A control method and device of a vehicle thermal management system, equipment and medium

Abstract

The embodiment of the application discloses a kind of control method, device, equipment and medium of vehicle thermal management system, it is related to electric vehicle thermal management technical field.The method comprises: in the loop of vehicle thermal management system, the potential of each node is determined according to the detection data at the preset node;According to the total potential loss power of vehicle thermal management system in the equivalent potential flow network of the vehicle thermal management system, the potential resistance of each component and the potential at each node is determined;Based on the optimization target of minimizing total potential loss power, the target control quantity of the vehicle thermal management system is predicted, and the target control quantity is controlled based on the control sequence obtained by prediction.The technical scheme is analyzed by equivalent potential flow network, and the irreversible loss: total potential loss power is calculated, and the control of thermal management system is realized by minimizing total potential loss power, which realizes global energy efficiency optimization, improves the energy utilization efficiency of whole vehicle, and further improves the cruising range of vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle thermal management, and particularly relates to a control method and device of a vehicle thermal management system, equipment and a medium. BACKGROUND

[0002] Pure electric vehicles face serious range challenges in low temperature environments, one of the reasons is that the existing vehicle thermal management system adopts a rule-based or a certain control strategy to control heat, and the control target of the thermal management system is usually to minimize the total energy consumption (electricity consumption) or track the target temperature.

[0003] These methods have inherent defects: the above-mentioned scheme only focuses on the "quantity" of energy and ignores the "quality", and cannot distinguish the value of different grade heat energy, which may lead to the use of high-grade electric energy for low-grade heating demand, which may result in the loss of high-grade electric energy although the total energy consumption is small. In addition, the optimization process is like a "black box" or "gray box" operation, and the controller cannot understand the specific source and size of the irreversible loss inside the system during the whole optimization process, and cannot perform accurate and root cause energy efficiency optimization. SUMMARY

[0004] The present application provides a control method, device, equipment and medium of a vehicle thermal management system, which can control the thermal management system of the vehicle based on an equivalent exergy flow network, improve the energy utilization efficiency of the whole vehicle, and improve the range of the vehicle.

[0005] According to an aspect of the present application, a control method of a vehicle thermal management system is provided, the method comprising:

[0006] In the loop of the vehicle thermal management system, the exergy potential at each node is determined according to the detection data at the preset node;

[0007] According to the exergy flow network of the vehicle thermal management system, the exergy resistance of each component and the exergy potential at each node, the total exergy loss power of the vehicle thermal management system is determined;

[0008] The target control quantity of the vehicle thermal management system is predicted based on the optimization target of minimizing the total exergy loss power, and the target control quantity is controlled based on the predicted control sequence.

[0009] The equivalent exergy flow network of the vehicle thermal management system is established according to the physical connection topology of the vehicle thermal management system.

[0010] According to another aspect of the present application, a control device of a vehicle thermal management system is provided, comprising:

[0011] a potential determination module, configured to determine potentials at nodes in a loop of a vehicle thermal management system according to detection data at preset nodes;

[0012] a total exergy loss power calculation module, configured to determine a total exergy loss power of the vehicle thermal management system according to exergy resistances of components and the potentials at the nodes in an equivalent exergy flow network of the vehicle thermal management system;

[0013] a target control variable control module, configured to predict a target control variable of the vehicle thermal management system based on an optimization target of minimizing the total exergy loss power, and control the target control variable based on a control sequence obtained by the prediction;

[0014] The equivalent exergy flow network of the vehicle thermal management system is established according to a physical connection topology of the vehicle thermal management system.

[0015] According to another aspect of the present application, an electronic device is provided, which comprises:

[0016] at least one processor; and

[0017] a memory connected to the at least one processor in communication; wherein

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the control method of the vehicle thermal management system according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the control method of the vehicle thermal management system according to any one of the embodiments of the present application when executed by the processor.

[0020] The technical scheme of the embodiments of the present application comprises: determining potentials at nodes in a loop of a vehicle thermal management system according to detection data at preset nodes; determining a total exergy loss power of the vehicle thermal management system according to exergy resistances of components and the potentials at the nodes in an equivalent exergy flow network of the vehicle thermal management system; predicting a target control variable of the vehicle thermal management system based on an optimization target of minimizing the total exergy loss power, and controlling the target control variable based on a control sequence obtained by the prediction; and the equivalent exergy flow network of the vehicle thermal management system is established according to a physical connection topology of the vehicle thermal management system. The technical scheme calculates the irreversible loss: total exergy loss power through exergy analysis of the equivalent exergy flow network, and realizes global energy efficiency optimization through control of the thermal management system by minimizing the total exergy loss power, thereby improving the whole vehicle energy utilization efficiency and further improving the vehicle range.

[0021] It should be understood that nothing in this section is intended to limit the scope of the embodiments of the present application nor are they intended to represent key or essential features of the embodiments of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0023] Figure 1 is a flow chart of a control method of a vehicle thermal management system according to the first embodiment of the present application;

[0024] Figure 2 is a flow chart of a control method of a vehicle thermal management system according to the second embodiment of the present application;

[0025] Figure 3 is a simple schematic diagram of a thermal management system according to the second embodiment of the present application;

[0026] Figure 4 is an architecture diagram of a thermal management system according to the second embodiment of the present application;

[0027] Figure 5 is a control flow chart of a vehicle thermal management system according to the second embodiment of the present application;

[0028] Figure 6 is a MPC rolling optimization flow chart according to the second embodiment of the present application;

[0029] Figure 7 is a structural schematic diagram of a control device of a vehicle thermal management system according to the third embodiment of the present application;

[0030] Figure 8 is a structural schematic diagram of an electronic device for implementing a control method of a vehicle thermal management system according to the present application. DETAILED DESCRIPTION

[0031] In order to make the technical personnel in the art better understand the present application scheme, the following will combine the drawings in the embodiments of the present application, and clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0032] It is to be understood that the terminology "first", "second", "target", etc. in the specification and claims of the application and above-described drawings is used to distinguish similar objects, and is not necessarily used to describe a particular sequential or chronological order. It should be understood that the data thus used can be interchanged, where appropriate, so that the embodiments of the application described herein can be carried out in other than the order shown or described herein. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or apparatus that comprises a list of steps or units not necessarily limited to those explicitly listed, but can include other steps or units not expressly listed or inherent to such process, method, product or apparatus.

