Compressor control method and device, electronic equipment and storage medium

Abstract

The invention discloses a compressor control method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first temperature difference value; generating first information according to the first temperature difference value, the compressor state change information and the current change information; generating a first control parameter according to the first information; and the rotating speed of the compressor is adjusted according to the first control parameter. According to the method, the first information is generated according to the temperature difference value, the compressor state change information and the current change information, the generated first information can accurately express the relation between the rotating speed and the current of the compressor, the first control parameter is generated according to the first information, the obtained first control parameter can have small overshoot, and the control precision is improved. And complex working conditions can be adapted. Meanwhile, due to automatic adjustment of the first control parameter, energy consumption caused by manual adjustment can be avoided.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a compressor control method, device, electronic equipment and storage medium. Background Art

[0002] The compressor control method of new energy pure electric vehicles is different from that of traditional fuel vehicles. New energy pure electric vehicles do not have internal combustion engines to provide power and can only use independent motors to drive the compressor, which will consume additional electricity and shorten the battery life of pure electric vehicles. The traditional automobile compressor control method uses PID control. Due to its stable and reliable advantages, it has the following limitations: fixed parameters: PID parameters Kp, Ki, and Kd need to be manually adjusted, and it is difficult to adapt to nonlinear and time-varying systems; large overshoot: under complex working conditions, large overshoot is easy to occur, affecting control accuracy; high energy consumption: because the parameters cannot be adaptively optimized, the actuators (such as compressors) are frequently adjusted, increasing energy consumption. Summary of the Invention

[0003] The present invention provides a compressor control method, device, electronic equipment and storage medium to solve the problems of fixed adjustment parameters, large overshoot and high energy consumption when controlling a vehicle compressor.

[0004] According to one aspect of the present invention, a compressor control method is provided, comprising:

[0005] Determine a first temperature difference; the first temperature difference is the difference between the temperature value inside the vehicle and a preset temperature value;

[0006] generating first information based on the first temperature difference, compressor state change information, and current change information; the compressor state change information is used to represent a change in the speed of a compressor configured in the vehicle; and the first information is used to represent a conversion relationship between the speed and current of the compressor;

[0007] Generate a first control parameter according to the first information; the first control parameter is an updated value of a proportional parameter, an integral parameter, and a differential parameter in a PID control structure; the updated value is a parameter value generated according to an initial proportional parameter, an initial differential parameter, an initial integral parameter, and an adjustment amount of each parameter;

[0008] The rotational speed of the compressor is adjusted according to the first control parameter.

[0009] According to another aspect of the present invention, there is provided a compressor control device, comprising:

[0010] A first temperature difference determination module is configured to determine a first temperature difference; the first temperature difference is the difference between the temperature inside the vehicle and a preset temperature value;

[0011] a first information determining module configured to generate first information based on the first temperature difference, compressor state change information, and current change information; the compressor state change information is used to represent a change in the speed of a compressor configured in the vehicle; and the first information is used to represent a conversion relationship between the speed and current of the compressor;

[0012] a first control parameter determination module, configured to generate a first control parameter based on the first information; the first control parameter being an updated value of a proportional parameter, an integral parameter, and a differential parameter in a PID control structure; the updated value being a parameter value generated based on an initial proportional parameter, an initial differential parameter, an initial integral parameter, and an adjustment amount of each parameter;

[0013] An adjustment module is used to adjust the speed of the compressor according to the first control parameter.

[0014] According to another aspect of the present invention, an electronic device is provided, comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the compressor control method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the compressor control method according to any embodiment of the present invention when executed.

[0019] The technical solution of an embodiment of the present invention determines a first temperature difference; generates first information based on the first temperature difference, compressor state change information, and current change information, wherein the generation of the first information can accurately represent the changing relationship between the compressor speed and current; generates a first control parameter based on the first information, which can adapt the first control parameter to complex operating conditions while improving the response speed of the vehicle temperature control system due to the adaptive generation of the first control parameter; adjusts the compressor speed based on the first control parameter, which can reduce the overshoot of the vehicle temperature control system while stabilizing the temperature value in the vehicle and reducing compressor energy consumption. This method generates the first information based on the temperature difference, compressor state change information, and current change information, which can accurately represent the relationship between the compressor speed and current; generates the first control parameter based on the first information, which can ensure that the obtained first control parameter has a smaller overshoot and can adapt to complex operating conditions. At the same time, due to the automatic adjustment of the first control parameter, the energy consumption caused by manual adjustment can be avoided.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flowchart of a compressor control method provided by an embodiment of the present invention;

[0023] Figure 2 A structural diagram of an RBF neural network provided in an embodiment of the present invention;

