Operation optimization method and device of vehicle thermal management system, vehicle and storage medium

Abstract

The invention relates to the technical field of vehicle thermal management, in particular to an operation optimization method and device for a vehicle thermal management system, a vehicle and a storage medium, and the method comprises the steps: obtaining a current operation mode of the vehicle thermal management system; determining an optimization target, a target function and a constraint condition according to the current operation mode, and solving the target function based on the optimization target and the constraint condition to obtain an operation solution set; and acquiring a Pareto optimal solution set from the operation solution set, performing subjective and objective combination weighting according to the Pareto optimal solution set to obtain target operation parameters meeting multi-target optimization, and controlling the vehicle thermal management system according to the target operation parameters. Therefore, based on the subjective and objective weighting method, multi-objective optimization is carried out on the operation mode of the vehicle thermal management system, the problems that in the related technology, the control mode is single, many optimization objectives cannot be considered, and consequently the control effect of the vehicle thermal management system is not ideal are solved, the operation parameters are optimized, and the operation effect is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle thermal management technology, and in particular to an operation optimization method, device, vehicle and storage medium of a vehicle thermal management system. Background Art

[0002] With the development of the automotive industry, the control of vehicle thermal management systems has received more and more attention from customers.

[0003] In the related art, when controlling a vehicle thermal management system, the operating parameters of the vehicle thermal management system are generally determined based on the vehicle thermal management system having the lowest energy consumption or the shortest time to reach the required temperature.

[0004] However, this control method is relatively simple and cannot take into account more optimization objectives, resulting in unsatisfactory control effects of the vehicle thermal management system, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a vehicle thermal management system operation optimization method, device, vehicle and storage medium to solve the problems in related technologies such as a relatively single control method and an inability to take into account multiple optimization targets, resulting in unsatisfactory control effects of the vehicle thermal management system, optimize operating parameters and improve operating effects.

[0006] The first aspect of the present application provides a method for optimizing the operation of a vehicle thermal management system, comprising the following steps:

[0007] Get the current operating mode of the vehicle thermal management system;

[0008] Determining an optimization objective, an objective function, and constraints according to the current operation mode, and solving the objective function based on the optimization objective and the constraints to obtain an operation solution set;

[0009] A Pareto optimal solution set is obtained from the operating solution set, and subjective and objective combined weighting is performed according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, and the vehicle thermal management system is controlled according to the target operating parameters.

[0010] Optionally, the current operation mode is a refrigerator single cold storage mode, and the optimization objectives are to minimize system energy consumption, maximize system cooling capacity, maximize system energy efficiency, minimize refrigerator cooling time, and minimize refrigerator temperature fluctuation;

[0011] The objective function includes:

[0012] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power;

[0013] System cooling capacity = refrigerator refrigerant mass flow rate × refrigerator evaporator inlet and outlet enthalpy difference;

[0014] System energy efficiency = system cooling capacity / system energy consumption;

[0015] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0016] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0017] The constraints include:

[0018] Compressor high pressure ≤ P max,1 ;

[0019] P min,1 ≤Compressor low pressure;

[0020] Compressor pressure ratio ≤P r,1 ;

[0021] -a≤actual temperature of refrigerator-preset temperature of refrigerator≤a;

[0022] Among them, P max,1 is the maximum allowable value of the compressor high pressure, P min,1 is the minimum allowable value of the compressor low pressure, P r,1 is the maximum allowable value of the compressor pressure ratio, and a is the allowable fluctuation value of the temperature in the refrigerator under the single cold storage mode of the refrigerator.

[0023] Optionally, performing subjective and objective combined weighting according to the Pareto optimal solution set to obtain target operating parameters that satisfy multi-objective optimization includes:

[0024] Based on the Pareto optimal solution set, a first initial weight of each operating parameter is calculated according to a preset hierarchical analysis method, and a first subjective weight is obtained according to a preset subjective judgment matrix and the first initial weight of each operating parameter;

[0025] Based on the Pareto optimal solution set, a preset CRITIC method is used to calculate the second initial weight of each operating parameter, and the second initial weight of each operating parameter is processed to obtain a first objective weight;

[0026] The target operating parameters satisfying the multi-objective optimization are obtained according to the first subjective weight and the first objective weight.

[0027] Optionally, the current operation mode is a joint operation mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time, shortest passenger compartment cooling time, minimum refrigerator temperature fluctuation and minimum passenger compartment temperature fluctuation as the optimization goals;

[0028] The objective function includes:

[0029] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power + blower power;

[0030] System cooling capacity = refrigerator evaporator refrigerant mass flow × refrigerator evaporator inlet and outlet enthalpy difference + air conditioning box evaporator refrigerant mass flow × air conditioning box evaporator inlet and outlet enthalpy difference + battery cooler refrigerant mass flow × battery cooler inlet and outlet enthalpy difference

[0031] System energy efficiency = system cooling capacity / system energy consumption;

[0032] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0033] Air conditioning cooling time = the time when the passenger compartment reaches the preset temperature - the time when cooling starts;

[0034] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0035] Passenger compartment temperature fluctuation = the maximum temperature change per unit time after the passenger compartment reaches the set temperature;

[0036] The constraints include:

[0037] Compressor high pressure ≤ P max,2 ;

[0038] P min,2 ≤Compressor low pressure;

[0039] Compressor pressure ratio ≤P r,2 ;

[0040] -b≤actual temperature of refrigerator-preset temperature of refrigerator≤b;

[0041] -c≤actual temperature of passenger compartment-preset temperature of passenger compartment≤c;

[0042] Among them, P max,2 is the maximum allowable value of the compressor high pressure in the combined operation mode, P min,2 is the minimum allowable value of the compressor low pressure in the combined operation mode, b is the allowable fluctuation value of the temperature in the refrigerator in the combined operation mode, and c is the allowable fluctuation value of the temperature in the passenger compartment in the combined operation mode.

[0043] Optionally, performing subjective and objective combined weighting according to the Pareto optimal solution set to obtain target operating parameters that satisfy multi-objective optimization includes:

[0044] Based on the Pareto optimal solution set, a third initial weight of each operating parameter is calculated according to a preset hierarchical analysis method, and a second subjective weight is obtained according to a preset subjective judgment matrix and the third initial weight of each operating parameter;

[0045] Based on the Pareto optimal solution set, a fourth initial weight of each operating parameter is calculated using a preset CRITIC method, and the fourth initial weight of each operating parameter is processed to obtain a second objective weight;

[0046] The target operating parameters satisfying the multi-objective optimization are obtained according to the second subjective weight and the second objective weight.

