Performance evaluation method and device for new energy automobile air conditioner, medium and equipment

Abstract

The invention provides a new energy automobile air conditioner performance evaluation method and device, a medium and equipment. The method comprises the steps that parameter data of a to-be-evaluated new energy automobile are collected; clustering to obtain a plurality of working condition subsets based on the vehicle operation parameters and the environment state parameters; calculating a plurality of evaluation indexes based on the vehicle operation parameters, the environment state parameters and the corresponding air conditioner operation parameters of each working condition subset; based on the multiple evaluation indexes, the evaluation result of the air conditioner performance is comprehensively determined; according to the method, vehicle operation parameters, air conditioner operation parameters and environment state parameters of a to-be-evaluated new energy vehicle in the operation process are actually collected, each set of parameters are clustered to obtain a plurality of working condition subsets, a plurality of evaluation indexes of the air conditioner performance of each working condition subset are calculated for each working condition subset, and then the air conditioner performance of the to-be-evaluated new energy vehicle is comprehensively determined. And multi-dimensional evaluation is carried out on the air conditioner performance in combination with the actual operation parameters of the vehicle, so that the accuracy of air conditioner performance evaluation is improved.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle air conditioning testing technology, specifically to a performance testing method, device, medium, and equipment for new energy vehicle air conditioning. Background Technology

[0002] With increasing global emphasis on environmental protection and energy structure transformation, new energy vehicles have experienced rapid development. As a key component for regulating the in-vehicle environment, the air conditioning system of new energy vehicles directly impacts vehicle comfort and driving range. Therefore, performance evaluation of the air conditioning system is necessary during the design of new energy vehicles. However, existing evaluation methods often rely on specific experimental environments with limited operating conditions, failing to accurately reflect the performance requirements and operational status of the air conditioning system under complex road conditions, weather, and varying driver habits encountered in real-world driving. Therefore, a method is needed to accurately evaluate the performance of air conditioning systems in new energy vehicles. Summary of the Invention

[0003] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method, apparatus, medium, and equipment for evaluating the performance of an air conditioner in a new energy vehicle.

[0004] According to one aspect of this application, a method for evaluating the performance of an air conditioner in a new energy vehicle is provided, comprising: collecting parameter data of the new energy vehicle to be evaluated; wherein the parameter data includes vehicle operating parameters of the new energy vehicle to be evaluated during operation, air conditioning operating parameters of the air conditioner installed in the new energy vehicle to be evaluated during operation, and environmental state parameters of the environment in which the new energy vehicle to be evaluated is located; clustering multiple operating condition subsets based on the vehicle operating parameters and the environmental state parameters; wherein each operating condition subset represents a vehicle operating parameter and an environmental state parameter; calculating multiple evaluation indicators of the air conditioning performance of the new energy vehicle to be evaluated based on the vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters of each operating condition subset; and comprehensively determining the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on the multiple evaluation indicators.

[0005] In one embodiment, the step of clustering multiple operating condition subsets based on the vehicle operating parameters and the environmental state parameters includes: arbitrarily selecting... k 1 data point is used as the initial cluster center point; among them, kThe number of operating condition subsets is given, and the data points are data vectors composed of a vehicle operating parameter and an environmental state parameter. The clustering distance between the remaining data points and each initial cluster center point is calculated. The remaining data points are assigned to the cluster corresponding to the initial cluster center point with the closest clustering distance. The deviation distance between the average value of all data points in each cluster and the corresponding initial cluster center point is calculated. If the deviation distance is greater than a preset deviation threshold, the average value is used as the new cluster center point. Based on the new cluster center point, all data points are clustered again to obtain new clusters, until the deviation distance between the average value of all data points in each cluster and the cluster center point is less than or equal to the deviation threshold.

[0006] In one embodiment, the evaluation index includes air conditioning energy consumption; wherein, the calculation of multiple evaluation indexes for the air conditioning performance of the new energy vehicle under test based on vehicle operating parameters, environmental state parameters and corresponding air conditioning operating parameters for each subset of operating conditions includes: calculating the air conditioning operating time based on the environmental state parameters and the vehicle operating parameters; and calculating the air conditioning energy consumption based on the air conditioning operating time and the air conditioning operating parameters.

[0007] In one embodiment, the evaluation index includes the degree of fluctuation in air conditioning energy consumption; wherein, the calculation of multiple evaluation indexes for the air conditioning performance of the new energy vehicle under test based on vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters for each subset of operating conditions includes: setting initial parameters based on the average air conditioning energy consumption over multiple historical periods; iteratively predicting air conditioning energy consumption over multiple future periods based on the initial parameters; and calculating the degree of fluctuation in air conditioning energy consumption based on the air conditioning energy consumption over the historical periods and the air conditioning energy consumption over the multiple future periods.

