Automatic air conditioner control method

Abstract

The invention discloses an automatic air conditioner control method which comprises the steps that under the conditions that an air conditioner system works and the temperature of a passenger compartment is stable, the actual temperature of the passenger compartment is obtained; on the basis of the actual temperature of the passenger compartment, the fading memory average temperature is calculated through a recursive least square method with a forgetting factor, and the air volume of an air blower is adjusted according to the fading memory average temperature; according to the difference value between the actual temperature of the passenger compartment and the preset target temperature, a control mode is switched through main ring control, a corresponding PID controller is enabled through auxiliary ring control, PID control with feedforward is adopted to adjust the temperature of the passenger compartment, the feedforward value is adjusted based on nonlinear mapping of temperature deviation of the passenger compartment, and PID parameters are automatically set through a PSO particle swarm optimization algorithm. According to the method, PID controller parameters are optimized by adopting the particle swarm optimization, and the dynamic performance of the system is improved; in combination with cascade control, the problem of cooperative control of multiple actuators is effectively solved, and the temperature control precision and the system stability are remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of air conditioning electric control system control, and particularly relates to an automatic air conditioner control method. BACKGROUND

[0002] With the development of automobile intelligence, the vehicle-mounted automatic air conditioning system has become a key configuration for improving the comfort of driving and riding. Multi-temperature zone independent control, rapid and accurate temperature regulation, and low energy consumption operation have become the core indicators for measuring the performance of modern vehicle air conditioners. Under this background, the coupling control effect of multiple actuators directly determines the overall performance of the system and the user experience.

[0003] Currently, the vehicle-mounted automatic air conditioning system generally adopts conventional PI control method, and some research attempts to optimize the control parameters through intelligent algorithms, but at least the following problems still exist: 1. Poor parameter setting effect: the traditional PI parameters rely on experience setting, and it is difficult to adapt to the strong coupling of multiple actuators; 2. Low efficiency of collaborative control: multiple actuators lack effective coordinated control, resulting in difficulty in balancing system stability and energy efficiency.

[0004] The application relates to the technical field of vehicle air conditioner control, and particularly relates to an automatic air conditioner multi-actuator control method based on PSO optimization and hierarchical control. The existing vehicle-mounted automatic air conditioner generally adopts conventional PI control, and has problems such as strong coupling of multiple actuators, difficult parameter setting, and slow dynamic response. SUMMARY

[0005] In view of the problems existing in the prior art, the purpose of the embodiments of the present application is to provide an automatic air conditioner control method, which optimizes the PID controller parameters by using a particle swarm algorithm to improve the dynamic performance of the system; and effectively solves the problem of multi-actuator collaborative control through cascade control, thereby significantly improving the temperature control accuracy and system stability.

[0006] According to a first aspect of the embodiments of the present application, an automatic air conditioner control method is provided, comprising: Step S1: acquiring an actual passenger cabin temperature when the air conditioning system is working and the passenger cabin temperature is stable; Step S2: calculating a fading memory average temperature based on the actual passenger cabin temperature by using a recursive least squares method with a forgetting factor, and adjusting the air volume of the air blower according to the fading memory average temperature; Step S3: switching the control mode through the main loop control according to the difference between the actual passenger cabin temperature and the predetermined target temperature, enabling the corresponding PID controller through the auxiliary loop control, and adjusting the temperature of the passenger cabin by using the PID control with feedforward, wherein the feedforward value is adjusted based on the nonlinear mapping of the passenger cabin temperature deviation, and the PID parameters are automatically set by using a PSO particle swarm optimization algorithm.

[0007] Furthermore, in step S2, the recursive formula for the recursive least squares method with forgetting factor is as follows: Gain matrix: , Update the covariance matrix: , Parameter update equation: , Kalman gain; The observation matrix; This is a forgetting factor used to reduce the weight of historical data; Here is the error covariance matrix; This is the estimated value of the system parameters, i.e., the gradually diminishing memory average temperature; This is the actual observed output value, i.e., the actual cabin temperature; For the current moment, For the previous time step, the H matrix is ​​1 when calculating the fading memory average temperature.

