Intelligent control method for new energy automobile air conditioning system based on COP optimization

Abstract

The invention discloses a new energy automobile air conditioning system intelligent control method based on COP optimization, and relates to the field of air conditioning simulation, the method comprises the following steps: S1, target and parameter initialization: S1.1, defining an optimization target as a maximum air conditioning system performance coefficient; s1.2, determining a core optimization variable as the rotating speed of the compressor; searching a value corresponding to the initial rotating speed for a fitness function value of the rotating speed; step S2, optimal variable position updating: step S2.1, based on the initial rotating speed group in the step S1, a fitness function value is screened out, and a new current optimal solution is determined; s4, iteration termination and optimal parameter output are carried out; S4.1, whether the current iteration number reaches the preset maximum iteration number or not is judged, or the value variation corresponding to the current optimal solution in M continuous iterations is smaller than or equal to delta is judged; and if any condition is met, iteration is terminated, and the compressor rotating speed corresponding to the current optimal solution is output.

Description

Technical Field

[0001] This application relates to the field of air conditioning simulation, and in particular to a COP-optimized intelligent control method for a new energy vehicle air conditioning system, a COP-optimized intelligent control system for a new energy vehicle air conditioning system, electronic equipment, storage media, and simulation platform. Background Technology

[0002] With the rapid development of pure electric vehicles, the impact of their thermal management systems on overall vehicle performance and battery life is becoming increasingly prominent. As a key component of the thermal management system, the air conditioning system's control strategy directly affects cooling efficiency and overall vehicle energy consumption. Currently, the industry mainly uses the following control methods: traditional threshold control, PID control, and fuzzy control. These methods suffer from the following main problems: insufficient control precision, making it difficult to achieve optimal heat dissipation under complex operating conditions; high energy consumption, affecting the vehicle's driving range; fixed parameters, unable to adapt to environmental changes; and a lack of comprehensive optimization for multiple objectives.

[0003] Unlike traditional optimization algorithms, intelligent algorithms are probabilistic search algorithms with the following characteristics: strong robustness, as interacting individuals in the group are distributed and there is no direct control center, so the failure of a few individuals will not affect the solution to the problem; simple structure and easy implementation, as each individual can only perceive local information and the rules followed by individuals are simple; easy to expand and with low overhead; and self-organizing, as the intelligent and complex behaviors exhibited by the group come from simple individual interactions. They can be applied in sensor networks, aircraft formation, data fusion, multi-robotic arm collaborative equipment, parallel computing, multi-human cooperative control, traffic vehicle control, and network data distribution. The intelligent algorithm proposed in this invention has fast convergence speed and strong global optimization ability, but it has not yet been applied to the field of thermal management in new energy vehicles. This invention is the first to combine intelligent algorithms with air conditioning system control, filling a technological gap. Summary of the Invention

[0004] The purpose of this invention is to provide a COP-optimized intelligent control method for a new energy vehicle air conditioning system, a COP-optimized intelligent control system for a new energy vehicle air conditioning system, electronic equipment, storage medium and simulation platform, and to solve at least one of a number of technical problems.

[0005] For example, achieving efficient, stable, and adaptive speed control of air conditioning systems, balancing energy efficiency and user experience.

[0006] This invention provides the following solution:

[0007] According to a first aspect of the present invention, a method for intelligent control of a new energy vehicle air conditioning system based on COP optimization is provided, comprising:

[0008] Step S1, Target and parameter initialization:

[0009] Step S1.1, define the optimization objective as maximizing the coefficient of performance of the air conditioning system. ;

[0010] Step S1.2: Determine the core optimization variable as compressor speed. ;

[0011] Find the initial rotational speed corresponding to The value used as the fitness function value for that rotational speed;

[0012] Step S2, Update the position of the optimal variable:

[0013] Step S2.1: Based on the initial rotational speed population in step S1, select fitness function values. Determine the new current optimal solution ;

[0014] Step S3, update the position of the inferior variable:

[0015] Step S3.1: Based on the initial rotational speed population in step S1, select fitness function values. To handle the inferior variable population, update the corresponding values ​​for each rotational speed. Value, until satisfied. The value increases and the rotational speed remains within the preset compliant range;

[0016] Step S4, Iteration Termination and Optimal Parameter Output:

[0017] Step S4.1: Determine whether the current iteration count has reached the preset maximum iteration count. Or the current optimal solution in M ​​consecutive iterations corresponding The change in value is ≤ Δ;

[0018] If any condition is met, the iteration terminates and the current optimal solution is output. The corresponding compressor speed;

[0019] If not satisfied, return to step S2 until the iteration terminates if any of the conditions in step S4.1 or other preset termination conditions are met.

[0020] Furthermore, step S1 also includes:

[0021] Step S1.3, randomly generate A group of initial compressor speeds;

[0022] Step S1.4, based on compressor suction density Compressor discharge capacity Compressor volumetric efficiency Specific enthalpy at compressor outlet Specific enthalpy after compressor throttling Compressor discharge pressure compressor isentropic efficiency and mechanical efficiency Combined with formula Calculate the refrigerant mass flow rate m om Then through Calculate the cooling capacity of the compressor ,pass Calculate compressor power Thus, the corresponding speed for each initial rotational speed is obtained. The value is the fitness function value used for this rotational speed.

