Methods, devices, vehicles, and storage media for optimizing the operation of vehicle thermal management systems

Abstract

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 for a vehicle thermal management system, including: obtaining the current operation mode of the vehicle thermal management system; determining the optimization goal, objective function, and constraints based on the current operation mode, and solving the objective function based on the optimization goal and constraints to obtain an operation solution set; obtaining a Pareto optimal solution set from the operation solution set, and performing subjective and objective combined weighting based on the Pareto optimal solution set to obtain target operation parameters that meet multi-objective optimization, and controlling the vehicle thermal management system based on the target operation parameters. Thus, based on the subjective and objective weighting method, the operation mode of the vehicle thermal management system is optimized for multiple objectives, solving the problems in related technologies such as the relatively single control method and the inability to take into account multiple optimization goals, resulting in unsatisfactory control effects of the vehicle thermal management system, optimizing the operation parameters, and improving the operation effect.

Description

Technical Field

[0001] This application relates to the field of vehicle thermal management technology, and in particular to a method, apparatus, vehicle, and storage medium for optimizing the operation of a vehicle thermal management system. Background Technology

[0002] With the development of the automotive industry, the control of vehicle thermal management systems is receiving increasing attention from customers.

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

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

[0005] This application provides a method, apparatus, vehicle, and storage medium for optimizing the operation of a vehicle thermal management system, in order to solve the problems in related technologies where the control method is relatively simple and cannot take into account multiple optimization objectives, resulting in unsatisfactory control effects of the vehicle thermal management system. The application optimizes the operating parameters and improves the operating effect.

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

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

[0008] The optimization objective, objective function, and constraints are determined based on the current operating mode, and the solution set is obtained by solving the objective function based on the optimization objective and the constraints.

[0009] A Pareto optimal solution set is obtained from the operating solution set, and a subjective and objective combination weighting is performed based on the Pareto optimal solution set to obtain target operating parameters that satisfy multi-objective optimization. The vehicle thermal management system is then controlled based on the target operating parameters.

[0010] Optionally, the current operating mode is a refrigerator single cold storage mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, and the minimum refrigerator temperature fluctuation.

[0011] The objective function includes:

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

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

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

[0015] Refrigerator cooling time = Time when the refrigerator reaches the preset temperature - Time when cooling 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 P represents the maximum permissible high pressure value of the compressor. min,1 P is the minimum permissible value for the low pressure of the compressor. r,1 denoted as the maximum allowable value of the compressor pressure ratio, and 'a' as the allowable temperature fluctuation value inside the refrigerator in the single-storage mode.

[0023] Optionally, the step of obtaining the target operating parameters that satisfy multi-objective optimization by combining subjective and objective weights based on the Pareto optimal solution set includes:

[0024] Based on the Pareto optimal solution set, the first initial weight of each operating parameter is calculated according to the preset analytic hierarchy process, and the first subjective weight is obtained according to the preset subjective judgment matrix and the first initial weight of each operating parameter.

[0025] 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;

[0026] The target operating parameters that satisfy multi-objective optimization are obtained based on the first subjective weight and the first objective weight.

[0027] Optionally, the current operating mode is a joint operating mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, the shortest passenger cabin cooling time, the minimum refrigerator temperature fluctuation, and the minimum passenger cabin temperature fluctuation.

[0028] The objective function includes:

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

[0030] System cooling capacity = Refrigerant mass flow rate of refrigerator evaporator × Enthalpy difference between inlet and outlet of refrigerator evaporator + Refrigerant mass flow rate of air conditioner evaporator × Enthalpy difference between inlet and outlet of air conditioner evaporator + Refrigerant mass flow rate of battery cooler × Enthalpy difference between inlet and outlet of battery cooler

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

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

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

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

[0035] Passenger cabin temperature fluctuation = the maximum temperature change per unit time after the set temperature is reached in the passenger cabin.

[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 refrigerator temperature-preset refrigerator temperature≤b;

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

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

[0043] Optionally, the step of obtaining the target operating parameters that satisfy multi-objective optimization by combining subjective and objective weights based on the Pareto optimal solution set includes:

[0044] Based on the Pareto optimal solution set, the third initial weight of each operating parameter is calculated according to the preset analytic hierarchy process, and the second subjective weight is obtained according to the preset subjective judgment matrix and the third initial weight of each operating parameter.

[0045] 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.

[0046] The target operating parameters that satisfy the multi-objective optimization are obtained based on the second subjective weight and the second objective weight.

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

[0048] The acquisition module is used to acquire the current operating mode of the vehicle thermal management system;

[0049] The calculation module is used to determine the optimization objective, objective function, and constraints according to the current operating mode, and to solve the objective function based on the optimization objective and the constraints to obtain the operating solution set;

[0050] The control module is used to obtain the Pareto optimal solution set from the running solution set, and to perform subjective and objective combination weighting based on the Pareto optimal solution set to obtain the target operating parameters that satisfy multi-objective optimization, and to control the vehicle thermal management system based on the target operating parameters.

[0051] Optionally, the current operating mode is a refrigerator single cold storage mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, and the minimum refrigerator temperature fluctuation.