[0008] Furthermore, in step S2, the blower air volume is adjusted by looking up a table based on the gradually diminishing average temperature.

[0009] Further, in step S3, based on the difference between the actual cabin temperature and the predetermined target temperature, the control mode is switched to enable the corresponding PID controller to adjust the cabin temperature, specifically as follows: (1) FastComp: Actual cabin temperature - target temperature > first threshold, enables the compressor's PID controller, which rapidly increases the compressor speed; (2) ApproachComp: The second threshold < actual cabin temperature - target temperature < first threshold enables the compressor's PID controller, which slowly regulates the compressor speed; (3) FastPTC: When the actual cabin temperature minus the target temperature is less than the third threshold, the PID controller of the PTC is enabled, which enables the PTC speed to be increased rapidly. (4) ApproachPTC: The third threshold < actual cabin temperature - target temperature < fourth threshold enables the PTC PID controller, which slowly adjusts the PTC speed.

[0010] Further, in step S3, the PSO particle swarm optimization algorithm includes: (1) Using the combination of parameters to be optimized by the PID controller as particles, the particle swarm is initialized by uniform random distribution, and the current position of each particle is set as P. i ; (2) Based on the difference between the actual crew cabin temperature and the predetermined target temperature, calculate the fitness value of each particle in the current particle swarm, and take the position of the particle with the highest fitness value as P. g ; (3) Initialize inertia weights Time information Learning factors and the maximum number of iterations; (4) Update the particle position and velocity, and calculate the fitness value of the updated particle; (5) Update the position P of each particle according to the updated fitness value of the particles. i The position P of the particle with the highest fitness value g Iteration count +1; (6) Determine whether the maximum number of iterations has been reached. If so, output P. g Otherwise update ω , k Then return to step (4).

[0011] Further, in step (2), the fitness value , where err is the deviation value, which is the difference between the actual cabin temperature and the predetermined target temperature.

[0012] Furthermore, in step (4), the particle updates its velocity V and position P through individual extreme values ​​and group extreme values:

[0013]

[0014] in, Inertial weighting is used to balance global and local search capabilities. To increase the randomness of the search, use random numbers between [0,1]. For particle position; in subscripts and superscripts Let represent the i-th particle, ibest represent the individual's historical best, and gbest represent the population's historical best. Indicates the previous moment, Indicates the current moment.

[0015] According to a second aspect of the embodiments of this application, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the method described in the first aspect.

[0016] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0017] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0018] The technical solutions provided by the embodiments of this application may include the following beneficial effects: 1. The gradually fading memory average passenger cabin temperature is used as a parameter for blower airflow control, adaptively recording passengers' air conditioning temperature usage habits. Compared to global averaging, it has several advantages, including the ability to track time-varying parameters and robustness to sudden changes and disturbances.

[0019] 2. The problem of multi-actuator coupled control is solved by cascade control. The main loop control decomposes the complex temperature control problem into four sub-problems with clear objectives through a clear state machine. The secondary loop adopts PID control. Depending on the control mode, the PID controller of the compressor or PTC is enabled respectively, and PID control with feedforward is used to improve the system response speed.

[0020] 2. The greatest advantage of PSO (Programmable Execution System) for optimizing PID parameters lies in its transformation of a complex, experience-dependent engineering parameter tuning problem into a standardized, automatically executable optimization problem. This method liberates engineers, freeing them from the burden of complex system modeling and tedious manual trial and error, enabling them to find high-performance, robust PID control parameters for various complex systems.

[0021] 3. Improvements are made to the operation, performance, and passenger experience of automatic air conditioning. Compared to the complex MPC model predictive control, PSO, as an optimization algorithm, is not directly deployed to the ECU. For microcontrollers with limited resources, PSO-PID calculation efficiency is greatly improved.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0024] Figure 1 This is a flowchart illustrating an automatic air conditioning control method according to an exemplary embodiment.

