Control of vapor compression cooling in thermal systems

Abstract

A method for controlling vapor compression cooling in a thermal system comprises: a step of changing a first operating parameter of a first actuator of the thermal system using a controller and according to a first function, the first function being defined by performing a data fitting, wherein the first actuator controls either the speed of a condenser fan or the speed of a compressor; and a step of changing a second operating parameter of a second actuator of the thermal system using the controller, wherein the second actuator controls either the speed of the condenser fan or the speed of the compressor, the second operating parameter being changed according to a second function that at least partially depends on the capacity requirements for the vapor compression cooling.

Description

Technical Field

[0001] [Cross - Reference to Related Applications] This application is a continuation of U.S. non - provisional patent application Ser. No. 18 / 329,032, filed Jun. 5, 2023, titled "CONTROLLING VAPOR COMPRESSION COOLING IN A THERMAL SYSTEM", which claims priority to U.S. provisional patent application Ser. No. 63 / 498,107, filed Apr. 25, 2023, titled "CONTROLLING VAPOR COMPRESSION COOLING IN A THERMAL SYSTEM", and the disclosure of which is hereby incorporated by reference in its entirety.

[0002] This application also claims priority to U.S. provisional patent application Ser. No. 63 / 498,107, filed Apr. 25, 2023, and the disclosure of which is hereby incorporated by reference in its entirety.

[0003] This document relates to the control of vapor compression cooling in a thermal system.

Background Art

[0004] Vapor compression cooling has been used in vehicles and other types of systems. A vehicle may use a thermal system to control the temperature of a passenger compartment, a battery, and / or a drive train. In some electric vehicles, the thermal system has been controlled by applying an algorithm referred to as control to a predefined efficient state. The predefined efficient state reflects the state of the thermal system under a given condition that is considered to be the most efficient. However, such an approach may have a lack of robustness.

[0005] There are other control algorithms such as real - time extremum seeking control and real - time model - based optimal control. These approaches may be associated with a lack of reliability and / or other drawbacks. [Overview of the Initiative]

[0006] In a first embodiment, a method for controlling vapor compression cooling in a thermal system comprises: a step of changing a first operating parameter of a first actuator of the thermal system using a controller and according to a first function, the first function being defined by performing a data fitting, wherein the first actuator controls either the speed of a condenser fan or the speed of a compressor; and a step of changing a second operating parameter of a second actuator of the thermal system using the controller, wherein the second actuator controls either the speed of the condenser fan or the speed of the compressor, the second operating parameter being changed according to a second function that at least partially depends on the capacity requirements for the vapor compression cooling.

[0007] The implementation may include any or all of the following features: The first actuator controls the speed of the fan of the condenser, wherein the step of changing the first operating parameter increases or decreases the speed of the fan of the condenser, wherein the second actuator controls the speed of the compressor, wherein the step of changing the second operating parameter increases or decreases the speed of the compressor. The step of changing the speed of the compressor according to the second function includes taking into account the required mass flow obtained from the capacity requirement. The first actuator controls the speed of the compressor, wherein the step of changing the first operating parameter increases or decreases the speed of the compressor, wherein the second actuator controls the speed of the fan of the condenser, wherein the step of changing the second operating parameter increases or decreases the speed of the fan of the condenser. The step of controlling the vapor compression cooling includes minimizing the cost function relating to the power consumption by the compressor and the power consumption by the fan. The cost function includes the sum of the power consumption by the compressor and the power consumption by the fan. The cost function is minimized using (i) a first partial derivative of the power consumption by the compressor and (ii) a second partial derivative of the power consumption by the fan. The first function and the second function represent the first and second partial derivatives, respectively. The first partial derivative is decomposed into a first related partial derivative, where the second partial derivative is decomposed into a second related partial derivative. The first actuator controls the speed of the fan of the condenser, where the step of changing the first operating parameter increases or decreases the speed of the fan of the condenser, and the method further includes the step of decomposing the first related partial derivative into a third and a fourth partial derivative that are multiplied together. The third partial derivative corresponds to the change in the power of the compressor with respect to the change in the saturated discharge temperature, and the fourth partial derivative corresponds to the change in the saturated discharge temperature with respect to the change in the speed of the fan of the condenser.The first function, defined by performing a fitting to the data, includes (i) a first model fitted to data reflecting the third partial derivative, (ii) a second model data reflecting the fourth partial derivative, and (iii) a third model fitted to data reflecting the second related partial derivative. The change in the first operating parameter is based on multiplying the first related partial derivative and the second related partial derivative by a gain. The data includes simulated data. The thermal system includes a non-electronic expansion device, and the first and second operating parameters are changed without changing the non-electronic expansion device. The non-electronic expansion device includes a passive expansion device or a mechanically tuned expansion device. The method further includes the step of changing a third operating parameter of the expansion device of the thermal system using the controller, the third operating parameter being changed to obtain a predefined value in the thermal system. The step of changing the third operating parameter includes the step of using a feedback loop. The aforementioned predefined values ​​are at least one of the following: superheating value, supercooling value, mass flow rate, suction pressure, capacity of the thermal system, discharge air temperature of the evaporator of the thermal system, or refrigerant temperature of the cooling device of the thermal system. The thermal system is part of a vehicle. The thermal system is part of a fixed energy storage system.