[0023] Furthermore, step S2 also includes:

[0024] Step S2.1 further includes selecting fitness function values ​​based on the initial rotational speed population in step S1. ;

[0025] Top rankings The rotational speed is taken as the optimal variable group, and the optimal variable group is determined. The rotational speed with the highest value is the current optimal solution. ;

[0026] Step S2.2, based on the formula Update the rotational speed for each variable in the optimal variable group;

[0027] in, For the first The optimal variable is at the _th ... The current position in 3D space. The current optimal solution is at the th The location of 4-dimensional space, , The first Upper and lower bounds of a dimensional variable , The coefficients change dynamically with the number of iterations and , This represents the current iteration number. To preset the maximum number of iterations, The random numbers are uniformly distributed in the interval 0-1.

[0028] Step S2.3, calculate the corresponding speed for each speed after the update. value;

[0029] If after the update Value greater than before update If the value and the speed are within the preset speed compliance range, then the updated speed is retained; otherwise, the update range is readjusted until the requirement is met. If the value is increased and the rotational speed is within the preset compliant range, a new current optimal solution is determined after the update. .

[0030] Furthermore, step S3 also includes:

[0031] Step S3.1 further includes selecting fitness function values ​​based on the initial rotational speed population in step S1. After ranking Rotational speed is considered as the inferior variable group;

[0032] Step S3.2, based on the formula Update the rotational speed for each variable in the inferior variable population;

[0033] in, The iteration time interval and , For the first The dominance variable in the first... Current velocity in 3D space, initial velocity , For acceleration and , For the first The dominance variable in the first... The present of dimensional space value;

[0034] Step S3.3, calculate the corresponding speed for each speed after the update. value;

[0035] If after the update Value greater than before update If the value and the speed are within the preset speed range, then the updated speed is retained; otherwise, the acceleration direction and magnitude are adjusted, and the update is repeated until the desired speed is met. The value increases and the speed is within the preset speed compliance range.

[0036] Furthermore, step S4 also includes:

[0037] Step S4.1 also includes determining whether the current iteration count has reached the preset maximum iteration count. , or continuous The current optimal solution in the next iteration corresponding Value change ;

[0038] in, For preset Stability threshold;

[0039] Step S4.2: If any of the conditions in step S4.1 are met, terminate the iteration and output the current optimal solution. The corresponding compressor speed, which is used to power the air conditioning system. The maximum optimal control speed;

[0040] If not satisfied, return to step S2 until the iteration terminates if any of the conditions in step S4.1 or other preset termination conditions are met.

[0041] Furthermore, it also includes:

[0042] The search range is the lower limit. Upper limit ;

[0043] Randomly generated A set of initial compressor speeds, each speed value in the initial speed set is at... Within the range, used to preset the compliant speed range.

[0044] Furthermore, it also includes:

[0045] The value corresponds to the ratio of compressor cooling capacity to compressor power.

[0046] in, .

[0047] According to a second aspect of the present invention, a smart control system for a new energy vehicle air conditioning system based on COP optimization is provided, comprising:

[0048] The objective and parameter initialization module is used to define the optimization objective as maximizing the coefficient of performance of the air conditioning system. The core optimization variable was determined to be the compressor speed. Find the initial rotational speed corresponding to The value used as the fitness function value for that rotational speed;

[0049] The optimal variable position update module is used to filter out fitness function values. Determine the new current optimal solution ;

[0050] The inferior variable position update module is used to filter out fitness function values. To handle the inferior variable population, update the corresponding values ​​for each rotational speed. Value, until satisfied. The value increases and the rotational speed remains within the preset compliant range;

[0051] The iteration termination and optimal parameter output module is used to determine whether the current iteration count has reached the preset maximum iteration count. Or the current optimal solution in M ​​consecutive iterations corresponding The change in value is ≤ Δ;

[0052] If any condition is met, the iteration terminates and the current optimal solution is output. The corresponding compressor speed;

[0053] If the condition is not met, return to either the superior variable position update module or the inferior variable position update module until the iteration terminates and the preset maximum number of iterations is reached. Or the current optimal solution in M ​​consecutive iterations corresponding The condition for determining if the change in value is less than or equal to Δ.

[0054] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0055] The memory stores a computer program, which, when executed by the processor, causes the processor to perform steps such as the intelligent control method for a new energy vehicle air conditioning system based on COP optimization.

[0056] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform steps such as those of a COP-optimized intelligent control method for a new energy vehicle air conditioning system.

[0057] According to a fifth aspect of the present invention, a simulation platform is provided, comprising:

[0058] Electronic devices for implementing steps such as intelligent control methods for new energy vehicle air conditioning systems based on COP optimization;

[0059] The processor runs a program, and when the program runs, it executes steps such as the intelligent control method for a new energy vehicle air conditioning system based on COP optimization from the data output by the electronic device.

[0060] Storage medium for storing programs that, when running, execute steps such as the COP-optimized intelligent control method for a new energy vehicle air conditioning system based on data output from an electronic device.

[0061] The above solution achieves the following beneficial technical effects:

[0062] This application leverages the advantages of intelligent algorithms in global search and multi-objective optimization to achieve efficient, stable, and adaptive speed control of air conditioning systems, balancing energy efficiency and user experience.