[0008] In one embodiment, the evaluation index includes air conditioning fuel consumption; wherein, the calculation of multiple evaluation indexes for the air conditioning performance of the new energy vehicle under test based on vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters for each subset of operating conditions includes: measuring a first fuel consumption under the condition of air conditioning off based on the vehicle operating parameters and the environmental state parameters; measuring a second fuel consumption under the condition of air conditioning on based on the vehicle operating parameters, the environmental state parameters, and the air conditioning operating parameters; and calculating the air conditioning fuel consumption based on the first fuel consumption and the second fuel consumption.

[0009] In one embodiment, the step of comprehensively determining the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on the multiple evaluation indicators includes: weighting the multiple evaluation indicators to obtain the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated.

[0010] In one embodiment, the step of comprehensively determining the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on the multiple evaluation indicators includes: determining the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on the multiple evaluation indicators and the corresponding target indicators; wherein, the target indicators are generated based on the vehicle operating parameters, the environmental state parameters and the air conditioning operating parameters.

[0011] According to another aspect of this application, a performance evaluation device for air conditioning in a new energy vehicle is provided, comprising: a parameter data acquisition module for acquiring parameter data of the new energy vehicle to be evaluated; wherein the parameter data includes vehicle operating parameters of the new energy vehicle to be evaluated during operation, air conditioning operating parameters of the air conditioning installed in the new energy vehicle to be evaluated during operation, and environmental state parameters of the environment in which the new energy vehicle to be evaluated is located; a working condition subset clustering module for clustering multiple working condition subsets based on the vehicle operating parameters and the environmental state parameters; wherein each working condition subset represents a vehicle operating parameter and an environmental state parameter; an evaluation index calculation module for calculating multiple evaluation indices of the air conditioning performance of the new energy vehicle to be evaluated based on the vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters of each working condition subset; and a test result determination module for comprehensively determining the test result of the air conditioning performance of the new energy vehicle to be evaluated based on the multiple evaluation indices.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.

[0013] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above.

[0014] This application provides a performance evaluation method, device, medium, and equipment for air conditioning systems in new energy vehicles. The method involves collecting parameter data from the new energy vehicle under test. This parameter data includes vehicle operating parameters, air conditioning operating parameters, and environmental state parameters of the environment in which the vehicle is located. Based on the vehicle operating parameters and environmental state parameters, multiple operating condition subsets are clustered. Each operating condition subset represents a type of vehicle operating parameter and a type of environmental state parameter. The method further involves evaluating the performance of each operating condition subset based on its vehicle operating parameters, environmental state parameters, and corresponding parameters. The system calculates multiple evaluation indicators for the air conditioning performance of the new energy vehicle under test based on its air conditioning operating parameters. Based on these indicators, the system comprehensively determines the evaluation results for the air conditioning performance of the new energy vehicle under test. Specifically, it collects vehicle operating parameters, air conditioning operating parameters, and environmental state parameters during the actual operation of the new energy vehicle under test. Each set of parameters is clustered to obtain multiple operating condition subsets. For each operating condition subset, multiple evaluation indicators for its air conditioning performance are calculated, and then the air conditioning performance of the new energy vehicle under test is comprehensively determined. This multi-dimensional evaluation of the air conditioning performance, combined with the actual operating parameters of the vehicle, improves the accuracy of the air conditioning performance evaluation. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a flowchart illustrating a performance evaluation method for air conditioning in new energy vehicles provided in an exemplary embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of a performance evaluation device for a new energy vehicle air conditioner provided in an exemplary embodiment of this application.

[0018] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0020] Figure 1This is a flowchart illustrating a performance evaluation method for a new energy vehicle air conditioner provided in an exemplary embodiment of this application. Figure 1 As shown, the performance evaluation method for the air conditioner of this new energy vehicle includes the following steps: Step 110: Collect parameter data of the new energy vehicle to be tested.

[0021] The parameter data includes vehicle operating parameters of the new energy vehicle under test, air conditioning operating parameters of the air conditioner installed in the new energy vehicle under test, and environmental status parameters of the environment in which the new energy vehicle is located. Specifically, this application involves installing multiple sensors and measuring devices on the new energy vehicle under test, including but not limited to in-vehicle temperature sensors, humidity sensors, air conditioning vent wind speed sensors, external ambient temperature sensors, vehicle speed sensors, battery management system data acquisition interfaces, thermal sensors, and 3D surface area measuring instruments. These sensors periodically collect (e.g., once every 5 seconds) various parameters of the vehicle and its occupants during vehicle operation, covering air conditioning operating parameters, vehicle driving status, environmental condition parameters, and occupant thermal comfort parameters, and transmit the data to the onboard data storage unit.