[0047] A second aspect of the present application provides an operation optimization device for a vehicle thermal management system, including:

[0048] An acquisition module, used to acquire the current operation mode of the vehicle thermal management system;

[0049] A calculation module, used to determine an optimization target, an objective function and constraints according to the current operation mode, and solve the objective function based on the optimization target and the constraints to obtain an operation solution set;

[0050] A control module is used to obtain a Pareto optimal solution set from the operating solution set, and to perform subjective and objective combined weighting according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, and to control the vehicle thermal management system according to the target operating parameters.

[0051] Optionally, the current operation mode is a refrigerator single cold storage mode, and the optimization objectives are to minimize system energy consumption, maximize system cooling capacity, maximize system energy efficiency, minimize refrigerator cooling time, and minimize refrigerator temperature fluctuation;

[0052] The objective function includes:

[0053] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power;

[0054] System cooling capacity = refrigerator refrigerant mass flow rate × refrigerator evaporator inlet and outlet enthalpy difference;

[0055] System energy efficiency = system cooling capacity / system energy consumption;

[0056] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0057] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0058] The constraints include:

[0059] Compressor high pressure ≤ P max,1 ;

[0060] P min,1 ≤Compressor low pressure;

[0061] Compressor pressure ratio ≤P r,1 ;

[0062] -a≤actual temperature of refrigerator-preset temperature of refrigerator≤a;

[0063] Among them, P max,1 is the maximum allowable value of the compressor high pressure, P min,1 is the minimum allowable value of the compressor low pressure, P r,1 is the maximum allowable value of the compressor pressure ratio, and a is the allowable fluctuation value of the temperature in the refrigerator under the single cold storage mode of the refrigerator.

[0064] Optionally, the control module is specifically used to:

[0065] Based on the Pareto optimal solution set, a first initial weight of each operating parameter is calculated according to a preset hierarchical analysis method, and a first subjective weight is obtained according to a preset subjective judgment matrix and the first initial weight of each operating parameter;

[0066] Based on the Pareto optimal solution set, a preset CRITIC method is used to calculate the second initial weight of each operating parameter, and the second initial weight of each operating parameter is processed to obtain a first objective weight;

[0067] The target operating parameters satisfying the multi-objective optimization are obtained according to the first subjective weight and the first objective weight.

[0068] Optionally, the current operation mode is a joint operation mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time, shortest passenger compartment cooling time, minimum refrigerator temperature fluctuation and minimum passenger compartment temperature fluctuation as the optimization goals;

[0069] The objective function includes:

[0070] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power + blower power;

[0071] System cooling capacity = refrigerator evaporator refrigerant mass flow × refrigerator evaporator inlet and outlet enthalpy difference + air conditioning box evaporator refrigerant mass flow × air conditioning box evaporator inlet and outlet enthalpy difference + battery cooler refrigerant mass flow × battery cooler inlet and outlet enthalpy difference

[0072] System energy efficiency = system cooling capacity / system energy consumption;

[0073] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0074] Air conditioning cooling time = the time when the passenger compartment reaches the preset temperature - the time when cooling starts;

[0075] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0076] Passenger compartment temperature fluctuation = the maximum temperature change per unit time after the passenger compartment reaches the set temperature;

[0077] The constraints include:

[0078] Compressor high pressure ≤ P max,2 ;

[0079] P min,2 ≤Compressor low pressure;

[0080] Compressor pressure ratio ≤P r,2 ;

[0081] -b≤actual temperature of refrigerator-preset temperature of refrigerator≤b;

[0082] -c≤actual temperature of passenger compartment-preset temperature of passenger compartment≤c;

[0083] Among them, P max,2 is the maximum allowable value of the compressor high pressure in the combined operation mode, P min,2 is the minimum allowable value of the compressor low pressure in the combined operation mode, b is the allowable fluctuation value of the temperature in the refrigerator in the combined operation mode, and c is the allowable fluctuation value of the temperature in the passenger compartment in the combined operation mode.

[0084] Optionally, the control module is specifically used to:

[0085] Based on the Pareto optimal solution set, a third initial weight of each operating parameter is calculated according to a preset hierarchical analysis method, and a second subjective weight is obtained according to a preset subjective judgment matrix and the third initial weight of each operating parameter;

[0086] Based on the Pareto optimal solution set, a fourth initial weight of each operating parameter is calculated using a preset CRITIC method, and the fourth initial weight of each operating parameter is processed to obtain a second objective weight;

[0087] The target operating parameters satisfying the multi-objective optimization are obtained according to the second subjective weight and the second objective weight.

[0088] The third aspect of the present application provides a vehicle, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the operation optimization method of the vehicle thermal management system as described in the above embodiment.

[0089] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the operation optimization method of the vehicle thermal management system as described in the above embodiment.

[0090] The fifth aspect of the present application provides a computer program product, which stores a computer program. When the program is executed by a processor, it implements the operation optimization method of the vehicle thermal management system as described in the above embodiment.

[0091] Therefore, after obtaining the current operating mode of the vehicle thermal management system, the optimization target, objective function and constraints are determined according to the current operating mode, and based on the optimization target and constraints, the objective function is solved to obtain the operating solution set, and then the Pareto optimal solution set is obtained from the operating solution set, and the subjective and objective combined weighting is performed according to the Pareto optimal solution set to obtain the target operating parameters that meet the multi-objective optimization, and the vehicle thermal management system is controlled according to the target operating parameters. Therefore, based on the subjective and objective weighting method, the operating mode of the vehicle thermal management system is optimized with multiple objectives, which solves the problems that the control method in the related technology is relatively single and cannot take into account more optimization targets, resulting in unsatisfactory control effects of the vehicle thermal management system, optimizes the operating parameters, and improves the operating effect.

