factor and the theoretical efficiency health factor to obtain a curve function, wherein the curve function is used to characterize the relationship between the health factor and the mechanical efficiency; and a standardized health factor is determined based on the curve function and the actual mechanical efficiency.

[0160] In an optional implementation: respectively obtain the factor value of the critical efficiency health factor, the efficiency value of the critical mechanical efficiency, the factor value of the theoretical efficiency health factor, and the efficiency value of the theoretical mechanical efficiency; determine the independent variable data according to the factor value of the critical efficiency health factor and the factor value of the theoretical efficiency health factor; determine the dependent variable data according to the efficiency value of the critical mechanical efficiency and the efficiency value of the theoretical mechanical efficiency; and perform curve fitting between the independent variable data and the dependent variable data by the least squares method.

[0161] In an optional embodiment: determining a critical power health factor corresponding to the critical output power of the electric compressor; determining a theoretical power health factor corresponding to the theoretical output power of the electric compressor; determining a standardized health factor based on the actual output power, the critical power health factor and the theoretical power health factor.

[0162] In an optional implementation, there are multiple target operating conditions, and the compressor speed under each target operating condition is different.

[0163] The storage unit 1220 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 12201 and / or a cache storage unit 12202 , and may further include a read-only storage unit (ROM) 12203 .

[0164] The storage unit 1220 may also include a program / utility 12204 having a set (at least one) of program modules 12205, such program modules 12205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0165] Bus 1230 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0166] The electronic device 1200 may also communicate with one or more external devices 1300 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1200, and / or communicate with any device that enables the electronic device 1200 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1250. In addition, the electronic device 1200 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 1260. As shown, the network adapter 1260 communicates with other modules of the electronic device 1200 via a bus 1230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0167] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0168] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0169] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application. The specification and embodiments are to be regarded as exemplary only, and the true scope and spirit of the present application are indicated by the claims.

Claims

1. A method for predicting the health status of an electric compressor, characterized in that: The method comprises: Acquiring characteristic data of the electric compressor operating under target working conditions; Inputting the characteristic data into a compressor efficiency model to obtain a performance index of the electric compressor, wherein the performance index is characterized by at least one of a mechanical efficiency and an output power of the electric compressor; Determining a standardized health factor according to the performance indicator, wherein the standardized health factor is used to evaluate the actual performance of the electric compressor under the target working condition; A degradation model of the electric compressor is determined based on the standardized health factor, and a health state of the electric compressor is predicted according to the degradation model.

2. The method according to claim 1, characterized in that The mechanical efficiency of the electric compressor includes: critical mechanical efficiency, theoretical mechanical efficiency, and actual mechanical efficiency, wherein the standardized health factor is determined according to the performance index, including: determining a critical efficiency health factor corresponding to a critical mechanical efficiency of the electric compressor; Determining a theoretical efficiency health factor corresponding to the theoretical mechanical efficiency of the electric compressor; The normalized health factor is determined according to the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor.

3. The method according to claim 2, characterized in that Determining the standardized health factor according to the actual mechanical efficiency, the critical efficiency health factor and the theoretical efficiency health factor includes: Performing curve fitting according to the critical efficiency health factor and the theoretical efficiency health factor to obtain a curve function, wherein the curve function is used to characterize the relationship between the health factor and the mechanical efficiency; The normalized health factor is determined based on the curve function and the actual mechanical efficiency.

4. The method according to claim 3, characterized in that Performing curve fitting according to the critical efficiency health factor and the theoretical efficiency health factor includes: Respectively obtaining the factor value of the critical efficiency health factor, the efficiency value of the critical mechanical efficiency, the factor value of the theoretical efficiency health factor, and the efficiency value of the theoretical mechanical efficiency; Determining independent variable data according to the factor value of the critical efficiency health factor and the factor value of the theoretical efficiency health factor; determining dependent variable data according to the efficiency value of the critical mechanical efficiency and the efficiency value of the theoretical mechanical efficiency; The independent variable data and the dependent variable data are curve-fitted by the least square method.

