A control method and terminal for a power cabinet temperature control system

Abstract

This invention relates to the field of temperature management technology, and more particularly to a control method and terminal for a power cabinet temperature control system. Based on pre-defined temperature zones, operating parameters are collected according to a preset sampling period. These operating parameters include ambient temperature, temperature control system power, and temperature zone temperature. An incremental PID controller model is constructed, and the adjustment control quantity is calculated based on the operating parameters collected the most recently (previously preset) number of times. The operating power of the temperature control system is adjusted according to the adjustment control quantity. This invention divides temperature zones, periodically collects ambient temperature and temperature zone temperature, and uses the data from the most recent period to calculate the adjustment control quantity using an incremental PID controller model. This allows for real-time adjustment of the operating power of the temperature control system, achieving better temperature control and reducing power consumption. Furthermore, the incremental PID controller model incorporates fuzzy adaptive tuning parameters, effectively suppressing the influence of equipment usage time and environmental interference, resulting in better temperature control.

Description

[0001] This case is a divisional application based on the invention patent filed on July 24, 2024, with application number 202410993552.0 and titled "A Method and Device for Controlling the Working Temperature of a Power Cabinet". Technical Field

[0002] This invention relates to the field of temperature management technology, and in particular to a control method and terminal for a power cabinet temperature control system. Background Technology

[0003] Currently, the new energy vehicle industry is booming, and the technology of new energy vehicles is constantly iterating. New energy vehicles with different battery voltage platforms, different charging powers, and different driving ranges are continuously entering the market. Charging and testing power cabinets need to meet these different charging requirements while ensuring their own smooth operation and sufficiently low power consumption and noise. Therefore, an excellent temperature control system is urgently needed for charging and testing power cabinets.

[0004] Currently, existing temperature control systems generally fall into the following three categories:

[0005] (1) Simply decide whether to turn the temperature control system on or off based on the upper and lower limits of the temperature.

[0006] (2) The decision to turn the temperature control system on or off is based solely on whether there is a vehicle charging.

[0007] (3) The temperature control system is turned on simply because there are vehicles charging, and then turned off after a certain period of forced operation when there are no vehicles charging.

[0008] In other words, the current temperature control system's temperature control strategy is relatively mechanical, resulting in poor temperature control performance and increased energy consumption. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a control method and terminal for a power cabinet temperature control system, so as to achieve better temperature control effect and reduce power consumption.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0011] A control method for a power cabinet temperature control system, comprising the following steps:

[0012] S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature.

[0013] S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times.

[0014] The preset number of iterations is 3, and the incremental PID controller model is represented as follows:

[0015] ΔP=A*e n +B*e n-1 +C*e n-2 ;

[0016] A=K P (1+T / T i +T d / T);

[0017] B=-K P (1+2T d / T);

[0018] C=K P *T d / T;

[0019] Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient;

[0020] S3. Adjust the operating power of the temperature control system according to the adjustment control quantity;

[0021] When the temperature of the temperature zone is lower than the target temperature or within the preset range of the target temperature, it is determined whether a vehicle is charging. If so, the temperature control system is maintained; otherwise, the temperature control system is shut down.

[0022] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0023] A method for controlling the operating temperature of a power cabinet, comprising the following steps:

[0024] S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature.

[0025] S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times.

[0026] The preset number of iterations is 3, and the incremental PID controller model is represented as follows:

[0027] ΔP=A*e n +B*en-1 +C*e n-2 ;

[0028] A=K P (1+T / T i +T d / T);

[0029] B=-K P (1+2T d / T);

[0030] C=K P *T d / T;

[0031] Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient;

[0032] S3. Adjust the operating power of the temperature control system according to the adjustment control quantity.

[0033] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0034] A control device for a power cabinet temperature control system includes a processor, a memory, and an ARM program stored in the memory and executable on the processor. When the processor executes the ARM program, it performs the following steps:

[0035] S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature.

[0036] S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times.

[0037] The preset number of iterations is 3, and the incremental PID controller model is represented as follows:

[0038] ΔP=A*e n +B*e n-1 +C*e n-2 ;

[0039] A=K P (1+T / T i +T d / T);

[0040] B=-K P (1+2T d / T);

[0041] C=K P *T d / T;

[0042] Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient;

[0043] S3. Adjust the operating power of the temperature control system according to the adjustment control quantity;

[0044] When the temperature of the temperature zone is lower than the target temperature or within the preset range of the target temperature, it is determined whether a vehicle is charging. If so, the temperature control system is maintained; otherwise, the temperature control system is shut down.