[0092] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0094] Figure 1 It is a structural schematic diagram of a cold-storage vehicle refrigerator with a hot and cold dual mode in the related art;

[0095] Figure 2 It is a structural schematic diagram of a method for adjusting the gear position of a vehicle refrigerator according to the load amount and the temperature difference in the related art;

[0096] Figure 3 It is a flow chart of a method for adjusting the gear position of a vehicle refrigerator according to the load amount and the temperature difference in the related art;

[0097] Figure 4 A flowchart of an operation optimization method of a vehicle thermal management system provided according to an embodiment of the present application;

[0098] Figure 5 A schematic diagram of the system operation principle of an operation optimization method for a vehicle thermal management system provided according to an embodiment of the present application;

[0099] Figure 6 A schematic diagram of a vehicle refrigerator according to an operation optimization method of a vehicle thermal management system provided in an embodiment of the present application;

[0100] Figure 7 A schematic diagram of a single cold storage mode of an operation optimization method of a vehicle thermal management system provided according to an embodiment of the present application;

[0101] Figure 8 A schematic diagram of a joint operation mode of an operation optimization method of a vehicle thermal management system provided according to an embodiment of the present application;

[0102] Fig. 9 A single cold storage mode operation flow chart of an operation optimization method of a vehicle thermal management system provided according to an embodiment of the present application;

[0103] Fig.10 A combined operation mode flow chart of an operation optimization method of a vehicle thermal management system provided according to an embodiment of the present application;

[0104] Fig.11 A schematic diagram of an operation optimization device for a vehicle thermal management system provided according to an embodiment of the present application;

[0105] Fig.12 It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application.

[0106] Figure numerals: compressor 100, condenser 201, cooling fan 202, liquid storage tank 300, coaxial tube 400, first electronic expansion valve 501, second electronic expansion valve 502, third electronic expansion valve 503, air conditioning box evaporator 601, blower 6011, vehicle refrigerator evaporator 602, battery cooler 603, vehicle refrigerator cold storage material 701, vehicle refrigerator storage space 702, refrigerator temperature equalizing electronic fan 703, vehicle thermal management system operation optimization device 10, acquisition module 1000, calculation module 2000, control module 3000. DETAILED DESCRIPTION

[0107] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0108] Before introducing the operation optimization method of the vehicle thermal management system according to the embodiment of the present application, the operation optimization method of the vehicle thermal management system in the related art is briefly introduced.

[0109] Specifically, Figure 1 As shown, Figure 1 The present invention is a schematic structural diagram of a cold-storage vehicle refrigerator with dual-modes of hot and cold in the related art.

[0110] A cold and hot dual-mode cold storage vehicle refrigerator in the field of automobile technology includes a shell, a cold storage module, a storage chamber, a flip cover, an upper shell, a lighting lamp, a cold storage agent temperature sensor, a cabin temperature sensor, a heating film, a controller, a refrigerant pipe outlet pressure plate, an electronic expansion valve, and a cold storage agent. The cold storage module is nested on the outer surface of the storage chamber, the storage chamber is nested in the shell, and the cold storage agent is arranged in the cold storage module; the controller, the refrigerant pipe outlet pressure plate, and the electronic expansion valve are all installed on the front outer wall of the shell; the upper shell is nested on the top of the shell near the controller side, and the flip cover is arranged on the top of the shell and matches the upper shell. In the implementation process of the present invention, during the heating process, the heating film at the bottom of the storage chamber is opened to heat the ambient temperature of the storage chamber; during the cooling process, the cold energy generated by the evaporator is stored in the cold storage agent, and then the cold energy is directly transferred to the storage chamber through the surface of the cold storage agent container close to the surface of the refrigerator storage chamber.

[0111] About the operation mode of the refrigerator:

[0112] Condition 1: A dual-mode cold storage vehicle refrigerator starts cooling, and the battery cooler and cabin evaporator are not working.

[0113] Under working condition 1, the vehicle refrigerator can adjust the evaporation temperature of the refrigerant through the electronic expansion valve 12 so that the evaporation temperature is lower than the evaporation temperature of the refrigerant 202. When the refrigerant temperature sensor 8 detects that the temperature of the refrigerant 202 drops below the phase change temperature, the refrigerant module 2 completes the storage of cold air and shuts down the compressor and condenser in the vehicle thermal management system.

[0114] Working condition 2: A dual-mode cold storage vehicle refrigerator starts cooling, the battery cooler or the cabin evaporator works, and the phase change temperature of the refrigerant in the vehicle refrigerator is higher than the evaporation temperature of the vehicle thermal management system.

[0115] In the case of working condition 2, the phase change point temperature of the coolant 202 is higher than the evaporation temperature of the vehicle thermal management system, and the coolant 202 can be cooled to below the phase change point temperature to complete cold storage. After the coolant temperature sensor 8 detects that the temperature of the coolant 202 drops below the phase change temperature, the electronic expansion valve 12 is closed, and the temperature is reduced by relying on the cold stored in the coolant 202.

[0116] Working condition 3: A dual-mode cold storage vehicle refrigerator starts cooling, the battery cooler or the cabin evaporator works, and the phase change temperature of the refrigerant in the vehicle refrigerator is lower than the evaporation temperature of the vehicle thermal management system.

[0117] Under working condition 3, the superheat of the refrigerant at the coil outlet 2012 in the refrigerant coil 201 is adjusted by adjusting the opening of the electronic expansion valve 12, and the coldness of the refrigerant is released into the storage compartment of the vehicle refrigerator through the refrigerant 202 and the refrigerant container 203 to maintain the temperature in the storage compartment of the vehicle refrigerator; when the thermal management system of the entire vehicle is adjusted to working condition 1 or working condition 2, the coldness storage of the refrigerant 202 is executed.

[0118] Furthermore, if Figure 2 and Figure 3 As shown, Figure 2 The structure diagram of a method for adjusting the gear position of a vehicle refrigerator according to the load and the temperature difference in the related art is shown in FIG. Figure 3 The present invention is a flow chart of a method for adjusting the gear position of a vehicle refrigerator according to the load amount and the temperature difference in the related art.

[0119] A method for adjusting the gear position of a vehicle refrigerator according to a load and a temperature difference belongs to the technical field of vehicle refrigerators. The method comprises a refrigerator, wherein a compressor is arranged in the refrigerator. The method comprises: detecting the ambient temperature and the temperature inside a box by a temperature sensor of the refrigerator, calculating according to the temperature inside the box and the ambient temperature, dividing the refrigerator into three intervals H1, H2, and H3 according to the difference, wherein the initial state of the load is 0, and subsequently calculating the difference according to the last load and the current load, dividing the refrigerator into three intervals G1, G2, and G3. The method comprises: dividing the refrigerator into three intervals H1, H2, and H3 according to the difference calculated according to the temperature inside the box and the ambient temperature, and dividing the refrigerator into three intervals G1, G2, and G3 according to the load. When the temperature difference and the load value are at: H1, H1, and H2, and H3, the refrigerator is in the first gear position; H1, G3, H2, and H2, and H3, the refrigerator is in the second gear position; and H3, G3, H3, and G2, and H2, and G3, the refrigerator is in the third gear position; the refrigerator is adjusted automatically according to the environment and the load, thereby improving the use efficiency and expanding the use scope.