[0008] In a second embodiment, a method for controlling vapor compression cooling in a thermal system, the method comprising: (i) setting a first operating parameter of a first actuator of the thermal system, and (ii) a second operating parameter of a second actuator of the thermal system; performing real-time optimization of the first and second operating parameters during operation of the thermal system based on minimizing a cost function that takes into account at least the first and second actuators; and adjusting the first and second operating parameters based on the real-time optimization.

[0009] The implementation may include any or all of the following features: The first actuator controls either the speed of the condenser fan or the speed of the compressor, wherein the second actuator controls the other of the speed of the condenser fan or the speed of the compressor. The real-time optimization is performed using a function fitted to data that reflects the relevant partial derivatives. The method further comprises (iii) setting a third operating parameter of the thermal system extension device, wherein the real-time optimization is also performed on the third operating parameter, and the third operating parameter is also adjusted based on the real-time optimization. [Brief explanation of the drawing]

[0010] [Figure 1] An example of a thermal system is shown.

[0011] [Figure 2] An example of a vapor compression cooling cycle that can be performed in the thermal system shown in Figure 1 is presented. [Figure 3] An example of a vapor compression cooling cycle that can be performed in the thermal system shown in Figure 1 is presented.

[0012] [Figure 4] An example of a cost function with a minimum value and a figure showing the effect of scaling mismatch on the minimum value is shown. [Figure 5] An example of a cost function with a minimum value and a figure showing the effect of scaling mismatch on the minimum value is shown. [Figure 6] An example of a cost function with a minimum value and a figure showing the effect of scaling mismatch on the minimum value is shown. [Figure 7] An example of a cost function with a minimum value and a figure showing the effect of scaling mismatch on the minimum value is shown.

[0013] [Figure 8]A graph showing an example of point cloud slicing under fixed conditions is shown. [Figure 9] A graph showing an example of point cloud slicing under fixed conditions is shown. [Figure 10] A graph showing an example of point cloud slicing under fixed conditions is shown. [Figure 11] A graph showing an example of point cloud slicing under fixed conditions is shown.

[0014] [Figure 12] A bar graph showing an example of total power consumption is shown. [Figure 13] A bar graph showing an example of total power consumption is shown.

[0015] Similar reference numerals in various drawings indicate the same elements. [Modes for carrying out the invention]

[0016] This book describes examples of systems and techniques for controlling vapor compression cooling in thermal systems. Robust real-time discharge pressure optimization for vapor compression cooling applications can be provided and used for thermal control systems in vehicles or fixed energy storage, to give just two examples. In some implementations, the control method can adjust the condenser fan speed and compressor speed to minimize a cost function that exhibits the cooling efficiency of the vapor compression cycle. Optionally, the position of extension devices can also be adjusted by the control method. The gradient of the cost function can be decomposed into several related partial derivatives, fitted offline using simulation data, and stored for real-time use. The function decomposition and adaptation methods can be designed to be robust to mismatches between model expectations and real-world performance. More specifically, the process may involve: (i) adjusting one actuator (e.g., for the condenser fan speed) as determined by a pre-calculated function, and (ii) solving the model in reverse to adjust another actuator (e.g., to change the compressor speed). In some implementations, non-electronic expansion valves (e.g., mechanically controlled or passive expansion devices) may be used in unregulated thermal systems as part of the process. In other implementations, the process may also include (iii) repositioning the expansion device to maintain a predefined value (e.g., superheat value, supercooling value, mass flow rate, suction pressure, thermal system capacity, discharge air temperature of the thermal system's evaporator, or refrigerant temperature of the thermal system's cooler). For example, minimization may be performed for one actuator, and solving the constrained model in reverse may be performed for one or more other actuators.

[0017] In previous approaches to control to a predefined efficient state, the system can be characterized to be most efficient in a certain state under given conditions. The points are stored in a table or fitted to an equation, and feedback control methods (such as proportional-integral-derivative (PID) controllers, or full state feedback control) can be used for control to those points. One issue for this approach is that it can be impossible to account for system variability in whether tests were used for characterization or unmodeled system dynamics if simulation data was used. This approach can become more difficult to implement when the definition of the conditions includes many variables. When the optimal points can be defined as a function of one or two variables, testing and fitting using those variables as inputs can be feasible, but in the case of coupled and highly non-linear systems such as the vapor compression cycle, the efficiency is a function of even more coupled variables. Thus, it becomes very difficult to account for their effects.

[0018] Existing real-time extremum seeking control may not be suitable for many applications. When the cost function is easily computable in real time, the extremum seeking controller can minimize it by adjusting its output to measure the effect of the oscillatory input on the cost. This form of control may not be suitable for thermal systems, as it requires continuously injecting noise into the input to find the direction and amplitude of the changes necessary to minimize the cost. In the case of applications within a vehicle thermal system, to optimize the speed of the fan and compressor, the controller would need to oscillate both the fan and compressor at a sufficiently high amplitude so that the effect on the cost is measurable. This can negatively impact performance, reliability, and / or noise, vibration, harshness (NVH) characteristics.