[0033] Embodiment one

[0034] Figure 1 A flowchart of a control method of a vehicle thermal management system is provided for the first embodiment of the present application. The first embodiment of the present application can be applied to the case of controlling a vehicle thermal management system. The method can be executed by a control device of the vehicle thermal management system. The control device of the vehicle thermal management system can be realized in the form of hardware and / or software. The control device of the vehicle thermal management system can be configured in an electronic device with data processing capability. As shown in the figure, the method comprises: Figure 1

[0035] S110, in the loop of the vehicle thermal management system, determining the potential at each node according to the detection data at the preset node.

[0036] The vehicle thermal management system is used to control the heat flow of the vehicle. The hardware thereof includes but is not limited to: pump, heat exchanger, sensor, cooling liquid pipeline, valve, etc. Exemplarily, the hardware part of the vehicle thermal management system includes: heat pump system and liquid cooling loop, low-grade heat recovery heat exchanger, controllable valve group and sensor network. The heat pump system and liquid cooling loop include: battery cooling loop, motor electric control cooling loop, heat pump system loop and passenger cabin heating loop. The low-grade heat recovery heat exchanger has a first channel connected to the motor electric control cooling loop and a second channel connected to the battery cooling loop. The heat exchanger is a plate heat exchanger, which is used for heat exchange between the motor cooling liquid and the battery cooling liquid. The controllable valve group includes multiple three-way valves and proportional regulating valves, which are used to dynamically change the flow direction and flow rate of the cooling liquid, so as to realize the reconfiguration of the heat flow path. The sensor network is used to collect real-time state data of the system, and is arranged with temperature sensors, flow rate sensors and pressure sensors on each loop.

[0037] ​The loop of the vehicle thermal management system can be a closed circulation network composed of pipes, pumps, heat exchangers, cooling liquid, etc. The cooling liquid can flow in the loop of the vehicle thermal management system to achieve heat transfer. For example, the loop of the vehicle thermal management system can be the heat pump system and the liquid cooling loop described above.

[0038] Specifically, the execution subject of the method described in the embodiments of the present application can be a vehicle controller or an independent domain controller. The execution subject can obtain detection data such as temperature, pressure, and mass flow rate at a preset node through a sensor pre-set at the node of the vehicle thermal management system, calculate the specific physical enthalpy at the node according to the detection data at the node, and determine the enthalpy potential at the node according to the specific physical enthalpy and the mass flow rate.

[0039] S120, determining the total enthalpy loss power of the vehicle thermal management system according to the enthalpy potential at each node and the enthalpy resistance of each component in the equivalent enthalpy flow network of the vehicle thermal management system.

[0040] The equivalent enthalpy flow network of the vehicle thermal management system is established according to the physical connection topology of the vehicle thermal management system. Specifically, it can be established according to the actual connection relationship of each component of the vehicle thermal management system.

[0041] Specifically, after obtaining the enthalpy potential at the node, the operating state of each component (such as valve opening, water pump speed, etc.) can be obtained, and the enthalpy resistance can be calculated accordingly. After obtaining the enthalpy resistance of each component and the enthalpy potential at each node, since the equivalent enthalpy flow network is established according to the physical connection topology of the vehicle thermal management system, each node and component has a corresponding position in the equivalent enthalpy flow network. The enthalpy flow can be calculated according to the enthalpy resistance of each node and the enthalpy potential of each component in the equivalent enthalpy flow network. Then, the enthalpy loss of each branch of the equivalent enthalpy flow network is calculated according to the enthalpy flow and the enthalpy resistance, and the total enthalpy loss power of the vehicle thermal management system is calculated.

[0042] S130, predicting the target control quantity of the vehicle thermal management system based on the optimization target of minimizing the total enthalpy loss power, and controlling the target control quantity based on the predicted control sequence.

[0043] The target control quantity can be an adjustable parameter of some components of the vehicle thermal management system, and the target control quantity can be modified by an instruction, such as valve opening, water pump speed, PTC (Positive Temperature Coefficient, electric heater) power, etc.

[0044] Specifically, after obtaining the total exergy loss power, an optimization algorithm is selected to predict the predicted value of the target control quantity, i.e., the control sequence, under the condition of minimizing the total exergy loss power, and then the target control quantity is controlled based on the predicted control sequence to minimize the total exergy loss power.

[0045] The technical scheme of the embodiment of the application comprises: determining exergy potentials at nodes in a loop of a vehicle thermal management system according to detection data at preset nodes; determining total exergy loss power of the vehicle thermal management system according to exergy resistances of components and the exergy potentials at the nodes in an equivalent exergy flow network of the vehicle thermal management system; predicting a target control quantity of the vehicle thermal management system based on an optimization target of minimizing the total exergy loss power, and controlling the target control quantity based on a predicted control sequence; wherein the equivalent exergy flow network of the vehicle thermal management system is established according to a physical connection topology of the vehicle thermal management system. The technical scheme calculates irreversible loss, i.e., total exergy loss power, through exergy analysis of the equivalent exergy flow network, and controls the thermal management system by minimizing the total exergy loss power to achieve global energy efficiency optimization, improve whole vehicle energy utilization efficiency, and further improve vehicle cruising range.