[0024] Figure 3 A structural diagram of an RBF neural network PID control module provided in an embodiment of the present invention;

[0025] Figure 4 A structural diagram of a PID control structure provided by an embodiment of the present invention;

[0026] Figure 5 A Simulink model diagram of an RBF neural network PID control module provided in an embodiment of the present invention;

[0027] Figure 6A Simulink model diagram of an RBF neural network module provided in an embodiment of the present invention;

[0028] Figure 7 A schematic diagram of temperature changes in a vehicle provided by an embodiment of the present invention;

[0029] Figure 8 A schematic diagram of a compressor speed change according to an embodiment of the present invention;

[0030] Figure 9 A schematic diagram of compressor power variation provided by an embodiment of the present invention;

[0031] Figure 10 A schematic diagram of energy consumption changes of a compressor provided by an embodiment of the present invention;

[0032] Figure 11 A schematic structural diagram of a compressor control device provided by an embodiment of the present invention;

[0033] Figure 12 A schematic structural diagram of an electronic device for implementing the compressor control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] Figure 1This is a flowchart of a compressor control method provided by an embodiment of the present invention. This embodiment is applicable to the case of controlling a compressor vehicle. The method can be executed by a compressor control device. The compressor control device can be implemented in the form of hardware and / or software. The compressor control device can be configured in any electronic device with network communication function. Figure 1 As shown, the method includes:

[0037] S110 , determining a first temperature difference; the first temperature difference is a difference between a temperature value inside the vehicle and a preset temperature value.

[0038] The temperature value inside the vehicle is the temperature value inside the vehicle passenger compartment, which is obtained by a temperature sensor arranged in the passenger compartment.

[0039] The preset temperature value is a temperature setting value pre-set and inputted into a human-machine interaction interface configured in the vehicle.

[0040] Specifically, a temperature value in the vehicle passenger compartment and a preset temperature value are obtained, and a first temperature difference value is obtained by subtracting the temperature value in the vehicle passenger compartment from the preset temperature value.

[0041] S120. Generate first information based on the first temperature difference, compressor state change information, and current change information; the compressor state change information is used to represent the speed change of the compressor configured in the vehicle; the first information is used to represent the conversion relationship between the speed and current of the compressor.

[0042] The first information, which may also be called Jacobi information, is used to describe the dynamic mapping relationship between the speed and current of the compressor.

[0043] The compressor state change information is obtained through a speed sensor configured on the compressor.

[0044] The current change information is collected by a current sensor configured in the compressor circuit.

[0045] Specifically, the first temperature difference, the compressor state change information and the current change information are input into the first model. The first model calculates the Euclidean distance between the compressor state change information and the current change information and the center vector through the radial basis function according to the compressor state change information and the current change information, and linearly combines the obtained Euclidean distance to obtain the first information.

[0046] The first model may adopt an RBF neural network model.

[0047] For example, Figure 2As shown in the figure, the RBF neural network consists of an input layer, a hidden layer, and an output layer. The input layer transmits compressor state change information and current change information to the hidden layer. The hidden layer is composed of radial basis function neurons, which calculate the distance between the compressor state change information and the current change information and the center vector and activate the neurons. The output layer linearly combines the outputs of the hidden layer to obtain the first information. The center vector is a vector with the same dimension as the compressor state change information and the current change information. The core function of the center vector is to measure the "distance" between the compressor state change information and the current change information and the neurons, thereby determining the activation strength of the neurons.

[0048] Furthermore, the step of performing linear combination on the obtained Euclidean distances to obtain the first information is: performing vector product calculation on the obtained Euclidean distances and a preset weight value to obtain the first information.

[0049] The preset weight values ​​are parameters of the second model corresponding to when the second model is trained and the error parameters meet the preset requirements.

[0050] The second model has the same structure as the first model but has not been trained in parameters.

[0051] Among them, the radial basis function can adopt the Gaussian function, which can be expressed as:

[0052]

[0053] Among them, ||xc j || 2 is the Euclidean norm; b j is the node width of the j-th hidden layer neuron; c j =[cj1,cj2,cj3] T is the center vector of the j-th hidden layer neuron node.

[0054] The first information can be expressed as:

[0055] J=w j ·R+b

[0056] Among them, w j =[w1,w2,…,wm] T is the output layer weight vector, and the base width parameter is greater than 0; b is the bias; R is the Euclidean distance between the compressor state change information and current change information and the center vector.