[0019] Existing real-time model-based optimal control may also not be suitable for many applications. When a simplified model of a system that can capture system dynamics is generated, an optimal controller can be used to improve efficiency in real time. However, the controller will be limited by the model and its accuracy. For example, when using vapor compression, even a highly discretized thermal-fluid model can deviate significantly from the behavior of an actual system.

[0020] The present subject matter can provide the advantage of being able to drive a system to an optimal point even when the model to which it is fitted does not match the actual system. An additional advantage can be that the method does not require being executed in real time with respect to a model of the system and, instead, can depend on a fitted related gradient such that the computational cost for execution is not more expensive. Compared to extremum seeking control, the present subject matter has a smoother output signal and is less affected by processing lags or limited measurement resolution.

[0021] Examples herein refer to vehicles. A vehicle is a machine that transports passengers or cargo, or both. A vehicle can have one or more motors that use at least one type of fuel or other energy source (e.g., electricity). Examples of vehicles include, but are not limited to, cars, trucks, and buses. The number of wheels can vary between vehicle types, and one or more (e.g., all) of the wheels can be used for propulsion of the vehicle, or the vehicle can be unpowered (e.g., when a trailer is attached to another vehicle). A vehicle can have one or more traction motors. For example, a traction motor can be an electric motor. As another example, a traction motor can be an internal combustion motor. A vehicle can include a passenger compartment that houses one or more people.

[0022] Figure 1 shows an example of the thermal system 100. The thermal system 100 may be used in conjunction with one or more other examples described elsewhere in this specification. The thermal system 100 is schematically shown as being implemented as part of system 102. System 102 represents any system in which the thermal system 100 may perform thermal operations such as vapor compression cooling. In some implementations, system 102 is a vehicle (e.g., an electric vehicle). For example, the thermal system 100 may provide vapor compression cooling to the passenger compartment and / or battery / drivetrain of the vehicle. In some implementations, system 102 is a stationary energy storage. For example, the stationary energy storage may include a number of electrochemical cells and be configured to supply electrical energy on demand.

[0023] The thermal system 100 includes a compressor 104 that acts on a refrigerant gas. The compressor 104 has an inlet 106 and an outlet 108, each connected to one or more refrigerant conduits in the thermal system 100. The compressor 104 can operate at any of several speeds (for example, within a range of operating speeds expressed in revolutions per minute or another unit). As another example, the operation of the compressor 104 can be characterized in terms of mass flow rate. The compressor 104 may have at least one actuator to facilitate control of its operation. For example, the actuator is used to set and change the operation of the compressor 104.

[0024] The thermal system 100 includes at least one condenser 110 that condenses a refrigerant gas into a liquid state. In some implementations, multiple condensers may be used in the thermal system 100. The condenser 110 has an input that receives the refrigerant output at the outlet 108 of the compressor 104. If the system 102 is a vehicle, the condenser 110 may function as part of the vehicle's air conditioning system. The condenser 110 may have a fan 112 that can be operated to control the condensation. For example, if multiple condensers are used, each condenser may have its own fan. The fan 112 may operate at any of several speeds (e.g., within a range of operating speeds expressed as revolutions per minute or another unit). The fan 112 may have at least one actuator to facilitate control of its operation. For example, the actuator is used to set and change the operation of the condenser 110.

[0025] The thermal system 100 includes an expansion device 114 that controls the flow of refrigerant. The expansion device 114 may be an electronic expansion device or a non-electronic expansion device (e.g., a mechanically controlled expansion device or a passive expansion device). The expansion device 114 may be, but is not limited to, a thermal expansion valve, an electronic expansion valve, a constant temperature expansion valve, a fixed orifice, a capillary tube, or any other type of flow restrictor. The inlet of the expansion device 114 may receive the refrigerant output at the outlet of the condenser 110. The expansion device 114 may be adjusted to any of a plurality of operating positions (e.g., within a range of positions, each providing a larger or smaller flow of refrigerant). If the expansion device 114 is electronically controllable, it may have at least one actuator to facilitate control of the operating position.

[0026] The thermal system 100 includes at least one cooling device 116 for cooling one or more aspects of system 102. For example, the cooling device 116 includes a component that receives heat to be removed using vapor compression cooling. In some implementations, system 102 is a vehicle, and then the cooling device 116 may include an evaporator for cooling the passenger compartment. In another example, the cooling device 116 may include a cooling system for the battery pack of an electric vehicle and / or for some aspects of the powertrain. Thus, the thermal system 100 may include one or more return paths for the refrigerant arriving at the inlet 106 of the compressor 104.