[0052] The objective function includes:

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

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

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

[0056] Refrigerator cooling time = Time when the refrigerator reaches the preset temperature - Time when cooling 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 P represents the maximum permissible high pressure value of the compressor. min,1 P is the minimum permissible value for the low pressure of the compressor. r,1 denoted as the maximum allowable value of the compressor pressure ratio, and 'a' as the allowable temperature fluctuation value inside the refrigerator in the single-storage mode.

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

[0065] Based on the Pareto optimal solution set, the first initial weight of each operating parameter is calculated according to the preset analytic hierarchy process, and the first subjective weight is obtained according to the preset subjective judgment matrix and the first initial weight of each operating parameter.

[0066] 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;

[0067] The target operating parameters that satisfy multi-objective optimization are obtained based on the first subjective weight and the first objective weight.

[0068] Optionally, the current operating mode is a joint operating mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, the shortest passenger cabin cooling time, the minimum refrigerator temperature fluctuation, and the minimum passenger cabin temperature fluctuation.

[0069] The objective function includes:

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

[0071] System cooling capacity = Refrigerant mass flow rate of refrigerator evaporator × Enthalpy difference between inlet and outlet of refrigerator evaporator + Refrigerant mass flow rate of air conditioner evaporator × Enthalpy difference between inlet and outlet of air conditioner evaporator + Refrigerant mass flow rate of battery cooler × Enthalpy difference between inlet and outlet of battery cooler

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

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

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

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

[0076] Passenger cabin temperature fluctuation = the maximum temperature change per unit time after the set temperature is reached in the passenger cabin.

[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 refrigerator temperature-preset refrigerator temperature≤b;

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

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

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

[0085] Based on the Pareto optimal solution set, the third initial weight of each operating parameter is calculated according to the preset analytic hierarchy process, and the second subjective weight is obtained according to the preset subjective judgment matrix and the third initial weight of each operating parameter.

[0086] 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.

[0087] The target operating parameters that satisfy the multi-objective optimization are obtained based on the second subjective weight and the second objective weight.

[0088] A third aspect of this application provides a vehicle, including: 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, the instructions being configured to perform an operation optimization method for a vehicle thermal management system as described in the above embodiments.

[0089] A fourth aspect of this 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 embodiments.

[0090] A fifth aspect of this application provides a computer program product storing a computer program that, when executed by a processor, implements the operation optimization method of the vehicle thermal management system as described in the above embodiments.

[0091] Therefore, after obtaining the current operating mode of the vehicle thermal management system, the optimization objective, objective function, and constraints are determined based on the current operating mode. Based on the optimization objective and constraints, the objective function is solved to obtain the operating solution set. Then, the Pareto optimal solution set is obtained from the operating solution set. Based on the Pareto optimal solution set, subjective and objective weighting is applied to obtain the target operating parameters that satisfy multi-objective optimization. The vehicle thermal management system is then controlled according to the target operating parameters. Thus, based on the subjective and objective weighting method, multi-objective optimization of the vehicle thermal management system's operating mode is performed, solving the problems of relatively singular control methods in related technologies, which cannot simultaneously consider multiple optimization objectives, leading to unsatisfactory control effects in the vehicle thermal management system. This optimizes the operating parameters and improves the operating performance.

[0092] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

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

[0094] Figure 1 This is a structural schematic diagram of a cold storage vehicle-mounted refrigerator with both cooling and heating modes in the related technology.

[0095] Figure 2 This is a structural schematic diagram of a vehicle-mounted refrigerator method that adjusts the gear according to the loading amount and temperature difference value in the related technology;

[0096] Figure 3 This is a flowchart of a method for adjusting the gear of a vehicle-mounted refrigerator according to the loading amount and temperature difference value in related technologies;

[0097] Figure 4 This is a flowchart of an operation optimization method for a vehicle thermal management system according to an embodiment of this application;

[0098] Figure 5 This is a schematic diagram illustrating the system operation principle of a vehicle thermal management system operation optimization method according to an embodiment of this application;

[0099] Figure 6 This is a schematic diagram of an onboard refrigerator according to an embodiment of the present application, which is a method for optimizing the operation of a vehicle thermal management system.

[0100] Figure 7 This is a schematic diagram of a single-cold storage mode for an operation optimization method of a vehicle thermal management system according to an embodiment of this application;

[0101] Figure 8 This is a schematic diagram of a combined operation mode of a vehicle thermal management system operation optimization method according to an embodiment of this application;

[0102] Figure 9 This is a flowchart illustrating the single-cold storage mode operation of a vehicle thermal management system operation optimization method according to an embodiment of this application;

[0103] Figure 10 This is a flowchart illustrating the combined operation mode of a vehicle thermal management system operation optimization method according to an embodiment of this application;

[0104] Figure 11 This is a schematic diagram of an operation optimization device for a vehicle thermal management system provided according to an embodiment of this application;

[0105] Figure 12 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application.

