Coupling and cooperative control method and device for refrigeration and driving

Abstract

This application relates to the field of energy consumption control technology for cold chain vehicles, and provides a coupled and coordinated control method and device for refrigeration and driving. The method includes: collecting operating parameters of the cold chain vehicle, including refrigeration system parameters, driving system parameters, and emission parameters; constructing a coupled dynamic prediction model for refrigeration-driving-emissions based on the operating parameters; the coupled dynamic prediction model is used to learn the dynamic coupling relationship between the systems under different operating conditions and output the predicted trend results of refrigeration energy efficiency and emission levels; and performing optimization calculations on the predicted trend results based on a preset multi-objective optimization function to determine the target control parameters of the cold chain vehicle. The coupled and coordinated control method and device for refrigeration and driving provided by this application can comprehensively collect refrigeration, driving, and emission parameters of the cold chain vehicle, accurately learn the dynamic coupling relationship between the systems under multiple operating conditions based on the coupled dynamic prediction model, and then accurately determine the target control parameters through a multi-objective optimization function.

Description

Technical Field

[0001] This invention relates to the field of energy consumption control technology for cold chain vehicles, and in particular to a coupled and coordinated control method and device for refrigeration and driving. Background Technology

[0002] Cold chain delivery vehicles are core equipment in cold chain logistics. Their refrigeration systems continuously consume power to maintain the low-temperature environment inside the vehicle, and there is a significant dynamic coupling relationship between refrigeration energy consumption and exhaust emissions during vehicle operation. In urban start-stop conditions, the refrigeration system and engine compete for power resources, easily leading to insufficient vehicle power, which in turn increases the frequency of rapid acceleration and causes emission concentrations to rise. Under high-speed conditions, excessively high refrigeration compressor speeds can trigger a surge in energy consumption, and for new energy cold chain vehicles, this can also severely deplete battery power and shorten the driving range. Existing vehicle control strategies mostly treat the refrigeration system and the driving system as independent control objects for isolated adjustment, and most are designed based on fixed operating conditions. At the same time, some solutions only focus on optimizing the single objective of refrigeration efficiency or emission control.

[0003] However, existing technologies do not fully consider the inherent relationship between refrigeration and vehicle emissions, making them unsuitable for dynamic operating conditions such as start-stop and hill climbing in urban areas. This leads to frequent issues of over-adjustment of vehicle compartment temperature or excessive emissions during operating condition switching. Furthermore, single-objective optimization models cannot simultaneously address environmental requirements and ensure the quality of cold chain goods. Although there are independent studies in related fields on improving refrigeration efficiency or controlling vehicle emissions, none have developed synergistic control schemes to address the coupling characteristics between the two. This fails to fundamentally resolve the industry dilemma of imbalance between efficiency, emissions, and quality, thus hindering the efficient and low-carbon development of cold chain logistics. Summary of the Invention

[0004] This application provides a method and apparatus for coupled and coordinated control of cooling and driving, which can solve the contradiction of not being able to simultaneously take into account the coupling of cooling and driving, and improve the coordinated control accuracy of the cooling and driving system.

[0005] In a first aspect, embodiments of this application provide a coupled and coordinated control method for cooling and driving, comprising:

[0006] Collect operating parameters of refrigeration vehicles, including refrigeration system parameters, driving system parameters, and emission parameters;

[0007] Based on operating parameters, a coupled dynamic prediction model for refrigeration, driving and emissions is constructed. The coupled dynamic prediction model is used to learn the dynamic coupling relationship between the systems under different operating conditions and output the prediction results of the changing trends of refrigeration energy efficiency and emission levels.

[0008] The target control parameters for cold chain vehicles are determined by optimizing the trend prediction results based on a preset multi-objective optimization function.

[0009] In one embodiment, the target control parameters for cold chain vehicles are determined by optimizing the trend prediction results based on a preset multi-objective optimization function, which can be specifically implemented as follows:

[0010] The parameters to be adjusted in the refrigeration system and the driving system are used as optimization variables;

[0011] Based on the operating conditions of cold chain vehicles, the value range of optimization variables is determined, and an initial set of optimization variables is randomly generated according to the value range.

[0012] The initial set of optimized variables and the predicted trend results are used to generate the optimal set of target control parameters through iterative optimization calculation using a multi-objective optimization function.

[0013] Based on the operating conditions and priority strategies of cold chain vehicles, the target control parameters of cold chain vehicles are determined from the optimal set of target control parameters.

[0014] In yet another embodiment, the multi-objective optimization function includes a cooling energy efficiency optimization function, a comprehensive emission optimization function, and a set of constraints.

[0015] The set of constraints includes at least one of the following:

[0016] Deviation constraint between actual temperature of refrigerated vehicle compartment and refrigeration set temperature: The difference between actual temperature of compartment and refrigeration set temperature is less than or equal to the preset temperature value.

[0017] Operating condition constraints on the speed of cold chain vehicles: The speed of cold chain vehicles must be greater than or equal to the minimum permissible speed corresponding to the current operating condition of the cold chain vehicle.

[0018] Battery state of charge constraint for cold chain vehicles: The state of charge of the power battery of cold chain vehicles is greater than or equal to the preset battery charge value.

[0019] In yet another embodiment, the above-mentioned cooling energy efficiency optimization function satisfies a first preset formula, which is:

[0020]

[0021] in, This indicates the target value for optimizing cooling energy efficiency. Indicates the coefficient of performance (COP) for cooling. Indicates the compressor speed of the refrigeration system. Indicates the set cooling temperature. Indicates the vehicle's speed. Cooling capacity of the refrigeration system per unit time This indicates the total operating power consumption of the refrigeration system.