[0022] Step 120: Based on vehicle operating parameters and environmental state parameters, cluster to obtain multiple operating condition subsets.

[0023] Each operating condition subset represents a vehicle operating parameter and an environmental state parameter. This application utilizes an onboard communication module to periodically (e.g., every 10 minutes) upload the collected data to a cloud data center, ensuring data timeliness and integrity. Simultaneously, a data verification mechanism is set up in the cloud data center to initially screen the uploaded data, removing erroneous or abnormal data to guarantee the data quality for subsequent analysis. A dedicated big data analysis platform for processing new energy vehicle data is built in the cloud data center, employing a distributed storage and computing architecture to meet the storage and computational needs of massive amounts of data. The platform incorporates multiple data analysis algorithm models, including clustering analysis algorithms to classify and aggregate collected data based on different road conditions (e.g., urban roads, highways, rural roads), seasons, and regions to uncover potential patterns in air conditioning energy consumption under different operating conditions; a time series analysis model to track the changing trends of air conditioning energy consumption of the same vehicle over different time periods, assisting in identifying potential performance degradation issues in the air conditioning system; and an adaptive learning rate optimization algorithm to detect and learn the driving and passenger's air conditioning usage habits, thereby achieving precise adjustment of the vehicle's interior temperature and reducing energy loss.

[0024] Step 130: Based on the vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters of each operating condition subset, calculate multiple evaluation indicators for the air conditioning performance of the new energy vehicle to be evaluated.

[0025] Based on the clustering analysis results of a big data analytics platform, this application selects representative operating condition combinations, covering different road conditions, seasons, temperature ranges, and air conditioning setting modes. In a simulated test environment, according to the selected operating condition combinations, high-precision measuring instruments are used to measure the real-time parameters of the air conditioning system of the new energy vehicle under test under various operating conditions. Combined with the operating time of the new energy vehicle under test, multiple evaluation indicators for the air conditioning performance are accurately calculated. Simultaneously, to ensure test accuracy, the simulated test environment is equipped with temperature and humidity control equipment similar to the actual environment, and a loading device simulating road driving resistance.

[0026] Step 140: Based on multiple evaluation indicators, comprehensively determine the evaluation results of the air conditioning performance of the new energy vehicle to be evaluated.

[0027] Based on big data analysis results and simulation test data, this application constructs an evaluation system for the air conditioning energy consumption of new energy vehicles. This system evaluates the air conditioning system of the new energy vehicle under test from multiple dimensions: In terms of energy efficiency, the proportion of air conditioning energy consumption to total vehicle energy consumption is compared under different vehicle models and operating conditions, as well as the energy consumption per unit of cooling / heating capacity. This is used to evaluate the energy utilization efficiency of the air conditioning system, determine whether it meets standard energy-saving requirements, and whether it includes intelligent energy-saving functions. In terms of comfort, the actual adjustment effects of key indicators such as in-vehicle temperature, humidity, noise, and air quality are combined to evaluate the performance of the air conditioning system in providing a comfortable driving environment. In terms of adaptability, the system's ability to respond quickly under extreme weather conditions (such as extreme heat, extreme cold, low pressure and oxygen deficiency) and complex road conditions is considered to meet the vehicle's basic usage needs. In terms of reliability, based on the abnormal fluctuations in air conditioning energy consumption tracked by time series analysis, we can determine whether there are potential faults in the air conditioning system, and evaluate its operational reliability in terms of long-term performance, safety, and maintainability.

[0028] This application provides a performance evaluation method for air conditioning in new energy vehicles. The method involves collecting parameter data from the new energy vehicle under test. This parameter data includes vehicle operating parameters, air conditioning operating parameters, and environmental state parameters of the environment in which the vehicle is located. Based on the vehicle operating parameters and environmental state parameters, multiple operating condition subsets are clustered. Each operating condition subset represents a type of vehicle operating parameter and a type of environmental state parameter. The method is further refined based on the vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters of each operating condition subset. This method involves calculating multiple evaluation indicators for the air conditioning performance of a new energy vehicle under test; based on these indicators, a comprehensive evaluation result is determined; specifically, the method involves actually collecting vehicle operating parameters, air conditioning operating parameters, and environmental state parameters during the operation of the new energy vehicle under test, clustering each set of parameters to obtain multiple operating condition subsets, calculating multiple evaluation indicators for the air conditioning performance of each operating condition subset, and then comprehensively determining the air conditioning performance of the new energy vehicle under test. By combining the parameters of actual vehicle operation, a multi-dimensional evaluation of its air conditioning performance is conducted to improve the accuracy of its air conditioning performance evaluation.