[0057] Furthermore, the determination process of the first model is as follows: the RBF model framework is used as the second model to obtain historical compressor state change information, historical current change information and finite difference labels. The historical compressor state change information and historical current change information are input into the second model. The second model determines the first distance through the Gaussian function based on the historical compressor state change information, historical current change information and the center vector, and linearly combines all the obtained first distances to obtain the second information. The second information and the finite difference label are subjected to error analysis to obtain the first data. The prediction effect of the second model is evaluated based on the first data and the preset error. If it does not meet the requirements, the second model is corrected until the obtained second information and the finite difference label meet the preset requirements, and the second model that meets the preset requirements is used as the first model. If it meets the requirements, the second model is directly used as the first model.

[0058] The first distance is the Euclidean distance between the historical compressor state change information, the historical current change information and the center vector.

[0059] The finite difference tag acquisition process is as follows: identify a compressor of the same model as that in the vehicle, apply a small disturbance to the current applied to the compressor during operation, and simultaneously measure the compressor speed to obtain the compressor speed change value. The finite difference tag is determined based on the ratio of the compressor change value to the small disturbance.

[0060] S130. Generate a first control parameter according to the first information; the first control parameter is an updated value of the proportional parameter, the integral parameter, and the differential parameter in the PID control structure; the updated value is a parameter value generated according to the initial proportional parameter, the initial differential parameter, the initial integral parameter, and the adjustment amount of each parameter.

[0061] Among them, the initial proportional parameter, initial differential parameter and initial integral parameter are obtained based on experience.

[0062] The adjustment amount of each parameter is generated according to the first information and the speed difference value group. The speed difference value group includes a first speed difference value, a second speed difference value, and a third speed difference value.

[0063] Among them, the first speed difference is:

[0064] a(2)=e(k)=r(k)-y(k);

[0065] The second speed difference is:

[0066] a(1)=e(k)-e(k-1);

[0067] Among them, the third speed difference is:

[0068] a(3)=e(k)-2e(k-1)+e(k-2);

[0069] Wherein, y(k) is the actual speed of the compressor, r(k) is the preset speed of the compressor; k is the first moment; k-1 is the second moment, that is, the moment before k; k-2 is the third moment, that is, the moment before k-1.

[0070] Specifically, a first control parameter is generated based on the first information and the speed difference value group. Specifically, a first speed difference and a second speed difference are determined from the speed difference value group, and the first information is multiplied by the first speed difference and the second speed difference to obtain a proportional adjustment variable. The proportional adjustment variable is then combined with an initial proportional parameter to determine a proportional control parameter. A first speed difference is obtained from the speed difference value group, and the first information is multiplied by the first speed difference to obtain an integral adjustment variable. The integral adjustment variable is then combined with an initial integral parameter to determine an integral control parameter. A first speed difference and a third speed difference are obtained from the speed difference value group, and the third speed difference, the first speed difference, and the first information are multiplied to obtain a differential adjustment variable. The differential adjustment variable is then combined with an initial differential parameter to determine a differential control parameter. The obtained proportional control parameter, integral control parameter, and differential control parameter are then combined to generate a first control parameter.

[0071] Among them, the proportional adjustment amount can be expressed as:

[0072]

[0073] Among them, the integral adjustment amount can be expressed as:

[0074]

[0075] Among them, the differential adjustment amount can be expressed as:

[0076]

[0077] Among them, the proportional control parameter can be expressed as:

[0078] k p =K p -Δk p .

[0079] Among them, the integral control parameter can be expressed as:

[0080] k i =K i -Δk i .

[0081] Among them, the differential control parameter can be expressed as:

[0082] k d =K d -Δk d .

[0083] Among them, K p is the initial scale parameter; K i Initial integration parameter; K d Initial differential parameter; Δk p is the proportional adjustment amount; Δk i is the integral adjustment amount; Δk d is the differential adjustment amount; k p is the proportional control parameter; k i is the integral control parameter; k d is the differential control parameter; η is the parameter; For the first information.

[0084] Furthermore, the generation of the first control parameter is realized through the PID control structure. The RBF neural network generates the first information according to the control instruction, the compressor state change information and the current change information and transmits it to the PID control structure. The PID control structure generates the first control parameter according to the obtained first information.

[0085] Furthermore, the structure diagram of PID control structure and RBF neural network is as follows: Figure 3 As shown, the RBF neural network transmits the first information to the PID control structure. The first information is Jacobian information, and the controlled object is the compressor.

[0086] For example, Figure 4 The figure shows the structure of the PID control structure. It can be seen from the figure that the PID control structure includes proportional parameters, integral parameters, and differential parameters. The sum of these parameters gives the control parameters of the controlled object. The controlled object is a compressor.