[0025] Figure 2This is a flowchart illustrating the PSO algorithm optimization according to an exemplary embodiment.

[0026] Figure 3 This is a block diagram illustrating an automatic air conditioning control device according to an exemplary embodiment.

[0027] Figure 4 This is a schematic diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0029] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0030] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0031] refer to Figure 1 This application provides an automatic air conditioning control method, comprising: Step S1: With the air conditioning system operating and the passenger cabin temperature stable, obtain the actual passenger cabin temperature; With the air conditioning system operating, once the passenger cabin temperature stabilizes, record the actual passenger cabin temperature. Step S2: Based on the actual cabin temperature, calculate the gradually fading memory average temperature using the recursive least squares method with a forgetting factor, and adjust the blower airflow according to the gradually fading memory average temperature. The temperature input is calculated using the Recursive Least Squares (RLS) method with a forgetting factor to achieve a gradually fading memory average, adaptively recording the passengers' air conditioning temperature usage habits. Compared to global averaging, it has several advantages, including the ability to track time-varying parameters and robustness to sudden changes and disturbances. The RLS recursive formula is as follows. When calculating the gradually fading memory average using RLS, the H matrix is ​​1. Gain matrix:

[0032] Update the covariance matrix:

[0033] Parameter update equation:

[0034] in, The Kalman gain determines the weight of the new measurement on the parameter update; The observation matrix (composed of the input data); The forgetting factor (0 < λ ≤ 1) is used to reduce the weight of historical data; This is the error covariance matrix, which reflects the uncertainty of the parameter estimation; These are estimated system parameters (gradually fading memory average temperature). This is the actual observed output value (actual cabin temperature). For the current moment, This refers to the previous moment.

[0035] Based on the gradually diminishing average temperature, a one-dimensional lookup table is used to obtain the air volume, thereby adjusting the blower air volume. The lookup table is obtained by experimental calibration based on the usage scenario.

[0036] Step S3: Based on the difference between the actual passenger cabin temperature and the predetermined target temperature, the control mode is switched through the main loop control, and the corresponding PID controller is enabled through the sub-loop control. PID control with feedforward is used to adjust the temperature of the passenger cabin. The feedforward value is adjusted based on the nonlinear mapping of the passenger cabin temperature deviation, and the PID parameters are automatically tuned using the PSO particle swarm optimization algorithm. The solution to the multi-actuator coupled control problem is decoupling control (DC). This application uses cascade control (CC) to decouple the system. This main-loop control decomposes the complex temperature control problem into four sub-problems with clear objectives through a clear state machine. It is essentially a variable gain control. In regions with large errors (Fast mode), high gain / strong output is used to pursue speed.

[0037] In the region with small errors (Approach mode), low gain / fine adjustment is used to pursue accuracy and stability.

[0038] This design perfectly resolves the core contradiction between "rapid response" and "prevention of overshoot" in automatic air conditioning systems, thereby achieving fast, stable, and comfortable cabin temperature control.

[0039] In one possible implementation, the following four control modes are adopted: FastComp – Actual cabin temperature - target temperature > 2.5℃. This temperature difference should be calibrated and determined according to system requirements. The compressor speed needs to be increased more quickly to rapidly cool the cabin. ApproachComp—1.5℃ < Actual cabin temperature - Target temperature < 2.5℃. This temperature difference should be calibrated and determined according to system requirements. The compressor speed needs to be adjusted slowly to prevent overshoot. FastPTC – Actual cabin temperature - target temperature < -2.5℃. This temperature difference should be calibrated and determined according to system requirements. It is necessary to rapidly increase the PTC speed to quickly reach the target temperature. ApproachPTC — -2.5℃ < Actual cabin temperature - Target temperature < -1.5℃. This temperature difference should be calibrated and determined according to system requirements. The PTC speed needs to be adjusted slowly to prevent overshoot. In addition, the switching between the compressor and the PTC not only takes into account the temperature difference between the actual cabin temperature and the target temperature, but also takes into account the negative regulation part of the PID. That is, when the compressor or PTC cannot change the trend of temperature change even when running at the minimum speed or power, the compressor / PTC will be switched.