[0029] In one embodiment, step 120 can be implemented by arbitrarily selecting... k 1 data point is used as the initial cluster center point; among them, k The number of operating condition subsets is given, and the data points are data vectors composed of one vehicle operating parameter and one environmental state parameter. The clustering distance between the remaining data points and each initial cluster center point is calculated. The remaining data points are assigned to the cluster corresponding to the initial cluster center point with the closest clustering distance. The deviation distance between the average value of all data points in each cluster and the corresponding initial cluster center point is calculated. If the deviation distance is greater than a preset deviation threshold, the average value is used as the new cluster center point. Based on the new cluster center point, all data points are clustered again to obtain new clusters until the deviation distance between the average value of all data points in the cluster and the cluster center point is less than or equal to the deviation threshold.

[0030] Specifically, this application first determines the number of clusters to be clustered (i.e., the number of the final generated subsets of operating conditions). k Then select any data point from the collected data points. k The nth data point is taken as the initial cluster center point, and the nth data point is denoted as the nth data point. i Each cluster is C i ,1≤ i ≤ kCalculate the cluster distance from the remaining data points (data points not assigned to clusters) to each cluster center, and assign the remaining data points to the clusters with the closest cluster distance to the cluster center according to the proximity principle. After all remaining data points have been assigned, calculate the objective function value with the cluster center of each cluster and the average value of all data points in each cluster as the objective. Specifically, the objective function is: ,in, J The objective function value, x As data points, this application uses respectively For the first i The cluster center of the first cluster and the second cluster center i The objective function is calculated by averaging all data points in each cluster. The difference between these objective functions (i.e., the deviation distance) is then calculated. If this deviation distance is large (i.e., greater than a preset deviation threshold), the average value is used as the new cluster center point to redistribute the data points and obtain new clusters. This process is repeated until the deviation distance is less than or equal to the deviation threshold, at which point the iterative update stops, and clustering is complete. Typical clustering scenarios include: Table 1 Typical Operating Condition Subset

[0031] In one embodiment, the evaluation index includes air conditioning energy consumption; wherein, the specific implementation of step 130 above may be: calculating the air conditioning operating time based on environmental state parameters and vehicle operating parameters; and calculating the air conditioning energy consumption based on the air conditioning operating time and air conditioning operating parameters.

[0032] This application calculates the air conditioning operating time based on environmental condition parameters and vehicle operating parameters, and then calculates the air conditioning energy consumption based on the air conditioning operating time and parameters. Specifically, the formula for calculating the air conditioning operating time is as follows: ; in, For the air conditioning system within a day i Within the hour (corresponding to the first) i Time to the i The runtime (within one hour of time +1) This represents the total operating time of the new energy vehicle to be tested throughout the day. For the new energy vehicles to be evaluated in the first i The proportion of trips occurring within one hour For the first i The thermal comfort of the human body at any given moment is a measure of the human body's response to the first... i Dissatisfaction rate of ambient temperature within one hour.

[0033] ; in, ; ; ; ; ; ; in, PMV This represents the average human body response to the current ambient temperature. L As for the body's heat load, M The human body's energy metabolism rate. W Mechanical work performed on the human body P a This refers to the actual output power of the air conditioner drive motor. The air temperature surrounding the human body It is the ratio of the surface area of ​​clothing to the surface area of ​​the naked body. h c The heat transfer coefficient of the clothing surface. The surface temperature of the clothing. The average radiant temperature inside the vehicle. V The air velocity around the human body For the thermal resistance of clothing, clo =0.155, This refers to relative humidity.

[0034] The formula for calculating air conditioner energy consumption is as follows: ; in, This represents the energy consumption of the air conditioning system over a single day. For the first i The output power of the air conditioning system within one hour.

[0035] In one embodiment, the evaluation index includes the degree of fluctuation in air conditioning energy consumption; wherein, the specific implementation of step 130 above may be: setting initial parameters based on the average air conditioning energy consumption over multiple historical periods; iteratively predicting air conditioning energy consumption over multiple future periods based on the initial parameters; and calculating the degree of fluctuation in air conditioning energy consumption based on the air conditioning energy consumption over historical periods and the air conditioning energy consumption over multiple future periods.