[0087] Furthermore, the PID control structure and RBF neural network were constructed using Amesim simulation software, and the RBF neural network PID control model was built using Simulink. The two were then connected via a co-simulation interface to complete the simulation analysis of the vehicle temperature control system. The input of the co-simulation interface is the vehicle's internal temperature value, and the output is the compressor speed.

[0088] Further, if Figure 5As shown in the figure, the RBF neural network PID control module has an outer layer of PID control structure. During the simulation process, the temperature signal of the passenger compartment input by Amesim is used as the feedback signal, and the first temperature difference, compressor state change information and current change information are used as the input of the RBF neural network PID control module. The control quantity of the compressor speed is calculated by the RBF neural network PID control module and output to the Amesim model. The real-time control function of the compressor speed is realized through repeated cycles. The inner layer is the RBF neural network real-time optimization module of the PID parameters Kp, Ki, and Kd, which uses a Gaussian activation function, such as Figure 6 As shown in the figure, the RBF neural network module uses the gradient descent principle to integrate these configuration values ​​into the loop operation process by presetting key parameters such as learning rate, update step size, and proportional coefficient, thereby dynamically calculating the parameter correction values ​​required by the PID controller.

[0089] Furthermore, the process for connecting the RBF neural network PID control module to the vehicle control system is as follows: First, configure the vehicle's environmental parameters in a computer system equipped with MATLAB software, and then configure the operating addresses of the vehicle control system and the computer system. After configuration is complete, open the MATLAB activation configuration file and run Amesim. Set the priority configuration in Amesim to the corresponding MATLAB version, build a joint simulation interface in Amesim, and configure the input and output. After configuration is complete, build the corresponding Amesim interface in the Simulink tool within MATLAB and connect it to the constructed RBF neural network PID control module. The output is the compressor speed, and the input is the vehicle's internal temperature.

[0090] The above steps introduce a nonlinear mapping function into the compressor control system to optimize the learning process. Key operating parameters can be pre-configured and dynamically adjusted. Online optimization of control parameters is achieved through continuous iterative calculations, and finally a real-time updated PID adjustment value is output. This allows the obtained compressor speed control parameters to adapt to changes in the compressor and achieve timely response.

[0091] S140: Adjust the rotation speed of the compressor according to the first control parameter.

[0092] Specifically, a second speed value is generated according to the first control parameter, and the speed of the compressor is adjusted according to the second speed value.

[0093] Furthermore, generating a second speed value based on the first control parameter involves: multiplying the first control parameter by a speed difference value within the speed difference value group, i.e., multiplying the first speed difference by an integral control parameter to obtain a first parameter; multiplying the second speed difference by a proportional control parameter to obtain a second parameter; and multiplying the third speed difference by a differential control parameter to obtain a third parameter. The second speed value is determined based on the obtained first, second, and third parameters and the speed value at the second moment.

[0094] The complete process of adjusting the compressor speed requires configuring simulation parameters. To accurately replicate the actual operating characteristics of the controller, the following parameters were configured during the simulation environment setup: First, the Simulink solver's calculation interval was set to 10 milliseconds to accurately capture the system's dynamic response. Furthermore, to ensure data synchronization during the co-simulation, the communication cycle between Amesim and Simulink was set to 1 second, aligning with the primary simulation step size. After all systems and parameters were set, the co-simulation was performed in Cautious mode.

[0095] In the above steps, Cautious mode significantly improves the numerical stability of the simulation process by optimizing the discontinuity handling mechanism, effectively preventing simulation interruptions caused by singularities. This configuration combination ensures both computational accuracy and reliability for long-term simulations.

[0096] Furthermore, to demonstrate the superiority of the RBF neural network PID control method compared to traditional PID control methods, a cooling simulation was performed in Amesim under a CLTC vehicle operating condition with an ambient temperature set to 35°C. The passenger compartment temperature was set to 22°C, the simulation time was 1800 seconds, and the print step size in both Amesim and Simulink was 1 second. The effectiveness of the RBF neural network PID control strategy in controlling compressor speed under these operating conditions was comprehensively compared using three indicators: real-time passenger compartment temperature fluctuations, compressor speed, and compressor energy consumption.

[0097] Furthermore, to verify the impact of the first control parameter on the compressor speed, a traditional PID control module and an RBF neural network PID control module were constructed for simulation comparison. Evaluations were conducted from three perspectives: vehicle temperature, compressor speed and power changes, and compressor energy consumption. The evaluation results are as follows:

[0098] Among them, the changes in vehicle temperature values ​​under RBF neural network PID control and traditional PID control are as follows: Figure 7As shown in the figure, the RBF neural network PID control reaches the preset temperature value for the first time in about 50 seconds, which is basically the same as the time of traditional PID control. However, the overshoot of traditional PID control is larger and it takes longer to reach stability. When the temperature value in the vehicle is stable, the temperature fluctuation range after RBF neural network optimization is also smaller, indicating that under the same working conditions and ambient temperature, the comfort of the passenger compartment can be improved under the RBF neural network PID control.