[0022] In yet another embodiment, the above-mentioned comprehensive emission optimization function satisfies a second preset formula, which is:

[0023]

[0024] in, This represents the overall emission optimization target value. This represents the weighting factor for nitrogen oxide (NOx) emissions. This represents the weighting factor for carbon dioxide (CO2) emissions. Indicates nitrogen oxide emissions, Indicates carbon dioxide emissions. This represents the engine speed / drive motor speed. Indicates the vehicle's speed.

[0025] In yet another embodiment, the above-described coupled dynamic prediction model for cooling-driving-emissions based on operating parameters can be specifically implemented as follows:

[0026] Based on the linear and nonlinear correlation analysis between refrigeration system parameters, driving system parameters and emission parameters, the key factors affecting refrigeration energy efficiency, driving status and emission levels are obtained.

[0027] Based on key factors and operating parameters, a coupled dynamic prediction model for refrigeration, driving, and emissions is constructed.

[0028] Secondly, embodiments of this application provide a coupled and coordinated control method and apparatus for cooling and driving, comprising: a data acquisition module, a construction module, and an execution module.

[0029] The aforementioned data acquisition module is used to collect operating parameters of cold chain vehicles, including refrigeration system parameters, driving system parameters, and emission parameters.

[0030] The aforementioned building modules are used to construct a coupled dynamic prediction model of refrigeration-driving-emission based on operating parameters. The coupled dynamic prediction model is used to learn the dynamic coupling relationship between the systems under different operating conditions and output the prediction results of the changing trends of refrigeration energy efficiency and emission levels.

[0031] The aforementioned execution module is used to perform optimization calculations on the trend prediction results based on a preset multi-objective optimization function to determine the target control parameters for cold chain vehicles.

[0032] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the cooling and driving coupled control method of the first aspect.

[0033] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the first aspect of the cooling and driving coupled coordinated control method.

[0034] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, implements the steps of the first aspect of the cooling and driving coupled coordinated control method.

[0035] The refrigeration and driving coupled control method and device provided in this application comprehensively collects multi-dimensional parameters of the refrigeration system, driving system and emission system of cold chain vehicles, uses a coupled dynamic prediction model to accurately capture the dynamic coupling relationship of the parameters of each system under multiple operating conditions, and then obtains the optimal control parameters by solving a multi-objective optimization function to achieve synergistic optimization of refrigeration effect, driving economy and emission compliance. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 One of the flowcharts of the coupled and coordinated control method for cooling and driving provided in the embodiments of this application;

[0038] Figure 2 A second schematic flowchart illustrating the coupled and coordinated control method for cooling and driving provided in an embodiment of this application;

[0039] Figure 3 A schematic diagram of the structure of the cooling and driving coupling control device provided in the embodiments of this application;

[0040] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] In the description of this application, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or relative positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and for simplification, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Unless otherwise specified, the above-mentioned orientational descriptions can be flexibly set in practical applications, provided that the relative positional relationships shown in the accompanying drawings are satisfied.

[0043] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0044] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "communication" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection. They can refer to a direct connection or an indirect connection through an intermediate medium, or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0045] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0046] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0047] In the embodiments of this application, at least one can also be described as one or more, and multiple can be two, three, four or more, and this application does not impose any restrictions.

[0048] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0049] To facilitate understanding, the terms used in the embodiments of this application will be explained first.

[0050] Time-series forecasting refers to the technology that uses historical time-series data (such as cooling parameters and driving parameters over a past period) to capture the time dependencies of the data and predict the evolution trend of target parameters (cooling efficiency and emission levels) within a specific future time period (e.g., the next 1-3 hours).

[0051] Orthogonal test schemes refer to test design methods that select matching orthogonal arrays based on key influencing factors (such as ambient temperature, driving conditions, and cooling set temperature) and their different levels in actual operating scenarios, to obtain operational data covering all operating conditions with the fewest number of tests and efficiently build model training datasets.

[0052] Figure 1 This is one of the flowcharts illustrating the coupled and coordinated control method for cooling and driving provided in an embodiment of this application. (Refer to...) Figure 1 This application provides a coupled and coordinated control method for cooling and driving, which may include:

[0053] Step S100: Collect the operating parameters of the cold chain vehicles.

[0054] Among them, operating parameters refer to various parameter data that can reflect the transportation status, working status and environmental impact status of cold chain vehicles.

[0055] Optionally, the operating parameters include at least one of the following: refrigeration system parameters, driving system parameters, and emission parameters. This application does not limit the specific type of operating parameters; they can be flexibly expanded according to actual needs.

[0056] For example, the refrigeration system parameters are used to characterize the working performance of the refrigeration equipment in the cold chain vehicle and the temperature and humidity environment inside the cold chain compartment, including but not limited to the start-stop status of the refrigeration compressor, compressor speed, operating power, exhaust temperature, suction pressure, condensing pressure, evaporator temperature, real-time temperature, humidity, temperature fluctuation range, humidity adjustment rate, refrigerant flow rate, and refrigeration efficiency coefficient inside the cold chain compartment.

[0057] Driving system parameters are used to characterize the driving status of cold chain vehicles and the operating status of core vehicle components, including but not limited to vehicle speed, acceleration, braking frequency, mileage, engine speed, transmission gear, tire pressure, tire temperature, and remaining battery charge percentage (State of Charge, SOC).

