Thermal management system control method

Abstract

The invention relates to a thermal management system control method, which reduces system oscillation caused by control change of a thermal management system through a thought of combining an internal controller and a discrete learning controller. By adding judgment of the system state, the controller can correct unreasonable control parameters in time, and the extra load caused by brain-free output to the heat management system is reduced; a discrete learning control module is added, a prediction model is established to predict the trend brought by control parameters, and then the existing control parameters are corrected, so that the control parameters of a controlled object are more reasonable and accurate; the storage unit adopts a discretized distribution idea, so that parameters of the learning control system are controllable, and consumption of computing resources can be reduced; through the scheme of the embedded dynamic control unit, the problem of simplification of control parameters is solved, and the efficiency of coordination work of the control system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile thermal management systems, and in particular to a thermal management system control method. Background Art

[0002] At present, due to the flexibility and reliability of PID controllers, about 95% of controllers in the field of industrial automation choose to use PID controllers. In the field of thermal management, PID controllers are also widely used. In addition, many workers have also optimized PID controllers to varying degrees in order to adapt to the control systems in their respective fields.

[0003] Patent (CN115774389A) discloses a fuzzy PID control method, and patent (CN110520803A) discloses a smart PID control method. The above two control methods provide good ideas for optimizing PID controllers; however, the optimization of these two control methods is centered on PID, which limits the intervention of other logic units to a certain extent. At the same time, for the coordinated development of thermal management systems, they ignore the actual state of the system and cannot reflect and adapt to the true state of the control system. In addition, the thermal management control system is not just a single control variable. Its core is to coordinate the action frequency of each part so that the thermal management loop can work efficiently to achieve the goal of new energy thermal management. In addition, in addition to coordinating the control methods of each part, the thermal management field also needs to ensure the safety of the thermal management system loop, which is a factor lacking in the existing control field. Summary of the invention

[0004] To solve the above problems, the present invention provides a thermal management system control method to solve and coordinate the coordination work between thermal management components, and provide safe and reliable brain control for thermal management components (compressor, electronic expansion valve, water pump, electronic fan).

[0005] The technical solution adopted by the present invention is: a thermal management system control method, characterized in that it includes the following steps:

[0006] S1. After receiving the external start command, the system performs self-diagnosis and transmits the self-diagnosis information to the supervision and feedback adaptation control unit. If the diagnosis passes, the self-diagnosis information will be transmitted to the adjustable self-start control unit;

[0007] S2. After completing self-start, the system will enter the multi-level open-loop control unit and wait for the system level SysDtcLevel transmitted by the supervision and feedback adaptation control unit. When SysDtcLevel=1, the initial control parameters of fast and slight oscillation will be output to the control response system; when SysDtcLevel=2, the initial control parameters of smooth integration will be output to the control response system; when SysDtcLevel=3, the initial control parameters of safe and low-speed trend will be output to the control response system.

[0008] S3. When all control schemes of the multi-level open loop are completed or the switching threshold is met, the embedded dynamic control unit will be automatically entered, where the threshold here is the set value and the actual value of the control target; the output parameters of the embedded dynamic control unit will be comprehensively judged with the output parameters of the discrete learning control unit;

[0009] S4, switching the control coefficients of the embedded dynamic control unit and the self-learning system;

[0010] S5. Continuously update control parameters according to control target values.

[0011] Preferably, in step S1, the self-diagnosis information includes high and low pressures, operating temperature, whether the compressor is in an operable state, and whether the expansion valve and high-pressure fan associated with the compressor are operating normally.

[0012] Preferably, in step S1, after receiving the external startup command, the control system sends a SysCheckFlag request diagnosis flag to the supervision and feedback adaptation control unit 4 for diagnosis. The supervision and feedback adaptation control unit 4 makes a comprehensive judgment based on the feedback parameter SysFb of the control response unit 6 and the Uni normalized parameter output by the system stability module. The judgment logic is as follows: an adder is used to accumulate and output SysDtcFlag, and then SysDtcFlag is diagnosed to be within the startup safety threshold. If the diagnosis passes, the startup is completed:

[0013] Assume that there are two calibration parameters A and B, and a safety threshold Up;

[0014] SysDtcFlag=A*KSFb+B*Uni

[0015] Then when SysDtcFlag-Up≤0, startup is allowed; KSFB is determined by the internal system state; Uni is calculated by normalizing the external input parameters. Since Uni is very small in static state, it is considered that SysDtcFlag=A*KSFb.