[0046] Embodiment Two

[0047] Figure 2 A flowchart of a control method of a vehicle thermal management system provided by Embodiment Two of the application is optimized based on the above-mentioned embodiments. It should be noted that the method described in the embodiment of the application calculates the thermal management system by abstracting it into an equivalent exergy flow network, which is composed of three types of elements, i.e., exergy potential sources (power sources), equivalent exergy resistances (internal resistances), and exergy potential sinks (loads), connected by topology, and the equivalent exergy flow network corresponds to the actual physical connection mode of the thermal management system.

[0048] The basic concepts are introduced as follows: specific enthalpy (h) ): represents the thermodynamic energy contained in unit mass of matter plus its flow energy (i.e., pressure energy).

[0049] Specific entropy (s) ): represents the entropy of unit mass of matter, which is a quantity in thermodynamics describing the degree of disorder of a system, and can also be understood as the "mass" of energy, i.e., how much energy can do work.

[0050] Exergy flow (E) ): analogous to electric current, represents the transmission rate of exergy, which is equal to the mass flow rate of working fluid in numerical value: ;

[0051] Among them, Mass flow rate of the working fluid (usually coolant in thermal management system), unit: kg / s; Note that flow rate is not flow power, which has unit of W and equals mass flow rate x specific flow.

[0052] Flow potential difference (Δφ): : Analogous to voltage, represents the flow potential difference between two points:

[0053] ;

[0054] Where, and are the specific physical flow of the two points, respectively.

[0055] Equivalent flow resistance (Rφ): : For any component that causes flow loss (such as heat exchanger, valve, pipeline, etc.), the equivalent flow resistance (unit: J·s / kg² or Pa·s² / m³) of the component is defined as the ratio of the flow potential difference (Δφ) upstream and downstream of the component to the flow (φ) passing through the component. Analogous to resistance, it represents the hindering effect of the component on flow transmission, and is defined as:

[0056] .

[0057] As shown in FIG. 1, the method according to an embodiment of the present application specifically includes the following steps: Figure 2

[0058] S210, in the loop of the vehicle thermal management system, according to the pressure and temperature at the preset node, the specific physical flow of the coolant in the loop is determined.

[0059] Specifically, the temperature , pressure , mass flow rate data at the preset node in the loop of the vehicle thermal management system are collected in real time. For the working fluid at any position in the vehicle thermal management system, the calculation of the specific physical flow is as follows:

[0060] ;

[0061] Where, and are the specific enthalpy and specific entropy of the fluid in the current state, respectively; , are the values of specific enthalpy and specific entropy in the ambient state (standard temperature , standard pressure ).

[0062] For incompressible fluids such as coolant, the calculation formula of specific physical flow can be simplified as:​​​​​

[0063] ;

[0064] ;

[0065] wherein, is the constant pressure specific heat capacity of the fluid; is the temperature of the fluid at the current state; is the density of the working fluid (coolant), in kg / m 3 ;

[0066] S220, determining the product of the mass flow rate of the coolant and the specific physical potential as the potential at the node.

[0067] Specifically, after obtaining the mass flow rate and the specific physical potential of the coolant at the node, the product of the two is determined as the potential; the potential can be calculated according to the following formula (unit: W):

[0068] .

[0069] S230, determining the potential flow of each branch in the vehicle thermal management system according to the potential of each node and the potential resistance of each component in the equivalent potential flow network of the vehicle thermal management system.

[0070] In the embodiments of the present application, optionally, the determination process of the potential resistance of each component includes: determining the potential resistance of the three-way valve and / or the proportional valve according to the valve opening; the potential resistance is inversely proportional to the valve opening; determining the potential resistance of the water pump according to the water pump speed; the potential resistance is inversely proportional to the water pump speed; determining the potential resistance of the plate heat exchanger according to the flow rate on both sides of the plate heat exchanger.

[0071] Exemplarily, the potential resistance can be pre-calibrated, specifically, an equivalent potential resistance parameter table can be pre-stored in the controller. The table is obtained through early bench test or high-fidelity simulation data fitting, and a mapping relationship between the operating state of each component and its potential resistance is established.

[0072] For the three-way valve / proportional valve: the potential resistance thereof is derived from throttling loss. The potential resistance is a function of the opening:

[0073] ; it usually decreases sharply with the increase of the opening.

[0074] For the water pump: the potential resistance thereof is derived from its mechanical efficiency and hydraulic efficiency. The potential resistance is a function of the speed : ; it usually decreases with the increase of the speed.

[0075] ​For plate heat exchanger: its main source of exergy loss is due to the irreversibility of heat transfer. The exergy loss of plate heat exchanger in its typical working range can be regarded as a constant, or a function weakly related to the geometric average of the flow rates on both sides: Where is the mass flow rate on both sides of the plate heat exchanger.

[0076] The present scheme pre-calibrates the exergy loss, so that according to the operating state of each component detected by the sensor (i.e. the operating parameters such as valve opening, water pump speed, etc.) and the pre-determined exergy loss function of each component, the exergy loss of each component can be obtained. Compared with the traditional method, it is reduced by 1-2 orders of magnitude, which can meet the real-time requirements of vehicle-mounted systems.

[0077] In the embodiments of the present application, optionally, according to the exergy flow network of the vehicle thermal management system, the exergy loss of each component and the exergy potential at each node, the exergy flow of each branch in the vehicle thermal management system is determined, including: determining the exergy flow corresponding to the exergy potential source according to the exergy flow power and the corresponding specific physical exergy of the exergy potential source in the equivalent exergy flow network of the vehicle thermal management system; determining the exergy flow corresponding to the exergy potential sink according to the consumption power and the corresponding specific physical exergy of the exergy potential sink in the equivalent exergy flow network of the vehicle thermal management system; determining the exergy flow of each branch in the vehicle thermal management system according to the exergy flow conditions satisfied by the exergy flow into any node in the equivalent exergy flow network of the vehicle thermal management system, and the exergy potential conditions satisfied by the exergy potential difference in any closed loop of the vehicle thermal management system.