[0036] This application predicts air conditioning energy consumption for future periods based on previous periods' energy consumption, thereby forecasting the degree of fluctuation in air conditioning energy consumption. This allows for early detection of abnormal fluctuations in energy consumption and provides advance warnings or appropriate measures when such fluctuations occur. Specifically, initial parameters are set based on the average energy consumption over multiple historical periods, including an initial energy consumption level. S 0. Initial energy consumption trend value b 0 and initial seasonal factorI 0, and set the data smoothing coefficient. α (0≤) α ≤1) Trend smoothing coefficient β (0≤) β ≤1) and seasonal smoothing coefficient γ (0≤) γ ≤1); Based on the initial energy consumption level, initial energy consumption trend, initial seasonal factor, data smoothing coefficient, trend smoothing coefficient, and seasonal smoothing coefficient, the air conditioning energy consumption in the future multiple periods is predicted through iteration. The specific prediction formula is as follows:

[0037] in, S t For time t The energy consumption level value (i.e., the value corresponding to the energy consumption range). Y t For time t The actual observed value (i.e. the value obtained by the sensor). I t-L The seasonal factor is the same time period in the previous cycle. L For seasonal cycles (e.g., spring, summer, autumn, winter). b t For time t The energy consumption trend value (i.e., the rate of change of energy consumption at that point in time). I t For time t Seasonal factors, F t+m For the future m The predicted value at the same time after one cycle.

[0038] In one embodiment, the evaluation index includes air conditioning fuel consumption; wherein, the specific implementation of step 130 above may be as follows: based on vehicle operating parameters and environmental state parameters, a first fuel consumption under the condition of air conditioning off is measured; based on vehicle operating parameters, environmental state parameters and air conditioning operating parameters, a second fuel consumption under the condition of air conditioning on is measured; based on the first fuel consumption and the second fuel consumption, the air conditioning fuel consumption is calculated.

[0039] This application measures the first fuel consumption under conditions of air conditioning off and the second fuel consumption under conditions of air conditioning on, based on vehicle operating parameters and environmental state parameters. Based on the first and second fuel consumption values, the air conditioning fuel consumption is calculated, where air conditioning fuel consumption = second fuel consumption - first fuel consumption. After calculating the air conditioning fuel consumption, a comparison is made between the calculated target fuel consumption and the actual air conditioning fuel consumption to determine whether the air conditioning fuel consumption of the new energy vehicle under evaluation meets the standard. The target fuel consumption... , CM This refers to the vehicle's curb weight. Furthermore, this application calculates the fuel consumption of the air conditioning system under various fuel types for the new energy vehicle being evaluated, and then converts these consumption figures into the consumption figures for liquid fuels such as diesel, gasoline, or methanol. Specifically, the consumption figures for gaseous fuels such as natural gas and hydrogen are converted to the liquid fuel consumption figures using the following formula: ; in, Q 1 represents the consumption of liquid fuel obtained by converting gaseous fuel consumption. P For gas pressure, V gas For gas volume, M For the molar volume of the gas, R Let be the ideal gas constant. R =8.314 J / (mol·K), T For gas temperature, The density is the liquid density.

[0040] Electricity consumption can be converted to liquid fuel consumption using the following formula: ; in, Q 2 represents the consumption of liquid fuel obtained by converting electricity consumption. E This refers to the vehicle's electrical energy consumption. The energy factor for fuel r p For refinery efficiency. t p To improve delivery and refueling efficiency, i ch For charging efficiency, when receiving electrical energy from the grid, we take 100%. i tr Line loss rate, The proportion of thermal power generation, s ge For power supply efficiency, T E Standard coal consumption for thermal power generation. T CThe carbon dioxide emission factor for fuel coal. T F It is a carbon dioxide emission factor for fuel. This is the conversion factor between fuel coal and standard coal.

[0041] In one embodiment, step 140 can be implemented by determining the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on multiple evaluation indicators and corresponding target indicators; wherein the target indicators are generated based on vehicle operating parameters, environmental state parameters and air conditioning operating parameters.