[0016] As a preference, KSFb has the following relationship:

[0017] KSFb=V*KV+A*KA+Rev*Kr

[0018] Among them, when Per is not permitted, KSFb is 0, otherwise it is calculated from various parameters, that is,

[0019]

[0020] In the formula: KV: voltage coefficient; KA: current coefficient; Per: permission mark; Kr: calibration coefficient; V: voltage; A: current; WorkPermit: work permit; Rev: working system calibration parameters.

[0021] Preferably, in step S3, after completing the open-loop control or receiving the instruction to enter the closed-loop control transmitted by the supervision and feedback adaptation control unit 4, the multi-level open-loop control unit 2 will maintain the current state until the threshold of system stability is reached and then enter the embedded dynamic control unit 3.

[0022] Preferably, in step S3, the embedded dynamic control unit 3 includes PID control, robust control and custom control. The embedded dynamic control unit 3 receives the system status level judgment from the supervision and feedback adaptation control unit 4 in real time, and only interferes with the original control parameters when the system is abnormal or it is judged that interference is required. Otherwise, it is output normally to the control response unit 6.

[0023] Preferably, in step S4, the control parameters of the compressor satisfy:

[0024] Y=M*A+N*C

[0025] Wherein: Y is the target speed of the compressor, M is the proportion coefficient of the dynamic adjustment unit, A is the control parameter of the dynamic adjustment unit, N is the proportion coefficient of the self-discrete learning control unit, and C is the control parameter of the self-learning unit; when A=C for a period of time, M is set to 0, that is, the resources of the dynamic adjustment control unit are released, and the self-learning control parameters are mainly used.

[0026] The beneficial effects achieved by the present invention are:

[0027] 1. By combining the internal controller with the discrete learning controller, the system oscillation caused by the change of control in the thermal management system can be reduced;

[0028] 2. By adding the judgment of system status, the controller can correct unreasonable control parameters in time, reducing the additional load on the thermal management system caused by mindless output;

[0029] 3. By adding a discrete learning control module and establishing a prediction model, the trend brought by the control parameters is predicted, and then the existing control parameters are corrected, so that the control parameters of the controlled object are more reasonable and accurate;

[0030] 4. The storage unit adopts the idea of ​​discrete distribution, which makes the parameters of the learning control system controllable and reduces the consumption of computing resources;

[0031] 5. The embedded dynamic control unit solution solves the problem of single control parameters and improves the efficiency of coordinated work of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the logic algorithm of the PID controller;

[0033] Figure 2 is a control logic diagram of the present invention;

[0034] Figure 3 It is a control logic diagram of an adjustable self-starting control unit;

[0035] Figure 4 It is the control logic diagram of KSFb;

[0036] Figure 5 is a control flow chart of the present invention;

[0037] In the figure: 1. Adjustable self-starting control unit; 2. Multi-level open-loop control unit; 3. Embeddable dynamic control unit; 4. Supervision and feedback adaptation control unit; 5. System stability module; 6. Control response unit; 7. Discrete learning control unit. DETAILED DESCRIPTION

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] like Figure 1 As shown in the figure, the PID controller is widely used in the control field. It can output a control variable Y through mathematical operation to achieve control based on the difference between the control target and the actual value.

[0040] pass Figure 1 It can be known that the mathematical logic of this PID controller is:

[0041] Y=KP+KI+KD

[0042] Among them: KP=Err*p; KI=ΣErr*i; KD=(Err-ErrLast)*d;

[0043] Err=TargetValue-Fb; TargetValue is the target value; Sub is the subtractor; Err is the error value; KP is the proportional value; KI is the integral value; KD is the differential value; ADD is the adder; Y is the control output; RealS is the current state of the controlled object; Fb is the feedback value; ErrLast is the last difference; KI_Last is the last integral value; p is the proportional coefficient; i is the integral coefficient; d is the differential coefficient.