[0040] The secondary loop employs PID control, enabling either the compressor's or PTC's PID controller depending on the control mode. This scheme uses PID control with feedforward to improve the system's response speed. The feedforward value is adjusted based on a nonlinear mapping of the cabin temperature deviation. The four control modes have four sets of proportional and integral coefficients. The PSO (Particle Swarm Optimization) algorithm is used to automatically tune the PID parameters, achieving high-precision control that is independent of the mathematical model of the controlled object and achieves global optimization. Details are as follows: (1) Initialize the particle swarm; Specifically, in the PSO optimization, the particles refer to the combination of parameters to be optimized in the PID controller. The initialization process is as follows: within the preset range of Kp, Ki, and Kd parameter values, an initial particle swarm is generated through uniform random distribution, so that the swarm uniformly covers the entire solution space. The current position of each particle is set as P. i .

[0041] (2) Based on the difference between the actual crew cabin temperature and the predetermined target temperature, the current particle fitness value is calculated. The fitness function of this invention takes improving the dynamic performance of the system as the core optimization goal, and achieves targeted optimization of the controller parameters through key indicators such as quantification adjustment time and overshoot. This embodiment adopts the ISE index:

[0042] Here, err is the deviation value, which is the difference between the actual cabin temperature and the predetermined target temperature.

[0043] Furthermore, the position of the particle with the highest fitness value in the particle swarm is taken as P. gbest .

[0044] (3) Initialization ω , k , c 1. c 2. Maximum number of iterations; (4) Update the particle position and velocity, and calculate the fitness value of the updated particle; In each iteration, the particle updates its velocity V and position P through individual and group extreme values:

[0045]

[0046] in, For speed; Inertial weights are used to balance global and local search capabilities; The learning factor adjusts the step size towards individual and group optima, respectively. To increase the randomness of the search, use random numbers between [0,1]. For particle position; in subscripts and superscripts Let represent the i-th particle, ibest represent the individual's historical best, and gbest represent the population's historical best. Indicates the previous moment, Indicates the current time; (5) Update P i and P g Specifically, if the current fitness calculated for the i-th particle is greater than the optimal fitness calculated at the previous time step, then... Assign to P i If the current fitness calculated for the i-th particle is greater than the population-optimal fitness calculated at the previous time step, then... Assign to P g Iteration count +1; (6) Determine whether the maximum number of iterations has been reached. If so, output P. g Otherwise update ω , k Then return to step (4).

[0047] It should be noted that the above method is for automatic control when the driver does not make any manual adjustments. If the driver manually adjusts the air conditioning system in automatic mode, the driver's preference should be given.

[0048] Corresponding to the aforementioned embodiments of the automatic air conditioning control method, this application also provides embodiments of an automatic air conditioning control device.

[0049] Figure 3 This is a block diagram illustrating an automatic air conditioning control device according to an exemplary embodiment. (Refer to...) Figure 3 The device may include: The acquisition module 21 is used to acquire the actual passenger cabin temperature when the air conditioning system is working and the passenger cabin temperature is stable. The air volume adjustment module 22 is used to calculate the gradually disappearing memory average temperature based on the actual passenger cabin temperature using the recursive least squares method with a forgetting factor, and adjust the blower air volume according to the gradually disappearing memory average temperature. The PID control module 23 is used to switch the control mode through the main loop control and enable the corresponding PID controller through the sub-loop control according to the difference between the actual passenger cabin temperature and the predetermined target temperature. The temperature of the passenger cabin is adjusted by PID control with feedforward, wherein the feedforward value is adjusted based on the nonlinear mapping of the passenger cabin temperature deviation, and the PID parameters are automatically tuned by the PSO particle swarm optimization algorithm.

[0050] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0051] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0052] Accordingly, this application also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the automatic air conditioning control method as described above.