[0106] Reference numerals: Compressor 100, Condenser 201, Cooling Fan 202, Liquid Receiver 300, Coaxial Tube 400, First Electronic Expansion Valve 501, Second Electronic Expansion Valve 502, Third Electronic Expansion Valve 503, Air Conditioning Unit Evaporator 601, Blower 6011, Vehicle Refrigerator Evaporator 602, Battery Cooler 603, Vehicle Refrigerator Cold Storage Material 701, Vehicle Refrigerator Storage Space 702, Refrigerator Temperature Equalization Electronic Fan 703, Vehicle Thermal Management System Operation Optimization Device 10, Acquisition Module 1000, Calculation Module 2000, Control Module 3000. Detailed Implementation

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

[0108] Before introducing the operation optimization method of the vehicle thermal management system according to the embodiments of this application, let me briefly introduce the operation optimization method of the vehicle thermal management system in the related art.

[0109] Specifically, such as Figure 1 As shown, Figure 1 This is a schematic diagram of a cold storage vehicle-mounted refrigerator with both cooling and heating modes, which is part of the related technology.

[0110] A dual-mode (heating and cooling) vehicle-mounted refrigerator in the automotive technology field includes an outer shell, a cold storage module, a storage compartment, a flip-top, an upper shell, a light, a cold storage refrigerant temperature sensor, a compartment temperature sensor, a heating film, a controller, a refrigerant pipe outlet pressure plate, an electronic expansion valve, and a cold storage refrigerant. The cold storage module is nested on the outer surface of the storage compartment, which is nested inside the outer shell. The cold storage refrigerant is disposed within the cold storage module. The controller, refrigerant pipe outlet pressure plate, and electronic expansion valve are all mounted on the front outer wall of the outer shell. The upper shell is nested on the top of the outer shell near the controller, and the flip-top is disposed on the top of the outer shell and matches the upper shell. In the implementation of this invention, during heating, the heating film at the bottom of the storage compartment is activated to heat the ambient temperature of the storage compartment. During cooling, the cold energy generated by the evaporator is stored in the cold storage refrigerant, and then transferred directly to the storage compartment through the surface of the cold storage refrigerant container, which is in close contact with the surface of the refrigerator storage compartment.

[0111] Regarding the refrigerator's operating modes:

[0112] Operating Condition 1: A dual-mode (heating and cooling) cold storage vehicle refrigerator begins cooling; the battery cooler and cabin evaporator are not operating.

[0113] Under operating 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 has dropped below the phase change temperature, the refrigerant storage module 2 completes the storage of cold energy and shuts down the compressor and condenser in the vehicle thermal management system.

[0114] Operating Condition 2: A dual-mode (hot and cold) cold storage vehicle refrigerator starts cooling, the battery cooler or the compartment evaporator works, and the phase change temperature of the cold storage refrigerant in the vehicle refrigerator is higher than the evaporation temperature of the vehicle's thermal management system.

[0115] Under operating condition 2, the phase change point temperature of the refrigerant 202 is higher than the evaporation temperature of the vehicle's thermal management system, which can cool the refrigerant 202 below the phase change point temperature, thus completing the cold storage. After the refrigerant temperature sensor 8 detects that the temperature of the refrigerant 202 has dropped below the phase change temperature, it closes the electronic expansion valve 12, relying on the cold energy stored in the refrigerant 202 for cooling.

[0116] Operating Condition 3: A dual-mode (hot and cold) cold storage vehicle refrigerator starts cooling, the battery cooler or the compartment evaporator works, and the phase change temperature of the cold storage refrigerant in the vehicle refrigerator is lower than the evaporation temperature of the vehicle's thermal management system.

[0117] Under operating condition 3, the superheat of the refrigerant at coil outlet 2012 in refrigerant coil 201 is adjusted by adjusting the opening of electronic expansion valve 12. The cold energy of the refrigerant is released into the storage compartment of the vehicle refrigerator through refrigerant 202 and refrigerant container 203 to maintain the temperature inside the storage compartment of the vehicle refrigerator. When the vehicle thermal management system is adjusted to operating condition 1 or operating condition 2, the cold energy storage of refrigerant 202 is then performed.

[0118] Furthermore, such as Figure 2 and Figure 3 As shown, Figure 2 This is a structural schematic diagram of a vehicle-mounted refrigerator method that adjusts the settings according to the load capacity and temperature difference, based on related technologies. Figure 3 This is a flowchart of a method for adjusting the gear of a vehicle-mounted refrigerator according to the loading amount and temperature difference value in related technologies.

[0119] A method for adjusting the load level of a vehicle-mounted refrigerator based on loading capacity and temperature difference belongs to the field of vehicle-mounted refrigerator technology. The refrigerator includes a compressor and operates as follows: A temperature sensor detects both the ambient and internal temperatures. Based on the temperature difference between the internal and ambient temperatures, the refrigerator is divided into three intervals: H1, H2, and H3. The initial loading capacity is 0. Subsequent loading is also divided into three intervals: G1, G2, and G3, based on the difference between the previous and current loading capacities. Specifically, the method divides the load level into three intervals based on the temperature difference between the internal and ambient temperatures: H1, H2, and H3. When the temperature difference and loading capacity values ​​are within the range of: H1&G1, H1&G2, and H2&G1, the first setting is applied; H1&G3, H2&G2, and H3&G1, the second setting is applied; and H3&G3, H3&G2, and H2&G3, the third setting is applied. The refrigerator automatically adjusts its load level according to the environment and the ambient temperature, improving efficiency and expanding its application range.