[0063] This application improves the global search capability, thereby enhancing the parameter tuning efficiency of air conditioning system speed control, effectively suppressing system disturbances, and strengthening anti-interference capabilities.

[0064] This application reduces energy consumption while ensuring user comfort by simultaneously balancing energy consumption, temperature, and cooling / heating efficiency. Attached Figure Description

[0065] Figure 1 This is a flowchart of an intelligent control method for a new energy vehicle air conditioning system based on COP optimization, provided by one or more embodiments of the present invention.

[0066] Figure 2 This is a structural diagram of an intelligent control system for a new energy vehicle air conditioning system based on COP optimization, provided by one or more embodiments of the present invention.

[0067] Figure 3 This is a schematic diagram of the pressure and enthalpy of an air conditioning system provided in a specific embodiment of the present invention.

[0068] Figure 4 This is a schematic diagram of the intelligent algorithm flow provided in a specific embodiment of the present invention.

[0069] Figure 5 This is an electronic device structural block diagram of an intelligent control method for a new energy vehicle air conditioning system based on COP optimization, provided by one or more embodiments of the present invention. Detailed Implementation

[0070] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Figure 1 This is a flowchart of an intelligent control method for a new energy vehicle air conditioning system based on COP optimization, provided by one or more embodiments of the present invention.

[0072] like Figure 1 The intelligent control method for new energy vehicle air conditioning systems based on COP optimization shown includes:

[0073] Step S1, Target and parameter initialization:

[0074] Step S1.1, define the optimization objective as maximizing the coefficient of performance of the air conditioning system. ;

[0075] Step S1.2: Determine the core optimization variable as compressor speed. ;

[0076] Find the initial rotational speed corresponding to The value used as the fitness function value for that rotational speed;

[0077] Step S2, Update the position of the optimal variable:

[0078] Step S2.1: Based on the initial rotational speed population in step S1, select fitness function values. Determine the new current optimal solution ;

[0079] Step S3, update the position of the inferior variable:

[0080] Step S3.1: Based on the initial rotational speed population in step S1, select fitness function values. To handle the inferior variable population, update the corresponding values ​​for each rotational speed. Value, until satisfied. The value increases and the rotational speed remains within the preset compliant range;

[0081] Step S4, Iteration Termination and Optimal Parameter Output:

[0082] Step S4.1: Determine whether the current iteration count has reached the preset maximum iteration count. Or the current optimal solution in M ​​consecutive iterations corresponding The change in value is ≤ Δ;

[0083] If any condition is met, the iteration terminates and the current optimal solution is output. The corresponding compressor speed;

[0084] If not satisfied, return to step S2 until the iteration terminates if any of the conditions in step S4.1 or other preset termination conditions are met.

[0085] Specifically, this application is applied to compressor speed control in heat pump air conditioning systems for new energy vehicles, with the core objective of maximizing the coefficient of performance (COP) of the air conditioning system.

[0086] In this embodiment, step S1 further includes:

[0087] Step S1.3, randomly generate A group of initial compressor speeds;

[0088] Step S1.4, based on compressor suction density Compressor discharge capacity Compressor volumetric efficiency Specific enthalpy at compressor outlet Specific enthalpy after compressor throttling Compressor discharge pressure compressor isentropic efficiency and mechanical efficiency Combined with formula Calculate the refrigerant mass flow rate m om Then through Calculate the cooling capacity of the compressor ,pass Calculate compressor power Thus, the corresponding speed for each initial rotational speed is obtained. The value is the fitness function value used for this rotational speed.

[0089] In this embodiment, step S2 further includes:

[0090] Step S2.1 further includes selecting fitness function values ​​based on the initial rotational speed population in step S1. ;

[0091] Top rankings The rotational speed is taken as the optimal variable group, and the optimal variable group is determined. The rotational speed with the highest value is the current optimal solution. ;

[0092] Step S2.2, based on the formula Update the rotational speed for each variable in the optimal variable group;

[0093] in, For the first The optimal variable is at the _th ... The current position in 3D space. The current optimal solution is at the th The location of 4-dimensional space, , The first Upper and lower bounds of a dimensional variable , The coefficients change dynamically with the number of iterations and , This represents the current iteration number. To preset the maximum number of iterations, The random numbers are uniformly distributed in the interval 0-1.

[0094] Step S2.3, calculate the corresponding speed for each speed after the update. value;

[0095] If after the update Value greater than before update If the value and the speed are within the preset speed compliance range, then the updated speed is retained; otherwise, the update range is readjusted until the requirement is met. If the value is increased and the rotational speed is within the preset compliant range, a new current optimal solution is determined after the update. .

[0096] In this embodiment, step S3 further includes:

[0097] Step S3.1 further includes selecting fitness function values ​​based on the initial rotational speed population in step S1. After ranking Rotational speed is considered as the inferior variable group;

[0098] Step S3.2, based on the formula Update the rotational speed for each variable in the inferior variable population;

[0099] in, The iteration time interval and , For the first The dominance variable in the first... Current velocity in 3D space, initial velocity , For acceleration and , For the first The dominance variable in the first... The present of dimensional space value;

[0100] Step S3.3, calculate the corresponding speed for each speed after the update. value;

[0101] If after the update Value greater than before update If the value and the speed are within the preset speed range, then the updated speed is retained; otherwise, the acceleration direction and magnitude are adjusted, and the update is repeated until the desired speed is met. The value increases and the speed is within the preset speed compliance range.