[0045] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0046] A device for controlling the operating temperature of a power cabinet includes a processor, a memory, and an ARM program stored in the memory and executable on the processor. When the processor executes the ARM program, it performs the following steps:

[0047] S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature.

[0048] S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times.

[0049] The preset number of iterations is 3, and the incremental PID controller model is represented as follows:

[0050] ΔP=A*e n +B*e n-1 +C*e n-2 ;

[0051] A=K P (1+T / T i +T d / T);

[0052] B=-K P (1+2T d / T);

[0053] C=K P *T d / T;

[0054] Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient;

[0055] S3. Adjust the operating power of the temperature control system according to the adjustment control quantity.

[0056] The beneficial effects of this invention are as follows: The control method and terminal of the power cabinet temperature control system of this invention divides the temperature zone, periodically collects the ambient temperature and the temperature zone temperature, and calculates the adjustment control quantity using the most recent period data and an incremental PID controller model, thereby adjusting the operating power of the temperature control system in real time to achieve better temperature control effect and reduce power consumption. Moreover, the incremental PID controller model introduces fuzzy adaptive tuning parameters, which effectively suppresses the influence of equipment usage time, environmental interference, etc., to achieve better temperature control effect. Attached Figure Description

[0057] Figure 1 This is a flowchart of a method for controlling the operating temperature of a power cabinet according to an embodiment of the present invention;

[0058] Figure 2 This is a structural diagram of a power cabinet operating temperature control device according to an embodiment of the present invention;

[0059] Figure 3 This is a model example diagram of a method for controlling the operating temperature of a power cabinet according to an embodiment of the present invention;

[0060] Label Explanation:

[0061] 1. A device for controlling the operating temperature of a power cabinet; 2. A processor; 3. A memory. Detailed Implementation

[0062] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0063] Please refer to Figure 1 as well as Figure 3A method for controlling the operating temperature of a power cabinet, comprising the following steps:

[0064] S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature.

[0065] S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times.

[0066] The preset number of iterations is 3, and the incremental PID controller model is represented as follows:

[0067] ΔP=A*e n +B*e n-1 +C*e n-2 ;

[0068] A=K P (1+T / T i +T d / T);

[0069] B=-K P (1+2T d / T);

[0070] C=K P *T d / T;

[0071] Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient;

[0072] S3. Adjust the operating power of the temperature control system according to the adjustment control quantity.

[0073] As can be seen from the above description, the beneficial effects of the present invention are as follows: The present invention provides a method for controlling the operating temperature of a power cabinet, which divides the temperature zone, periodically collects the ambient temperature and the temperature zone temperature, and uses the data from the most recent period to calculate the adjustment control quantity using an incremental PID controller model, thereby adjusting the operating power of the temperature control system in real time, achieving better temperature control effect and reducing power consumption. Furthermore, the incremental PID controller model introduces fuzzy adaptive tuning parameters, which effectively suppresses the influence of equipment usage time, environmental interference, etc., to achieve better temperature control effect.

[0074] Furthermore, the determination of the adaptive tuning parameters specifically involves:

[0075] With the current deviation signal e n The deviation change Δe is taken as input and calculated using a fuzzy adaptive tuning method based on a preset fine-tuning value table.

[0076] Where, △e=e n -e n-1 ;

[0077] Based on the difference between the ambient temperature and the preset target temperature, the proportional coefficient of the adaptive tuning parameter is determined, and the adaptive tuning parameter is adjusted according to the proportional coefficient.

[0078] As described above, the demand control quantity of the previous cycle is corrected based on the adjustment control quantity calculated in the current cycle, and then the demand control quantity of the current cycle is obtained, thereby controlling the operating power of the temperature control system.

[0079] Furthermore, the steps between S1 and S2 include:

[0080] Determine whether the temperature of the temperature zone is lower than the preset target temperature. If so, determine whether there is vehicle charging. If there is no vehicle charging, shut down the temperature control system without waiting for the command from the PID controller. Otherwise, proceed to step S2.

[0081] As described above, if the current temperature is lower than the target temperature, the temperature control system will be shut down directly without waiting for the PID controller's command, thus achieving more efficient control.

[0082] Furthermore, the integral coefficient T i The value range is [240, 480], and the differential coefficient T d The value range is [3, 150].