[0078] Exemplarily, the exergy potential source: the exergy potential source is an element in the equivalent exergy flow network that provides exergy potential, and its output is taken as the input quantity of system control. The main exergy potential sources (motor / electronic control waste heat source and PTC electric heating source) are modeled as follows:

[0079] Motor / electronic control waste heat source: the output exergy flow power of this source is determined by its power loss and heat exchange efficiency, which can be calculated by the following formula and introduced into the network as a known input quantity:

[0080] ;

[0081] Where, is the power loss of the motor or electronic controller at the moment (unit: W), which can be estimated in real time by querying the pre-stored efficiency characteristic map or through the built-in loss model by the vehicle controller; is the heat exchange efficiency of the circuit under the current working condition, which is a coefficient between 0 and 1, which can be obtained by looking up the table.

[0082] PTC electric heating source:

[0083] ​​The source is a controlled potential source, whose maximum output current is equal to its input electric power, but its actual output is affected by the network state. It is characterized in the optimization problem as:

[0084]

[0085] where, is the input electric power of PTC at time PTC (unit: W), which is one of the optimization variables. The inequality constraint shows that the actual current output of PTC will not exceed its electric power input.

[0086] Further, the current power of each potential source is determined After that, the current of each potential source is determined according to the current power and the specific physical potential, which is equal to the mass flow rate in numerical value;

[0087] The conversion formula: mass flow rate = current power / specific physical potential. Where the specific physical potential is calculated by the fluid state at the node , .

[0088] Mainly the calculation of the specific physical potential part.

[0089] For the "potential source" (motor / PTC), the conversion formula is:

[0090]

[0091] where, is equal to the output current of the potential source in numerical value; is the output current power of the potential source; motor source: ; PTC source: , which is an optimization variable, but its maximum value is limited by the electric power of PTC. is the specific physical potential of the working fluid at the source output node.

[0092] For the motor source, its output loop coolant temperature under working conditions can be measured through bench test; Then the corresponding specific physical potential is calculated, and the motor source can be recorded as: , which is stored and looked up later.

[0093] For the PTC source, the same can be obtained .

[0094] Further, after determining the current corresponding to the potential source, the current corresponding to the potential sink can be determined according to the consumption power of the potential sink and the corresponding specific physical potential in the equivalent current network of the vehicle thermal management system.

[0095] ​​​Where, the potential well is the element in the network that consumes potential, and its demand is the boundary condition for the system control. The major thermal load demand can be characterized as potential wells (potential wells include: cabin heating well and battery heating / insulation well):

[0096] Cabin heating well:

[0097] The minimum potential flow consumption required to maintain the cabin set temperature is determined by the thermal load demand and is converted into an output constraint of the optimization problem by the following formula:

[0098] ;

[0099] Where, is the potential flow consumption power of the cabin heating well; is the predicted cabin thermal load at time k+j (unit: W) at time k; is the ideal heating cycle coefficient, which is used to convert the thermal load into the theoretically lowest required potential flow consumption. This constraint ensures that the cabin comfort requirement is met. Usually, an empirical constant (e.g., 3.0) is taken, which represents the efficiency of an approximate ideal heat pump.

[0100] The calculation formula of the battery heating / insulation well is as follows:

[0101] ;

[0102] Where, is the potential flow consumption power of the battery heating / insulation well; is the predicted battery thermal demand (unit: W), which mainly depends on the difference between the target temperature and the predicted future battery temperature. This constraint ensures that the battery operates in the optimal temperature range.

[0103] For potential wells (battery / cabin), an offline calibration method similar to that of potential sources can also be used to determine the ratio of potential well consumption power to specific physical potential as the potential flow corresponding to the potential well.

[0104] For example, for the battery well, according to the optimal working temperature range of the battery, the target temperature of the heating circuit coolant is determined , and then the corresponding specific physical potential is calculated according to the target temperature , and then the potential flow corresponding to the battery well is calculated.

[0105] Specifically, after obtaining the vortex flow corresponding to the vortex source and the vortex flow corresponding to the vortex sink, the vortex flow of each branch in the equivalent vortex flow network can be determined according to the vortex potential at each node and the vortex resistance of each component or according to the vortex potential at each node and the vortex resistance of each branch in the equivalent vortex flow network. Since the Kirchhoff's voltage law (KVL) and the Kirchhoff's current law (KCL) in the circuit theory are known, the sum of the vortex flows flowing into any node is zero and the sum of the vortex potential differences around any closed loop is zero, and thus the entire equivalent vortex flow network can be solved to obtain the vortex flow of each branch.

[0106] For example, the vortex resistance of a single component can be obtained by table lookup, and there can be multiple components on a branch. In this case, the vortex resistance of each component obtained by table lookup can be assembled into a complete and calculable vortex flow network according to the actual physical connection topology of the system. For example, the total vortex resistance of a series branch is equal to the sum of the vortex resistances of the components, for example, a branch including a valve and a heat exchanger has a total vortex resistance ; the reciprocal of the total vortex resistance of a parallel branch is equal to the sum of the reciprocals of the vortex resistances of the components; and the total vortex resistance of a parallel branch can be represented by the following formula: .