[0102] In this embodiment, step S4 further includes:

[0103] Step S4.1 also includes determining whether the current iteration count has reached the preset maximum iteration count. , or continuous The current optimal solution in the next iteration corresponding Value change ;

[0104] in, For preset Stability threshold;

[0105] Step S4.2: If any of the conditions in step S4.1 are met, terminate the iteration and output the current optimal solution. The corresponding compressor speed, which is used to power the air conditioning system. The maximum optimal control speed;

[0106] If not satisfied, return to step S2 until the iteration terminates if any of the conditions in step S4.1 or other preset termination conditions are met.

[0107] In this embodiment, it also includes:

[0108] The search range is the lower limit. Upper limit ;

[0109] Randomly generated A set of initial compressor speeds, each speed value in the initial speed set is at... Within the range, used to preset the compliant speed range.

[0110] In this embodiment, it also includes:

[0111] The value corresponds to the ratio of compressor cooling capacity to compressor power.

[0112] in, .

[0113] Figure 2 This is a structural diagram of an intelligent control system for a new energy vehicle air conditioning system based on COP optimization, provided by one or more embodiments of the present invention.

[0114] like Figure 2 The intelligent control system for the air conditioning system of a new energy vehicle based on COP optimization shown includes:

[0115] The objective and parameter initialization module is used to define the optimization objective as maximizing the coefficient of performance of the air conditioning system. The core optimization variable was determined to be the compressor speed. Find the initial rotational speed corresponding to The value used as the fitness function value for that rotational speed;

[0116] The optimal variable position update module is used to filter out fitness function values. Determine the new current optimal solution ;

[0117] The inferior variable position update module is used to filter out fitness function values. To handle the inferior variable population, update the corresponding values ​​for each rotational speed. Value, until satisfied. The value increases and the rotational speed remains within the preset compliant range;

[0118] The iteration termination and optimal parameter output module is used to determine whether the current iteration count has reached the preset maximum iteration count. Or the current optimal solution in M ​​consecutive iterations corresponding The change in value is ≤ Δ;

[0119] If any condition is met, the iteration terminates and the current optimal solution is output. The corresponding compressor speed;

[0120] If the condition is not met, return to either the superior variable position update module or the inferior variable position update module until the iteration terminates and the preset maximum number of iterations is reached. Or the current optimal solution in M ​​consecutive iterations corresponding The condition for determining if the change in value is less than or equal to Δ.

[0121] It is worth noting that although this system / device only discloses the above-mentioned modules / units, it does not mean that this system / device is limited to the above-mentioned basic functional modules. On the contrary, what this invention intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can add one or more functional modules in combination with the prior art to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. It cannot be assumed that the scope of protection of the claims of this invention is limited to the above-disclosed basic functional modules just because this embodiment only discloses a few basic functional modules.

[0122] In one specific embodiment, an air conditioning system is disclosed:

[0123] Step 1: Working principle of the air conditioning system

[0124] The heating and cooling cycles of an air conditioning system operate on the same principle, only the heat flow is reversed. Process 1→2 is the compression process, where the low-temperature, low-pressure refrigerant gas from the evaporator is compressed by the compressor, becoming a high-temperature, high-pressure superheated refrigerant gas. Process 2→3 is the condensation process, where the superheated refrigerant gas from the compressor, flowing through the condenser, is subjected to forced convection from the outside air, resulting in heat transfer with the environment. After condensation and heat release, the gaseous refrigerant becomes a high-pressure subcooled liquid. Process 3→4 is the throttling process, where the refrigerant pressure drops significantly as it passes through the electronic expansion valve, transforming from a liquid state into a low-temperature, low-pressure gas-liquid mixture. Throughout this transformation, the refrigerant's enthalpy remains constant. Process 4→1 is the evaporation process, where the two-phase refrigerant enters the evaporator, evaporating and absorbing heat to lower the cabin temperature. Simultaneously, the refrigerant undergoes a phase change, becoming a low-temperature, low-pressure superheated gas, which then flows back into the compressor to begin the next refrigeration cycle.

[0125] Step 2: Compressor Model

[0126] The compressor is the most important component in the refrigerant circuit. It primarily uses mechanical work to compress gaseous refrigerant into a high-temperature, high-pressure superheated gas, thus transferring heat from a low-temperature heat source to a high-temperature heat source. This invention does not consider its internal work process, but mainly studies the impact of the compressor on the performance of the entire thermal management system. Therefore, when establishing the compressor model, its structural and performance parameters are mainly considered, and parameters such as the compressor's mechanical efficiency and isentropic efficiency are input into the model. During the operation of the thermal management system, the refrigerant mass flow rate at the compressor outlet can be controlled by adjusting the compressor speed. Adjustment, mass flow rate The calculation formula is as follows: In the formula, The compressor suction density; This refers to the compressor speed; This refers to the compressor's discharge capacity; This refers to the compressor's volumetric efficiency.