5. The method according to claim 1, characterized in that: The output power of the electric compressor includes: critical output power, theoretical output power, and actual output power, wherein the standardized health factor is determined according to the performance index, including: Determining a critical power health factor corresponding to a critical output power of the electric compressor; Determining a theoretical power health factor corresponding to the theoretical output power of the electric compressor; The standardized health factor is determined according to the actual output power, the critical power health factor and the theoretical power health factor.

6. The method according to any one of claims 1 to 5, characterized in that There are multiple target operating conditions, and the compressor speed under each target operating condition is different.

7. A method for predicting the health status of an electric compressor, characterized in that: The method comprises: Obtaining a standardized health factor, wherein the standardized health factor is obtained by obtaining characteristic data of the electric compressor operating under a target operating condition, inputting the characteristic data into a compressor efficiency model, obtaining a performance index of the electric compressor, and obtaining the performance index according to the performance index, wherein the performance index is characterized by at least one of the mechanical efficiency and the output power of the electric compressor, wherein the standardized health factor is used to evaluate an actual performance index of the electric compressor under the target operating condition; determining a degradation model of the electric compressor based on the normalized health factor; The health status of the electric compressor is predicted according to the degradation model.

8. The method according to claim 7, characterized in that The standardized health factor includes at least one of the following: a standardized factor of a first working condition, a standardized factor of a second working condition, and a standardized factor of a third working condition, wherein the speed of the electric compressor under the first working condition is greater than the speed of the electric compressor under the second working condition, and the speed of the electric compressor under the second working condition is greater than the speed of the electric compressor under the third working condition, and determining the degradation model of the electric compressor based on the standardized health factor includes: Selecting the minimum factor among the normalization factor of the first working condition, the normalization factor of the second working condition, and the normalization factor of the third working condition; The degradation model is determined according to the minimum factor.

9. The method according to claim 8, characterized in that Determining the degradation model according to the minimum factor includes: Determining the mileage of the vehicle when it runs at the performance parameter corresponding to the minimum factor, wherein the performance parameter includes at least one of the following: mechanical efficiency, output power; Determine the mileage time corresponding to the mileage; Calculate the degradation rate of the minimum factor according to the minimum factor, the mileage and the mileage time; The degradation model is constructed based on the degradation rate.

10. The method according to claim 8, characterized in that Predicting the health state of the electric compressor according to the degradation model includes: Obtain vehicle historical degradation data through big data algorithms; The degradation model is trained by fitting the historical degradation data of the vehicle by the least square method to obtain a target degradation model; The health status of the electric compressor is predicted by the target degradation model.

11. A device for predicting the health status of an electric compressor, characterized in that: The device comprises: A first acquisition unit, used to acquire characteristic data of the electric compressor operating under a target working condition; a second acquisition unit, configured to input the characteristic data into a compressor efficiency model to obtain a performance index of the electric compressor, wherein the performance index is characterized by at least one of a mechanical efficiency and an output power of the electric compressor; a first determining unit, configured to determine a standardized health factor according to the performance indicator, wherein the standardized health factor is used to evaluate an actual performance of the electric compressor under the target operating condition; The second determining unit determines a degradation model of the electric compressor based on the standardized health factor, and predicts a health state of the electric compressor according to the degradation model.

12. A device for predicting the health status of an electric compressor, characterized in that: The device comprises: a third acquisition unit, configured to acquire a standardized health factor, wherein the standardized health factor is obtained by acquiring characteristic data of the electric compressor operating under a target operating condition, inputting the characteristic data into a compressor efficiency model, and obtaining a performance index of the electric compressor, and is obtained according to the performance index, wherein the performance index is characterized by at least one of a mechanical efficiency and an output power of the electric compressor, wherein the standardized health factor is used to evaluate an actual performance index of the electric compressor under the target operating condition; a third determining unit, configured to determine a degradation model of the electric compressor based on the standardized health factor; A prediction unit is used to predict the health state of the electric compressor according to the degradation model.

13. A computer-readable storage medium, characterized in that: The storage medium includes a stored program, wherein the program executes the health status prediction method of the electric compressor according to any one of claims 1 to 10.

14. An electronic device, characterized in that: include: A processor, a memory, a display device and a program, wherein the program is stored in the memory and is configured to be executed by the processor, and the program includes a method for executing the health status prediction method of the electric compressor described in any one of claims 1 to 6 or claims 7 to 10.

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