[0027] The thermal system 100 includes a controller 118 for controlling some or all aspects of vapor compression cooling. The controller 118 may include any processor-based device or component that executes instructions. The controller 118 may generate control signals transmitted in the thermal system 100 by wire or wirelessly. The controller 118 may control compression in the thermal system 100 using a signal 120 to the actuator of the compressor 104. The controller 118 may control concentration in the thermal system 100 using a signal 122 to the actuator of the fan 112 of the condenser 110. In an implementation in which an extension device 114 is controlled as a component that adjusts vapor compression cooling, the controller 118 may control the position of the extension device 114 using a signal 124 to the actuator of the extension device 114. The controller 118 may additionally or alternatively control one or more other aspects of the thermal system 100. In contrast, the cooling device 116 may not be controlled by the controller 118.

[0028] The controller 118 may attempt to optimize the operation of the thermal system 100 in terms of power consumption. For example, more efficient vapor compression cooling would result in lower energy consumption and therefore an increased range for electric vehicles or improved utility of fixed energy storage. In some implementations, the controller 118 performs real-time optimization of the operating parameters of the compressor 104 and the fan 112, respectively. Optionally, the controller 118 may also adjust the extension device 114 as part of the real-time optimization. For example, the real-time optimization may be performed in the form of minimizing the cost functions of at least the compressor speed and the fan speed using partial derivatives and at least one fitted function.

[0029] Figures 2 and 3 show examples of vapor compression cooling cycles 200 that can be performed in the thermal system 100 of Figure 1. The vapor compression cycle 200 may be used in conjunction with one or more other examples described elsewhere in this specification. The vapor compression cycle 200 is shown in a figure having pressure shown on the vertical axis and a specific enthalpy shown on the horizontal axis. The figure includes isotherms 202, each marked with a line indicating that the respective state has a specific refrigerant temperature. The figure includes an area 204 covering those states in the figure, where the refrigerant is a mixture of liquid and vapor. The vapor compression cycle 200 includes a step 206 in which a compressor performs work to increase the pressure and temperature of the refrigerant; a step 208 in which heat is transferred to the surroundings using a condenser with a fan; a step 210 in which an expansion device lowers the pressure and temperature of the refrigerant; and a step 212 in which heat from a cooling device is transferred to the refrigerant.

[0030] Multiple different versions of the vapor compression cycle 200 can satisfy a given requirement for cooling capacity. These different solutions yield the same thermal result, but they can use different amounts of energy, meaning the thermal system will have higher or lower efficiency depending on which solution is used. In Figure 2, arrow 214 schematically shows the decrease in compressor power in stage 206, which is related to arrow 216 schematically showing the increase in power used by the condenser fan in stage 208. In contrast, in Figure 3, arrow 300 schematically shows the increase in compressor power in stage 206, which is related to arrow 302 schematically showing the decrease in power used by the condenser fan in stage 208. Thus, while operating at a given suction pressure with at least one fixed value (e.g., superheating, supercooling, mass flow rate, suction pressure, thermal system capacity, evaporator discharge air temperature of the thermal system, or refrigerant temperature of the cooling device of the thermal system), the effect of increasing the condenser fan speed (i.e., condenser air flow rate), as indicated by arrow 216, is a lower discharge pressure, which reduces compressor power at the expense of higher fan power, as indicated by arrow 214, and vice versa, as indicated by arrows 300 and 302. Reducing the discharge pressure at certain supercooling and superheating increases the difference in cooling effect (enthalpy) across the evaporator / cooling device. This can further reduce compressor power by reducing the required compressor speed to achieve the same cooling demand, which can be characterized as a secondary effect of fan adjustment. Additional secondary effects may also exist in the thermal system: when the fan is adjusted, the compressor will be adjusted based on a separate controller, which affects the total power.

[0031] Thus, there are optimal operating conditions that supply the required cooling capacity with minimum operating power. This subject attempts to discover and operate a thermal system under such optimal operating conditions by modifying some or all of the characteristics of the vapor compression period 200, as schematically shown by arrows 214-216 and 300-302 in Figures 2-3. Several examples will be provided here.

[0032] The power used for vapor compression cooling, the operating point performed by the actuator, and the cooling capacity provided are all coupled together. Since the supplied cooling capacity is controlled by the load, optimizing the efficiency of the cycle can be expressed in terms of minimizing the power of the components under fixed cooling capacity conditions.

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[0033] In electric vehicles, multiple cooling devices may be used in the thermal system. Cooling devices may be used to cool the powertrain and battery via coolant, and evaporators may be used to cool the cabin via air. Under most operating conditions where cooling is required, the potential adjustments to the coolant conditions in the compressor suction are limited. For cabin cooling, the evaporator fan is controlled to a specific target discharge air temperature and flow rate based on user input. This limits the possibility of performing optimization on the suction side. For powertrain or battery cooling, the suction conditions are a function of the coolant temperature and coolant flow rate entering the cooling device. Changes to the coolant flow rate are limited to satisfy the thermal load distribution requirements (i.e., to prevent large deviations in battery cell temperature). This ensures that the suction pressure is highly coupled to the temperature in the battery and drive unit components. The above situation leaves the compressor speed, condenser fan speed, and (optionally) the position of the expansion device as the three main components that have an impact on the efficiency of cooling. The above minimization can therefore be expressed as follows:

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[0034] In some implementations, the challenge can be further simplified by defining the operating point of the compressor speed and the position of the extension device as a function of the assumed optimized state and fixed demand requirements. In other implementations, the operating point of another component may be used instead. Thus, optimization can be performed directly through one actuator (e.g., adjusting the fan speed) or indirectly through another actuator (e.g., the compressor speed and the position of the extension device). As a more concrete example, assuming that we want to operate at a given superheat and supercooling value, the compressor speed will have a one-to-one mapping to the required capacity at any point, when the condenser fan is driving the discharge conditions to the optimal value while considering the impact on other components. The position of the extension device will then have a single solution for setting the superheat to the required value. When multiple suction-side paths are used, each having its own required capacity, then at a given superheat value, there is only one setting of the extension device limit that results in the correct capacity distribution among them.

[0035] Excluding the suction-side components (e.g., the cooling system pump and / or evaporator fan), the power to be minimized from the compressor and condenser fan can be used as a cost function.

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[0036] Optimization can be performed using one or more partial derivatives. To directly minimize the cost by adjusting the fan speed, the partial derivatives of the cost function in (1) with respect to the fan speed may be used.

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[0037] The two partial derivatives in (2) can be fitted offline using data (e.g., simulation data). To make the minimization more robust to model uncertainty, the partial derivatives can be decomposed into relevant partial derivatives with respect to offline power data multiplied by the measured power term. Each partial derivative can be expressed as follows:

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[0038] Using the relevant partial derivatives, when there is a scale mismatch (i.e., when the function is extended by a scalar across the power axis), the partial derivatives will still drive the system to its most optimal point after rescaling. A scale mismatch can have an impact on the optimal point, as illustrated here.

[0039] Figures 4–7 show examples of diagrams 400, 500, 600, and 700, which have a cost function with a minimum value and the effect of scaling mismatch on the minimum value. The cost function may be used in conjunction with one or more other examples described elsewhere in this specification. In each of diagrams 400–700, power is shown on the vertical axis and fan speed is shown on the horizontal axis. Graph 402 represents the measured compressor power, and graph 404 represents the modeled compressor power. Graph 406 represents the measured fan power, and graph 408 represents the modeled fan power. Discrepancies may occur between modeled and measured values ​​due to model uncertainties, system aging, system variability, etc. For example, similar to graphs 406 and 408 (i.e., graph 406 is scaled relative to graph 408), graphs 402 and 404 here are different from each other (i.e., graph 402 is scaled relative to graph 404). In diagram 500, graph 502 corresponds to the sum of graphs 402 and 406 (i.e., the sum of measured power), and graph 504 corresponds to the sum of graphs 404 and 408 (i.e., the sum of modeled power). Graph 502 has a minimum value at fan speed 506, and graph 504 has a minimum value at fan speed 508. Fan speeds 506 and 508 are different from each other, which means that the minimum value of the actual cost function does not have to occur at the point indicated by the model. However, this subject can also be optimized when such discrepancies occur by using gradients normalized by the power of the compressor and fan.

[0040] To also take into account the shift between the expected operating conditions and the model, the partial derivative of the compressor power in (3) can be decomposed into two partial derivatives.

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[0041] Graph 602 represents the measured compressor power, and Graph 604 represents the modeled compressor power. Graph 606 represents the measured fan power, and Graph 608 represents the modeled fan power. Discrepancies can occur between modeled and measured values. For example, as with Graphs 606 and 608 (i.e., Graph 606 is shifted relative to Graph 608), here Graphs 602 and 604 are different from each other (i.e., Graph 602 is scaled relative to Graph 604). In Diagram 700, Graph 702 corresponds to the sum of Graphs 602 and 606 (i.e., the sum of measured power), and Graph 704 corresponds to the sum of Graphs 604 and 608 (i.e., the sum of modeled power). Graph 702 has a minimum value at fan speed 706, and Graph 704 has a minimum value at fan speed 708. Fan speeds 706 and 708 are different from each other, which means that the actual minimum value of the cost function does not necessarily occur at the point indicated by the model.

[0042] Therefore, where there will be discrepancies between the modeled and actual compressor and fan power, or between the modeled and actual saturated discharge temperatures, the optimization method can still robustly move the system toward the optimal point by using the gradient normalized by the respective cost function component (i.e., the compressor or condenser fan power).

[0043] The partial derivatives in (3), (4), and / or (5) may be used when searching for the optimal operating conditions for the thermal system. To avoid the need to provide the controller with existing data with a large volume to be used during operation, one or more functions may be fitted to representative data, and the controller may then evaluate the functions in real time.