[0127] The mechanical efficiency of a compressor is used to calculate the power loss due to friction, and can be calculated using the following formula: In the formula, For compressor mechanical efficiency; Specific enthalpy at compressor outlet; Specific enthalpy at the compressor inlet; This refers to the compressor torque.

[0128] The cooling capacity R of the compressor is expressed as: ;

[0129] Isentropic efficiency represents the degree of deviation between the actual compression process and the isentropic compression process of a compressor. It can be calculated by the ratio of isentropic power to actual compression power, as shown in the following formula: In the formula, The compressor has isentropic efficiency; This is the isentropic outlet enthalpy.

[0130] The power consumption P of the compressor can be calculated using the following formula: ;

[0131] Where Q is the compressor's discharge flow rate, and p is the compressor's discharge pressure. The compressor's motor efficiency is expressed as the product of isentropic efficiency and mechanical efficiency. Table 1 shows the relevant parameters for compressor modeling.

[0132] Table 1

[0133] parameter numerical values unit refrigerant R134a / volume 34 cc Rated voltage 350 V Speed ​​range 800-6000 rpm Operating temperature range -20~110 ℃

[0134] Step 3: Intelligent Algorithm and Model Parameters

[0135] Algorithm introduction, algorithm flow as follows Figure 4 As shown:

[0136] It mainly consists of the following three steps.

[0137] a) Initialization. Assume the search space for variable X is represented by M, the number of variables is N, the optimal solution F is the overall optimization objective, and the upper bound of the search space is... The lower limit of the search space is The initialization variables for this algorithm are then expressed as:

[0138] ;

[0139] b) Update of the position of optimal variables. The direction in which the entire variable population moves towards the optimal solution is determined by the optimal variables, which play a leading role. The formula for updating the position of optimal variables is expressed as:

[0140] ;

[0141] in, Let i be the position of the current variable i in the j-dimensional space. It is the position of the current optimal solution in the j-dimensional space. and Let be the upper and lower bounds of the j-dimensional search space, respectively. and It is a random number that is uniformly distributed between (0,1). The value changes dynamically with the number of iterations. The value is represented as:

[0142] ;

[0143] In the formula, l represents the number of iterations at this point, and Max_iter is the maximum number of iterations. Following the movement pattern shown in the formula, the optimal solution serves as the center of the entire search process, continuously prompting the optimal variable to search across the entire search range.

[0144] c) Dominant variable position update. Followers change their position according to Newton's laws of motion, using the following formula:

[0145] ;

[0146] in, , This represents the position of the i-th follower in the j-th dimension, where t is the current time. Let represent the initial velocity, and 'a' represent the acceleration. Since time is represented by the number of iterations during algorithm optimization, and the interval between iterations is 1, t=1 initially. Considering , Therefore, the above formula can also be expressed as: ;

[0147] Core fitness function formula: Coefficient of performance (COP) is an important indicator of compressor. The higher the COP value, the higher the compressor efficiency, the lower the energy consumption, and the better the cooling or heating performance.

[0148] By using intelligent algorithms to optimize the rotational speed, the optimal rotational speed value is obtained, ultimately achieving a better COP.

[0149] In another specific embodiment, a smart control method for a new energy vehicle air conditioning system based on COP optimization is disclosed. This method is applied to the compressor speed control of a new energy vehicle heat pump air conditioning system, with the core objective of maximizing the COP of the air conditioning system. The steps include:

[0150] Step A1, Initialization of basic parameters and optimization objectives:

[0151] Step A1.1: Input the compressor's fixed parameters, including the refrigerant being R134a and the compressor's volume. Compressor rated voltage 350V, compressor speed range 800-6000rpm, compressor operating temperature range -20~110℃, compressor suction density Compressor volumetric efficiency compressor isentropic efficiency Compressor mechanical efficiency Compressor discharge pressure Specific enthalpy at the compressor inlet Specific enthalpy at compressor outlet compressor isentropic outlet specific enthalpy Specific enthalpy after compressor throttling Compressor torque ;

[0152] Step A1.2 defines the optimization objective as maximizing the coefficient of performance (COP) of the air conditioning system. COP is the ratio of compressor cooling capacity to compressor power, and its basic formula is: At the same time, the core correlation formula of the compressor is clarified:

[0153] Refrigerant mass flow rate: ;

[0154] Mechanical efficiency: ;

[0155] Cooling capacity: ;

[0156] Isoentropy efficiency: ;

[0157] Motor efficiency: ;

[0158] Compressor power: (Q is the compressor discharge flow rate, and...) (positive correlation)

[0159] Step A1.3: Determine the core optimization variable as compressor speed. Its search range is the lower limit. Upper limit Simultaneously, algorithm iteration parameters are set, including the initial rotational speed population size N and the proportion of optimal variables. Maximum number of iterations Number of COP stability checks (M), COP stability threshold ;

[0160] Step A1.4: Randomly generate N initial compressor speeds within the range of 800-6000 rpm to form an initial speed group. Based on the fixed parameters in step S1.1 and the core formula in step A1.2, calculate the cooling capacity R, compressor power P and COP value corresponding to each initial speed. The COP value is the fitness function value of the corresponding speed.