[0083] As can be seen from the above description, the values ​​of the integral coefficients and differential coefficients are taken as a specific embodiment of the present invention.

[0084] Please refer to Figure 2 A device for controlling the operating temperature of a power cabinet includes a processor, a memory, and an ARM program stored in the memory and executable on the processor. When the processor executes the ARM program, it performs the following steps:

[0085] S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature.

[0086] S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times.

[0087] The preset number of iterations is 3, and the incremental PID controller model is represented as follows:

[0088] ΔP=A*e n +B*e n-1 +C*e n-2 ;

[0089] A=K P (1+T / T i +T d / T);

[0090] B=-K P (1+2T d / T);

[0091] C=K P *T d / T;

[0092] Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient;

[0093] S3. Adjust the operating power of the temperature control system according to the adjustment control quantity.

[0094] As can be seen from the above description, the beneficial effects of the present invention are as follows: The present invention provides a method for controlling the operating temperature of a power cabinet, which divides the temperature zone, periodically collects the ambient temperature and the temperature zone temperature, and uses the data from the most recent period to calculate the adjustment control quantity using an incremental PID controller model, thereby adjusting the operating power of the temperature control system in real time, achieving better temperature control effect and reducing power consumption. Furthermore, the incremental PID controller model introduces fuzzy adaptive tuning parameters, which effectively suppresses the influence of equipment usage time, environmental interference, etc., to achieve better temperature control effect.

[0095] Furthermore, the determination of the adaptive tuning parameters specifically involves:

[0096] With the current deviation signal e n The deviation change Δe is taken as input and calculated using a fuzzy adaptive tuning method based on a preset fine-tuning value table.

[0097] Where, △e=e n -e n-1 ;

[0098] Based on the difference between the ambient temperature and the preset target temperature, the proportional coefficient of the adaptive tuning parameter is determined, and the adaptive tuning parameter is adjusted according to the proportional coefficient.

[0099] As described above, the fuzzy adaptive tuning parameters are calculated based on the deviation signal through fuzzy adaptive tuning.

[0100] Furthermore, step S3 specifically includes:

[0101] Calculate the current demand control quantity P based on the aforementioned adjustment control quantity. n :

[0102] P n =ΔP+P n-1 ;

[0103] Where ΔP represents the control quantity, P n-1 This represents the demand control quantity from the previous sampling period;

[0104] The operating power of the temperature control system is adjusted according to the required control quantity.

[0105] As described above, the demand control quantity of the previous cycle is corrected based on the adjustment control quantity calculated in the current cycle, and then the demand control quantity of the current cycle is obtained, thereby controlling the operating power of the temperature control system.

[0106] Furthermore, the steps between S1 and S2 include:

[0107] Determine whether the temperature of the temperature zone is lower than the preset target temperature. If so, determine whether there is vehicle charging. If there is no vehicle charging, shut down the temperature control system without waiting for the command from the PID controller. Otherwise, proceed to step S2.

[0108] As described above, if the current temperature is lower than the target temperature, the temperature control system will be shut down directly without waiting for the PID controller's command, thus achieving more efficient control.

[0109] Furthermore, the integral coefficient T i The value range is [240, 480], and the differential coefficient T d The value range is [3, 150].

[0110] As can be seen from the above description, the values ​​of the integral coefficients and differential coefficients are taken as a specific embodiment of the present invention.

[0111] The present invention provides a method and apparatus for controlling the operating temperature of a power cabinet, applicable to the temperature control of a power cabinet.

[0112] Please refer to Figure 1 and Figure 3 Embodiment 1 of the present invention is as follows:

[0113] A method for controlling the operating temperature of a power cabinet, comprising the following steps:

[0114] S1. Based on the pre-divided temperature zones, collect operating parameters according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature.

[0115] In this embodiment, the power cabinet is divided into multiple different temperature zones, and the relationship between the different temperature zones is established.

[0116] The system periodically collects parameters such as ambient temperature, output power, and temperature zone, and uses comprehensive calculations to control the temperature control system.

[0117] The process can be referred to Figure 3 As shown in the figure, T1, T2, and T3 are the temperature values ​​in the three most recent sampling periods, ΔT1, ΔT2, and ΔT3 are the deviation signals in the three most recent sampling periods, P1 is the current required control value, P2 is the required control value in the previous period, ΔP is the control quantity that needs to be adjusted calculated by the incremental PID controller, and K... P Fuzzy adaptive tuning parameters.