[0044] To obtain the equations for the partial derivatives, a simulation setup covering the expected operating range may be performed, and the partial derivatives may be calculated numerically. A reasonable form of equation may be selected as a diminishing-order model of the gradient, and a minimization algorithm (e.g., using regression analysis) may be used to fit the model parameters. In the case of the partial derivative of the power of the compressor with respect to the saturated discharge temperature in (5), the model may take the following form:

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[0045] Fitting can be performed using multiple models. In some implementations, a compressor model, a condenser model, and / or a fan model may be used. For example, the compressor model may be based on an efficiency map to reflect isentropic efficiency and volumetric efficiency. Data for performing fitting can be obtained by collecting performance data or by running simulations. In some implementations, simulations are performed within the ranges of the cooling capacity target, saturated intake temperature, and saturated discharge temperature, respectively. For the simulation, superheating and supercooling may be fixed. An example of fitting is described here.

[0046] Figures 8–11 show graphs 800, 900, 1000, and 1100, which have examples of point cloud slices under fixed conditions. The fitted functions may be used in conjunction with one or more other examples described elsewhere in this specification. Each of graphs 800–1100 shows a three-dimensional figure with two horizontal axes and one vertical axis. In graph 800, one of the horizontal axes represents the saturated discharge temperature and the other represents the saturated suction temperature. The vertical axis represents the change in compressor (cmp) power due to the change in saturated discharge temperature (SDT). Graph 800 includes point 802 (indicated by a cross), which is modeled (e.g., simulated) data. Parameters of a selected function (e.g., parameters P0 to P7 in (6) above) may be selected, and as a result, the function may be closely fitted to point 802. Here, point 804, which coincides with point 802, represents the fitted function.

[0047] One or more other functions may be fitted. In the case of the partial derivative of the saturated discharge temperature with respect to the fan speed in (5), the following may be used:

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[0048] The parameters in (7) can be fitted using a compressor model, condenser model, evaporator model, extension device model, and condenser fan model. For example, the compressor model may be based on an efficiency map. As another example, the extension device model may feature a flow coefficient map. Data for performing the fitting can be obtained by collecting performance data or by running simulations. In some implementations, simulations are performed within the ranges of the cooling capacity target, saturated suction temperature, ambient temperature, and fan duty cycle, respectively. In the case of simulations, values ​​(e.g., superheating, supercooling, mass flow rate, suction pressure, thermal system capacity, evaporator discharge air temperature of the thermal system, or refrigerant temperature of the cooling device of the thermal system) can be fixed. An example of fitting is described here.

[0049] In Graph 900 of Figure 9, one of the horizontal axes represents the saturated discharge temperature, and the other represents the compressor speed. The vertical axis represents the change in saturated discharge temperature due to the change in fan speed. Graph 900 includes point 902 (indicated by a cross), which is modeled (e.g., simulated) data. The parameters of the selected function (e.g., parameters P0 to P7 in (7) above) are selected, and as a result, the function can be closely fitted to point 902. Here, point 904, which coincides with point 902, represents the fitted function. If the data in Graph 800 (Figure 8) were to be multiplied by the data in Graph 900, the product would show how the associated compressor power fluctuates with the change in fan speed.

[0050] One or more other functions may be fitted. In the case of the partial derivative of the fan power with respect to the fan speed in (4), the following may be used:

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[0051] After one actuator (e.g., the speed of the condenser fan) is adjusted, another actuator (e.g., the speed of the compressor) may be solved in the reverse direction to ensure that the required cooling capacity is maintained. In some implementations, the volumetric efficiency of the compressor may be fitted to data for use in solving in the reverse direction for the compressor speed. The calculation of the gradient using a pre-fitted function for adjusting the first controller may take into account the change (secondary effect) in the second actuator as a result of solving in the reverse direction. For example, if it is initially determined that the fan speed will change the compressor speed by a certain amount, this also takes into account the fact that the compressor speed will change in response to the fan speed, using the model illustrated below.

[0052] The data for performing the fitting can be obtained by collecting performance data or by running simulations. In some implementations, the simulations are performed within the ranges of the compressor speed, saturated intake temperature, saturated discharge temperature, and superheat value. The model can be fitted to the compressor's volumetric efficiency as a function of the saturated intake temperature and saturated discharge temperature.

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[0053] In Graph 1000 of Figure 10, one of the horizontal axes represents the saturated discharge temperature, and the other represents the saturated intake temperature. The vertical axis represents the mass flow rate. Graph 1000 includes point 1002 (indicated by a cross), which is modeled (e.g., simulated) data. The parameters of the selected function (e.g., parameters E0 to E5 in (10) above, and D0 to D5 in (11) above) are selected, and as a result, the function can be closely fitted to point 1002. Here, point 1004, which coincides with point 1002, represents the fitted function.

[0054] As described above, the controller may perform real-time optimization using measurement data and one or more functions fitted as illustrated herein. In some implementations, the real-time optimization technique may make discrete calls to a controller in which the cost gradient has been calculated and at least one actuator has been adjusted. For example, the fan speed may be adjusted in the negative direction of the slope of the cost function.