[0120] However, the methods in the related technologies are relatively simple, and only make simple adjustments to the throttle valve and the compressor according to the operating conditions, lacking further optimization methods; unable to take into account more optimization objectives; and lacking a scientific and reasonable decision-making method for balancing multiple objectives when obtaining the Pareto frontier solution set of multi-objective optimization.

[0121] Based on the above problems, this application proposes an operation optimization method for a vehicle thermal management system. In this method, after obtaining the current operation mode of the vehicle thermal management system, the optimization target, objective function and constraint conditions are determined according to the current operation mode, and based on the optimization target and constraint conditions, the objective function is solved to obtain the operation solution set, and then the Pareto optimal solution set is obtained from the operation solution set, and the subjective and objective combination weighting is performed according to the Pareto optimal solution set to obtain the target operation parameters that meet the multi-objective optimization, and the vehicle thermal management system is controlled according to the target operation parameters. Therefore, based on the subjective and objective weighting method, the operation mode of the vehicle thermal management system is optimized with multiple objectives, which solves the problems that the control method in the related technology is relatively single and cannot take into account more optimization targets, resulting in unsatisfactory control effect of the vehicle thermal management system, optimizes the operation parameters, and improves the operation effect.

[0122] Specifically, Figure 4 A schematic flow chart of a method for optimizing the operation of a vehicle thermal management system provided in an embodiment of the present application.

[0123] In this embodiment, the structure of the vehicle thermal management system can be as follows: Figure 5 As shown, the vehicle thermal management system includes: an air-conditioning box evaporator 601, a blower 6011, a vehicle refrigerator evaporator 602, and a battery cooler 603 are configured in parallel.

[0124] Further, Figure 6 This is a schematic diagram of a vehicle refrigerator according to an embodiment of the present application, which includes: a cold storage material 701, a vehicle refrigerator storage space 702, and a temperature-averaging electronic fan 703. The cold storage material 701 is filled between the vehicle refrigerator shell and the storage chamber, and the vehicle refrigerator evaporator 602 is placed in the cold storage material 701. The shape of the vehicle refrigerator evaporator 602 can be adaptively adjusted according to the shape of the vehicle refrigerator. The temperature-averaging electronic fan 703 can be arranged at the top center of the vehicle refrigerator storage space for forced convection to make the temperature in the storage chamber uniform as quickly as possible.

[0125] The embodiment of the present application divides the operation mode of the vehicle thermal management system into a refrigerator single cold storage mode and a combined operation mode according to whether the refrigerator refrigeration process interacts with the passenger compartment and the battery.

[0126] Specifically, Figure 7 As shown, Figure 7This is a schematic diagram of a refrigerator single cold storage mode according to an embodiment of the present application. When the vehicle thermal management system only needs to cool the vehicle refrigerator, the system operation mode is as follows Figure 7 In this mode, the running components include the compressor 100, the cooling fan 202, the second electronic expansion valve 502, and the temperature-averaging electronic fan 703. The first electronic expansion valve 501 and the third electronic expansion valve 503 are closed. The multi-objective optimization of the system is achieved by optimizing the speed of the compressor 100, the speed of the cooling fan 202, the speed of the temperature-averaging electronic fan 703, and the opening of the second electronic expansion valve 502.

[0127] Specifically, Figure 8 As shown, Figure 8 This is a schematic diagram of a joint operation mode of an embodiment of the present application. When the vehicle thermal management system needs to cool the refrigerator, the passenger compartment and the battery together, in this mode, the operating components including the compressor 100, the cooling fan 202, the first electronic expansion valve 501, the second electronic expansion valve 502, the third electronic expansion valve 503, the temperature-averaging electronic fan 703, and the blower 6011 are all started.

[0128] Furthermore, if Figure 4 As shown, the operation optimization method of the vehicle thermal management system includes the following steps:

[0129] In step S401, the current operation mode of the vehicle thermal management system is obtained.

[0130] Among them, the current operation mode is divided into separate cold storage mode and joint operation mode.

[0131] Specifically, if the vehicle thermal management system operates in the refrigerator single cold storage mode, then the current operating mode of the vehicle thermal management system obtained is the refrigerator single cold storage mode. For example, if the vehicle thermal management system operates in the combined operation mode, then the current operating mode of the vehicle thermal management system obtained is the combined operation mode.

[0132] In step S402, the optimization objective, objective function and constraint conditions are determined according to the current operation mode, and based on the optimization objective and constraint conditions, the objective function is solved to obtain an operation solution set.

[0133] Among them, the optimization goal is set according to the current operating mode of the vehicle thermal management system, aiming to achieve efficient operation and optimal performance of the system; the objective function is a mathematical expression used to quantify and evaluate the optimization goal; the constraints are restrictions that ensure that the vehicle thermal management system meets specific performance and safety requirements during the optimization process.

[0134] Specifically, the embodiments of the present application significantly improve the overall performance of the system and user experience by determining the optimization goal, constructing the objective function and constraints, and applying the optimization algorithm to obtain the operating solution set. While ensuring stability and safety, it achieves reduced energy consumption, improved cooling effect and precise temperature control.

[0135] Optionally, in some embodiments, the current operation mode is a refrigerator single cold storage mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time and minimum refrigerator temperature fluctuation as optimization goals;

[0136] The objective functions include:

[0137] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power;

[0138] System cooling capacity = refrigerator refrigerant mass flow rate × refrigerator evaporator inlet and outlet enthalpy difference;

[0139] System energy efficiency = system cooling capacity / system energy consumption;

[0140] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0141] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0142] Constraints include:

[0143] Compressor high pressure ≤ P max,1 ;

[0144] P min,1 ≤Compressor low pressure;

[0145] Compressor pressure ratio ≤P r,1 ;

[0146] -a≤actual temperature of refrigerator-preset temperature of refrigerator≤a;

[0147] Among them, P max,1 is the maximum allowable value of the compressor high pressure, P min,1 is the minimum allowable value of the compressor low pressure, P r,1 is the maximum allowable value of the compressor pressure ratio, and a is the allowable fluctuation value of the temperature inside the refrigerator in the refrigerator single cold storage mode.

[0148] It can be understood that in the refrigerator single cold storage mode of the vehicle thermal management system, by setting the minimum system energy consumption, maximum cooling capacity, highest energy efficiency, shortest cooling time and minimum temperature fluctuation as the optimization goals, combined with specific objective functions (including calculation formulas for system energy consumption, cooling capacity, energy efficiency, cooling time and temperature fluctuation) and constraints (including compressor high pressure, low pressure, pressure ratio limit and refrigerator temperature fluctuation range), the overall performance of the system can be significantly improved, which not only helps to save energy and reduce consumption, improve refrigeration efficiency and energy efficiency ratio, but also ensure the temperature stability in the refrigerator and improve user experience; through reasonable goal setting and constraints, the vehicle thermal management system can maintain efficient and stable operation under different working conditions, meeting users' needs for high-quality car refrigerators.