[0120] However, the methods in the relevant technologies are relatively simple, only adjusting the throttle valve and compressor according to the operating conditions, lacking further optimization methods; they cannot take into account multiple optimization objectives; and they lack a scientific and reasonable decision-making method to balance and trade off multiple objectives when obtaining the Pareto front solution set for multi-objective optimization.

[0121] This application addresses the aforementioned problems by proposing an operation optimization method for a vehicle thermal management system. In this method, after obtaining the current operating mode of the vehicle thermal management system, the optimization objective, objective function, and constraints are determined based on the current operating mode. Based on the optimization objective and constraints, the objective function is solved to obtain an operational solution set. Then, a Pareto optimal solution set is obtained from the operational solution set. Finally, based on the Pareto optimal solution set, a subjective and objective weighting method is applied to obtain target operating parameters that satisfy multi-objective optimization. The vehicle thermal management system is then controlled according to these target operating parameters. Therefore, based on the subjective and objective weighting method, multi-objective optimization of the vehicle thermal management system's operating mode is performed, solving the problems of relatively singular control methods in related technologies, which cannot simultaneously consider multiple optimization objectives, leading to unsatisfactory control effects in the vehicle thermal management system. This optimizes the operating parameters and improves the operational performance.

[0122] Specifically, Figure 4 This is a flowchart illustrating an operation optimization method for a vehicle thermal management system provided in an embodiment of this 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 unit evaporator 601, a blower 6011, an on-board refrigerator evaporator 602, and a battery cooler 603, all configured in parallel.

[0124] Furthermore, Figure 6 This is a schematic diagram of a vehicle-mounted refrigerator according to an embodiment of this application. The vehicle-mounted refrigerator includes: a cold storage material 701, a vehicle-mounted refrigerator storage space 702, and a temperature-regulating electronic fan 703. The cold storage material 701 fills the space between the vehicle-mounted refrigerator outer shell and the storage space, and the vehicle-mounted refrigerator evaporator 602 is placed within the cold storage material 701. The shape of the vehicle-mounted refrigerator evaporator 602 can be adaptively adjusted according to the shape of the vehicle-mounted refrigerator. The temperature-regulating electronic fan 703 can be arranged at the top center of the vehicle-mounted refrigerator storage space for forced convection, so that the temperature inside the storage space can be uniformly distributed as quickly as possible.

[0125] This application embodiment divides the vehicle thermal management system's operation mode into a refrigerator-only cold storage mode and a combined operation mode based on whether the refrigerator's cooling process interacts with the passenger compartment and battery.

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

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

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

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

[0130] The current operating modes are divided into standalone cold storage mode and combined operation mode.

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

[0132] In step S402, the optimization objective, objective function, and constraints are determined according to the current operating mode, and the solution set is obtained by solving the objective function based on the optimization objective and constraints.

[0133] The optimization objective is set based on 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 objective; and the constraints are limitations that ensure the vehicle thermal management system meets specific performance and safety requirements during the optimization process.

[0134] Specifically, the embodiments of this application significantly improve the overall performance and user experience of the system by determining the optimization objective, constructing the objective function and constraints, and applying the optimization algorithm to solve the solution set. Under the premise of ensuring stability and security, energy consumption is reduced, cooling effect is improved and temperature control is more precise.

[0135] Optionally, in some embodiments, the current operating mode is a refrigerator single cold storage mode, with the optimization goals of minimizing system energy consumption, maximizing system cooling capacity, maximizing system energy efficiency, minimizing refrigerator cooling time, and minimizing refrigerator temperature fluctuation.

[0136] The objective function includes:

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

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

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

[0140] Refrigerator cooling time = Time when the refrigerator reaches the preset temperature - Time when cooling 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 P represents the maximum permissible high pressure value of the compressor. min,1 P is the minimum permissible value for the low pressure of the compressor. r,1 denoted as the maximum allowable value for the compressor pressure ratio, and 'a' as the allowable temperature fluctuation value inside the refrigerator in single-storage mode.

[0148] Understandably, in the refrigerator-only cold storage mode of a vehicle thermal management system, by setting optimization goals such as minimizing system energy consumption, maximizing cooling capacity, achieving the highest energy efficiency, minimizing cooling time, and minimizing temperature fluctuations, and combining specific objective functions (including calculation formulas for system energy consumption, cooling capacity, energy efficiency, cooling time, and temperature fluctuations) and constraints (including compressor high pressure, low pressure, pressure ratio limits, and refrigerator temperature fluctuation range), the overall performance of the system can be significantly improved. This not only helps to save energy and reduce consumption, improve cooling efficiency and energy efficiency ratio, but also ensures stable temperature inside the refrigerator, enhancing the user experience. Through reasonable target setting and constraints, the vehicle thermal management system can maintain efficient and stable operation under different working conditions, meeting users' needs for high-quality in-vehicle refrigerators.