[0099] Among them, Figure 8-Figure 9 The following figure shows the changes in compressor speed and power under RBF neural network PID control and traditional PID control strategies. As can be seen from the figure, the average values ​​of compressor speed and power under RBF neural network PID control are not much different, but the speed and power fluctuation range after RBF neural network optimization is smaller, and the operation is more stable. This shows that RBF neural network PID control has better adjustment capabilities than traditional PID control in the face of complex working conditions.

[0100] Among them, Figure 10 As shown in the figure, the energy consumption of the compressor under RBF neural network PID control and traditional PID control strategies is changed. As can be seen from the figure, under CLTC working conditions, RBF neural network PID control has lower energy consumption than traditional PID control methods. Under a CLTC working condition, energy consumption can be reduced by 4.87%.

[0101] In summary, when using traditional PID control for compressors, there are problems such as poor control accuracy, lack of adaptability, and poor following performance. However, RBF neural network PID control can achieve dynamic identification. With the learning ability of the neural network, the proportional, integral, and differential parameters of the PID control can be corrected online according to the control environment, making the parameters more in line with the adjustment requirements, thereby improving the real-time performance and adaptability of the system. By building an RBF neural network control model in Simulink and co-simulating it with the physical model of Amesim, after introducing the RBF neural network optimized PID control, compared with the original vehicle and traditional PID control, the RBF neural network PID control can improve the system response speed, reduce the overshoot of the cockpit temperature, improve the comfort of the passenger compartment, reduce the speed fluctuation range of the compressor, and make the operation more stable. Compared with traditional PID control, it can save 4.87% of the compressor energy consumption. The control effect is significantly superior to traditional PID control in many aspects.

[0102] Optionally, generating the first information according to the first temperature difference, the compressor state change information, and the current change information includes steps A1-A2:

[0103] Step A1: If the first temperature difference is greater than or equal to a preset difference, a control instruction is generated; the control instruction is used to instruct the generation of a first control parameter of the compressor.

[0104] Specifically, the first temperature difference is compared with a preset difference. If the first temperature difference is greater than or equal to the preset difference, it indicates that the temperature inside the vehicle does not meet the requirements and needs to be adjusted, so a control instruction is generated. If the first temperature difference is less than the preset difference, it indicates that although there is a temperature change inside the vehicle, the temperature change is not sufficient to affect objects inside the vehicle, so no temperature adjustment is required.

[0105] Step A2: Generate first information according to the control instruction, the compressor state change information, and the current change information.

[0106] Specifically, after receiving the control instruction, the RBF neural network OID control module responds to the control instruction and inputs the first temperature difference, compressor state change information and current change information into the first model. The first model calculates the Euclidean distance between the compressor state change information and the current change information and the center vector through a Gaussian function based on the compressor state change information and the current change information, and linearly combines the obtained Euclidean distance to obtain the first information.

[0107] Optionally, generating first information according to the compressor state change information and the current change information includes steps B1-B2:

[0108] Step B1: Determine at least one first distance using a radial basis function based on the compressor state change information and the current change information; the first distance is used to represent the distance between the compressor state change information, the current change information and the center vector.

[0109] Specifically, the compressor state change information and the current change information are input into a radial basis function, and the Euclidean distance between the compressor state change information, the current change information and the center vector is calculated to obtain at least one first distance.

[0110] Step B2: Generate first information according to at least one first distance and a preset weight value.

[0111] Specifically, the obtained Euclidean distance is vector-multiplied by a preset weight value to obtain the first information.

[0112] The preset weight values ​​are parameters of the second model corresponding to when the second model is trained and the error parameters meet the preset requirements.

[0113] Optionally, generating the first control parameter according to the first information includes steps C1-C3:

[0114] Step C1, determining a first speed difference according to the speed value at the first moment and a preset speed value; the preset speed value is a speed value that the preset speed needs to reach.

[0115] The preset speed value is set according to the actual needs of the vehicle.

[0116] Specifically, the speed value y(k) of the compressor at the first moment is determined, and the speed value obtained at the first moment is subtracted from the preset speed value r(k) to obtain a first speed difference.

[0117] Among them, the first speed difference:

[0118] e(k)=r(k)-y(k).

[0119] Step C2: Determine a speed difference value group according to the first speed difference value.