[0161] Step A2, Update the position of the optimal variable:

[0162] Step A2.1: From the initial rotational speed population in Step A1, select the top-ranked populations based on their fitness function values. The rotational speeds are considered as a group of optimal variables, and the rotational speed with the highest COP value among them is determined as the current optimal solution. ;

[0163] Step A2.2: Calculate the dynamic coefficients in real time based on the current iteration number l. , The initial value of l is 1, and the maximum value is 1. ;

[0164] Step A2.3, based on the formula Update each rotational speed in the optimal variable group, where... Let be the current position of the i-th optimal variable in the j-th dimension. Let this be the position of the current optimal solution in the j-th dimension. , Let be the upper and lower bounds of the j-th dimension variable, respectively. A fixed coefficient in the range of 0.5-1.0. The random numbers are uniformly distributed in the interval 0-1.

[0165] Step A2.4: Calculate the COP value for each speed after the update. If the updated speed is within the range of 800-6000 rpm and the COP value is greater than before the update, retain that speed; otherwise, readjust the update range until the conditions are met, and synchronously update the current optimal solution. ;

[0166] Step A3, Dynamic Update of Dominant Variable Location and Velocity:

[0167] Step A3.1: From the initial rotational speed population in Step A1, select the population ranked by fitness function values. Rotational speed is considered as the inferior variable group;

[0168] Step A3.2, based on the latest optimal solution after updating the optimal variables. Calculate acceleration , , where t is the iteration time interval and , Let i be the current position of the i-th inferior variable in the j-th dimension space during the t-th iteration;

[0169] Step A3.3, based on the formula The speed of updating inferior variables, where Let be the velocity of the i-th inferior variable in the j-th dimension during the t-th iteration. The initial velocity;

[0170] Step A3.4, based on the formula Update the position of the inferior variable to complete the speed adjustment;

[0171] Step A3.5: Calculate the COP value corresponding to each speed after the update. If the speed after the update is in the range of 800-6000 rpm and the COP value is greater than before the update, then retain the speed; otherwise, adjust the direction and magnitude of the acceleration, and re-execute steps A3.2 and S3.4 until the conditions are met.

[0172] Step A4, Iteration termination judgment and optimal parameter output:

[0173] Step A4.1: First, determine whether the current iteration number l has reached the preset maximum iteration number. If the condition is met, proceed directly to step A4.2; otherwise, continue to determine if the current optimal solution has been reached in M ​​consecutive iterations. Corresponding change in COP value ;

[0174] Step A4.2: If any of the above termination conditions are met, terminate the iteration and verify the current optimal solution. After the corresponding COP value is the global maximum value, the compressor speed corresponding to the optimal solution is output. This speed is the optimal control speed that maximizes the COP of the air conditioning system.

[0175] In step A4.3, if the termination condition is not met, increment the iteration count l by 1, return to step A2, and repeat the process of updating the superior and inferior variables until the termination condition is met.

[0176] It also includes that in step A1, the value range of N is 10-50, and the value range of K is 20-30.

[0177] The range of values ​​for N or K depends on the experimental budget and the anticipated experimental plan.

[0178] It also includes step A2 The value of decreases with increasing iteration number, starting from the initial iteration. When the number of iterations reaches hour .

[0179] This also includes the fact that the value of M in step A4 ranges from 3 to 5. The value range is 0.01-0.05.

[0180] It also includes the fact that the heat pump air conditioning system controls the direction of refrigerant flow by opening and closing valve groups to achieve the switching between refrigeration and heating cycles. In the refrigeration cycle, the refrigerant completes four thermodynamic processes in sequence: isentropic compression, isobaric condensation, adiabatic expansion, and isobaric evaporation.

[0181] It also includes the ability for the algorithm to be expanded to optimize the dimension of variables, such as compressor speed. Based on this, the opening degree of the electronic expansion valve is added. At this time, the search space in step A1 is expanded to two dimensions, and the variable updates in steps A2 and A3 are performed synchronously for the two-dimensional space parameters.

[0182] It also includes the acceleration of the inferior variable in step A3. Each iteration is based on the latest update of the optimal variables. Real-time calculation ensures that inferior variables always dynamically follow the current global optimum.

[0183] In another specific embodiment, a control method for a heat pump air conditioning system for new energy vehicles based on intelligent algorithms is disclosed:

[0184] 1. Establish a model of the heat pump air conditioning system and compressor:

[0185] A heat pump air conditioning system forms a closed loop through an electric compressor, control valve and heat exchanger. The refrigerant completes four thermodynamic processes in the loop: isentropic compression, isobaric condensation, adiabatic expansion and isobaric evaporation. The heating cycle and the refrigeration cycle have the same thermodynamic processes but the heat flow is opposite.

[0186] The core parameter calculation formulas for the compressor model are as follows:

[0187] ① Formula for refrigerant mass flow rate: ,in The compressor intake density, This refers to the compressor speed. This refers to the compressor's discharge capacity. For compressor volumetric efficiency;

[0188] ② Formula for compressor mechanical efficiency: ,in For mechanical efficiency, The specific enthalpy at the compressor outlet. Here, T is the compressor inlet specific enthalpy, and T is the compressor torque.