[0058] Emission parameters are used to characterize the exhaust emissions and environmental performance of cold chain vehicles, including but not limited to the concentrations of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), particulate matter (PM), and exhaust temperature.

[0059] It should be noted that the operating parameters listed above are merely illustrative examples. In practical applications, other types of operating parameters can be added according to the specific needs of cold chain transportation scenarios, such as vehicle power supply system parameters (e.g., battery voltage, current, and remaining power), cargo status monitoring parameters (e.g., cargo weight and cargo position offset), and environmental parameters (ambient temperature, humidity, and light intensity). As long as effective monitoring of the cold chain vehicle transportation process can be achieved, they all fall within the scope of protection of the embodiments of this application.

[0060] In this embodiment, suitable sensors can be deployed at corresponding locations on the cold chain vehicle according to the monitoring requirements of different operating parameters, and the operating parameters of the cold chain vehicle can be collected through multi-sensor fusion technology.

[0061] In this embodiment, operating parameters are collected using multi-sensor fusion technology involving sensor deployment and data synchronization processing, specifically including:

[0062] (1) Deployment of sensors and data interfaces.

[0063] The On-Board Diagnostics (OBD) interface connects to the vehicle's OBD system, enabling real-time acquisition of critical information such as the vehicle's engine operating status and fault codes.

[0064] Refrigeration system-specific sensors are specially designed for specific application scenarios in refrigeration systems, and can accurately measure parameters such as temperature, pressure, and flow rate in the refrigeration system.

[0065] Exhaust gas emission analyzers utilize high-precision exhaust gas emission analyzers to perform detailed analysis of various components in vehicle exhaust gases.

[0066] The Global Positioning System (GPS) positioning module obtains the vehicle's geographical location information in real time.

[0067] (2) Data synchronous collection.

[0068] Data denoising (e.g., wavelet transform denoising) is necessary because data acquisition is inevitably subject to various noise interferences. Wavelet transform has the characteristics of multi-resolution analysis, which can effectively separate signals and noise at different scales.

[0069] Outlier removal (3σ criterion): In real-world data, outliers may exist due to sensor malfunctions, external interference, or other reasons. The 3σ criterion is used to remove outliers. Based on the principle of normal distribution, assuming the data follows a normal distribution, data points exceeding three standard deviations from the mean are considered outliers. By calculating the mean and standard deviation of the data, data points outside the range [μ-3σ, μ+3σ] (where μ is the mean and σ is the standard deviation) are identified as outliers and removed, thus ensuring the accuracy and stability of the data.

[0070] Parameter standardization (Z-score standardization) transforms the original data into a standard normal distribution with a mean of 0 and a standard deviation of 1 by subtracting the mean from the original data and then dividing by the standard deviation.

[0071] In this embodiment, due to differences in the working principles and sampling frequencies of different sensors, an advanced data synchronization acquisition mechanism is employed to ensure the accuracy and consistency of subsequent data processing. Through a precise time synchronization algorithm, it is ensured that each sensor acquires data at the same time, thereby avoiding data inconsistency caused by time deviations.

[0072] The application embodiment employs multi-sensor fusion technology. Compared to single-sensor acquisition, multi-sensor fusion technology can integrate data from sensors of different types and installation locations, thereby overcoming the limitations of single sensors in terms of measurement range, accuracy, and anti-interference capability, and improving the accuracy of operational parameter acquisition.

[0073] Step S110: Based on the operating parameters, construct a coupled dynamic prediction model for refrigeration-driving-emissions.

[0074] Among them, the coupled dynamic prediction model is used to learn the dynamic coupling relationship between various systems under different operating conditions and output the prediction results of the changing trends of cooling energy efficiency and emission levels.

[0075] The trend forecast results refer to the quantitative dynamic information on the evolution of cooling energy efficiency and emission levels over time in the next few hours.

[0076] In this embodiment of the application, the steps of constructing a coupled dynamic prediction model of refrigeration-driving-emission include: based on the linear and nonlinear correlation analysis between refrigeration system parameters, driving system parameters and emission parameters, obtaining key factors affecting refrigeration energy efficiency, driving status and emission levels; and based on the key factors and operating parameters, constructing a coupled dynamic prediction model of refrigeration-driving-emission.

[0077] In some embodiments, the running parameters are preprocessed, including but not limited to timestamp alignment, filtering and denoising, and handling of missing values.

[0078] Based on the pre-processed operating parameters, the data is divided according to the typical operating conditions of cold chain delivery vehicles (start-stop condition, constant speed condition, acceleration condition, and hill climbing condition).

[0079] For example, the criteria for defining each operating condition can be as follows:

[0080] Start-stop conditions: vehicle speed ≤ 5km / h and engine / drive motor start-stop times ≥ 1 time / 5min;

[0081] Constant speed operation: Vehicle speed fluctuation range ≤ ±2km / h and absolute acceleration value ≤ 0.2m / s² 2 Duration ≥ 1 minute;

[0082] Acceleration condition: Acceleration ≥ 0.3 m / s² 2 Furthermore, the vehicle speed increases from ≤30km / h to ≥50km / h for a duration of ≤30s;

[0083] Climbing conditions: slope ≥ 5° and engine load / drive motor load ≥ 70%, duration ≥ 30s.

[0084] For operating parameters under various conditions, calculate the statistical characteristics of the refrigeration system parameters, driving system parameters, and emission parameters, including but not limited to:

[0085] Central tendency characteristics: parameter mean (e.g., mean compressor speed under constant speed condition, mean NOx concentration), median;

[0086] Dispersion characteristics: parameter variance (such as refrigerant flow variance under acceleration conditions), standard deviation, coefficient of variation;

[0087] Dynamic change characteristics: parameter peak value (such as the peak engine speed under climbing conditions), valley value, fluctuation range (the difference between the peak value and the valley value), and rate of change (the average change of the parameter per unit time).