[0053] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the automatic air conditioning control method described above. Figure 3 The diagram shown is a hardware structure diagram of any device with data processing capabilities, including an automatic air conditioning control device provided in an embodiment of the present invention. Except for... Figure 3 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0054] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the automatic air conditioning control method described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0055] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. An automatic air conditioning control method, characterized in that, include: Step S1: With the air conditioning system operating and the passenger cabin temperature stable, obtain the actual passenger cabin temperature; Step S2: Based on the actual cabin temperature, calculate the gradually fading memory average temperature using the recursive least squares method with a forgetting factor, and adjust the blower airflow according to the gradually fading memory average temperature. Step S3: Based on the difference between the actual passenger cabin temperature and the predetermined target temperature, switch the control mode through the main loop control, enable the corresponding PID controller through the sub-loop control, and use PID control with feedforward to adjust the passenger cabin temperature. The feedforward value is adjusted based on the nonlinear mapping of the passenger cabin temperature deviation, and the PID parameters are automatically tuned using the PSO particle swarm optimization algorithm.

2. The method according to claim 1, characterized in that, In step S2, the recursive formula for the recursive least squares method with forgetting factor is as follows: Gain matrix: , Update the covariance matrix: , Parameter update equation: , Kalman gain; The observation matrix; This is a forgetting factor used to reduce the weight of historical data; Here is the error covariance matrix; This is the estimated value of the system parameters, i.e., the gradually diminishing memory average temperature; This is the actual observed output value, i.e., the actual cabin temperature; For the current moment, For the previous time step, the H matrix is ​​1 when calculating the fading memory average temperature.

3. The method according to claim 1, characterized in that, In step S2, the blower air volume is adjusted by looking up a table based on the gradually decreasing average temperature.

4. The method according to claim 1, characterized in that, In step S3, based on the difference between the actual cabin temperature and the predetermined target temperature, the control mode is switched, enabling the corresponding PID controller to adjust the cabin temperature. Specifically: (1) FastComp: Actual cabin temperature - target temperature > first threshold, enables the compressor's PID controller, which rapidly increases the compressor speed; (2) ApproachComp: The second threshold < actual cabin temperature - target temperature < first threshold enables the compressor's PID controller, which slowly regulates the compressor speed; (3) FastPTC: When the actual cabin temperature minus the target temperature is less than the third threshold, the PID controller of the PTC is enabled, which enables the PTC speed to be increased rapidly. (4) ApproachPTC: The third threshold < actual cabin temperature - target temperature < fourth threshold enables the PTC PID controller, which slowly adjusts the PTC speed.

5. The method according to claim 1, characterized in that, In step S3, the PSO particle swarm optimization algorithm includes: (1) Using the combination of parameters to be optimized by the PID controller as particles, the particle swarm is initialized by uniform random distribution, and the current position of each particle is set as P. i ; (2) Based on the difference between the actual crew cabin temperature and the predetermined target temperature, calculate the fitness value of each particle in the current particle swarm, and take the position of the particle with the highest fitness value as P. g ; (3) Initialize inertia weights Time information Learning factors and the maximum number of iterations; (4) Update the particle position and velocity, and calculate the fitness value of the updated particle; (5) Update the position P of each particle according to the updated fitness value of the particles. i The position P of the particle with the highest fitness value g Iteration count +1; (6) Determine whether the maximum number of iterations has been reached. If so, output P. g Otherwise update ω , k Then return to step (4).

6. The method according to claim 5, characterized in that, In step (2), the fitness value , where err is the deviation value, which is the difference between the actual cabin temperature and the predetermined target temperature.

7. The method according to claim 5, characterized in that, In step (4), the particle updates its velocity V and position P through individual extreme values ​​and group extreme values: ， ， in, Inertial weighting is used to balance global and local search capabilities. To increase the randomness of the search, use random numbers between [0,1]. For particle position; in subscripts and superscripts Let represent the i-th particle, ibest represent the individual's historical best, and gbest represent the population's historical best. Indicates the previous moment, Indicates the current moment.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method as described in any one of claims 1-7.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

Images