[0107] For example, the vortex flow condition satisfied by the vortex flow flowing into any node in the equivalent vortex flow network of the vehicle thermal management system is that the sum of the vortex flows flowing into any node is zero. The KCL (sum of the vortex flows flowing into any node is zero) expression is:

[0108] ;

[0109] The vortex potential condition satisfied by the vortex potential difference in any closed loop of the vehicle thermal management system is that the sum of the vortex potential differences around any closed loop is zero. The KVL (sum of the vortex potential differences around any closed loop is zero) expression is:

[0110] ;

[0111] According to the above vortex flow condition and the vortex potential condition, a linear equation set of the equivalent vortex flow network can be established. By bringing the information such as the calculated vortex potential at each node and the vortex resistance of each branch into the linear equation set, the vortex flow of each branch in the network can be obtained ; wherein, is the vortex flow, and the subscript i is the identification of the i th branch.

[0112] In this way, the entire equivalent vortex flow network can be solved based on the Kirchhoff's voltage law and the Kirchhoff's current law to obtain the vortex flow of each branch.

[0113] In one specific example, Figure 3 is a simple schematic diagram of a thermal management system; the vortex flow power of the motor waste heat source is The PTC heating source is A resistor (R) and a battery trap are The crew cabin is .

[0114] Let the current in the motor power source branch be Let the current of the PTC source branch be... (This current can be calculated based on current power and specific physical current.) Let the total current be... According to KCL, we have: ;

[0115] For the resistance R, the KVL equation is: ;

[0116] Total flow Power is split at the nodes of the battery trap to meet the needs of the battery and the crew compartment respectively. Power allocated to the battery trap... ≥800W. Power allocated to the crew cabin. Must be ≥ 500W. According to KCL, there is... .

[0117] Solving the above equations simultaneously will yield the flow of each branch.

[0118] S240, determine the total power loss based on the current of each branch and the resistance of each component in the vehicle thermal management system.

[0119] For example, after obtaining the current in each branch and the resistance of each component in the vehicle thermal management system, the instantaneous power loss of each branch and the total power loss of the system are calculated based on the analogy of Joule's law:

[0120] ;

[0121] ;

[0122] in, Let the power loss of the i-th branch be , The total power loss of the system. Let N be the resistance of the i-th branch, and N be the total number of branches.

[0123] The entire calculation process of this technical solution involves scalar multiplication and solving linear equations, resulting in extremely low computational load. It can be completed within milliseconds, fully meeting the real-time requirements of vehicle control.

[0124] In a specific example, the method described in this application embodiment is executed by a controller, which can read sensor data in real time. , , ) and the current status of each actuator (valve opening) Pump speed ).according to Calculate the potential of the critical nodes based on the current state of each actuator. The corresponding component's real-time parameters are retrieved and called from the pre-stored equivalent resistance parameter table. The values ​​are then assembled into a complete and computable equivalent current network according to the actual physical connection topology of the system. Based on the equivalent current network, KCL and KVL are solved to obtain the current of each branch, and then the total current loss power of the thermal management system is calculated.

[0125] S250, the target control quantity of the vehicle thermal management system is predicted based on the optimization objective of minimizing total power loss, and the target control quantity is controlled based on the predicted control sequence.

[0126] In this embodiment of the application, optionally, the target control quantity of the vehicle thermal management system is predicted based on the optimization objective of minimizing total power loss, and the target control quantity is controlled based on the predicted control sequence, including: when the optimization objective is to minimize the total power loss, processing the current state variables of the vehicle thermal management system based on the model predictive control algorithm to obtain the control sequence of the target control quantity; and controlling the target control quantity based on the control sequence.

[0127] Model Predictive Control (MPC) is a dynamic control algorithm. Current state variables include, but are not limited to, the current values ​​of each branch. Quantities directly related to flow; target control quantities are the quantities controlled by the instructions of each actuator, such as valve opening, pump speed, PTC power, etc.

[0128] For example, MPC uses the following discrete state-space equations as a prediction model:

[0129] ;

[0130] ;

[0131] in, The current state variable is selected as the current value of each branch. Or quantities directly related to flow.

[0132] The control input (i.e. the target control quantity) can be the command of each actuator, such as valve opening, water pump speed, PTC power, etc.

[0133] For output variable, for controlled variable (such as battery temperature, passenger cabin temperature).

[0134] A is a system matrix. It is determined by the flow network topology and the flow resistance of the system, and describes how the internal state of the system evolves by itself.

[0135] B is a control input matrix. It describes how the control input influences the system state x(k+1).

[0136] C is an output matrix. It describes how the system state is mapped to the system output .

[0137] Specifically, at each control period k, the following finite-time optimization problem is solved, i.e., the objective function is calculated:

[0138] ;

[0139] wherein: is the prediction horizon; is the total flow loss of the system at time k+j predicted at time k; is the optimization variable (i.e., the target control variable), i.e., the set of control commands of the actuators (such as the valve opening vector, the pump speed vector);

[0140] is a penalty term added to avoid too drastic control commands. Wherein, is the rate of change of the control variable, which represents the degree of drasticness of the control command. is a penalty factor. This term is to penalize the drastic and frequent changes of the control action. This penalty factor can avoid the MPC calculating a strategy of making the valve switch crazily and the pump speed change drastically, thereby protecting the actuators and guaranteeing the user's driving experience.

[0141] In this way, the optimization objective of minimizing the total flow loss power of the system can be achieved in the iteration process of the model predictive control algorithm through the objective function, and the penalty term is set in the objective function to avoid the drastic change of the control sequence obtained by prediction, and the use experience of the user is also guaranteed when the target control variable is controlled based on the control sequence.

[0142] Optionally, in the embodiment of the application, the model predictive control algorithm is further provided with a passenger cabin comfort constraint, a battery temperature constraint and / or a physical boundary constraint of the actuators of the vehicle thermal management system in the prediction process.

[0143] For example, the passenger cabin comfort constraint is realized according to the following formula:

[0144]

[0145] wherein, is the air flow consumption power of the passenger cabin heating sink; is the predicted is the passenger cabin thermal load at time (unit: W); is the predicted is the ideal heating cycle coefficient.