[0088] Through statistical characteristic analysis, the influence of different operating conditions on the parameters of each system is clarified.

[0089] For example, during start-stop operation, the compressor speed of the refrigeration system fluctuates by 150-200 r / min, which is significantly higher than the 50-80 r / min during constant speed operation. During ramp operation, the average NOx concentration in the emission parameters increases by 30%-40% compared to the constant speed operation, and the fluctuation coefficient increases by 25%, providing support for the subsequent coupling correlation analysis from the operating condition perspective.

[0090] Based on the statistical characteristics of parameters under various operating conditions, linear and nonlinear correlation analysis methods are used to quantitatively calculate the correlation strength between refrigeration system parameters and driving emission parameters, and to determine the coupling relationship between the two.

[0091] By ranking the correlation strength and performing significance tests, we can identify the key factors that affect the coupling relationship.

[0092] Key factors include, but are not limited to, cooling load, vehicle speed, engine load (for gasoline vehicles) / drive motor load (for electric vehicles), and ambient temperature.

[0093] Association strength ranking: Weights are assigned according to the proportion of cold chain vehicle operation under all working conditions (start-stop / constant speed / acceleration / climbing), and the weighted mean of the association degree of potential factors (refrigeration, driving, and environmental parameters) is calculated. Strong association factors with a mean of ≥0.6 are selected in descending order.

[0094] Significance test: Set the significance level α=0.05, remove non-significant factors with an association strength greater than or equal to 0.05, and distinguish between fuel vehicles (retaining engine load) and electric vehicles (replacing drive motor load).

[0095] The key factors were ultimately determined to be: cooling load, vehicle speed, engine load (for gasoline vehicles) / drive motor load (for electric vehicles), and ambient temperature, which provide core input features for the subsequent coupled dynamic prediction model.

[0096] Based on the key influencing factors (cooling load, vehicle speed, engine load / drive motor load, ambient temperature) identified in the aforementioned steps and the periodically monitored operating parameters, a coupled dynamic prediction model for cooling and driving emissions is constructed to achieve highly accurate time-series prediction of cooling load and driving emissions. This model includes the following sub-steps:

[0097] Step 1: Design an orthogonal experimental scheme based on key factors.

[0098] First, set the parameter levels of each key factor to match the actual operating scenario of cold chain delivery vehicles (e.g., set the ambient temperature to 5℃ / 25℃ / 35℃, and the vehicle speed to 20km / h / 40km / h / 60km / h), and then select the appropriate orthogonal array to determine the number of experiments.

[0099] Under different combinations of key factors, vehicle operation data are collected periodically at sampling intervals of 1-5 minutes to ensure that the dataset covers all operating conditions including start-stop, constant speed, acceleration, and hill climbing, forming a periodic monitoring dataset for model training and validation.

[0100] It should be noted that the orthogonal experimental design should generate no less than 500 sets of full-condition sample data (covering key factor combinations of start-stop, constant speed, acceleration, and climbing conditions) to ensure the generalization ability of the model training.

[0101] Step 2: Perform feature engineering on the periodic monitoring dataset.

[0102] The Z-score normalization method is used to normalize features and eliminate the interference of different dimensional parameters (such as temperature in °C and vehicle speed in km / h) on model training.

[0103] By using sliding window statistics (e.g., setting the window size to 3-5 sampling periods), time-series features (such as the rate of change of cooling load and the amplitude of vehicle speed fluctuation) are extracted, and a feature set for model training that is dimensionally adapted and information-complete is constructed.

[0104] Step 3: Based on periodic monitoring data, a dynamic prediction model coupling cooling and driving emissions is constructed using a fusion architecture of Convolutional Neural Network - Long Short-Term Memory Hybrid Model (CNN-LSTM).

[0105] A convolutional neural network (CNN) is used to extract local spatiotemporal features from monitoring data to capture the local correlation patterns between key factors and the coupling relationship between cooling and driving emissions. The high-dimensional features output by the CNN are then input into a long short-term memory (LSTM) network. The LSTM's gating mechanism is used to capture the long-term temporal dependencies of the input sequence, ultimately constructing a coupled dynamic prediction model that can achieve highly accurate time-series prediction.

[0106] It should be noted that the dynamic prediction model for coupled refrigeration and driving emissions based on the CNN-LSTM fusion architecture needs to determine the dynamic prediction time step, extract features from the input data, and optimize hyperparameters to ensure that the model's prediction accuracy and real-time performance are adapted to the needs of cold chain operations. Specific details are as follows:

[0107] (1) Determining the dynamic prediction time step.

[0108] Using the data collection cycle of periodic monitoring (e.g., 1-5 minutes) as the smallest unit time step, three typical time step schemes of 5 min, 10 min, and 15 min were selected to test the prediction accuracy and computation time of the model at different time steps:

[0109] Prediction accuracy is quantified and evaluated using the root mean squared error (RMSE) and the mean absolute error (MAE).

[0110] Specifically, the root mean square error satisfies the following formula:

[0111]

[0112] in, This represents the root mean square error. This represents the actual experimental value of the i-th group of parameters (such as the cooling energy efficiency and NO collected by multi-sensor fusion technology in this application). x (Concentration and other actual operational data); The i-th group of parameters represents the model prediction value (such as the prediction results of cooling energy efficiency and emission parameters in the next 1-3 hours output by the coupled dynamic prediction model); m represents the total number of samples participating in the evaluation (i.e., the number of multi-condition data sets collected in the test).