[0044] Therefore, through the analysis of mathematical logic, it can be known that the logic of this PID controller is to use the difference to perform trend logic operations on the controlled object. It uses the ideas of proportion, integration and differentiation to well control the controlled object within the required target range.

[0045] However, this controller only focuses on the relationship between the controlled object and the control target, ignoring other system parameters and the stability of the system. In a thermal management system, if only one control parameter is focused on, there will often be a situation where the control parameter is always substandard but the PID controller is always working, resulting in the inability of the controlled object to make correct adjustments when the system oscillates. At the same time, the existing thermal management control systems are all adjusted by a core controller, and the rest of the control logic is auxiliary. Therefore, when the control logic changes, the PID controller sometimes cannot perfectly adapt to the oscillation caused by the change, so it also needs to be modified to varying degrees as the control logic changes.

[0046] In order to adapt to the coordinated control required in the field of thermal management and improve the commonly used unified control, the present invention breaks through the barriers of multiple control flows and adapts to the coordinated work requirements of multiple components in the field of thermal management, while solving the problem of resource allocation of existing control systems, and the present invention can smoothly switch between different control logics according to needs. At the same time, the present invention monitors the state of the system in real time by adding a system stability module 5 and a supervision and feedback adaptation control unit 4, and at the same time, evaluates the state level of the system by establishing a mathematical model, and adapts different control parameters with the help of different state levels. Under the premise of coordinating multiple control targets, the stability of the system is taken into account, thereby ensuring the safety, reliability and efficiency of the entire thermal management control system, and at the same time, the oscillation frequency of the system can be reduced.

[0047] like Figure 2-5 As shown, a thermal management system control method of the present invention comprises the following steps:

[0048] S1, receiving an external start command, the system performs self-diagnosis, and the self-diagnosis information is transmitted to the supervision and feedback adaptation control unit 4. If the diagnosis passes, the self-diagnosis information is transmitted to the adjustable self-start control unit 1;

[0049] S2. After completing self-start, the system will enter the multi-level open-loop control unit 2 and wait for the system level SysDtcLevel transmitted by the supervision and feedback adaptation control unit 4. When SysDtcLevel=1, the initial control parameters of fast and slight oscillation will be output to the control response unit 6; when SysDtcLevel=2, the initial control parameters of smooth integration will be output to the control response unit 6; when SysDtcLevel=3, the initial control parameters of safe and low-speed trend will be output to the control response unit 6.

[0050] S3, when all control schemes of the multi-level open loop are completed or the switching threshold is met, the embedded dynamic control unit 3 will be automatically entered, wherein the threshold here is the set value and the actual value of the control target; the output parameters of the embedded dynamic control unit 3 will be comprehensively judged with the output parameters of the discrete learning control unit;

[0051] S4, switching the control coefficients of the embedded dynamic control unit 3 and the self-learning system;

[0052] S5. Continuously update control parameters according to control target values.

[0053] In this embodiment, an adjustable self-starting control unit 1 is designed according to the state of the controlled system. After receiving an external start command, the controller will self-check the conditions necessary for pre-starting components. For example, before starting the compressor, the core component of the thermal management control unit, it is necessary to ensure the reliability of high and low pressures and ensure that the expansion valve and the electronic fan can work normally. Therefore, the self-check unit in the controller will judge these parameters to prevent dangers caused by blind startup.

[0054] The details of the adjustable self-start control unit 1 are as follows Figure 3 As shown, a new system state coefficient KSFb is added. After receiving the external startup command, the controller will send the SysCheckFlag request diagnosis flag to the supervision and feedback adaptation control unit 4 for diagnosis, and then 4 will make a comprehensive judgment based on the feedback parameter SysFb of the control response unit 6 and the Uni normalized parameter output by the system stability module. The judgment logic is as follows: the adder is used to accumulate and output SysDtcFlag, and then the SysDtcFlag is diagnosed to be within the startup safety threshold. If the diagnosis passes, the startup is completed. Therefore, combined with the above description, there will be the following mathematical logic:

[0055] Assume that there are two calibration parameters A and B, and a safety threshold Up;

[0056] SysDtcFlag=A*KSFb+B*Uni

[0057] Then when SysDtcFlag-Up≤0, it is allowed to start; KSFB is determined by the internal system state; Uni is calculated by normalizing the external input parameters. Since Uni is very small in static state, it is considered that SysDtcFlag=A*KSFb; the normalization operation logic is not described too much in static state. The detailed logic can be referred to Figure 4 .