[0042] Specifically, this application obtains the score for each evaluation indicator by comparing the differences between individual evaluation indicators and their corresponding target indicators, and determines the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on the scores of all evaluation indicators. The target indicators can be obtained by inputting relevant parameters of the new energy vehicle to be evaluated into a neural network model to directly obtain the optimal indicators (i.e., target indicators) of the air conditioning in the corresponding scenario. Relevant parameters may include: driving habits (e.g., temperature setting, wind speed preference), environmental parameters (e.g., temperature and humidity, solar radiation), and vehicle status (e.g., speed, acceleration), thereby obtaining the optimal air conditioning control strategy and corresponding target indicators suitable for the new energy vehicle to be evaluated. For example, in high-speed conditions (vehicle speed > 80 km / h), the compressor load is reduced by using windward cooling; in congested conditions (vehicle speed < 30 km / h), an intermittent air supply mode is activated, and the energy-saving mode is switched 5 minutes in advance based on the predicted arrival time to avoid ineffective cooling; special road sections such as tunnels and uphill sections are identified in real time by fusion of GPS and high-precision maps; the wind speed is automatically reduced and the internal circulation ratio is increased before entering a tunnel; when climbing a hill, the engine thermal management system is linked to use waste heat to assist heating and improve the energy efficiency ratio.

[0043] The training methods for neural network models include: First, set initial metrics, and then introduce activation functions to map the initial metrics to each convolutional layer, calculating the corresponding feature results layer by layer. The feature results of the layer are as follows: , For the first The feature results of the layer, For the first The convolution function of the layer, For the first The weight matrix of the layer, For the first The bias vector of the layer; calculate the error between the predicted result and the actual result. , N For the sample size, h This represents the true value (real result) of the sample. To predict the results; calculate the loss function. , The regularization coefficient is . The weight parameters are used; the iterative gradient is calculated based on the loss function. , for t Gradient at time, For learning rate, For the first i The loss value for each sample is calculated; the weight matrix and bias vector are updated, where the update formula for the bias vector is as follows: , for t The bias vector at time step. The derivative of the error with respect to the bias vector is given. The model is updated based on the updated weight matrix and bias vector, and the above calculation is repeated until the loss function is less than the preset value or the number of iterations reaches the set value.

[0044] In one embodiment, step 140 can be implemented by weighting multiple evaluation indicators to obtain the evaluation results of the air conditioning performance of the new energy vehicle to be evaluated.

[0045] This application calculates multiple evaluation indicators (or scores for the evaluation indicators), and then weights these indicators to obtain the evaluation result of the air conditioning performance of the new energy vehicle under test. For example, the formula for calculating the comprehensive score of the air conditioning performance of the new energy vehicle under test is as follows: ; in, S This represents the total score for the air conditioning performance of the new energy vehicle being evaluated. A, B, C, D These correspond to the scores for energy efficiency, comfort, adaptability, and reliability evaluation indicators, respectively. 、 、 、 These are the weighting coefficients for each evaluation indicator. Possible values: 0.2 Possible values: 0.2 Possible values: 0.3, The possible value is 0.3.

[0046] Specifically, the evaluation indicators for the air conditioning performance of the new energy vehicle to be tested may include: Table 2 Air Conditioner Performance Evaluation Indicators

[0047] Figure 2This is a schematic diagram of the structure of a performance evaluation device for a new energy vehicle air conditioner provided in an exemplary embodiment of this application. Figure 2 As shown, the performance evaluation device 20 for the air conditioning of a new energy vehicle includes: a parameter data acquisition module 21, used to collect parameter data of the new energy vehicle to be evaluated; wherein, the parameter data includes vehicle operating parameters of the new energy vehicle to be evaluated during operation, air conditioning operating parameters of the air conditioning installed in the new energy vehicle to be evaluated during operation, and environmental state parameters of the environment in which the new energy vehicle to be evaluated is located; a working condition subset clustering module 22, used to cluster multiple working condition subsets based on vehicle operating parameters and environmental state parameters; wherein, each working condition subset represents a vehicle operating parameter and an environmental state parameter; an evaluation index calculation module 23, used to calculate multiple evaluation indexes of the air conditioning performance of the new energy vehicle to be evaluated based on the vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters of each working condition subset; and an evaluation result determination module 24, used to comprehensively determine the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on multiple evaluation indexes.