[0149] Optionally, in some embodiments, the current operation mode is a joint operation mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time, shortest passenger compartment cooling time, minimum refrigerator temperature fluctuation, and minimum passenger compartment temperature fluctuation as optimization goals;

[0150] The objective functions include:

[0151] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power + blower power;

[0152] System cooling capacity = refrigerator evaporator refrigerant mass flow × refrigerator evaporator inlet and outlet enthalpy difference + air conditioning box evaporator refrigerant mass flow × air conditioning box evaporator inlet and outlet enthalpy difference + battery cooler refrigerant mass flow × battery cooler inlet and outlet enthalpy difference

[0153] System energy efficiency = system cooling capacity / system energy consumption;

[0154] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0155] Air conditioning cooling time = the time when the passenger compartment reaches the preset temperature - the time when cooling starts;

[0156] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0157] Passenger compartment temperature fluctuation = the maximum temperature change per unit time after the passenger compartment reaches the set temperature;

[0158] Constraints include:

[0159] Compressor high pressure ≤ P max,2 ;

[0160] P min,2 ≤Compressor low pressure;

[0161] Compressor pressure ratio ≤Pr,2 ;

[0162] -b≤actual temperature of refrigerator-preset temperature of refrigerator≤b;

[0163] -c≤actual temperature of passenger compartment-preset temperature of passenger compartment≤c;

[0164] Among them, P max,2 is the maximum allowable value of the compressor high pressure in the combined operation mode, P min,2 is the minimum allowable value of the compressor low pressure in the combined operation mode, b is the allowable fluctuation value of the temperature in the refrigerator in the combined operation mode, and c is the allowable fluctuation value of the temperature in the passenger compartment in the combined operation mode.

[0165] It can be understood that in the joint operation mode of the vehicle thermal management system, the system performance is fully optimized by comprehensively considering multiple optimization objectives such as system energy consumption, cooling capacity, energy efficiency, cooling time and temperature fluctuation of the refrigerator and passenger compartment, combining the objective function and constraints. This mode not only greatly improves the cooling efficiency and energy efficiency ratio, effectively reduces energy consumption, but also ensures the rapid stabilization of the temperature in the refrigerator and the passenger compartment, greatly improving the user's comfort experience.

[0166] In step S403, a Pareto optimal solution set is obtained from the operating solution set, and subjective and objective combined weighting is performed according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, and the vehicle thermal management system is controlled according to the target operating parameters.

[0167] The Pareto optimal solution set refers to a set of solutions in a multi-objective optimization problem, in which any improvement in a certain objective will lead to the deterioration of at least one other objective, that is, each solution in the Pareto optimal solution set is non-disposable, and no other solution can be better than it in all objectives.

[0168] Specifically, the embodiment of the present application selects the Pareto optimal solution set from the operating solution set, calculates the subjective weight and the objective weight based on the analytic hierarchy process (AHP) and the CRITIC method, and calculates the final combined weight by weighted calculation based on the subjective and objective weights; constructs a set of performance indicators of the system, calculates the comprehensive score of each solution according to the performance of each solution on different objectives and the corresponding combined weight, selects the optimal solution as the target operating parameter, and sets the vehicle thermal management system according to the determined target operating parameters to ensure that the embodiment of the present application operates according to the optimized parameters, thereby achieving the purposes of energy saving and emission reduction, improving energy efficiency, and improving user experience.

[0169] As a possible implementation method, in some embodiments, when the current operating mode is the refrigerator single cold storage mode, subjective and objective combined weighting is performed according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, including: based on the Pareto optimal solution set, the first initial weight of each operating parameter is calculated according to the preset hierarchical analysis method, and the first subjective weight is obtained according to the preset subjective judgment matrix and the first initial weight of each operating parameter; based on the Pareto optimal solution set, the second initial weight of each operating parameter is calculated using the preset CRITIC method, and the second initial weight of each operating parameter is processed to obtain the first objective weight; according to the first subjective weight and the first objective weight, the target operating parameters that meet multi-objective optimization are obtained.

[0170] It is understandable that in the refrigerator single cold storage mode, the target operating parameters that meet the multi-objective optimization are selected from the Pareto optimal solution set by integrating the hierarchical analysis method and the CRITIC method. The detailed process has been described in the above embodiment and will not be repeated here.

[0171] As another possible implementation method, in some embodiments, when the current operating mode is the joint operating mode, subjective and objective combined weighting is performed according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, including: based on the Pareto optimal solution set, the third initial weight of each operating parameter is calculated according to the preset hierarchical analysis method, and the second subjective weight is obtained according to the preset subjective judgment matrix and the third initial weight of each operating parameter; based on the Pareto optimal solution set, the fourth initial weight of each operating parameter is calculated using the preset CRITIC method, and the fourth initial weight of each operating parameter is processed to obtain the second objective weight; according to the second subjective weight and the second objective weight, the target operating parameters that meet multi-objective optimization are obtained.

[0172] It is understandable that in the joint operation mode, the objective and subjective combined weighting strategy of the fusion of the hierarchical analysis method and the CRITIC method is used to accurately select the target operation parameters that meet the multi-objective optimization from the Pareto optimal solution set. The detailed process has been described in the above embodiment and will not be repeated here.

[0173] Therefore, this application performs multi-objective optimization on the vehicle refrigerator currently integrated into the vehicle thermal management system, and performs targeted optimization and adjustment according to different operating modes to achieve minimum energy consumption, maximum cooling capacity, highest energy efficiency, shortest cooling time, and minimum temperature fluctuation; when the parameter Pareto front solution set is obtained, the subjective and objective weighting methods are further combined to obtain a specific operating parameter solution. In addition, in terms of energy consumption, the embodiments of this application reduce system energy consumption, increase system cooling capacity, and improve system energy efficiency; in terms of effect, shorten cooling time and reduce temperature fluctuations; by reasonably choosing different optimization goals, the performance is optimized and the user experience is improved.

[0174] To facilitate those skilled in the art to further understand the operation optimization method of the vehicle thermal management system of the embodiment of the present application, it is elaborated in detail below in combination with the refrigerator single cold storage mode and the combined operation mode.