[0058] like Figure 4 As shown, the logic of KSFb is further analyzed, and KSFb has the following relationship:

[0059] KSFb=V*KV+A*KA+Rev*Kr

[0060] Among them, when Per is not permitted, KSFb is 0, otherwise it is calculated from various parameters, that is,

[0061]

[0062] In the formula: KV: voltage coefficient; KA: current coefficient; Per: permission mark; Kr: calibration coefficient; V: voltage; A: current; WorkPermit: work permit; Rev: working system calibration parameters.

[0063] like Figure 2 As shown, the supervised multi-element embedded control system of the present invention includes an adjustable self-starting control unit 1, a multi-level open-loop control unit 2, an embeddable closed-loop control unit 3 (i.e., an embeddable dynamic control unit), a supervisory and feedback adaptation control unit 4, a system stability module 5, a control response unit 6 and a discrete discrete learning control unit 7.

[0064] The meanings of the parameters in the figure are as follows:

[0065] Prci: low pressure; Prco: high pressure; Trci: low pressure temperature; Trco: high pressure temperature; TW: water temperature; IAMB: scene temperature of system operation; Res: calibration parameter; Uni: normalization parameter; SysDtcFlag: system diagnosis flag; CellWorkSta: unit working status; SysCheckFlag: system detection flag; SysDtcLevel: system diagnosis level; SysFb: controlled object feedback parameter; Y: unit control parameter; KY: control parameter after diagnosis; TX: control target parameter after diagnosis; X: unit target parameter.

[0066] First, after the system is started, the adjustable self-start control unit 1 receives the system status judgment from the supervision and feedback adaptation control unit 4. If the system status is good, the self-start control parameters are output to the control response unit 6, otherwise the current status is maintained to wait for the next instruction from the supervision and feedback adaptation control unit 4. Then, after completing the self-start operation, the system will enter the multi-level open-loop control unit 2 and wait for the system level SysDtcLevel transmitted by the supervision and feedback adaptation control unit 4. When SysDtcLevel=1, the initial control parameters of fast and slight oscillation will be output to the control response unit 6; when SysDtcLevel=2, the initial control parameters of smooth integration will be output to the control response unit 6; when SysDtcLevel=3, the initial control parameters of safe and low-speed trend will be output to the control response unit 6.

[0067] Secondly, after completing the open-loop control or receiving the instruction to enter the closed-loop control from the supervisory and feedback adaptation control unit 4, the multi-level open-loop control unit 2 will maintain the current state until the threshold of system stability is reached and then enter the embedded closed-loop control unit 3. The embedded closed-loop control unit 3 includes but is not limited to PID, robust control, custom control and other dynamic adjustment control systems involved in the system. At the same time, this unit also receives the system status level judgment from the supervisory and feedback adaptation control unit 4 in real time. The original control parameters will only be interfered with when the system is abnormal or it is judged that interference is needed. Otherwise, it will be output normally to the control response unit 6.

[0068] In this embodiment, the supervisory and feedback adaptation control unit 4 is at the central point of the system. It not only participates in coordinating the workflow of each unit, but also needs to judge the system stability module 5 in real time, and then inputs the current system state after normalization processing by integrating various feedback parameters. At the same time, the supervisory and feedback adaptation control unit 4 will record multiple groups of control data according to the changing state of the control target, and then perform differentiated intervention after each parameter calculation of the multi-level open-loop control unit 2 or the embedded closed-loop control unit 3. Then, after receiving the updated feedback parameters, it is compared with the predicted parameters, and the optimal value is taken to update the previously stored data group. In addition, this unit will also judge the feasibility of the control parameters. If the output parameters of a certain state are greatly different from the recorded parameters due to the change of the dynamic control system, intervention will be carried out, which is similar to the method of de-extreme value. If unreasonable values ​​appear, according to the idea of ​​interval linearization, the system will automatically lock the extreme values ​​in the learning interval, thereby bearing the risks brought by the changes to a certain extent.