[0120] Specifically, a difference between an actual compressor speed and a preset speed of the vehicle compressor at a second moment is determined as a first difference; the first speed difference is subtracted from the first difference to obtain a second speed difference. A difference between an actual compressor speed and a preset speed of the vehicle compressor at a third moment is determined as a second difference; the first speed difference, the second difference, and the first difference are combined to generate a third speed difference. The first speed difference, the second speed difference, and the third speed difference are combined to form a speed difference set.

[0121] The second speed difference is:

[0122] a(1)=e(k)-e(k-1);

[0123] Among them, the third speed difference is:

[0124] a(3)=e(k)-2e(k-1)+e(k-2).

[0125] Step C3: Generate a first control parameter according to the rotation speed difference group and the first information.

[0126] Specifically, a first speed difference and a second speed difference are determined from the speed difference value group, and the first speed difference and the second speed difference are multiplied by the first information to obtain a proportional adjustment amount. A proportional control parameter is determined by combining the proportional adjustment amount with an initial proportional parameter. A first speed difference is obtained from the speed difference value group, and an integral adjustment amount is obtained by multiplying the first speed difference and the first information. An integral control parameter is determined by combining the integral adjustment amount with an initial integral parameter. A first speed difference and a third speed difference are obtained from the speed difference value group, and a differential adjustment amount is obtained by multiplying the third speed difference, the first speed difference, and the first information. A differential control parameter is determined by combining the differential adjustment amount with an initial differential parameter. The obtained proportional control parameter, integral control parameter, and differential control parameter are used to generate a first control parameter.

[0127] Optionally, generating the first control parameter according to the speed difference group and the first information includes steps D1-D4:

[0128] Step D1, obtain the first speed difference and the second speed difference from the speed difference group, and generate a proportional control parameter based on the first speed difference, the second speed difference and the first information; the second speed difference is the difference between the speed value at the second moment and the speed value at the first moment; the second moment is before the first moment.

[0129] Specifically, a first speed difference and a second speed difference are determined from the speed difference group, and the first information is multiplied by the first speed difference and the second speed difference to obtain a proportional adjustment amount. The proportional adjustment amount and the initial proportional parameter are used to determine a proportional control parameter.

[0130] Among them, the proportional adjustment amount can be expressed as:

[0131]

[0132] Among them, the proportional control parameter can be expressed as:

[0133] k p =K p -Δk p .

[0134] Step D2: Obtain a first speed difference from the speed difference group, and generate an integral control parameter according to the first speed difference and the first information.

[0135] Specifically, a first speed difference is obtained from the speed difference group, and the first speed difference is multiplied by the first information to obtain an integral adjustment amount, and the integral control parameter is determined by combining the integral adjustment amount and the initial integral parameter.

[0136] Among them, the integral adjustment amount can be expressed as:

[0137]

[0138] Among them, the integral control parameter can be expressed as:

[0139] k i =K i -Δk i .

[0140] Step D3, obtaining the first speed difference and the third speed difference from the speed difference group, and generating a differential control parameter based on the third speed difference, the first speed difference and the first information; the third speed difference is determined based on the speed difference at the second moment, the speed difference at the third moment and the speed difference at the first moment; the third moment is before the second moment.

[0141] Specifically, the first speed difference and the third speed difference are obtained from the speed difference group, and the differential adjustment amount is obtained by multiplying the third speed difference, the first speed difference and the first information. The differential control parameter is determined by combining the differential adjustment amount and the initial differential parameter.

[0142] Among them, the differential adjustment amount can be expressed as:

[0143]

[0144] Among them, the differential control parameter can be expressed as:

[0145] k d =K d -Δk d .

[0146] Furthermore, the proportional control parameter, the integral control parameter and the differential control parameter are combined to obtain a first control parameter.

[0147] Optionally, adjusting the speed of the compressor according to the first control parameter includes steps E1-E2:

[0148] Step E1: Obtain a second speed value according to the first control parameter, the speed value at the second moment, and the speed difference value group; the speed difference value group is determined according to the speed difference value and the corresponding first control parameter.

[0149] Specifically, the first control parameter is multiplied by the speed difference in the speed difference group, that is, the first speed difference is multiplied by the integral control parameter to obtain the first parameter; the second speed difference is multiplied by the proportional control parameter to obtain the second parameter; and the third speed difference is multiplied by the differential control parameter to obtain the third parameter. The obtained first parameter, second parameter, third parameter, and speed value at the second moment are summed to obtain the second speed value.

[0150] Step E2: adjusting the speed of the compressor according to the second speed value.

[0151] Specifically, the PID control module sends the second speed value to the compressor to adjust the speed until the speed value reaches the second speed value.