[0175] Specifically, Fig. 9 As shown, Fig. 9 A multi-objective optimization flow chart of a refrigerator single cold storage mode of an operation optimization method of a vehicle thermal management system provided by an embodiment of the present application, the operation optimization method of the vehicle thermal management system comprises the following steps:

[0176] S901: Establish an integrated thermal management system simulation model.

[0177] S902: Enter the refrigerator cold storage mode.

[0178] S903: Determine the optimization goal: minimize system energy consumption, maximize system cooling capacity, maximize system energy efficiency, minimize refrigerator cooling time, and minimize refrigerator temperature fluctuation as the optimization goals.

[0179] S904: Define the objective function:

[0180] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power;

[0181] System cooling capacity = refrigerator refrigerant mass flow rate × refrigerator evaporator inlet and outlet enthalpy difference;

[0182] System energy efficiency = system cooling capacity / system energy consumption;

[0183] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0184] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0185] S905: Setting constraints:

[0186] Compressor high pressure ≤ P max,1 ;

[0187] P min,1 ≤Compressor low pressure;

[0188] Compressor pressure ratio ≤P r,1 ;

[0189] -a≤actual refrigerator temperature-preset refrigerator temperature≤a.

[0190] S906: Select optimization algorithm: There are many existing optimization algorithms. You can choose an appropriate optimization algorithm according to your needs, such as genetic algorithm, particle swarm optimization algorithm, simulated annealing method, etc.

[0191] S907: Perform joint solution: Run the optimization algorithm to adjust the compressor speed, the electronic expansion valve opening, the front-end cooling fan speed, and the refrigerator temperature averaging fan speed to generate a solution set.

[0192] S908: Obtain Pareto front: Obtain the Pareto front from the feasible solution obtained.

[0193] S909: Construct a system performance indicator set with an optimization goal.

[0194] S9010: Use the analytic hierarchy process to calculate the weights of each optimization indicator.

[0195] S9011: The objective weight of each indicator is calculated using the CRITIC method.

[0196] S9012: Carry out subjective and objective combined weighting.

[0197] S9013: Use the ideal point method to calculate the comprehensive score of each group of solutions.

[0198] S9014: Obtain operating parameters that satisfy multi-objective optimization.

[0199] S9015: Output specific operating parameters.

[0200] Therefore, by comprehensively considering multiple optimization objectives and applying a variety of methods and technical means, the operation of the vehicle thermal management system in the refrigerator single cold storage mode is optimized, thereby improving the overall performance and efficiency of the system.

[0201] Furthermore, if Fig.10 As shown, Fig.10 A combined operation mode flow chart of an operation optimization method of a vehicle thermal management system provided in one embodiment of the present application, the operation optimization method of the vehicle thermal management system comprises the following steps:

[0202] S1001: Establish an integrated thermal management system simulation model.

[0203] S1002: Entering the joint operation mode.

[0204] S1003: Determine optimization goals: minimize system energy consumption, maximize system cooling capacity, maximize system energy efficiency, minimize refrigerator cooling time, minimize passenger compartment cooling time, minimize refrigerator temperature fluctuation, and minimize passenger compartment temperature fluctuation as optimization goals.

[0205] S1004: Define the objective function:

[0206] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power + blower power;

[0207] System cooling capacity = refrigerator evaporator refrigerant mass flow × refrigerator evaporator inlet and outlet enthalpy difference + air conditioning box evaporator refrigerant mass flow × air conditioning box evaporator inlet and outlet enthalpy difference + battery cooler refrigerant mass flow × battery cooler inlet and outlet enthalpy difference;

[0208] System energy efficiency = system cooling capacity / system energy consumption;

[0209] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0210] Air conditioning cooling time = the time when the cabin reaches the preset temperature - the time when cooling starts

[0211] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0212] Passenger compartment temperature fluctuation = the maximum temperature change per unit time after the passenger compartment reaches the set temperature.

[0213] S1005: Set constraints:

[0214] Compressor high pressure ≤ P max,2 ;

[0215] P min,2 ≤Compressor low pressure;

[0216] Compressor pressure ratio ≤P r,2 ;

[0217] -b≤actual temperature of refrigerator-preset temperature of refrigerator≤b;

[0218] -c≤actual temperature of the passenger compartment-preset temperature of the passenger compartment≤c.

[0219] S1006: Select optimization algorithm: There are many optimization algorithms currently available, and you can choose an appropriate optimization algorithm according to your needs, such as genetic algorithm, particle swarm optimization algorithm, simulated annealing method, etc.

[0220] S1007: Perform joint solution: Run the optimization algorithm to adjust the compressor speed, the electronic expansion valve opening, the cooling fan speed, the blower speed, and the refrigerator temperature averaging fan speed to generate a solution set.

[0221] S1008: Obtain Pareto front: Obtain the Pareto front from the feasible solution obtained.

[0222] S1009: Construct a system performance indicator set with optimization goals.

[0223] S1010: Calculate the weight of each optimization index using the hierarchical analysis method.

[0224] S1011: The objective weight of each indicator is calculated using the CRITIC method.

[0225] S1012: Carry out subjective and objective combined weighting.

[0226] S1013: Calculate the comprehensive score of each group of solutions using the ideal point method.

[0227] S1014: Obtaining operating parameters that satisfy multi-objective optimization.

[0228] S1015: Output specific operating parameters.

[0229] Therefore, the multi-objective optimization method of the vehicle thermal management system in the joint operation mode ensures that the system achieves efficient and reliable operation while meeting multiple optimization objectives.

[0230] According to the operation optimization method of the vehicle thermal management system proposed in the embodiment of the present application, after obtaining the current operation mode of the vehicle thermal management system, the optimization target, objective function and constraint conditions are determined according to the current operation mode, and based on the optimization target and constraint conditions, the objective function is solved to obtain the operation solution set, and then the Pareto optimal solution set is obtained from the operation solution set, and the subjective and objective combination weighting is performed according to the Pareto optimal solution set to obtain the target operation parameters that meet the multi-objective optimization, and the vehicle thermal management system is controlled according to the target operation parameters. Therefore, based on the subjective and objective weighting method, the operation mode of the vehicle thermal management system is optimized with multiple objectives, which solves the problems in the related technology that the control method is relatively single and cannot take into account more optimization targets, resulting in unsatisfactory control effect of the vehicle thermal management system, optimizes the operation parameters, and improves the operation effect.

[0231] Next, the operation optimization device of the vehicle thermal management system proposed in accordance with the embodiment of the present application is described with reference to the accompanying drawings.