[0113] Specifically, the mean absolute error satisfies the following formula:

[0114]

[0115] in, This represents the root mean square error. Represented as actual experimental values, This represents the model's predicted value, and m represents the total number of samples.

[0116] Combining the temperature control requirements of cold chain goods (temperature deviation of the compartment ≤ 0.5℃) and the real-time control requirements (control strategy update delay ≤ prediction time step), the scheme that meets the engineering error requirements (RMSE ≤ 5%, MAE ≤ 3%) and has the shortest calculation time is selected, and the optimal prediction time step is determined.

[0117] (2) Feature extraction of model input data.

[0118] For multi-dimensional parameters (including refrigeration system parameters and driving emission parameters) in periodic monitoring data, CNN is used to perform spatial feature extraction: the convolutional layers of CNN (with 3-5 convolutional kernels and a kernel size of 3×3) are used to capture local features of the multi-dimensional parameter matrix and explore the spatial correlation between parameters (such as the local coupling features between ambient temperature and refrigeration load).

[0119] After dimensionality reduction by pooling layers (max pooling, 2×2 pooling kernels), a high-dimensional compressed feature vector is output. This feature vector is used as the input of LSTM to construct a CNN-LSTM two-stage prediction architecture. The long-term temporal dependencies of the feature sequence are learned through the gating mechanism of LSTM (input gate, forget gate, output gate), and finally, dynamic prediction of cooling energy efficiency and emission levels in the next 1-3 hours is achieved.

[0120] (3) Model hyperparameter optimization.

[0121] An improved particle swarm optimization (IPSO) algorithm is used to iteratively optimize the model's hyperparameters, with the goal of minimizing the model training error RMSE.

[0122] IPSO introduces an adaptive inertia weight adjustment strategy: as the number of iterations increases, the inertia weight dynamically decreases from 0.9 to 0.4, balancing the algorithm's global search capability (early stage of iteration) and local optimization accuracy (late stage of iteration).

[0123] The hyperparameters to be optimized include: learning rate (0.001-0.01), number of iterations (100-500), number of hidden layers in LSTM (1-3 layers), number of neurons in hidden layers (32-128), optimal prediction time step (5min / 10min / 15min), and batch size (16-64).

[0124] Through IPSO iterative search, the hyperparameter combination that minimizes the model training error is output, thus completing the model parameter configuration and ensuring the model's prediction stability and accuracy under all operating conditions.

[0125] The coupled and coordinated control method for cooling and driving provided in this application learns parameter samples under multiple operating conditions through a coupled dynamic prediction model, quantifies the coupling strength and response law of each system parameter, and provides accurate data basis for coordinated control.

[0126] Step S120: Optimize the trend prediction results based on the preset multi-objective optimization function to determine the target control parameters for cold chain vehicles.

[0127] Among them, the multi-objective optimization function refers to a mathematical function that simultaneously carries the dual optimization objectives of maximizing cooling energy efficiency and minimizing comprehensive emissions, and incorporates actual engineering constraints.

[0128] Optionally, the multi-objective optimization function includes a cooling energy efficiency optimization function, a comprehensive emission optimization function, and a set of constraints.

[0129] The set of constraints includes at least one of the following:

[0130] The actual temperature T in the compartment of a refrigerated vehicle C Deviation constraint from the cooling set temperature Tset: The difference between the actual temperature of the passenger compartment and the cooling set temperature is less than or equal to the preset temperature value;

[0131] Operating condition constraints for cold chain vehicle speed: The cold chain vehicle speed v is greater than or equal to the minimum permissible speed v corresponding to the current operating condition of the cold chain vehicle. min ;

[0132] Battery state of charge constraint for cold chain vehicles: The state of charge (SOC) of the power battery of cold chain vehicles must be greater than or equal to the preset battery charge value.

[0133] For example, the set of constraints can be: , as well as At least one of them.

[0134] Specifically, the cooling energy efficiency optimization function satisfies the following formula:

[0135]

[0136] in, This indicates the target value for optimizing cooling energy efficiency. Indicates the coefficient of performance (COP) for cooling. Indicates the compressor speed of the refrigeration system. Indicates the set cooling temperature. Indicates the vehicle's speed. Cooling capacity of the refrigeration system per unit time This indicates the total operating power consumption of the refrigeration system.

[0137] Specifically, the comprehensive emission optimization function satisfies the following formula:

[0138]

[0139] in, This represents the overall emission optimization target value. This represents the weighting factor for nitrogen oxide (NOx) emissions. This represents the weighting factor for carbon dioxide (CO2) emissions. Indicates nitrogen oxide emissions, Indicates carbon dioxide emissions. This represents the engine speed / drive motor speed. Indicates the vehicle's speed.

[0140] It should be noted that the weighting coefficients α and β can be adjusted according to regional environmental policies (such as NO). xThe emission limits and carbon reduction targets are dynamically adjusted for cold chain transportation scenarios to ensure that α+β=1. For example, in urban conditions, α=0.6 and β=0.4 (emphasizing NO). x (Emission reduction), α=0.3 and β=0.7 under high-speed conditions (focusing on carbon emission reduction).

[0141] like Figure 2 As shown, the sub-steps for determining the target control parameters of cold chain vehicles in this embodiment of the application specifically include:

[0142] Step S200: Use the parameters to be adjusted in the refrigeration system and the parameters to be adjusted in the driving system as optimization variables.