[0152] The technical solution of this embodiment determines a first temperature difference; generates first information based on the first temperature difference, compressor state change information, and current change information, wherein the generated first information can accurately represent the changing relationship between the compressor speed and current; generates a first control parameter based on the first information, which can adapt the first control parameter to complex operating conditions while improving the response speed of the vehicle temperature control system due to the adaptive generation of the first control parameter; adjusts the compressor speed based on the first control parameter, which can reduce the overshoot of the vehicle temperature control system while stabilizing the temperature value in the vehicle and reducing compressor energy consumption. This method generates the first information based on the temperature difference, compressor state change information, and current change information, which can accurately represent the relationship between the compressor speed and current; generates the first control parameter based on the first information, which can ensure that the obtained first control parameter has a smaller overshoot and can adapt to complex operating conditions. At the same time, the automatic adjustment of the first control parameter can avoid the energy consumption caused by manual adjustment.

[0153] Figure 11 This is a schematic diagram of the structure of a compressor control device provided by an embodiment of the present invention. This embodiment is applicable to the case of controlling a compressor vehicle. The compressor control device can be implemented in the form of hardware and / or software, and can be configured in any electronic device with network communication function. Figure 11 As shown, the device includes: a first temperature difference determination module 210, a first information determination module 220, a first control parameter determination module 230 and an adjustment module 240, wherein:

[0154] The first temperature difference determination module 210 is used to determine a first temperature difference; the first temperature difference is the difference between the temperature inside the vehicle and a preset temperature value;

[0155] First information determination module 220: configured to generate first information based on the first temperature difference, compressor state change information, and current change information; the compressor state change information is used to represent a change in the speed of a compressor configured in the vehicle; and the first information is used to represent a conversion relationship between the speed and current of the compressor;

[0156] First control parameter determination module 230: used to generate a first control parameter based on the first information; the first control parameter is an updated value of the proportional parameter, the integral parameter, and the differential parameter in the PID control structure; the updated value is a parameter value generated based on the initial proportional parameter, the initial differential parameter, the initial integral parameter, and the adjustment amount of each parameter;

[0157] The adjustment module 240 is configured to adjust the rotation speed of the compressor according to the first control parameter.

[0158] Optionally, the first information determining module 220 includes:

[0159] If the first temperature difference is greater than or equal to the preset difference, a control instruction is generated; the control instruction is used to instruct the generation of a first control parameter of the compressor;

[0160] The first information determining unit is configured to generate the first information according to the control instruction, the compressor state change information and the current change information.

[0161] Optionally, the first information determining unit includes:

[0162] A first distance determination subunit is configured to determine at least one first distance using a radial basis function according to the compressor state change information and the current change information; the first distance is configured to represent the distance between the compressor state change information, the current change information and the center vector;

[0163] The second information determination subunit is configured to generate first information according to at least one first distance and a preset weight value.

[0164] Optionally, the first control parameter determination module 230 includes:

[0165] The first speed difference determination unit is used to determine the first speed difference according to the speed value at the first moment and the preset speed value; the preset speed value is the speed value that the preset speed needs to reach.

[0166] a speed difference value group determining unit, configured to determine a speed difference value group according to the first speed difference value;

[0167] The first control parameter determination unit is configured to generate a first control parameter according to the rotation speed difference group and the first information.

[0168] Optionally, the first control parameter determination unit includes:

[0169] a proportional control parameter determination subunit configured to obtain a first speed difference value and a second speed difference value from the speed difference value group, and generate a proportional control parameter based on the first speed difference value, the second speed difference value, and the first information; the second speed difference value is the difference between the speed value at a second moment and the speed value at the first moment; the second moment being before the first moment;

[0170] An integral control parameter determination subunit is configured to obtain a first speed difference from the speed difference group and generate an integral control parameter according to the first speed difference and the first information;

[0171] a differential control parameter determination subunit configured to obtain a first speed difference and a third speed difference from the speed difference group, and generate a differential control parameter based on the third speed difference, the first speed difference, and the first information; the third speed difference is determined based on the speed difference at the second moment, the speed difference at the third moment, and the speed difference at the first moment; the third moment being before the second moment;

[0172] Optionally, the adjustment module 240 includes:

[0173] A second speed value determining unit is configured to obtain a second speed value based on the first control parameter, the speed value at the second moment, and a speed difference value group; the speed difference value group is determined based on the speed difference value and the corresponding first control parameter;

[0174] Adjustment unit: used to adjust the speed of the compressor according to the second speed value.