[0232] Fig.11 It is a block diagram of an operation optimization device of a vehicle thermal management system according to an embodiment of the present application.

[0233] like Fig.11 As shown, the operation optimization device 10 of the vehicle thermal management system includes: an acquisition module 1000, a calculation module 2000 and a control module 3000.

[0234] Wherein, the acquisition module 1000 is used to obtain the current operation mode of the vehicle thermal management system;

[0235] The calculation module 2000 is used to determine the optimization target, the objective function and the constraint conditions according to the current operation mode, and solve the objective function based on the optimization target and the constraint conditions to obtain the operation solution set;

[0236] The control module 3000 is used to obtain the Pareto optimal solution set from the operating solution set, and perform subjective and objective combined weighting according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, and control the vehicle thermal management system according to the target operating parameters.

[0237] Optionally, the current operation mode is a refrigerator single cold storage mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time and minimum refrigerator temperature fluctuation as optimization goals;

[0238] The objective function includes:

[0239] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power;

[0240] System cooling capacity = refrigerator refrigerant mass flow rate × refrigerator evaporator inlet and outlet enthalpy difference;

[0241] System energy efficiency = system cooling capacity / system energy consumption;

[0242] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0243] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0244] Constraints include:

[0245] Compressor high pressure ≤ P max,1 ;

[0246] P min,1 ≤Compressor low pressure;

[0247] Compressor pressure ratio ≤P r,1 ;

[0248] -a≤actual temperature of refrigerator-preset temperature of refrigerator≤a;

[0249] Among them, P max,1 is the maximum allowable value of the compressor high pressure, P min,1 is the minimum allowable value of the compressor low pressure, P r,1 is the maximum allowable value of the compressor pressure ratio, and a is the allowable fluctuation value of the temperature inside the refrigerator in the refrigerator single cold storage mode.

[0250] Optionally, the control module 3000 is specifically used to: based on the Pareto optimal solution set, calculate the first initial weight of each operating parameter according to the preset hierarchical analysis method, and obtain the first subjective weight according to the preset subjective judgment matrix and the first initial weight of each operating parameter; based on the Pareto optimal solution set, calculate the second initial weight of each operating parameter using the preset CRITIC method, and process the second initial weight of each operating parameter to obtain the first objective weight; and obtain the target operating parameter that meets multi-objective optimization according to the first subjective weight and the first objective weight.

[0251] Optionally, the current operation mode is a joint operation mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time, shortest passenger compartment cooling time, minimum refrigerator temperature fluctuation and minimum passenger compartment temperature fluctuation as optimization goals;

[0252] The objective functions include:

[0253] System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power + blower power;

[0254] System cooling capacity = refrigerator evaporator refrigerant mass flow × refrigerator evaporator inlet and outlet enthalpy difference + air conditioning box evaporator refrigerant mass flow × air conditioning box evaporator inlet and outlet enthalpy difference + battery cooler refrigerant mass flow × battery cooler inlet and outlet enthalpy difference

[0255] System energy efficiency = system cooling capacity / system energy consumption;

[0256] Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts;

[0257] Air conditioning cooling time = the time when the passenger compartment reaches the preset temperature - the time when cooling starts;

[0258] Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature;

[0259] Passenger compartment temperature fluctuation = the maximum temperature change per unit time after the passenger compartment reaches the set temperature;

[0260] Constraints include:

[0261] Compressor high pressure ≤ P max,2 ;

[0262] P min,2 ≤Compressor low pressure;

[0263] Compressor pressure ratio ≤P r,2 ;

[0264] -b≤actual temperature of refrigerator-preset temperature of refrigerator≤b;

[0265] -c≤actual temperature of passenger compartment-preset temperature of passenger compartment≤c;

[0266] Among them, P max,2 is the maximum allowable value of the compressor high pressure in the combined operation mode, P min,2 is the minimum allowable value of the compressor low pressure in the combined operation mode, b is the allowable fluctuation value of the temperature in the refrigerator in the combined operation mode, and c is the allowable fluctuation value of the temperature in the passenger compartment in the combined operation mode.

[0267] Optionally, the control module 3000 is specifically used to: based on the Pareto optimal solution set, calculate the third initial weight of each operating parameter according to the preset hierarchical analysis method, and obtain the second subjective weight according to the preset subjective judgment matrix and the third initial weight of each operating parameter; based on the Pareto optimal solution set, calculate the fourth initial weight of each operating parameter using the preset CRITIC method, and process the fourth initial weight of each operating parameter to obtain the second objective weight; and obtain the target operating parameters that meet multi-objective optimization according to the second subjective weight and the second objective weight.

[0268] It should be noted that the aforementioned explanation of the embodiment of the vehicle thermal management system operation optimization method is also applicable to the vehicle thermal management system operation optimization device of this embodiment, and will not be repeated here.

[0269] According to the operation optimization device of the vehicle thermal management system proposed in the embodiment of the present application, after obtaining the current operation mode of the vehicle thermal management system, the optimization target, objective function and constraint conditions are determined according to the current operation mode, and based on the optimization target and constraint conditions, the objective function is solved to obtain the operation solution set, and then the Pareto optimal solution set is obtained from the operation solution set, and the subjective and objective combination weighting is performed according to the Pareto optimal solution set to obtain the target operation parameters that meet the multi-objective optimization, and the vehicle thermal management system is controlled according to the target operation parameters. Therefore, based on the subjective and objective weighting method, the operation mode of the vehicle thermal management system is optimized for multiple objectives, which solves the problems in the related technology that the control method is relatively single and cannot take into account more optimization targets, resulting in unsatisfactory control effect of the vehicle thermal management system, optimizes the operation parameters, and improves the operation effect.

[0270] Fig.12 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0271] A memory 1201 , a processor 1202 , and a computer program stored in the memory 1201 and executable on the processor 1202 .

[0272] When the processor 1202 executes the program, the operation optimization method of the vehicle thermal management system provided in the above embodiment is implemented.

[0273] Furthermore, the vehicle also includes:

[0274] The communication interface 1203 is used for communication between the memory 1201 and the processor 1202 .

[0275] The memory 1201 is used to store computer programs that can be executed on the processor 1202 .

[0276] The memory 1201 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0277] If the memory 1201, the processor 1202 and the communication interface 1203 are implemented independently, the communication interface 1203, the memory 1201 and the processor 1202 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.12 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0278] Optionally, in a specific implementation, if the memory 1201, the processor 1202 and the communication interface 1203 are integrated on a chip, the memory 1201, the processor 1202 and the communication interface 1203 can communicate with each other through an internal interface.