[0143] For example, the parameters to be adjusted in the refrigeration system include compressor speed and refrigeration set temperature. The compressor speed is directly related to the refrigeration load and refrigeration power consumption, while the refrigeration set temperature determines the temperature control benchmark and refrigeration load requirements of the passenger compartment.

[0144] Driving system parameters to be adjusted include recommended vehicle speed, engine load distribution ratio (gasoline vehicle) / drive motor load distribution ratio (electric vehicle). These parameters directly affect driving energy consumption, exhaust emissions, and the power supply capacity of the power system to the cooling system.

[0145] Step S210: Based on the operating conditions of cold chain vehicles, determine the value range of the optimization variables respectively, and randomly generate an initial set of optimization variables according to the value range.

[0146] For example, based on the current operating conditions of the cold chain vehicle (start-stop condition, constant speed condition, acceleration condition, and hill-climbing condition) and actual engineering constraints, the reasonable value range of each optimization variable is determined:

[0147] Compressor speed: The value range is set to 1500-3500 r / min based on the rated power of the refrigeration system and the actual refrigeration load requirements;

[0148] Refrigeration setting temperature: Based on the temperature control requirements of cold chain goods (such as 2-8℃ for fresh goods), the set value range is 2-10℃;

[0149] Recommended vehicle speed: Limited according to the type of working condition: 10-30km / h for start-stop working conditions, 30-60km / h for constant speed working conditions, 20-50km / h for acceleration working conditions, and 15-40km / h for climbing working conditions;

[0150] Engine / drive motor load distribution ratio: Set the power distribution ratio of the refrigeration system to 10%-30% to ensure a balance between the supply and demand of driving power and refrigeration power.

[0151] Within the above value range, an initial set of optimization variables of size N (N=50-200, set according to the solution accuracy requirements) is randomly generated using real number encoding. Each element of the set corresponds to a complete combination of parameters to be adjusted for the cooling-driving system.

[0152] Step S220: The initial set of optimization variables and the predicted trend results are used to generate the optimal set of target control parameters through iterative optimization calculation using a multi-objective optimization function.

[0153] For example, the initial set of optimization variables and the trend prediction results output by the coupled dynamic prediction model are substituted into the aforementioned multi-objective optimization function (balancing maximizing cooling energy efficiency and minimizing comprehensive emissions), and constraints are incorporated (cabin temperature deviation ≤ 0.5℃, SOC of new energy vehicles ≥ 20%, etc.), and iterative optimization calculations are performed through a multi-objective optimization algorithm:

[0154] For each combination of optimization variables, an fitness assessment is performed, and the corresponding cooling energy efficiency coefficient and comprehensive emission value are calculated.

[0155] Through fast nondominated sorting and crowding calculation, nondominated solutions that meet the constraints are selected.

[0156] The combination of variables is iteratively updated through genetic operations such as crossover and mutation until the maximum number of iterations is reached. Finally, the Pareto optimal solution set is output, which is the optimal set of objective control parameters. This set contains multiple sets of optimal parameter combinations that take into account different optimization objectives and priorities.

[0157] Step S230: Based on the operating conditions and priority strategy of the cold chain vehicle, determine the target control parameters of the cold chain vehicle from the optimal set of target control parameters.

[0158] Based on the real-time operating conditions of cold chain vehicles and the preset priority strategy, target control parameters are selected from the optimal set of target control parameters:

[0159] If the current operation is in an urban start-stop condition (with strict emission constraints), the "emission priority" strategy is adopted, and the parameter combination with the lowest comprehensive emission value and meeting the basic requirements of cooling energy efficiency is selected.

[0160] If the current operating condition is high-speed and constant speed (energy efficiency demand is prominent), adopt the "energy efficiency first" priority strategy and select the parameter combination with the highest cooling energy efficiency coefficient and emission level compliance.

[0161] For general operating conditions (without special constraints), a "balanced" priority strategy is adopted to select the parameter combination that is optimal in synergy between cooling energy efficiency and emission levels.

[0162] Through the above screening process, the target control parameters of the cold chain vehicle that are suitable for the current working conditions and requirements are determined, providing a direct basis for the subsequent dynamic coupling control of the refrigeration system and the driving system.

[0163] In summary, the refrigeration and driving coupled coordinated control method and device provided in this application comprehensively collects multi-dimensional parameters of the refrigeration system, driving system and emission system of cold chain vehicles, uses a coupled dynamic prediction model to accurately capture the dynamic coupling relationship of the parameters of each system under multiple operating conditions, and then obtains the optimal control parameters by solving a multi-objective optimization function, thereby achieving coordinated optimization of refrigeration effect, driving economy and emission compliance.

[0164] The foregoing mainly describes the solution provided in this application. Accordingly, this application also provides a coupled and coordinated control method and apparatus for cooling and driving, which is used to implement the above-described method embodiments.

[0165] The following describes the coupled and coordinated control method and apparatus for cooling and driving provided in the embodiments of this application. The coupled and coordinated control method and apparatus for cooling and driving described below can be referred to in conjunction with the coupled and coordinated control method for cooling and driving described above.

[0166] Figure 3 This is a schematic diagram of the structure of the cooling and driving coupled coordinated control method device provided in the embodiments of this application. Figure 3 As shown, this application embodiment provides a coupled and coordinated control device for cooling and driving, which may include:

[0167] The data acquisition module 301 is used to collect the operating parameters of the cold chain vehicle, including refrigeration system parameters, driving system parameters and emission parameters.