[0118] S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times.

[0119] In this embodiment, the preset number of times is 3, and the incremental PID controller model is represented as follows:

[0120] ΔP=A*e n +B*e n-1 +C*e n-2 ;

[0121] A=K P (1+T / T i +T d / T);

[0122] B=-K P (1+2T d / T);

[0123] C=K P *T d / T;

[0124] Where A, B, and C represent control constants, e n en-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d This represents the differential coefficient.

[0125] In this embodiment, the temperature change trend is identified by short-term continuous temperature data acquisition, and the sampling period T and the deviation signal within the most recent three sampling periods are determined for calculation (in this embodiment, the sampling period is selected as 5~10s; in other equivalent embodiments, it can be adjusted according to actual needs). The fuzzy adaptive tuning parameter K is then determined by combining the difference between the ambient temperature and the target temperature. P .

[0126] The determination of the adaptive tuning parameters is specifically as follows:

[0127] With the current deviation signal e n The deviation change Δe is taken as input and calculated using a fuzzy adaptive tuning method based on a preset fine-tuning value table.

[0128] Where, △e=e n -e n-1 ;

[0129] Based on the difference between the ambient temperature and the preset target temperature, the proportional coefficient of the adaptive tuning parameter is determined, and the adaptive tuning parameter is adjusted according to the proportional coefficient.

[0130] In this embodiment, the current deviation signal e n Using the deviation change Δe as input, seven value ranges are defined: negative large, negative medium, negative small, exactly, positive small, positive medium, and positive large. Three parameters K are then established. P \T i \T d These correspond to fine-tuning value tables within a 7×7 value range. The fuzzy adaptive tuning parameter K is obtained through a step-by-step table lookup and approximation method. p The lower the temperature in the cooling zone is than the target temperature, the better the cooling effect, and the lower the required Kelvin temperature. p The smaller the value, the better. The adjusted K is determined based on the difference between the ambient temperature and the preset target temperature. p The scaling factor of the value, and whether it needs to be relatively amplified, reduced, or left unadjusted. If the ambient temperature is much lower than the set temperature, reduce it; if it is much higher, amplify it; if they are not much different, leave it unadjusted.

[0131] In this embodiment, the integral coefficient T i The value range of T is generally [240, 480], and the differential coefficient Td The value range is generally [3, 150].

[0132] S3. Adjust the operating power of the temperature control system according to the adjustment control quantity;

[0133] Step S3 is as follows:

[0134] Calculate the current demand control quantity P based on the aforementioned adjustment control quantity. n :

[0135] Pn=ΔP+P n-1 ;

[0136] Where ΔP represents the control quantity, P n-1 This represents the demand control quantity from the previous sampling period;

[0137] The operating power of the temperature control system is adjusted according to the required control quantity.

[0138] Furthermore, in this embodiment, when the actual temperature is lower than or close to the target temperature, the system determines whether to shut down based on whether any vehicles are currently charging, without waiting for the PID controller to shut down. When the actual temperature is lower than or close to the target temperature, it indicates that the control has essentially achieved its goal. If no vehicles are charging at this time, it means that no more heat will be generated, and the system can shut down. If vehicles are still charging at this time, it means that heat will be generated, and the system needs to continue operating.

[0139] In this embodiment, the target temperature can be a temperature range or a specific temperature value.

[0140] Please refer to Figure 2 Embodiment two of the present invention is as follows:

[0141] A device 1 for controlling the operating temperature of a power cabinet includes a processor 2, a memory 3, and an ARM program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the ARM program, it implements the steps in the method for controlling the operating temperature of a power cabinet as described in Embodiment 1 above.

[0142] In summary, the present invention provides a method and device for controlling the operating temperature of a power cabinet. It periodically collects ambient temperature and zone temperature data, and uses an incremental PID controller model to calculate and adjust control quantities based on the most recent data. This allows for real-time adjustment of the operating power of the temperature control system, achieving better temperature control and reducing power consumption. Furthermore, the incremental PID controller model incorporates fuzzy adaptive tuning parameters, effectively suppressing the effects of equipment usage time and environmental interference, thus achieving even better temperature control.