[0189] ③ Formula for compressor cooling capacity: ,in The specific enthalpy of the refrigerant at the evaporator inlet. Specific enthalpy of refrigerant at condenser outlet;

[0190] ④ Formula for compressor isentropic efficiency: ,in For isentropic efficiency, The isentropic compression outlet specific enthalpy;

[0191] ⑤ Compressor power consumption formula: Where Q is the compressor discharge flow rate and p is the compressor discharge pressure. For motor efficiency and ;

[0192] 2. Construct intelligent algorithms and optimize compressor speed parameters:

[0193] ① Variable initialization: Set the search space dimension of the variable X to be optimized to M, the number of variables to N, and the upper limit of the search space. Lower limit The initialization variable formula is: ,in , ;

[0194] ②Optimal variable position update: First calculate the iterative decay factor Where l is the current iteration number, To determine the maximum number of iterations, further calculations are used.

[0195] ;

[0196] Update the position of the optimal variable, where This is the current optimal solution. for Uniformly distributed random numbers within an interval;

[0197] ③ Dominant variable position update: The update formula is simplified based on Newton's laws of motion. ;

[0198] Parameter tuning is completed with the goal of optimizing system energy efficiency: the compressor speed obtained in step 2 is used for iteration. Substituting the compressor model formula from step 1, through Calculate the coefficient of performance (COP) of the air conditioning system, determine the optimal compressor speed with the goal of maximizing COP, and achieve a synergistic balance between energy consumption and cooling / heating efficiency of the air conditioning system.

[0199] The compressor's core parameters meet the following requirements: refrigerant is R134a, and displacement is [missing information]. Rated voltage 350V, speed control range The operating temperature range is -20℃ to 110℃.

[0200] In step 2, the variable to be optimized is the compressor speed. The upper limit of the variable search space The lower limit is 6000 rpm. It is 800 rpm.

[0201] In step 2, ②, the optimal variable position is updated with the current optimal solution. With the core as the method, through iterative decay factors The search range is dynamically adjusted to achieve global optimization and avoid local optima.

[0202] In step 2, ③, the position update of the inferior variable simplifies Newton's laws of motion by replacing the time dimension with the number of iterations and setting the initial velocity to 0, thereby achieving rapid convergence of the inferior variable toward the optimal solution.

[0203] Among them, the goal of optimizing system energy efficiency in step 3 also includes taking into account the temperature stability of the crew cabin, ensuring that the temperature fluctuation range inside the cabin does not exceed ±1℃ while maximizing COP.

[0204] The robustness of the intelligent algorithm is achieved through distributed individual interaction, and fluctuations in a single variable parameter will not affect the overall optimization process of the algorithm.

[0205] In another specific embodiment, a smart control system for heat pump air conditioning in new energy vehicles is disclosed, including a data acquisition module, a model calculation module, an algorithm optimization module, and an execution control module:

[0206] The data acquisition module is used to collect the compressor's suction density. Import / export enthalpy and Real-time operating parameters such as exhaust pressure p;

[0207] The model calculation module has a built-in compressor model and corresponding formula, which can output the refrigerant mass flow rate in real time. Cooling capacity R and power consumption P;

[0208] The algorithm optimization module is equipped with intelligent algorithms that can iteratively calculate the optimal compressor speed. ;

[0209] The execution control module adjusts the compressor speed based on the optimal speed output by the algorithm optimization module, ultimately achieving the energy efficiency target of maximizing COP.

[0210] The system also includes a multi-objective balancing module, which can simultaneously balance the weights of air conditioning energy consumption, cabin temperature, and cooling / heating efficiency based on optimal speed control.

[0211] Among them, intelligent algorithms can be extended to other control fields of new energy vehicles, including battery thermal management and parameter tuning of motor cooling systems.

[0212] Figure 5 This is an electronic device structural block diagram of an intelligent control method for a new energy vehicle air conditioning system based on COP optimization, provided by one or more embodiments of the present invention.

[0213] like Figure 5 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0214] The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of an intelligent control method for a new energy vehicle air conditioning system based on COP optimization.

[0215] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a COP-optimized intelligent control method for a new energy vehicle air conditioning system.

[0216] This application also provides a simulation platform, including:

[0217] Electronic equipment, used to implement a COP-optimized intelligent control method for a new energy vehicle air conditioning system;

[0218] The processor runs a program, and when the program runs, it executes the steps of a COP-optimized intelligent control method for a new energy vehicle air conditioning system based on data output from electronic devices.

[0219] A storage medium for storing a program that, when running, executes steps of a COP-optimized intelligent control method for a new energy vehicle air conditioning system based on data output from an electronic device.

[0220] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0221] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0222] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0223] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0224] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0225] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0226] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0227] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0228] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart control method for a new energy vehicle air conditioning system based on COP optimization, characterized in that, The intelligent control method for new energy vehicle air conditioning systems based on COP optimization includes: Step S1, Initialize the target and parameters: Step S1.1, define the optimization objective as maximizing the coefficient of performance of the air conditioning system. ; Step S1.2: Determine the core optimization variable as compressor speed. ; Find the initial rotational speed corresponding to The value used as the fitness function value for that rotational speed; Step S2, Update the position of the optimal variable: Step S2.1: Based on the initial rotational speed population in step S1, select fitness function values. Determine the new current optimal solution ; Step S3, update the position of the inferior variable: Step S3.1: Based on the initial rotational speed population in step S1, select fitness function values. To handle the inferior variable population, update the corresponding values ​​for each rotational speed. Value, until satisfied. The value increases and the rotational speed remains within the preset compliant range; Step S4, Iteration Termination and Optimal Parameter Output: Step S4.1: Determine whether the current iteration count has reached the preset maximum iteration count. Or the current optimal solution in M ​​consecutive iterations corresponding The change in value is ≤ Δ; If any condition is met, the iteration terminates and the current optimal solution is output. The corresponding compressor speed; If not satisfied, return to step S2 until the iteration terminates if any of the conditions in step S4.1 or other preset termination conditions are met.