[0149] Optionally, in some embodiments, the current operating mode is a joint operating mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, the shortest passenger cabin cooling time, the minimum refrigerator temperature fluctuation, and the minimum passenger cabin temperature fluctuation.

[0150] The objective function includes:

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

[0152] System cooling capacity = Refrigerant mass flow rate of refrigerator evaporator × Enthalpy difference between inlet and outlet of refrigerator evaporator + Refrigerant mass flow rate of air conditioner evaporator × Enthalpy difference between inlet and outlet of air conditioner evaporator + Refrigerant mass flow rate of battery cooler × Enthalpy difference between inlet and outlet of battery cooler

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

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

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

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

[0157] Passenger cabin temperature fluctuation = the maximum temperature change per unit time after the set temperature is reached in the passenger cabin.

[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 refrigerator temperature-preset refrigerator temperature≤b;

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

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

[0165] Understandably, in the joint operation mode of the vehicle thermal management system, 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, and combining objective functions and constraints, the system performance is fully optimized. This mode not only significantly improves cooling efficiency and energy efficiency ratio and effectively reduces energy consumption, but also ensures rapid stabilization of the temperature in the refrigerator and passenger compartment, greatly enhancing the user's comfort experience.

[0166] In step S403, the Pareto optimal solution set is obtained from the running solution set, and the target operating parameters that satisfy multi-objective optimization are obtained by combining subjective and objective factors according to the Pareto optimal solution set. The vehicle thermal management system is then controlled according to the target operating parameters.

[0167] In a Pareto optimal solution set, a set of solutions in a multi-objective optimization problem is defined as follows: any improvement of any solution on one objective will lead to the deterioration of at least one other objective. That is, each solution in the Pareto optimal solution set is undoable, and no other solution is superior to it on all objectives.

[0168] Specifically, in this embodiment, a Pareto optimal solution set is selected from the operational solution set. Subjective and objective weights are calculated based on the Analytic Hierarchy Process (AHP) and the CRITIC method. The final combined weight is calculated by combining the subjective and objective weights. A system performance index set is constructed. Based on the performance of each solution on different objectives and the corresponding combined weights, the comprehensive score of each solution is calculated. The optimal solution is selected as the target operating parameter. The vehicle thermal management system is set according to the determined target operating parameter to ensure that this embodiment operates according to the optimized parameters, thereby achieving the goals of energy saving and emission reduction, improving energy efficiency, and improving user experience.

[0169] As one possible implementation, in some embodiments, when the current operating mode is the refrigerator's single-storage mode, the target operating parameters that satisfy multi-objective optimization are obtained by combining subjective and objective weights based on the Pareto optimal solution set. This includes: calculating the first initial weight of each operating parameter based on the Pareto optimal solution set using a preset analytic hierarchy process (AHP), and obtaining the first subjective weight based on a preset subjective judgment matrix and the first initial weight of each operating parameter; calculating the second initial weight of each operating parameter based on the Pareto optimal solution set using a preset CRITIC method, and processing the second initial weight of each operating parameter to obtain the first objective weight; and obtaining the target operating parameters that satisfy multi-objective optimization based on the first subjective weight and the first objective weight.

[0170] Understandably, in the refrigerator's single-storage mode, a combined subjective and objective weighting method, integrating the analytic hierarchy process (AHP) and the CRITIC method, is used to select target operating parameters that satisfy multi-objective optimization from the Pareto optimal solution set. The detailed process has been described in the above embodiments and will not be repeated here.

[0171] As another possible implementation, in some embodiments, when the current operating mode is a joint operating mode, the target operating parameters that satisfy multi-objective optimization are obtained by combining subjective and objective weights based on the Pareto optimal solution set. This includes: calculating the third initial weight of each operating parameter based on the Pareto optimal solution set using a preset analytic hierarchy process (AHP), and obtaining the second subjective weight based on a preset subjective judgment matrix and the third initial weight of each operating parameter; calculating the fourth initial weight of each operating parameter based on the Pareto optimal solution set using a preset CRITIC method, and processing the fourth initial weight of each operating parameter to obtain the second objective weight; and obtaining the target operating parameters that satisfy multi-objective optimization based on the second subjective weight and the second objective weight.

[0172] Understandably, in the joint operation mode, by combining the subjective and objective weighting strategies of the analytic hierarchy process (AHP) and the CRITIC method, target operating parameters that satisfy multi-objective optimization are accurately selected from the Pareto optimal solution set. The detailed process has been described in the above embodiments and will not be repeated here.

[0173] Therefore, this application performs multi-objective optimization on in-vehicle refrigerators currently integrated into the vehicle's thermal management system. Based on different operating modes, targeted optimization adjustments are made to achieve minimum energy consumption, maximum cooling capacity, highest energy efficiency, shortest cooling time, and minimal temperature fluctuation. Having obtained the Pareto front solution set of parameters, a combination of subjective and objective weighting methods is used to obtain specific operating parameter solutions. Furthermore, 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 performance, they shorten cooling time and reduce temperature fluctuation. By reasonably selecting different optimization objectives, optimal performance is achieved, improving the user experience.