[0146] The formula means that the MPC optimization algorithm will not give a solution that results in "air flow allocated to the cabin" being less than "the minimum required air flow of the cabin" when finding the optimal solution (minimizing total air loss). In low temperature environment, the temperature demand of the passenger cabin is guaranteed; thus, the comfort requirement of the passenger cabin is forcibly guaranteed.

[0147] The battery temperature constraint is realized according to the following formula:

[0148] ;

[0149] The boundary constraint condition is as follows:

[0150] ;

[0151] wherein, wherein, is the air flow consumption power of the battery heating / insulation sink; is the predicted battery thermal demand (unit: W), is the battery temperature; is the predicted is the value at time .

[0152] The scheme sets the passenger cabin comfort constraint, the battery temperature constraint and / or the physical boundary constraint of the actuator of the vehicle thermal management system, so that the predicted target control quantity is a value under which each component of the thermal management system is located in the normal working interval, and has practicability.

[0153] Further, the MPC algorithm is solved to obtain the optimal control sequence ; the control instruction of the first step is issued to the actuator. In the next period, the optimization initial value is refreshed according to the new system state measurement value, and the process is repeated. The optimal control instruction calculated by the MPC optimization is issued to each actuator (valve, water pump, compressor, PTC, etc.), and the system operating state is dynamically adjusted to realize the air optimal distribution of the heat flow.

[0154] Compared with the prior art, the technical scheme of the application embodiment has the following advantages: the scheme adopts a thermodynamics-circuit network conversion method based on equivalent thermal resistance, a complex thermodynamic system is converted into an easy-to-handle circuit network by constructing an analogy relationship between the thermodynamic system and the circuit system, and a theoretical basis is provided for application of the thermal analysis in real-time control. In the calculation optimization link, through comprehensive use of offline calibration, parameterization method and table lookup technology, complex nonlinear thermal calculation is simplified into efficient algebraic operation and table lookup operation, the calculation load is reduced by 1-2 orders of magnitude compared with the traditional method, and the real-time requirement of the vehicle-mounted system is met. The technology implements root cause optimization from the energy "quality" level, relies on a model predictive control (MPC) framework of the thermal flow network, can identify and accurately locate the largest irreversible loss source, realizes true global energy efficiency optimization by minimizing the key loss, and ensures that the thermal management system operates in an efficient and reliable state.

[0155] In one specific example, Figure 4 is a schematic diagram of a thermal management system. Figure 5 is a control flowchart of a vehicle thermal management system. Figure 6 is a MPC rolling optimization flowchart. The following will be further described in detail in combination with the drawings. The specific example can include steps S1-S4:

[0156] S1: system initialization and real-time parameter acquisition.

[0157] In each control period (such as 100 ms), the controller synchronously performs the following operations:

[0158] 1. Data acquisition: the control unit reads the state signals of each node of the thermal management system in real time through the sensor network:

[0159] (1) Collect the temperature values of the key nodes of each circuit (such as the battery inlet and outlet, the motor inlet and outlet, the passenger compartment heat exchanger inlet and outlet, etc.);

[0160] (2) Collect the pressure values of the key nodes of each circuit ;

[0161] (3) Collect the volume flow rate of the cooling liquid of each circuit , and convert it into mass flow rate through fluid density ;

[0162] (4) Collect the real-time power of the motor ;

[0163] (5) Collect the current state of each actuator, including the three-way valve opening degree , the water pump speed , the current power of the PTC , etc.

[0164] 2. Data pre-processing: The collected raw data is filtered (e.g. first-order low-pass filter) and normalized to eliminate noise and prepare for subsequent calculations.

[0165] S2: Online construction and solution of the water network.

[0166] The optimization algorithm built-in the control unit, based on the real-time data collected in step 1, performs the following sequence of operations:

[0167] 1. Water potential calculation: Based on the pre-processed temperature , pressure and mass flow rate data, the specific physical water of each key measurement point is calculated according to the following formula:

[0168] ;

[0169] The water potential of each node is calculated according to the following formula:

[0170] .

[0171] 2. Water resistance query: According to the current state of each actuator (valve opening , pump speed ), the equivalent water resistance parameter table pre-stored in the controller is queried to obtain the equivalent water resistance value of each component under the current state.

[0172] 3. Network construction and solution: The values obtained by querying are assembled into a complete equivalent water network according to the actual physical connection topology of the system. The Kirchhoff's law (KCL, KVL) is applied to establish a linear equation system, and the water flow (i.e. mass flow rate ) distribution of each branch is obtained by solving the network; then, the total water loss power of the system is calculated according to the following formula:

[0173] .

[0174] S3: MPC rolling optimization solution based on the water network model constructed in step S2, start the model predictive control (MPC) rolling optimization process.

[0175] 1. State prediction: Taking the current system state as the initial value, the system behavior in the future time domain (e.g. 60 seconds in the future) is predicted using the prediction model.

[0176] 2. Optimization solution: Minimize the predicted total water loss ​For the core target, while meeting the comfort constraints of the passenger compartment, the battery temperature constraints, and the physical boundary constraints of each actuator (such as valve opening, pump speed, etc.), an optimal future control command sequence is obtained .

[0177] 3. Instruction output: the first control quantity in the optimal control sequence is sent to the corresponding actuator (such as a three-way valve, a water pump, a PTC, etc.).

[0178] For example, the process of obtaining the optimal control sequence by the MPC optimization algorithm is as follows: is the optimal control action obtained by solving the optimization problem (minimizing the objective function + constraints) based on the current state at the current time , and then the system executes the action and re-optimizes at the next time. Specifically, according to the current system state and the given control command , the prediction model predicts the state of the system at the next time . Through this model, the possible behaviors of the system in the future can be obtained , , etc. Then, by comparing the objective function values under different control command sequences, the optimal control command sequence is selected.