2. The intelligent control method for a new energy vehicle air conditioning system based on COP optimization according to claim 1, characterized in that, Step S1 also includes: Step S1.3, randomly generate A group of initial compressor speeds; Step S1.4, based on compressor suction density Compressor discharge capacity Compressor volumetric efficiency Specific enthalpy at compressor outlet Specific enthalpy after compressor throttling Compressor discharge pressure compressor isentropic efficiency and mechanical efficiency Combined with formula Calculate the refrigerant mass flow rate m om Then through Calculate the cooling capacity of the compressor ,pass Calculate compressor power Thus, the corresponding speed for each initial rotational speed is obtained. The value is the fitness function value used for this rotational speed.

3. The intelligent control method for a new energy vehicle air conditioning system based on COP optimization according to claim 1, characterized in that, Step S2 also includes: Step S2.1 further includes selecting fitness function values ​​based on the initial rotational speed population in step S1. ; Top rankings The rotational speed is taken as the optimal variable group, and the optimal variable group is determined. The rotational speed with the highest value is the current optimal solution. ; Step S2.2, based on the formula Update the rotational speed for each rotational speed in the population of superior variables; in, For the first The optimal variable is at the _ ... The current position in 3D space. The current optimal solution is at the th The location of 4-dimensional space, , The first Upper and lower bounds of a dimensional variable , The coefficients change dynamically with the number of iterations and , This represents the current iteration number. To preset the maximum number of iterations, The random numbers are uniformly distributed in the interval 0-1. Step S2.3, calculate the corresponding speed for each speed after the update. value; If after the update Value greater than before update If the value and the speed are within the preset speed compliance range, then the updated speed is retained; otherwise, the update range is readjusted until the requirement is met. If the value is increased and the rotational speed is within the preset compliant range, a new current optimal solution is determined after the update. .

4. The intelligent control method for a new energy vehicle air conditioning system based on COP optimization according to claim 1, characterized in that, Step S3 also includes: Step S3.1 further includes selecting fitness function values ​​based on the initial rotational speed population in step S1. After ranking Rotational speed is considered as the inferior variable group; Step S3.2, based on the formula Update the rotational speed for each variable in the inferior variable population; in, The iteration time interval and , For the first The dominance variable in the first... Current velocity in dimensional space, initial velocity , For acceleration and , For the first The dominance variable in the first... The present of dimensional space value; Step S3.3, calculate the corresponding speed for each speed after the update. value; If after the update Value greater than before update If the value and the speed are within the preset speed range, then the updated speed is retained; otherwise, the acceleration direction and magnitude are adjusted, and the update is repeated until the desired speed is met. The value increases and the speed is within the preset speed compliance range.

5. The intelligent control method for a new energy vehicle air conditioning system based on COP optimization according to claim 1, characterized in that, Step S4 also includes: Step S4.1 also includes determining whether the current iteration count has reached the preset maximum iteration count. , or continuous The current optimal solution in the next iteration corresponding Value change ; in, For preset Stability threshold; Step S4.2: If any of the conditions in step S4.1 are met, terminate the iteration and output the current optimal solution. The corresponding compressor speed, which is used to power the air conditioning system. The maximum optimal control speed; If not satisfied, return to step S2 until the iteration terminates if any of the conditions in step S4.1 or other preset termination conditions are met.

6. The intelligent control method for a new energy vehicle air conditioning system based on COP optimization according to claim 1, characterized in that, Also includes: The The search range is the lower limit. Upper limit ; Randomly generated A set of initial compressor speed groups, wherein each speed value in the initial speed group is at... Within the range, used to preset the compliant speed range.

7. The intelligent control method for a new energy vehicle air conditioning system based on COP optimization according to claim 1, characterized in that, Also includes: The value corresponds to the ratio of compressor cooling capacity to compressor power. in, .

8. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory stores a computer program, which, when executed by a processor, causes the processor to perform the steps of the intelligent control method for a new energy vehicle air conditioning system based on COP optimization as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The device stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the intelligent control method for a new energy vehicle air conditioning system based on COP optimization as described in any one of claims 1 to 7.

10. A simulation platform, characterized in that, include: An electronic device for implementing the steps of the intelligent control method for a new energy vehicle air conditioning system based on COP optimization as described in any one of claims 1 to 7; The processor runs a program, and when the program runs, it executes the steps of the intelligent control method for a new energy vehicle air conditioning system based on COP optimization as described in any one of claims 1 to 7 from the data output by the electronic device. A storage medium for storing a program that, when running, executes the steps of the intelligent control method for a new energy vehicle air conditioning system based on COP optimization as described in any one of claims 1 to 7 on data output from an electronic device.

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