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[0055] Combining (12) with (5), (4), and (2) yields the following:

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[0056] In Graph 1100 of Figure 11, one horizontal axis represents the saturated discharge temperature, and the other horizontal axis represents the saturated suction temperature. The vertical axis represents power. Graph 1100 includes point 1102 (shown as a dot) representing the fan power, point 1104 (shown as a cross) representing the compressor power, and point 1106 (shown as a cross) representing the sum of points 1102 and 1104, respectively. Graph 1100 includes arrow 1108 which schematically shows the operation of iteratively stepping toward the minimum value according to (13). The algorithm may attempt to move in state space along the slope toward the minimum value of point 1106. This slice reflects the assumption that overheating is constant. Also, the saturated suction temperature does not have to be directly controlled, and therefore the saturated suction temperature can be considered fixed, and the optimization may move in two dimensions toward the minimum value of point 1106. The actual slope may be adapted to real-time measurement of actual costs. For example, the derivatives at points 1102 and 1104 can be scaled in real time to account for the difference-pair model between systems. As another example, scaling can be performed. As yet another example, partial derivatives can be decomposed to improve optimization.

[0057] In some implementations, the compressor speed can be indirectly optimized by adjusting the fan speed. Since the fan speed is modified to minimize cost, the suction and discharge conditions may also be changed; thereby, the supplied cooling capacity can be kept constant, and the compressor speed can be solved in reverse using a fitted compressor model. The compressor speed can be solved as a function of the required mass flow, which can be derived from the capacity requirement through the inlet and outlet enthalpies.

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[0058] The position of the expansion device can be indirectly optimized. The optimization gradient fitting can be evaluated under assumed fixed overheating. In some implementations, a feedback loop is used. For example, the expansion device may be a feedback controlled using a PID controller to maintain overheating around its target. In some implementations, the position of the expansion device is not adjusted as part of the optimization.

[0059] Figures 12 and 13 show bar graphs 1200 and 1300, which have examples of total power consumption. In each of the bar graphs 1200 and 1300, the vertical axis represents the simulation of energy used during a charging session, and the horizontal axis represents the ambient temperature. The charging sessions that occurred during the same length of time in bar graphs 1200 and 1300, and the charging session in bar graph 1200, were performed at a higher power rate than the charging session in bar graph 1300. At each ambient temperature, the bar on the right represents the power used according to the implementation of this subject, and the bar on the left (having a thicker outline) represents the power used according to the reference procedure at the same ambient temperature. Thus, bar graph 1200 shows the power used by the reference procedure and this implementation at six different ambient temperatures, respectively. Bar graph 1300 shows the power used by the reference procedure and this implementation at four different ambient temperatures, respectively. Here, the reference procedure involves pre-determining the optimal condenser and suction pressure points, and controlling the expansion device proportionally as a function of battery temperature.

[0060] Each bar in bar graphs 1200 and 1300 represents the distribution of energy consumed in the thermal system. At each ambient temperature in bar graphs 1200 and 1300, this subject reduces thermal power consumption compared to the reference procedure. The reduction is significant at many ambient temperatures. Similar optimizations can be achieved with cabin-only cooling and dual-cabin and cooling unit cooling.

[0061] Thus, this subject may provide more efficient and / or faster charging in the case of cabin cooling at low speeds; an increased vehicle range; improved effective round-trip efficiency for fixed energy storage and vehicle-grid applications; and improved NVH characteristics by reducing noise and vibration of the compressor cabin in vehicle implementations.

[0062] The terms “substantially” and “about” as used throughout this specification are used to describe and account for small variations, such as those resulting from processing variability. For example, they may mean less than or equal to ±5%, less than or equal to ±2%, less than or equal to ±1%, less than or equal to ±0.5%, less than or equal to ±0.2%, less than or equal to ±0.1%, less than or equal to ±0.05%. Also, as used herein, indefinite articles such as “a” or “an” mean “at least one.”

[0063] It should be understood that all combinations of the aforementioned concepts and any additional concepts discussed in more detail below (provided that such concepts are not mutually contradictory) are intended to be part of the subject matter of the invention disclosed herein. In particular, all combinations of the claimed subject matter appearing at the end of this disclosure are intended to be part of the subject matter of the invention disclosed herein.

[0064] Several implementations have been described. Nevertheless, it should be understood that various modifications may be made without deviating from the intent and scope of this specification.

[0065] Furthermore, the logical flow shown in the diagram does not require a specific or sequential order to achieve the desired result. In addition, other processes may be provided, or processes may be excluded from the described flow; other components may be added to or removed from the described system. Therefore, other implementations fall within the scope of the following claims.

[0066] While specific features of the described implementations have been shown as described herein, many modifications, substitutions, alterations, and equivalents will now come to mind for those skilled in the art. It should be understood that the appended claims are intended to encompass all such modifications and alterations that fall within the scope of these implementations. They are presented merely as examples and not as limitations, and it should be understood that various modifications in form and detail are possible. Any part of the apparatus and / or method described herein may be combined in any combination, except for mutually exclusive combinations. The implementations described herein may include various combinations and / or partial combinations of the functions, components, and / or features of the different implementations described herein.