[0179] S4: closed-loop feedback and continuous operation.

[0180] After the above steps S1 to S3 are completed in a control period, the system enters the next period. The controller reads the updated system state under the action of the new control command, refreshes the optimization initial value, and repeats steps S1 to S3. This cycle continues to form a closed-loop feedback control, so as to realize dynamic real-time optimization of the system under various working conditions and external disturbances, and always track and operate in the optimal state with the minimum overall loss.

[0181] In this embodiment, step S1 is mainly used for system initialization and real-time parameter acquisition. In each control period (such as 100 ms), the system synchronously carries out multiple operations. The control unit acquires the state signals of each node of the system in real time by means of sensors: on the one hand, the temperature values T of the key nodes of each circuit are collected, including the battery inlet and outlet, the motor inlet and outlet, the passenger compartment heat exchanger inlet and outlet, etc.; on the other hand, the pressure values of the key nodes of each circuit are collected; at the same time, the volume flow rates of the cooling fluids of each circuit are collected and converted into mass flow rates through fluid density; in addition, the real-time power of the motor is collected.and the current state of each actuator, including the three-way valve opening, water pump speed After the raw data is collected, filtering (such as first-order low-pass filtering) and unit conversion are performed to eliminate noise interference and prepare for subsequent calculations.

[0182] In this embodiment, step S2 is mainly used for online construction and solving of the water network. The optimization algorithm built in the control unit performs the following operations in order according to the real-time data collected in step S1: first, water potential calculation, according to the pre-processed temperature , pressure and mass flow rate data, the specific physical water of each key measurement point is calculated according to the corresponding formula, and then the water potential of each node is calculated; then, water resistance query, according to the current state of each actuator (valve opening , pump speed ), the equivalent water resistance parameter table pre-stored in the controller is queried to obtain the equivalent water resistance value of each component under the current state; finally, network construction and solving, according to the actual physical connection topology of the system, the values obtained by querying are assembled into a complete equivalent water network, Kirchhoff's law (KCL, KVL) is applied to establish a linear equation system and solve it, to obtain the water flow (i.e. mass flow rate) distribution of each branch, and then the total water loss power of the system is calculated according to the formula.

[0183] In this embodiment, step S3 is mainly used for MPC rolling optimization solving. Based on the water network model constructed in step S2, the model predictive control (MPC) rolling optimization process is started: first, state prediction, taking the current system state as the initial value, the system behavior in the future time domain (such as the next 60 seconds) is predicted by using the prediction model; then, optimization solving, taking the minimization of the predicted total water loss as the core target, while satisfying the passenger compartment comfort constraint, the battery temperature constraint and the physical boundary constraint of each actuator (such as valve opening, pump speed, etc.), an optimal future control command sequence is solved; finally, command output, the first control quantity in the optimal control sequence is sent to the corresponding actuator (such as three-way valve, water pump, PTC, etc.).

[0184] In this embodiment, step S4 is mainly used for closed-loop feedback and continuous operation. After the above steps S1 to S3 are successfully completed in a control period, the thermal management system enters the next control period. The controller reads the updated state of the thermal management system under the action of the new control command, refreshes the optimization initial value, and repeats steps S1 to S3. This cycle forms a closed-loop feedback control, realizes dynamic real-time optimization of the thermal management system under various working conditions and external disturbances, and ensures that the thermal management system always tracks and operates in the optimal state of minimum global water loss.

[0185] Example Three

[0186] Figure 7 A structural schematic diagram of a control device of a vehicle thermal management system is provided for Embodiment Three of the present application. The device can execute the control method of the vehicle thermal management system provided by any embodiment of the present application, and has the function modules and beneficial effects corresponding to the execution method. As shown in the figure, the device comprises: Figure 7

[0187] a potential determination module 310, configured to determine the potential at each node in the loop of the vehicle thermal management system according to the detection data at the preset nodes;

[0188] a total exergy loss power calculation module 320, configured to determine the total exergy loss power of the vehicle thermal management system according to the exergy resistance of each component and the potential at each node in the equivalent exergy flow network of the vehicle thermal management system;

[0189] a target control quantity control module 330, configured to predict the target control quantity of the vehicle thermal management system based on the optimization target of minimizing the total exergy loss power, and control the target control quantity based on the control sequence obtained by the prediction;

[0190] wherein the equivalent exergy flow network of the vehicle thermal management system is established according to the physical connection topology of the vehicle thermal management system.

[0191] The technical solution of the present application comprises: a potential determination module 310, configured to determine the potential at each node in the loop of the vehicle thermal management system according to the detection data at the preset nodes; a total exergy loss power calculation module 320, configured to determine the total exergy loss power of the vehicle thermal management system according to the exergy resistance of each component and the potential at each node in the equivalent exergy flow network of the vehicle thermal management system; and a target control quantity control module 330, configured to predict the target control quantity of the vehicle thermal management system based on the optimization target of minimizing the total exergy loss power, and control the target control quantity based on the control sequence obtained by the prediction. The equivalent exergy flow network is used for exergy analysis, and the total exergy loss power, which is the irreversible loss, is calculated. The control of the thermal management system based on the minimization of the total exergy loss power achieves the global energy efficiency optimization, improves the energy utilization efficiency of the whole vehicle, and further improves the cruising range of the vehicle.

[0192] In the present application, the total exergy loss power calculation module 320 can comprise:

[0193] an exergy flow calculation unit, configured to determine the exergy flow of each branch in the vehicle thermal management system according to the exergy resistance of each component and the potential at each node in the equivalent exergy flow network of the vehicle thermal management system;

[0194] ​The total entropy loss power determination unit is configured to determine a total entropy loss power according to the entropy flow of each branch in the vehicle thermal management system and the entropy resistance of each component.