[0168] Module 302 is used to build a coupled dynamic prediction model of refrigeration-driving-emission based on operating parameters. The coupled dynamic prediction model is used to learn the dynamic coupling relationship between the systems under different operating conditions and output the prediction results of the changing trends of refrigeration energy efficiency and emission levels.

[0169] The execution module 303 is used to perform optimization calculations on the trend prediction results based on a preset multi-objective optimization function to determine the target control parameters of the cold chain vehicle.

[0170] In one embodiment, execution module 303 is specifically used for:

[0171] The parameters to be adjusted in the refrigeration system and the driving system are used as optimization variables;

[0172] Based on the operating conditions of cold chain vehicles, the value range of optimization variables is determined, and an initial set of optimization variables is randomly generated according to the value range.

[0173] The initial set of optimized variables and the predicted trend results are used to generate the optimal set of target control parameters through iterative optimization calculation using a multi-objective optimization function.

[0174] Based on the operating conditions and priority strategies of cold chain vehicles, the target control parameters of cold chain vehicles are determined from the optimal set of target control parameters.

[0175] In yet another embodiment, the multi-objective optimization function includes a cooling energy efficiency optimization function, a comprehensive emission optimization function, and a set of constraints.

[0176] The set of constraints includes at least one of the following:

[0177] Deviation constraint between actual temperature of refrigerated vehicle compartment and refrigeration set temperature: The difference between actual temperature of compartment and refrigeration set temperature is less than or equal to the preset temperature value.

[0178] Operating condition constraints on the speed of cold chain vehicles: The speed of cold chain vehicles must be greater than or equal to the minimum permissible speed corresponding to the current operating condition of the cold chain vehicle.

[0179] Battery state of charge constraint for cold chain vehicles: The state of charge of the power battery of cold chain vehicles is greater than or equal to the preset battery charge value.

[0180] In yet another embodiment, the above-mentioned cooling energy efficiency optimization function satisfies a first preset formula, which is:

[0181]

[0182] in, This indicates the target value for optimizing cooling energy efficiency. Indicates the coefficient of performance (COP) for cooling. Indicates the compressor speed of the refrigeration system. Indicates the set cooling temperature. Indicates the vehicle's speed. Cooling capacity of the refrigeration system per unit time This indicates the total operating power consumption of the refrigeration system.

[0183] In yet another embodiment, the above-mentioned comprehensive emission optimization function satisfies a second preset formula, which is:

[0184]

[0185] in, This represents the overall emission optimization target value. This represents the weighting factor for nitrogen oxide (NOx) emissions. This represents the weighting factor for carbon dioxide (CO2) emissions. Indicates nitrogen oxide emissions, Indicates carbon dioxide emissions. This represents the engine speed / drive motor speed. Indicates the vehicle's speed.

[0186] In yet another embodiment, the construction module 302 is specifically used for:

[0187] Based on the linear and nonlinear correlation analysis between refrigeration system parameters, driving system parameters and emission parameters, the key factors affecting refrigeration energy efficiency, driving status and emission levels are obtained.

[0188] Based on key factors and operating parameters, a coupled dynamic prediction model for refrigeration, driving, and emissions is constructed.

[0189] In some embodiments, the cooling and driving coupled coordinated control method apparatus includes hardware structures and / or software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art will readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0190] This application embodiment can divide the coupled and coordinated control method device for cooling and driving into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into a feature extraction module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0191] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application, such as... Figure 4As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute the steps of a coupled and coordinated control method for refrigeration and driving. This method includes: collecting operating parameters of the cold chain vehicle, including refrigeration system parameters, driving system parameters, and emission parameters; constructing a coupled dynamic prediction model for refrigeration-driving-emissions based on the operating parameters, the coupled dynamic prediction model being used to learn the dynamic coupling relationship between the systems under different operating conditions and outputting prediction results of the changing trends of refrigeration energy efficiency and emission levels; and performing optimization calculations on the prediction results of the changing trends based on a preset multi-objective optimization function to determine the target control parameters of the cold chain vehicle.

[0192] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0193] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the coupled and coordinated control method for refrigeration and driving provided in the above embodiments, such as: collecting the operating parameters of the cold chain vehicle, including refrigeration system parameters, driving system parameters, and emission parameters; constructing a coupled dynamic prediction model for refrigeration-driving-emission based on the operating parameters, wherein the coupled dynamic prediction model is used to learn the dynamic coupling relationship between the systems under different operating conditions and output the prediction results of the changing trends of refrigeration energy efficiency and emission levels; and performing optimization calculations on the prediction results of the changing trends based on a preset multi-objective optimization function to determine the target control parameters of the cold chain vehicle.

[0194] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program. The computer program is used to cause the processor to execute the steps of the methods provided in the above embodiments, such as: collecting operating parameters of the cold chain vehicle, including refrigeration system parameters, driving system parameters, and emission parameters; constructing a coupled dynamic prediction model of refrigeration-driving-emission based on the operating parameters, the coupled dynamic prediction model being used to learn the dynamic coupling relationship between the systems under different operating conditions and output the prediction results of the changing trends of refrigeration energy efficiency and emission levels; and performing optimization calculations on the prediction results of the changing trends based on a preset multi-objective optimization function to determine the target control parameters of the cold chain vehicle.