[0069] Combination Figure 5 As shown, the workflow of the patented invention is further explained in detail by taking the compressor, a core component in the thermal management control system, as an example.

[0070] In the first step, when the controller of the present invention acts on the compressor, after receiving the external start command, the controller will perform a self-check, wherein the self-check content includes high and low pressure, working temperature, whether the compressor is in an operable state, and whether the expansion valve and high-pressure fan associated with it are working properly. Among them, these information to be diagnosed will be transmitted to the supervision and feedback adaptation control unit. If the diagnosis passes, it will be transmitted to the control unit SysDtcFlag=1 to allow the start signal.

[0071] In the second step, the adjustable self-start control unit 1 in the control unit will start slowly at a speed of αrpm / s. When the compressor is successfully started, it will enter the multi-stage open-loop control unit 2. This unit will automatically adjust the speed of the compressor in combination with the system status. For example, if the current speed of the compressor is 800rpm and the reference target speed is 3000rpm, in the actual control system, if the compressor is blindly started to 3000rpm, the expansion valve may not respond in time, causing the system pressure to rise rapidly, affecting the stability and safety of the system. At the same time, if the PID control algorithm is adopted, it will increase the time for the controller to stabilize.

[0072] Therefore, when dealing with similar situations, the multi-stage open-loop control unit 2 will perform linear speed regulation in combination with the current speed of the compressor. For example, the lowest speed regulation rate allowed by the compressor is αrpm / s, the highest speed regulation rate is βrpm / s, and the speed compensation is B. Then, when this happens, the target speed output after the controller is 800±n*α+B, that is, if the system is in a stable state during speed regulation, it will quickly approach 3000rpm at an increasing rate, otherwise it will slow down the rate of approaching 3000rpm. At the same time, the multi-stage open-loop control unit 2 will synchronously transmit the actual control parameters to the discrete learning system for self-learning.

[0073] In the third step, when all control schemes of the multi-stage open loop are completed or the switching threshold is met, the embedded dynamic control unit 3 will be automatically entered. The threshold here is the set value and the actual value of the control target. For the general compressor control target superheat, if the actual superheat is 25K when the system is not started, the target value is 5K, that is, the superheat difference is 20K, then when the control system is started, if the superheat difference reaches a certain threshold γ, then the embedded dynamic control unit 3 can be entered at this time. If the embedded dynamic control unit 3 is a PID controller, the time for the PID controller to reach stability can be reduced to a certain extent.

[0074] Furthermore, no matter what kind of controller the embedded dynamic control unit 3 is, the control parameters it outputs will not directly affect the compressor control. The output parameters of the embedded dynamic control unit 3 will be comprehensively judged with the output parameters of the discrete learning control unit 7.

[0075] The fourth step is to switch the control coefficients of the dynamic adjustment system and the self-learning system. Assume that after a period of operation, the control parameters of the compressor satisfy Y = M*A + N*C, where Y is the target speed of the compressor, and Y, M, A, N, and C are the target speed of the compressor, the dynamic adjustment unit ratio coefficient, the dynamic adjustment unit control parameter, the self-discrete learning control unit ratio coefficient, and the self-learning unit control parameter. When A = C for a period of time, set M to 0, that is, release the resources of the dynamic adjustment control unit, and use the self-learning control parameters as the main ones.

[0076] Step 5: continuously update the control parameters according to the control target value. If the compressor control target superheat is 5K, when the discrete learning control unit 7 takes over, assuming that there is a set of control parameters such as [10, 1000], [9, 1200], [8, 1500], [7, 1700], [6, 2000], [5, 2200] in the learning array, then if the current superheat is 11, the self-learning unit will guide the output of the compressor target speed of 1000rpm with the target value of 10. If the trend of reaching the target is too slow, it will gradually increase at the minimum allowable adjustment rate based on the actual compressor speed until the target superheat reaches 5K.