[0048] This application provides a performance evaluation device for a new energy vehicle air conditioner. The device collects parameter data from the new energy vehicle under test via a parameter data acquisition module 21. This parameter data includes vehicle operating parameters, air conditioner operating parameters, and environmental state parameters of the environment in which the new energy vehicle is located. A working condition subset clustering module 22 clusters multiple working condition subsets based on the vehicle operating parameters and environmental state parameters. Each working condition subset represents a vehicle operating parameter and an environmental state parameter. An evaluation index calculation module 23 calculates the evaluation index based on the vehicle operating parameters and environmental state parameters of each working condition subset. The system calculates multiple evaluation indicators for the air conditioning performance of the new energy vehicle under test based on the data and corresponding air conditioning operating parameters. The evaluation result determination module 24 comprehensively determines the evaluation result of the air conditioning performance of the new energy vehicle under test based on multiple evaluation indicators. Specifically, it collects the vehicle operating parameters, air conditioning operating parameters, and environmental state parameters of the new energy vehicle under test during operation, and clusters each set of parameters to obtain multiple operating condition subsets. For each operating condition subset, it calculates multiple evaluation indicators for its air conditioning performance, and then comprehensively determines the air conditioning performance of the new energy vehicle under test. It combines the parameters of actual vehicle operation to conduct a multi-dimensional evaluation of its air conditioning performance in order to improve the accuracy of its air conditioning performance evaluation.

[0049] In one embodiment, the above-mentioned working condition subset clustering module 22 can be further configured to: arbitrarily select k 1 data point is used as the initial cluster center point; among them, kThe number of operating condition subsets is given, and the data points are data vectors composed of one vehicle operating parameter and one environmental state parameter. The clustering distance between the remaining data points and each initial cluster center point is calculated. The remaining data points are assigned to the cluster corresponding to the initial cluster center point with the closest clustering distance. The deviation distance between the average value of all data points in each cluster and the corresponding initial cluster center point is calculated. If the deviation distance is greater than a preset deviation threshold, the average value is used as the new cluster center point. Based on the new cluster center point, all data points are clustered again to obtain new clusters until the deviation distance between the average value of all data points in the cluster and the cluster center point is less than or equal to the deviation threshold.

[0050] In one embodiment, the evaluation index includes air conditioning energy consumption; wherein, the evaluation index calculation module 23 can be further configured to: calculate the air conditioning operating time based on environmental state parameters and vehicle operating parameters; and calculate the air conditioning energy consumption based on the air conditioning operating time and air conditioning operating parameters.

[0051] In one embodiment, the evaluation index includes the degree of fluctuation in air conditioning energy consumption; wherein, the evaluation index calculation module 23 can be further configured to: set initial parameters based on the average air conditioning energy consumption over multiple historical periods; iteratively predict air conditioning energy consumption over multiple future periods based on the initial parameters; and calculate the degree of fluctuation in air conditioning energy consumption based on the air conditioning energy consumption over historical periods and the air conditioning energy consumption over multiple future periods.

[0052] In one embodiment, the evaluation index includes air conditioning fuel consumption; wherein, the evaluation index calculation module 23 can be further configured to: measure a first fuel consumption under the condition of air conditioning off based on vehicle operating parameters and environmental state parameters; measure a second fuel consumption under the condition of air conditioning on based on vehicle operating parameters, environmental state parameters and air conditioning operating parameters; and calculate the air conditioning fuel consumption based on the first fuel consumption and the second fuel consumption.

[0053] In one embodiment, the above-mentioned evaluation result determination module 24 can be further configured to: weight multiple evaluation indicators to obtain the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated.

[0054] In one embodiment, the above-mentioned evaluation result determination module 24 can be further configured to: determine the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on multiple evaluation indicators and corresponding target indicators; wherein, the target indicators are generated based on vehicle operating parameters, environmental state parameters and air conditioning operating parameters.

[0055] Below, for reference Figure 3This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0056] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0057] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0058] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0059] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0060] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0061] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0062] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0063] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0064] Of course, for the sake of simplicity, Figure 3Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0065] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0066] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0067] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0068] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0069] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0070] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0071] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0072] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0073] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A performance evaluation method for air conditioning in new energy vehicles, characterized in that, include: Collect parameter data of the new energy vehicle to be tested; wherein, the parameter data includes the vehicle operating parameters of the new energy vehicle to be tested when it is running, the air conditioning operating parameters of the air conditioner installed in the new energy vehicle to be tested when it is running, and the environmental status parameters of the environment in which the new energy vehicle to be tested is located. Based on the vehicle operating parameters and the environmental state parameters, multiple operating condition subsets are obtained by clustering; wherein each operating condition subset represents a vehicle operating parameter and an environmental state parameter. Based on the vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters of each operating condition subset, multiple evaluation indicators of the air conditioning performance of the new energy vehicle to be evaluated are calculated. Based on the aforementioned multiple evaluation indicators, the evaluation results of the air conditioning performance of the new energy vehicle to be tested are comprehensively determined.