[0143] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A control method for a power cabinet temperature control system, characterized in that, Including the following steps: S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature. S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times. The preset number of iterations is 3, and the incremental PID controller model is represented as follows: ΔP=A*e n +B*e n-1 +C*e n-2 ; A=K P (1+T / T i +T d / T); B=-K P (1+2T d / T); C=K P *T d / T; Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient; The determination of the adaptive tuning parameters is specifically as follows: based on the current deviation signal e n The deviation change Δe is taken as input and calculated using a fuzzy adaptive tuning method based on a preset fine-tuning value table; where Δe = e n -e n-1 Based on the difference between the ambient temperature and the preset target temperature, determine the proportional coefficient of the adjusted adaptive tuning parameter, and adjust the adaptive tuning parameter according to the proportional coefficient. S3. Adjust the operating power of the temperature control system according to the adjustment control quantity; When the temperature of the temperature zone is lower than the target temperature or within the preset range of the target temperature, it is determined whether a vehicle is charging. If so, the temperature control system is maintained; otherwise, the temperature control system is shut down.

2. The control method for a power cabinet temperature control system according to claim 1, characterized in that, Step S3 is as follows: Calculate the current demand control quantity P based on the aforementioned adjustment control quantity. n : P n =ΔP+P n-1 ; Where ΔP represents the control quantity, P n-1 This represents the demand control quantity from the previous sampling period; The operating power of the temperature control system is adjusted according to the required control quantity.

3. The control method for a power cabinet temperature control system according to claim 1, characterized in that, The steps between S1 and S2 include: Determine whether the temperature of the temperature zone is lower than the preset target temperature. If so, determine whether there is vehicle charging. If there is no vehicle charging, shut down the temperature control system without waiting for the command from the PID controller. Otherwise, proceed to step S2.

4. The control method for a power cabinet temperature control system according to claim 1, characterized in that, Integral coefficient T i The value range is [240, 480], and the differential coefficient T d The value range is [3, 150].

5. A control terminal for a power cabinet temperature control system, comprising a processor, a memory, and an ARM program stored in the memory and executable on the processor, characterized in that, When the processor executes the ARM program, it performs the following steps: S1. According to the pre-divided temperature zones, the operating parameters are collected according to the preset sampling period. The operating parameters include ambient temperature, temperature control system power, and temperature zone temperature. S2. Construct an incremental PID controller model and calculate the adjustment control quantity by combining the operating parameters collected the most recently preset number of times. The preset number of iterations is 3, and the incremental PID controller model is represented as follows: ΔP=A*e n +B*e n-1 +C*e n-2 ; A=K P (1+T / T i +T d / T); B=-K P (1+2T d / T); C=K P *T d / T; Where A, B, and C represent control constants, e n e n-1 and e n-2 This represents the deviation signal within the most recent three sampling periods, where the deviation signal is the difference between the temperature of the temperature zone and the preset target temperature, K. P T represents the fuzzy adaptive tuning parameters. i T represents the integral coefficient. d Represents the differential coefficient; The determination of the adaptive tuning parameters is specifically as follows: based on the current deviation signal e n The deviation change Δe is taken as input and calculated using a fuzzy adaptive tuning method based on a preset fine-tuning value table; where Δe = e n -e n-1 Based on the difference between the ambient temperature and the preset target temperature, determine the proportional coefficient of the adjusted adaptive tuning parameter, and adjust the adaptive tuning parameter according to the proportional coefficient. S3. Adjust the operating power of the temperature control system according to the adjustment control quantity; When the temperature of the temperature zone is lower than the target temperature or within the preset range of the target temperature, it is determined whether a vehicle is charging. If so, the temperature control system is maintained; otherwise, the temperature control system is shut down.

6. The control terminal of the power cabinet temperature control system according to claim 5, characterized in that, Step S3 is as follows: Calculate the current demand control quantity P based on the aforementioned adjustment control quantity. n : P n =ΔP+P n-1 ; Where ΔP represents the control quantity, P n-1 This represents the demand control quantity from the previous sampling period; The operating power of the temperature control system is adjusted according to the required control quantity.

7. The control terminal of the power cabinet temperature control system according to claim 5, characterized in that, The steps between steps S1 and S2 include: Determine whether the temperature of the temperature zone is lower than the preset target temperature. If so, determine whether there is vehicle charging. If there is no vehicle charging, shut down the temperature control system without waiting for the command from the PID controller. Otherwise, proceed to step S2.

8. The control terminal of the power cabinet temperature control system according to claim 5, characterized in that, Integral coefficient T i The value range is [240, 480], and the differential coefficient T d The value range is [3, 150].

Images