[0175] The compressor control device provided in the embodiment of the present invention can execute the compressor control method provided in any embodiment of the present invention mentioned above, and has the corresponding functions and beneficial effects of executing the compressor control method. For detailed process, please refer to the relevant operations of the compressor control method in the above embodiment.

[0176] Figure 12 A schematic diagram of the structure of an electronic device for implementing a compressor control method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0177] like Figure 12 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0178] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0179] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the compressor control method.

[0180] In some embodiments, the compressor control method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the compressor control method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the compressor control method in any other suitable manner (e.g., via firmware).

[0181] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0182] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0183] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0184] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0185] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0186] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0187] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0188] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A compressor control method, characterized in that: include: determining a first temperature difference; The first temperature difference is the difference between the temperature inside the vehicle and a preset temperature value; generating first information according to the first temperature difference, the compressor state change information, and the current change information; The compressor state change information is used to represent the speed change of the compressor configured in the vehicle; the first information is used to represent the conversion relationship between the speed and current of the compressor; Generate a first control parameter according to the first information; the first control parameter is an updated value of a proportional parameter, an integral parameter, and a differential parameter in a PID control structure; the updated value is a parameter value generated according to an initial proportional parameter, an initial differential parameter, an initial integral parameter, and an adjustment amount of each parameter; The rotational speed of the compressor is adjusted according to the first control parameter.

2. The method according to claim 1, characterized in that The generating the first information according to the first temperature difference, the compressor state change information, and the current change information includes: If the first temperature difference is greater than or equal to a preset difference, a control instruction is generated; the control instruction is used to instruct the generation of a first control parameter of the compressor; First information is generated according to the control instruction, the compressor state change information, and the current change information.

3. The method according to claim 2, characterized in that Generating first information according to the compressor state change information and the current change information includes: Determining at least one first distance using a radial basis function based on the compressor state change information and the current change information; the first distance is used to represent the distance between the compressor state change information, the current change information and the center vector; First information is generated according to the at least one first distance and a preset weight value.

4. The method according to claim 1, wherein Generating a first control parameter according to the first information includes: Determining a first speed difference based on the speed value at the first moment and a preset speed value; the preset speed value is a speed value that the preset speed needs to reach; determining a speed difference value group according to the first speed difference value; A first control parameter is generated according to the rotation speed difference group and the first information.

5. The method according to claim 4, characterized in that Generating the first control parameter according to the rotation speed difference group and the first information includes: Obtaining a first speed difference and a second speed difference from the speed difference group, and generating a proportional control parameter based on the first speed difference, the second speed difference, and the first information; the second speed difference being the difference between a speed value at a second moment and a speed value at a first moment; the second moment being before the first moment; Obtaining a first speed difference from the speed difference group, and generating an integral control parameter according to the first speed difference and first information; A first speed difference and a third speed difference are obtained from the speed difference group, and a differential control parameter is generated based on the third speed difference, the first speed difference and the first information; the third speed difference is determined based on the speed difference at the second moment, the speed difference at the third moment and the speed difference at the first moment; the third moment is located before the second moment.

6. The method according to claim 1, characterized in that The adjusting the speed of the compressor according to the first control parameter includes: Obtaining a second speed value according to the first control parameter, the speed value at the second moment, and a speed difference value group; the speed difference value group is determined according to the speed difference value and the corresponding first control parameter; The speed of the compressor is adjusted according to the second speed value.

7. A compressor control device, characterized in that: include: A first temperature difference determination module, configured to determine a first temperature difference; The first temperature difference is the difference between the temperature inside the vehicle and a preset temperature value; a first information determining module, configured to generate first information according to the first temperature difference, the compressor state change information, and the current change information; The compressor state change information is used to represent the speed change of the compressor configured in the vehicle; the first information is used to represent the conversion relationship between the speed and current of the compressor; a first control parameter determination module, configured to generate a first control parameter based on the first information; the first control parameter being an updated value of a proportional parameter, an integral parameter, and a differential parameter in a PID control structure; the updated value being a parameter value generated based on an initial proportional parameter, an initial differential parameter, an initial integral parameter, and an adjustment amount of each parameter; An adjustment module is used to adjust the speed of the compressor according to the first control parameter.

8. The device according to claim 7, characterized in that The first control parameter determination module includes: A first speed difference determination unit is configured to determine a first speed difference based on a speed value at a first moment and a preset speed value; the preset speed value is a speed value that a predetermined speed needs to reach; a speed difference value group determining unit, configured to determine a speed difference value group according to the first speed difference value; The first control parameter determination unit is configured to generate a first control parameter according to the rotation speed difference group and the first information.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the compressor control method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the compressor control method according to any one of claims 1 to 6 when executed.

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