[0174] To facilitate those skilled in the art to further understand the operation optimization method of the vehicle thermal management system in the embodiments of this application, the following detailed explanation is provided in conjunction with the single-storage mode and the combined operation mode of a refrigerator.

[0175] Specifically, such as Figure 9 As shown, Figure 9 The flowchart illustrates a multi-objective optimization method for a single-cold storage mode of a refrigerator, provided as an embodiment of this application, for optimizing the operation of a vehicle thermal management system. The method includes the following steps:

[0176] S901: Establish a simulation model for the integrated thermal management system.

[0177] S902: Enter refrigerator single-storage mode.

[0178] S903: Determine the optimization objectives: 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.

[0179] S904: Define the objective function:

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

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

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

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

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

[0185] S905: Set 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 temperature of refrigerator -preset temperature of refrigerator≤a.

[0190] S906: Selecting an optimization algorithm: There are many existing optimization algorithms. You can select the 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, electronic expansion valve opening, front-end cooling fan speed, and refrigerator temperature equalization fan speed to generate a solution set.

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

[0193] S909: Construct a set of system performance indicators with optimization objectives.

[0194] S9010: The weights of each optimization index are calculated using the analytic hierarchy process (AHP).

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

[0196] S9012: Perform subjective and objective weighting.

[0197] S9013: The ideal point method is used to calculate the comprehensive score of each set of solutions.

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

[0199] S9015: Outputs specific operating parameters.

[0200] Therefore, by comprehensively considering multiple optimization objectives and employing various methods and technologies, the operation of the vehicle thermal management system in the refrigerator single-storage mode was optimized, thereby improving the overall performance and efficiency of the system.

[0201] Furthermore, such as Figure 10 As shown, Figure 10 The following is a flowchart illustrating a combined operation mode of a vehicle thermal management system operation optimization method according to an embodiment of this application. The vehicle thermal management system operation optimization method includes the following steps:

[0202] S1001: Establish a simulation model for the integrated thermal management system.

[0203] S1002: Enter joint operation mode.

[0204] S1003: Determine the optimization objectives: The optimization objectives are to minimize system energy consumption, maximize system cooling capacity, maximize system energy efficiency, minimize refrigerator cooling time, minimize passenger cabin cooling time, minimize refrigerator temperature fluctuation, and minimize passenger cabin temperature fluctuation.

[0205] S1004: Define the objective function:

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

[0207] System cooling capacity = Refrigerant mass flow rate of refrigerator evaporator × Enthalpy difference between inlet and outlet of refrigerator evaporator + Refrigerant mass flow rate of air conditioner evaporator × Enthalpy difference between inlet and outlet of air conditioner evaporator + Refrigerant mass flow rate of battery cooler × Enthalpy difference between inlet and outlet of battery cooler;

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

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

[0210] Air conditioning cooling time = Time when the passenger cabin reaches the preset temperature - Time when cooling begins

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

[0212] Passenger cabin temperature fluctuation = the maximum temperature change per unit time after the set temperature is reached inside the passenger cabin.

[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 refrigerator temperature-preset refrigerator temperature≤b;

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

[0219] S1006: Selecting an optimization algorithm: There are many existing optimization algorithms. You can select the 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, electronic expansion valve opening, cooling fan speed, blower speed, and refrigerator temperature equalization fan speed to generate a solution set.

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

[0222] S1009: Construct a set of system performance indicators with optimization objectives.

[0223] S1010: The weights of each optimization index are calculated using the analytic hierarchy process (AHP).

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

[0225] S1012: Perform a combination of subjective and objective weighting.

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

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

[0228] S1015: Outputs 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 can achieve efficient and reliable operation while meeting multiple optimization objectives.

[0230] According to the vehicle thermal management system operation optimization method proposed in this application, after obtaining the current operating mode of the vehicle thermal management system, the optimization objective, objective function, and constraints are determined based on the current operating mode. Based on the optimization objective and constraints, the objective function is solved to obtain an operating solution set. Then, a Pareto optimal solution set is obtained from the operating solution set. Subjective and objective weighting is applied to the Pareto optimal solution set to obtain target operating parameters that satisfy multi-objective optimization. The vehicle thermal management system is then controlled based on these target operating parameters. Therefore, based on the subjective and objective weighting method, multi-objective optimization of the vehicle thermal management system's operating mode is performed, solving the problems in related technologies where the control method is relatively singular and cannot accommodate multiple optimization objectives, leading to unsatisfactory control effects in the vehicle thermal management system. This optimizes the operating parameters and improves the operating performance.

[0231] Next, referring to the accompanying drawings, an operation optimization device for a vehicle thermal management system proposed according to an embodiment of this application is described.

[0232] Figure 11 This is a block diagram of the operation optimization device of the vehicle thermal management system according to an embodiment of this application.

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

[0234] The acquisition module 1000 is used to acquire the current operating mode of the vehicle thermal management system.