Claims

1. A method for controlling vapor compression cooling in a thermal system, wherein the method is: The steps include: changing a first operating parameter of a first actuator of the thermal system using a controller and according to a first function, the first function being defined by performing a data fitting, and the first actuator controlling either the speed of the condenser fan or the speed of the compressor; and The step of changing a second operating parameter of a second actuator of the thermal system using the controller, wherein the second actuator controls the other of the speed of the fan of the condenser or the speed of the compressor, and the second operating parameter is changed according to a second function that depends at least in part on the capacity requirements for the vapor compression cooling. A method that includes [a certain feature].

2. The method according to claim 1, wherein the first actuator controls the speed of the fan of the condenser, and the step of changing the first operating parameter increases or decreases the speed of the fan of the condenser, and the second actuator controls the speed of the compressor, and the step of changing the second operating parameter increases or decreases the speed of the compressor.

3. The method of claim 2, wherein the step of changing the speed of the compressor according to the second function includes the step of considering a required mass flow obtained from the capacity requirement.

4. The method according to any one of claims 1 to 3, wherein the first actuator controls the speed of the compressor, and the step of changing the first operating parameter increases or decreases the speed of the compressor, wherein the second actuator controls the speed of the fan of the condenser, and the step of changing the second operating parameter increases or decreases the speed of the fan of the condenser.

5. The method according to any one of claims 1 to 3, wherein the step of controlling the vapor compression cooling includes a step of minimizing a cost function relating to the power consumption by the compressor and the power consumption by the fan.

6. The method according to claim 5, wherein the cost function includes the sum of the power consumption by the compressor and the power consumption by the fan.

7. The method according to claim 5, wherein the cost function is minimized using (i) a first partial derivative of the power consumption by the compressor and (ii) a second partial derivative of the power consumption by the fan.

8. The method according to claim 7, wherein the first function and the second function represent the first partial derivative and the second partial derivative, respectively.

9. The method according to claim 7, wherein the first partial derivative is decomposed into a first related partial derivative, where the second partial derivative is decomposed into a second related partial derivative.

10. The method according to claim 9, wherein the first actuator controls the speed of the fan of the condenser, and the step of changing the first operating parameter is to increase or decrease the speed of the fan of the condenser, and the method further comprises the step of decomposing the first associated partial derivative into a third partial derivative and a fourth partial derivative that are multiplied together.

11. The method according to claim 10, wherein the third partial derivative corresponds to the change in the power of the compressor with respect to the change in the saturated discharge temperature, and the fourth partial derivative corresponds to the change in the saturated discharge temperature with respect to the change in the speed of the fan of the condenser.

12. The method according to claim 11, wherein the first function defined by performing a fitting to the data includes (i) a first model fitted to data reflecting the third partial derivative, (ii) second model data reflecting the fourth partial derivative, and (iii) a third model fitted to data reflecting the second related partial derivative.

13. The method according to claim 9, wherein the modification of the first operating parameter is performed based on the step of multiplying the first related partial derivative and the second related partial derivative by a gain.

14. The method according to any one of claims 1 to 3, wherein the data includes simulated data.

15. The method according to any one of claims 1 to 3, wherein the thermal system includes a non-electronic expansion device, and the first and second operating parameters are changed without changing the non-electronic expansion device.

16. The method according to claim 15, wherein the non-electronic expansion device includes a passive expansion device or a mechanically regulated expansion device.

17. The method according to any one of claims 1 to 3, further comprising the step of changing a third operating parameter of an extension device of the thermal system using the controller, wherein the third operating parameter is changed to obtain a predefined value in the thermal system.

18. The method according to claim 17, wherein the step of changing the third operating parameter includes the step of using a feedback loop.

19. The method according to claim 17, wherein the predefined value is at least one of the following: superheating degree value, supercooling degree value, mass flow rate, suction pressure, capacity of the thermal system, discharge air temperature of the evaporator of the thermal system, or refrigerant temperature of the cooling device of the thermal system.

20. The method according to any one of claims 1 to 3, wherein the thermal system is part of a vehicle.

21. The method according to any one of claims 1 to 3, wherein the thermal system is part of a fixed energy storage system.

22. A method for controlling vapor compression cooling in a thermal system, wherein the method is: (i) setting a first operating parameter of the first actuator of the thermal system, and (ii) setting a second operating parameter of the second actuator of the thermal system; A step of performing real-time optimization of the first and second operating parameters during the operation of the thermal system based on minimizing a cost function that takes into account at least the first and second actuators; and A step of adjusting the first operating parameter and the second operating parameter based on the real-time optimization described above. A method that includes [a certain feature].

23. The method according to claim 22, wherein the first actuator controls either the speed of the condenser fan or the speed of the compressor, and the second actuator controls the other of the speed of the condenser fan or the speed of the compressor.

24. The method according to claim 22 or 23, wherein the real-time optimization is performed using a function fitted to data that reflects the relevant partial derivatives.

25. The method according to claim 22 or 23, further comprising (iii) setting a third operating parameter of the thermal system extension device, wherein the execution of real-time optimization is also performed on the third operating parameter, and the third operating parameter is also adjusted based on the real-time optimization.

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