[0195] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0196] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0197] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0198] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A coupled and coordinated control method for cooling and driving, characterized in that, The method includes: Collect operating parameters of refrigeration vehicles, including refrigeration system parameters, driving system parameters, and emission parameters; Based on the operating parameters, a coupled dynamic prediction model for refrigeration-driving-emissions is constructed. The coupled dynamic prediction model is used to learn the dynamic coupling relationship between the systems under different operating conditions and output the prediction results of the changing trends of refrigeration energy efficiency and emission levels. The prediction results of the changing trends are used to represent the quantitative dynamic information of the evolution of refrigeration energy efficiency and emission levels over time within a future preset time period. The target control parameters of the cold chain vehicle are determined by performing optimization calculations on the predicted trend results based on a preset multi-objective optimization function. The process involves optimizing the predicted trend based on a preset multi-objective optimization function to determine the target control parameters for the cold chain vehicle, including: The parameters to be adjusted in the refrigeration system and the parameters to be adjusted in the driving system are used as optimization variables; the parameters to be adjusted in the refrigeration system include compressor speed and refrigeration set temperature, and the parameters to be adjusted in the driving system include suggested vehicle speed and engine load distribution ratio or drive motor load distribution ratio. Based on the operating conditions of the cold chain vehicles, the value ranges of the optimization variables are determined respectively, and an initial set of optimization variables is randomly generated according to the value ranges. The initial set of optimized variables and the predicted trend are used in the multi-objective optimization function to generate the optimal set of target control parameters through iterative optimization calculation. Based on the operating conditions and priority strategy of the cold chain vehicle, the target control parameters of the cold chain vehicle are determined from the optimal set of target control parameters.

2. The coupled and coordinated control method for cooling and driving according to claim 1, characterized in that, The multi-objective optimization function includes a cooling energy efficiency optimization function, a comprehensive emission optimization function, and a set of constraints. The set of constraints includes at least one of the following: The deviation constraint between the actual temperature of the cold chain vehicle compartment and the set refrigeration temperature is: the difference between the actual temperature of the compartment and the set refrigeration temperature is less than or equal to the preset temperature value. The operating condition constraint for the cold chain vehicle's driving speed is: the driving speed of the cold chain vehicle is greater than or equal to the minimum permissible driving speed corresponding to the current operating condition of the cold chain vehicle. The state of charge (SOC) constraint of the battery in the cold chain vehicle: The SOC of the power battery in the cold chain vehicle is greater than or equal to a preset battery charge value.

3. The coupled and coordinated control method for cooling and driving according to claim 2, characterized in that, The cooling energy efficiency optimization function satisfies a first preset formula, which is: ； in, This indicates the target value for optimizing cooling energy efficiency. Indicates the coefficient of performance (COP) for cooling. Indicates the compressor speed of the refrigeration system. Indicates the set cooling temperature. Indicates the vehicle's speed. Cooling capacity of the refrigeration system per unit time This indicates the total operating power consumption of the refrigeration system.

4. The coupled and coordinated control method for cooling and driving according to claim 2, characterized in that, The comprehensive emission optimization function satisfies a second preset formula, which is: ； in, This represents the overall emission optimization target value. This represents the weighting factor for nitrogen oxide (NOx) emissions. This represents the weighting factor for carbon dioxide (CO2) emissions. Indicates nitrogen oxide emissions, This indicates carbon dioxide emissions. This represents the engine speed / drive motor speed. Indicates the vehicle's speed.

5. The coupled and coordinated control method for cooling and driving according to claim 1, characterized in that, The construction of a coupled dynamic prediction model for cooling, driving, and emissions based on the operating parameters includes: Based on the linear and nonlinear correlation analysis between the refrigeration system parameters, the driving system parameters, and the emission parameters, the key factors affecting refrigeration efficiency, driving status, and emission levels are obtained. Based on the key factors and operating parameters, the coupled dynamic prediction model of refrigeration-driving-emissions is constructed.

6. A coupled and coordinated control device for cooling and driving, characterized in that, include: The data acquisition module is used to collect the operating parameters of the cold chain vehicle, including refrigeration system parameters, driving system parameters, and emission parameters. The module is used to construct a coupled dynamic prediction model of refrigeration-driving-emission based on the operating parameters. The coupled dynamic prediction model is used to learn the dynamic coupling relationship between the systems under different operating conditions and output the prediction results of the changing trends of refrigeration energy efficiency and emission levels. The prediction results of the changing trends are used to represent the quantitative dynamic information of the evolution of refrigeration energy efficiency and emission levels over time within a future preset time period. The execution module is used to perform optimization calculations on the trend prediction results based on a preset multi-objective optimization function to determine the target control parameters of the cold chain vehicle. The process involves optimizing the predicted trend based on a preset multi-objective optimization function to determine the target control parameters for the cold chain vehicle, including: The parameters to be adjusted in the refrigeration system and the parameters to be adjusted in the driving system are used as optimization variables; the parameters to be adjusted in the refrigeration system include compressor speed and refrigeration set temperature, and the parameters to be adjusted in the driving system include suggested vehicle speed and engine load distribution ratio or drive motor load distribution ratio. Based on the operating conditions of the cold chain vehicles, the value ranges of the optimization variables are determined respectively, and an initial set of optimization variables is randomly generated according to the value ranges. The initial set of optimized variables and the predicted trend are used in the multi-objective optimization function to generate the optimal set of target control parameters through iterative optimization calculation. Based on the operating conditions and priority strategy of the cold chain vehicle, the target control parameters of the cold chain vehicle are determined from the optimal set of target control parameters.

7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the coupled and coordinated control method for cooling and driving as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the coupled and coordinated control method for cooling and driving as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the coupled and coordinated control method for cooling and driving as described in any one of claims 1 to 5.

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