[0235] The calculation module 2000 is used to determine the optimization objective, objective function and constraints according to the current running mode, and to obtain the running solution set by solving the objective function based on the optimization objective and constraints;

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

[0237] Optionally, the current operating mode is the refrigerator single cold storage mode, with the optimization goals of minimizing system energy consumption, maximizing system cooling capacity, maximizing system energy efficiency, minimizing refrigerator cooling time, and minimizing refrigerator temperature fluctuation.

[0238] The objective function includes:

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

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

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

[0242] Refrigerator cooling time = Time when the refrigerator reaches the preset temperature - Time when cooling 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 P represents the maximum permissible high pressure value of the compressor. min,1 P is the minimum permissible value for the low pressure of the compressor. r,1 denoted as the maximum allowable value for the compressor pressure ratio, and 'a' as the allowable temperature fluctuation value inside the refrigerator in single-storage mode.

[0250] Optionally, the control module 3000 is specifically used for: calculating the first initial weight of each operating parameter based on the Pareto optimal solution set and according to the preset analytic hierarchy process, and obtaining the first subjective weight based on the preset subjective judgment matrix and the first initial weight of each operating parameter; calculating the second initial weight of each operating parameter based on the Pareto optimal solution set and using the preset CRITIC method, and processing the second initial weight of each operating parameter to obtain the first objective weight; and obtaining the target operating parameters that satisfy multi-objective optimization based on the first subjective weight and the first objective weight.

[0251] Optionally, the current operating mode is a joint operating mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, the shortest passenger cabin cooling time, the minimum refrigerator temperature fluctuation, and the minimum passenger cabin temperature fluctuation.

[0252] The objective function includes:

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

[0254] System cooling capacity = Refrigerant mass flow rate of refrigerator evaporator × Enthalpy difference between inlet and outlet of refrigerator evaporator + Refrigerant mass flow rate of air conditioner evaporator × Enthalpy difference between inlet and outlet of air conditioner evaporator + Refrigerant mass flow rate of battery cooler × Enthalpy difference between inlet and outlet of battery cooler

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

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

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

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

[0259] Passenger cabin temperature fluctuation = the maximum temperature change per unit time after the set temperature is reached in the passenger cabin.

[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 refrigerator temperature-preset refrigerator temperature≤b;

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

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

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

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

[0269] The vehicle thermal management system operation optimization device proposed in this application, after obtaining the current operating mode of the vehicle thermal management system, determines the optimization objective, objective function, and constraints based on the current operating mode. Based on the optimization objective and constraints, it solves the objective function to obtain an operating solution set. Then, it obtains the Pareto optimal solution set from the operating solution set and performs subjective and objective weighting based on the Pareto optimal solution set to obtain target operating parameters that satisfy multi-objective optimization. Finally, it controls the vehicle thermal management system based on the target operating parameters. Therefore, based on the subjective and objective weighting method, it performs multi-objective optimization of the vehicle thermal management system's operating mode, solving the problems of relatively simple control methods in related technologies, which cannot simultaneously consider multiple optimization objectives, leading to unsatisfactory control effects in the vehicle thermal management system. This optimizes the operating parameters and improves the operating effect.

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

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

[0272] When the processor 1202 executes the program, it implements the operation optimization method of the vehicle thermal management system provided in the above embodiments.

[0273] Furthermore, the vehicle also includes:

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

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

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

[0277] If the memory 1201, processor 1202, and communication interface 1203 are implemented independently, then the communication interface 1203, memory 1201, and processor 1202 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

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

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

[0280] This application also provides a computer-readable storage medium storing a computer program thereon, characterized in that the program, when executed by a processor, implements the above-described method for optimizing the operation of a vehicle thermal management system.

[0281] This application also provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for optimizing the operation of a vehicle thermal management system.

[0282] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

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

[0284] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0285] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0286] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for optimizing the operation of a vehicle thermal management system, characterized in that, Includes the following steps: Obtain the current operating mode of the vehicle thermal management system; The optimization objective, objective function, and constraints are determined based on the current operating mode, and the solution set is obtained by solving the objective function based on the optimization objective and the constraints. A Pareto optimal solution set is obtained from the running solution set, and a subjective and objective combination weighting is performed based on the Pareto optimal solution set to obtain target operating parameters that satisfy multi-objective optimization. The vehicle thermal management system is then controlled based on the target operating parameters. The current operating mode is a combined operating mode, with the optimization objectives being minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time, shortest passenger cabin cooling time, minimum refrigerator temperature fluctuation, and minimum passenger cabin temperature fluctuation. The objective functions include: refrigerator cooling time = time when the refrigerator reaches the preset temperature - time when cooling begins; air conditioning cooling time = time when the passenger cabin reaches the preset temperature - time when cooling begins; refrigerator temperature fluctuation = maximum temperature change per unit time after the system reaches the set temperature; passenger cabin temperature fluctuation = maximum temperature change per unit time after the passenger cabin 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 refrigerator temperature - Preset refrigerator temperature ≤ b ;- c ≤ Actual temperature of the passenger compartment - Preset temperature of the passenger compartment ≤ c ;in, P max,2 The maximum permissible value of the compressor high pressure under the aforementioned combined operation mode. P min,2 This refers to the minimum permissible low-pressure value of the compressor under the aforementioned combined operation mode. b This refers to the allowable temperature fluctuation value inside the refrigerator under the combined operation mode. c This refers to the permissible temperature fluctuation value inside the crew cabin under the combined operation mode.