2. The performance evaluation method for air conditioning in new energy vehicles according to claim 1, characterized in that, The clustering based on the vehicle operating parameters and the environmental state parameters to obtain multiple operating condition subsets includes: Choose any k 1 data point is used as the initial cluster center point; among them, k The number of operating condition subsets; the data points are data vectors composed of a vehicle operating parameter and an environmental state parameter. Calculate the clustering distance between the remaining data points and each of the initial cluster centers; The remaining data points are assigned to the clusters corresponding to the initial cluster center points with the closest clustering distance. Calculate the deviation distance between the average value of all data points in each cluster and the corresponding initial cluster center point; If the deviation distance is greater than the preset deviation threshold, then the average value is used as the new cluster center point; Based on the new cluster center point, all data points are clustered again to obtain new clusters until the average value of all data points in the cluster and the deviation distance between the cluster center point are less than or equal to the deviation threshold.

3. The performance evaluation method for air conditioning in new energy vehicles according to claim 1, characterized in that, The evaluation indicators include air conditioning energy consumption; wherein, the multiple evaluation indicators for calculating the air conditioning performance of the new energy vehicle under test, based on vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters for each subset of operating conditions, include: Based on the environmental state parameters and the vehicle operating parameters, the air conditioning operating time is calculated; The energy consumption of the air conditioner is calculated based on the air conditioner's operating time and operating parameters.

4. The performance evaluation method for air conditioning in new energy vehicles according to claim 1, characterized in that, The evaluation indicators include the degree of fluctuation in air conditioning energy consumption; wherein, the multiple evaluation indicators for calculating the air conditioning performance of the new energy vehicle under test based on vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters for each subset of operating conditions include: Initial parameters are set based on the average air conditioning energy consumption over multiple historical periods; Iterative prediction of air conditioning energy consumption over multiple future cycles based on initial parameters; The degree of fluctuation in air conditioning energy consumption is calculated based on the air conditioning energy consumption in the historical period and the air conditioning energy consumption in the future multiple periods.

5. The performance evaluation method for air conditioning in new energy vehicles according to claim 1, characterized in that, The evaluation indicators include air conditioning fuel consumption; wherein, the multiple evaluation indicators for calculating the air conditioning performance of the new energy vehicle under test, based on vehicle operating parameters, environmental state parameters, and corresponding air conditioning operating parameters for each subset of operating conditions, include: Based on the vehicle operating parameters and the environmental state parameters, the first fuel consumption under the condition of air conditioning off was measured. Based on the vehicle operating parameters, the environmental state parameters, and the air conditioning operating parameters, the second fuel consumption under the condition of air conditioning being turned on is measured. The air conditioning fuel consumption is calculated based on the first fuel consumption and the second fuel consumption.

6. The performance evaluation method for air conditioning in new energy vehicles according to claim 1, characterized in that, The evaluation result of the air conditioning performance of the new energy vehicle under test, determined comprehensively based on the multiple evaluation indicators, includes: By weighting the multiple evaluation indicators, the evaluation results of the air conditioning performance of the new energy vehicle to be evaluated are obtained.

7. The performance evaluation method for air conditioning in new energy vehicles according to claim 1, characterized in that, The evaluation result of the air conditioning performance of the new energy vehicle under test, determined comprehensively based on the multiple evaluation indicators, includes: Based on the multiple evaluation indicators and corresponding target indicators, the evaluation results of the air conditioning performance of the new energy vehicle to be evaluated are determined; wherein, the target indicators are generated based on the vehicle operating parameters, the environmental state parameters, and the air conditioning operating parameters.

8. A performance evaluation device for air conditioning in new energy vehicles, characterized in that, include: The parameter data acquisition module is used to collect parameter data of the new energy vehicle to be tested; wherein, the parameter data includes the vehicle operating parameters of the new energy vehicle to be tested during operation, the air conditioning operating parameters of the air conditioner installed in the new energy vehicle to be tested during operation, and the environmental status parameters of the environment in which the new energy vehicle to be tested is located. The working condition subset clustering module is used to cluster multiple working condition subsets based on the vehicle operating parameters and the environmental state parameters; wherein each working condition subset represents a vehicle operating parameter and an environmental state parameter. The evaluation index calculation module is used to calculate multiple evaluation indexes of the air conditioning performance of the new energy vehicle to be evaluated based on the vehicle operating parameters, environmental state parameters and corresponding air conditioning operating parameters of each operating condition subset. The evaluation result determination module is used to comprehensively determine the evaluation result of the air conditioning performance of the new energy vehicle to be evaluated based on the multiple evaluation indicators.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method described in any one of claims 1-7.

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