[0279] The processor 1202 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0280] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-mentioned method for optimizing the operation of a vehicle thermal management system.

[0281] An embodiment of the present application also provides a computer program product, which stores a computer program, and when the program is executed by a processor, it implements the above-mentioned vehicle thermal management system operation optimization method.

[0282] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0283] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0284] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0285] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0286] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A method for optimizing the operation of a vehicle thermal management system, characterized in that: The following steps are involved: Get the current operating mode of the vehicle thermal management system; Determining an optimization objective, an objective function, and constraints according to the current operation mode, and solving the objective function based on the optimization objective and the constraints to obtain an operation solution set; A Pareto optimal solution set is obtained from the operating solution set, and subjective and objective combined weighting is performed according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, and the vehicle thermal management system is controlled according to the target operating parameters.

2. The method according to claim 1, characterized in that The current operation mode is a refrigerator single cold storage mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time and minimum refrigerator temperature fluctuation as the optimization goals; The objective function includes: System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power; System cooling capacity = refrigerator refrigerant mass flow rate × refrigerator evaporator inlet and outlet refrigerant enthalpy difference; System energy efficiency = system cooling capacity / system energy consumption; Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts; Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature; The constraints include: Compressor high pressure ≤ P max,1 ; P min,1 ≤Compressor low pressure; Compressor pressure ratio ≤P r,1 ; -a≤actual temperature of refrigerator-preset temperature of refrigerator≤a; Among them, P max,1 is the maximum allowable value of the compressor high pressure in the refrigerator single cold storage mode, P min,1 is the minimum allowable value of the compressor low pressure in the refrigerator single cold storage mode, P r,1 is the maximum allowable value of the compressor pressure ratio, and a is the allowable fluctuation value of the temperature in the refrigerator under the single cold storage mode of the refrigerator.

3. The method according to claim 2, characterized in that The objective operating parameters satisfying multi-objective optimization are obtained by performing subjective and objective combined weighting according to the Pareto optimal solution set, including: Based on the Pareto optimal solution set, a first initial weight of each operating parameter is calculated according to a preset hierarchical analysis method, and a first subjective weight is obtained according to a preset subjective judgment matrix and the first initial weight of each operating parameter; Based on the Pareto optimal solution set, a preset CRITIC method is used to calculate the second initial weight of each operating parameter, and the second initial weight of each operating parameter is processed to obtain a first objective weight; The target operating parameters satisfying the multi-objective optimization are obtained according to the first subjective weight and the first objective weight.

4. The method according to claim 1, characterized in that: The current operation mode is a joint operation mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time, shortest passenger compartment cooling time, minimum refrigerator temperature fluctuation and minimum passenger compartment temperature fluctuation as the optimization goals; The objective function includes: System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power + blower power; System cooling capacity = refrigerator evaporator refrigerant mass flow × refrigerator evaporator inlet and outlet enthalpy difference + air conditioning box evaporator refrigerant mass flow × air conditioning box evaporator inlet and outlet enthalpy difference + battery cooler refrigerant mass flow × battery cooler inlet and outlet enthalpy difference System energy efficiency = system cooling capacity / system energy consumption; Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts; Air conditioning cooling time = the time when the passenger compartment reaches the preset temperature - the time when cooling starts; Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature; Passenger compartment temperature fluctuation = the maximum temperature change per unit time after the passenger compartment reaches the set temperature; The constraints include: Compressor high pressure ≤ P max,2 ; P min,2 ≤Compressor low pressure; Compressor pressure ratio ≤P r,2 ; -b≤actual temperature of refrigerator-preset temperature of refrigerator≤b; -c≤actual temperature of passenger compartment-preset temperature of passenger compartment≤c; Among them, P max,2 is the maximum permissible value of the compressor high pressure in the combined operation mode, P min,2 is the minimum allowable value of the compressor low pressure in the combined operation mode, b is the allowable fluctuation value of the temperature in the refrigerator in the combined operation mode, and c is the allowable fluctuation value of the temperature in the passenger compartment in the combined operation mode.

5. The method according to claim 4, characterized in that The objective operating parameters satisfying multi-objective optimization are obtained by performing subjective and objective combined weighting according to the Pareto optimal solution set, including: Based on the Pareto optimal solution set, a third initial weight of each operating parameter is calculated according to a preset hierarchical analysis method, and a second subjective weight is obtained according to a preset subjective judgment matrix and the third initial weight of each operating parameter; Based on the Pareto optimal solution set, a fourth initial weight of each operating parameter is calculated using a preset CRITIC method, and the fourth initial weight of each operating parameter is processed to obtain a second objective weight; The target operating parameters satisfying the multi-objective optimization are obtained according to the second subjective weight and the second objective weight.

6. An operation optimization device for a vehicle thermal management system, characterized in that: include: An acquisition module, used to acquire the current operation mode of the vehicle thermal management system; A calculation module, used to determine an optimization target, an objective function and constraints according to the current operation mode, and solve the objective function based on the optimization target and the constraints to obtain an operation solution set; A control module is used to obtain a Pareto optimal solution set from the operating solution set, and to perform subjective and objective combined weighting according to the Pareto optimal solution set to obtain target operating parameters that meet multi-objective optimization, and to control the vehicle thermal management system according to the target operating parameters.

7. The device according to claim 6, characterized in that The current operation mode is a refrigerator single cold storage mode, with minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time and minimum refrigerator temperature fluctuation as the optimization goals; The objective function includes: System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature averaging fan power; System cooling capacity = refrigerator refrigerant mass flow rate × refrigerator evaporator inlet and outlet enthalpy difference; System energy efficiency = system cooling capacity / system energy consumption; Refrigerator cooling time = the time when the refrigerator reaches the preset temperature - the time when refrigeration starts; Refrigerator temperature fluctuation = the maximum temperature change per unit time after the system reaches the set temperature; The constraints include: Compressor high pressure ≤ P max,1 ; P min,1 ≤Compressor low pressure; Compressor pressure ratio ≤P r,1 ; -a≤actual temperature of refrigerator-preset temperature of refrigerator≤a; Among them, P max,1 is the maximum allowable value of the compressor high pressure, P min,1 is the minimum allowable value of the compressor low pressure, P r,1 is the maximum allowable value of the compressor pressure ratio, and a is the allowable fluctuation value of the temperature in the refrigerator under the single cold storage mode of the refrigerator.

8. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the operation optimization method of the vehicle thermal management system as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the operation optimization method of the vehicle thermal management system as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the operation optimization method of the vehicle thermal management system as described in any one of claims 1 to 5 is implemented.

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