2. The method according to claim 1, characterized in that, The current operating mode is the refrigerator single cold storage mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, and the minimum refrigerator temperature fluctuation. The objective function includes: System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature equalization 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 = Time when the refrigerator reaches the preset temperature - Time when cooling 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 refrigerator temperature - Preset refrigerator temperature ≤ a ; in, P max,1 This refers to the maximum permissible high pressure value of the compressor in the single-storage mode of the refrigerator. P min,1 This refers to the minimum permissible low-pressure value of the compressor in the single-storage mode of the refrigerator. P r,1 This is the maximum allowable value for the compressor pressure ratio. a This refers to the allowable temperature fluctuation value inside the refrigerator under the single-storage cold mode.

3. The method according to claim 2, characterized in that, The process of obtaining target operating parameters that satisfy multi-objective optimization by combining subjective and objective weights based on the Pareto optimal solution set includes: Based on the Pareto optimal solution set, the first initial weight of each operating parameter is calculated according to the preset analytic hierarchy process, 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; The target operating parameters that satisfy multi-objective optimization are obtained based on the first subjective weight and the first objective weight.

4. The method according to claim 1, characterized in that, The objective function also includes: System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature equalization fan power + blower power; System cooling capacity = Refrigerant mass flow rate of refrigerator evaporator × Enthalpy difference between inlet and outlet of refrigerator evaporator + Refrigerant mass flow rate of air conditioner evaporator × Enthalpy difference between inlet and outlet of air conditioner evaporator + Refrigerant mass flow rate of battery cooler × Enthalpy difference between inlet and outlet of battery cooler; System energy efficiency = system cooling capacity / system energy consumption.

5. The method according to claim 1, characterized in that, The process of obtaining target operating parameters that satisfy multi-objective optimization by combining subjective and objective weights based on the Pareto optimal solution set includes: Based on the Pareto optimal solution set, the third initial weight of each operating parameter is calculated according to the preset analytic hierarchy process, 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. The target operating parameters that satisfy the multi-objective optimization are obtained based on the second subjective weight and the second objective weight.

6. An operation optimization device for a vehicle thermal management system, characterized in that, include: The acquisition module is used to acquire the current operating mode of the vehicle thermal management system; The calculation module is used to determine the optimization objective, objective function, and constraints according to the current operating mode, and to solve the objective function based on the optimization objective and the constraints to obtain the operating solution set; The control module is used to obtain the Pareto optimal solution set from the running solution set, and to perform subjective and objective combination weighting based on the Pareto optimal solution set to obtain the target operating parameters that satisfy multi-objective optimization, and to control the vehicle thermal management system based on the target operating parameters. The current operating mode is a combined operating mode, with the optimization objectives being minimum system energy consumption, maximum system cooling capacity, highest system energy efficiency, shortest refrigerator cooling time, shortest passenger cabin cooling time, minimum refrigerator temperature fluctuation, and minimum passenger cabin temperature fluctuation. The objective functions include: refrigerator cooling time = time when the refrigerator reaches the preset temperature - time when cooling begins; air conditioning cooling time = time when the passenger cabin reaches the preset temperature - time when cooling begins; refrigerator temperature fluctuation = maximum temperature change per unit time after the system reaches the set temperature; passenger cabin temperature fluctuation = maximum temperature change per unit time after the passenger cabin 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 refrigerator temperature - Preset refrigerator temperature ≤ b ;- c ≤ Actual temperature of the passenger compartment - Preset temperature of the passenger compartment ≤ c ;in, P max,2 The maximum permissible value of the compressor high pressure under the aforementioned combined operation mode. P min,2 This refers to the minimum permissible low-pressure value of the compressor under the aforementioned combined operation mode. b This refers to the allowable temperature fluctuation value inside the refrigerator under the combined operation mode. c This refers to the permissible temperature fluctuation value inside the crew cabin under the combined operation mode.

7. The apparatus according to claim 6, characterized in that, The current operating mode is the refrigerator single cold storage mode, with the optimization objectives being the minimum system energy consumption, the maximum system cooling capacity, the highest system energy efficiency, the shortest refrigerator cooling time, and the minimum refrigerator temperature fluctuation. The objective function includes: System energy consumption = compressor power + front-end cooling fan power + refrigerator temperature equalization fan power; System cooling capacity = refrigerator refrigerant mass flow rate × enthalpy difference between refrigerator evaporator inlet and outlet; System energy efficiency = system cooling capacity / system energy consumption; Refrigerator cooling time = Time when the refrigerator reaches the preset temperature - Time when cooling 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 refrigerator temperature - Preset refrigerator temperature ≤ a ; in, P max,1 This is the maximum permissible value for the compressor's high pressure. P min,1 This is the minimum permissible value for the low pressure of the compressor. P r,1 This is the maximum allowable value for the compressor pressure ratio. a This refers to the allowable temperature fluctuation value inside the refrigerator under the single-storage cold mode.

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

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

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

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