[0077] The above shows and describes the basic principles and main structural features of the present invention. The present invention is not limited to the above examples. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A thermal management system control method, characterized in that: The following steps are involved: S1. After receiving the external start command, the system performs self-diagnosis and transmits the self-diagnosis information to the supervision and feedback adaptation control unit. If the diagnosis passes, the self-diagnosis information will be transmitted to the adjustable self-start control unit; S2. After completing the self-start, the system will enter the multi-level open-loop control unit and wait for the system level SysDtcLevel transmitted by the supervision and feedback adaptation control unit. According to different system levels SysDtcLevel, the initial control parameters are output to the control response unit in different ways; S3. When all control schemes of the multi-level open loop are completed or the switching threshold is met, the embedded dynamic control unit will be automatically entered, where the threshold here is the set value and the actual value of the control target; the output parameters of the embedded dynamic control unit will be comprehensively judged with the output parameters of the discrete learning control unit; S4, switching the control coefficients of the embedded dynamic control unit and the self-learning system; S5. Continuously update control parameters according to control target values.

2. The thermal management system control method according to claim 1, characterized in that: In step S1, the self-diagnosis information includes high and low pressure, operating temperature, whether the compressor is in an operable state, and whether the expansion valve and high-pressure fan associated with the compressor are operating normally.

3. The thermal management system control method according to claim 1, characterized in that: In step S1, after receiving the external startup command, the control system will send the SysCheckFlag diagnostic request flag to the supervision and feedback adaptation control unit for diagnosis. The supervision and feedback adaptation control unit makes a comprehensive judgment based on the feedback parameter SysFb of the control response unit and the Uni normalized parameter output by the system stability module. The judgment logic is as follows: the adder is used to accumulate and output SysDtcFlag, and then the SysDtcFlag is diagnosed to be within the startup safety threshold. If the diagnosis passes, the startup is completed: Assume that there are two calibration parameters A and B, and a safety threshold Up; SysDtcFlag=A*KSFb+B*Uni Then when SysDtcFlag-Up≤0, startup is allowed; KSFB is determined by the internal system state; Uni is calculated by normalizing the external input parameters. Since Uni is very small in static state, it is considered that SysDtcFlag=A*KSFb.

4. The thermal management system control method according to claim 3, characterized in that: KSFb has the following relations: KSFb=V*KV+A*KA+Rev*Kr Among them, when Per is not permitted, KSFb is 0, otherwise it is calculated from various parameters, that is, In the formula: KV: voltage coefficient; KA: current coefficient; Per: permission mark; Kr: calibration coefficient; V: voltage; A: current; WorkPermit: work permit; Rev: working system calibration parameters.

5. The thermal management system control method according to claim 1, characterized in that: In step S3, after completing the open-loop control or receiving the instruction to enter the closed-loop control transmitted by the supervision and feedback adaptation control unit, the multi-level open-loop control unit will maintain the current state until the threshold of system stability is reached and then enter the embedded dynamic control unit.

6. The thermal management system control method according to claim 1, characterized in that: In step S3, the embedded dynamic control unit includes PID control, robust control and custom control. The embedded dynamic control unit receives the system status level judgment from the supervision and feedback adaptation control unit in real time. The original control parameters will be interfered with only when the system is abnormal or it is judged that interference is required. Otherwise, it will be output normally to the control response unit.

7. The thermal management system control method according to claim 1, characterized in that: In step S4, the control parameters of the compressor satisfy: Y=M*A+N*C Wherein: Y is the target speed of the compressor, M is the proportion coefficient of the dynamic adjustment unit, A is the control parameter of the dynamic adjustment unit, N is the proportion coefficient of the self-discrete learning control unit, and C is the control parameter of the self-learning unit; when A=C for a period of time, M is set to 0, that is, the resources of the dynamic adjustment control unit are released, and the self-learning control parameters are mainly used.

8. The thermal management system control method according to claim 1, characterized in that: In step S2, when SysDtcLevel=1, the initial control parameters of fast and slight oscillation are output to the control response unit; when SysDtcLevel=2, the initial control parameters of smooth integration are output to the control response unit; when SysDtcLevel=3, the initial control parameters of safe and low-speed trend are output to the control response unit.

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