[0195] In the embodiments of the present application, the entropy flow calculation unit comprises:

[0196] The entropy flow calculation subunit of the entropy source is configured to determine the entropy flow of the entropy source according to the entropy flow power of the entropy source in the equivalent entropy flow network of the vehicle thermal management system and the corresponding specific physical entropy.

[0197] The entropy flow calculation subunit of the entropy sink is configured to determine the entropy flow of the entropy sink according to the consumption power of the entropy sink in the equivalent entropy flow network of the vehicle thermal management system and the corresponding specific physical entropy.

[0198] The entropy flow calculation subunit is configured to determine the entropy flow of each branch in the vehicle thermal management system according to the entropy flow condition satisfied by the entropy flow flowing into any node in the equivalent entropy flow network of the vehicle thermal management system and the entropy condition satisfied by the entropy difference in any closed loop of the vehicle thermal management system.

[0199] In the embodiments of the present application, the device further comprises an entropy resistance determination module, which is specifically configured to:

[0200] determine the entropy resistance of the three-way valve and / or the proportional valve according to the valve opening degree; the entropy resistance is inversely proportional to the valve opening degree;

[0201] determine the entropy resistance of the water pump according to the water pump rotating speed; the entropy resistance is inversely proportional to the water pump rotating speed;

[0202] determine the entropy resistance of the plate heat exchanger according to the flow rates on both sides of the plate heat exchanger.

[0203] In the embodiments of the present application, the entropy potential determination module 310 comprises:

[0204] The specific physical entropy calculation unit is configured to determine the specific physical entropy of the cooling liquid in the loop according to the pressure and the temperature at the preset node.

[0205] The entropy potential determination unit is configured to determine the entropy potential at the node as the product of the mass flow rate of the cooling liquid and the specific physical entropy.

[0206] In the embodiments of the present application, the target control quantity control module 330 comprises:

[0207] The control sequence prediction unit is configured to, in the case that the optimization target is the minimum total entropy loss power, process the current state variable of the vehicle thermal management system based on a model predictive control algorithm to obtain a control sequence of the target control quantity.

[0208] The target control quantity control unit is configured to control the target control quantity based on the control sequence.

[0209] Optionally, in the embodiments of the present application, the model predictive control algorithm is further provided with, in the prediction process:

[0210] a passenger cabin comfort constraint, a battery temperature constraint, and / or a physical boundary constraint of an actuator of the vehicle thermal management system.

[0211] The control device of the vehicle thermal management system provided in the embodiments of the present application can execute the control method of the vehicle thermal management system provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0212] Embodiment Four

[0213] Figure 8 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0214] As shown in Figure 8 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0215] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0216] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the control method of the vehicle thermal management system.

[0217] In some embodiments, the control method of the vehicle thermal management system can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the control method of the vehicle thermal management system described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the control method of the vehicle thermal management system by any other appropriate means, such as by means of firmware.

[0218] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0219] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, enables the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0220] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0221] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0222] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0223] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0224] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the technical solutions of the present disclosure are achieved, and the present disclosure is not limited herein.

[0225] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A control method for a vehicle thermal management system, characterized in that, include: In the loop of the vehicle thermal management system, the potential at each node is determined based on the detection data at the preset nodes; Based on the resistance of each component and the potential at each node in the equivalent current network of the vehicle thermal management system, the total power loss of the vehicle thermal management system is determined. The target control quantity of the vehicle thermal management system is predicted based on the optimization objective of minimizing total power loss, and the target control quantity is controlled based on the predicted control sequence. The equivalent current network of the vehicle thermal management system is established based on the physical connection topology of the vehicle thermal management system. The total power loss of the vehicle thermal management system is determined based on the resistance of each component and the potential at each node in the equivalent current network of the vehicle thermal management system, including: Based on the resistance of each component and the potential at each node in the equivalent current network of the vehicle thermal management system, the current of each branch in the vehicle thermal management system is determined. The total power loss is determined based on the current in each branch of the vehicle thermal management system and the resistance of each component. Specifically, based on the resistance of each component and the potential at each node in the equivalent current network of the vehicle thermal management system, the current of each branch in the vehicle thermal management system is determined, including: Based on the current power of the potential source in the equivalent current network of the vehicle thermal management system and the corresponding physical current, the current corresponding to the potential source is determined. Based on the power consumption of the potential well in the equivalent potential network of the vehicle thermal management system and the corresponding physical potential, the current corresponding to the potential well is determined. Based on the current conditions satisfied by the current at any node in the equivalent current network flowing into the vehicle thermal management system, and the potential conditions satisfied by the potential difference in any closed loop of the vehicle thermal management system, the current of each branch in the vehicle thermal management system is determined. The process of determining the resistance of each component includes: The resistance of the three-way valve and / or proportional valve is determined based on the valve opening degree; this resistance is inversely proportional to the valve opening degree. The resistance of the water pump is determined based on the pump speed; this resistance is inversely proportional to the pump speed. The resistance of a plate heat exchanger is determined based on the flow rates on both sides.

2. The method according to claim 1, characterized in that, The potential at each node is determined based on the detection data at the preset nodes, including: Based on the pressure and temperature at the preset nodes, determine the specific physical properties of the coolant in the circuit; The product of the mass flow rate of the coolant and the specific physical potential is determined as the potential at the node.

3. The method according to claim 1, characterized in that, The target control quantity of the vehicle thermal management system is predicted based on the optimization objective of minimizing total power loss, and the target control quantity is controlled based on the predicted control sequence, including: With the optimization objective being to minimize the total power loss, the current state variables of the vehicle thermal management system are processed based on the model predictive control algorithm to obtain the control sequence of the target control quantity; The target control quantity is controlled based on this control sequence.

4. The method according to claim 3, characterized in that, The model predictive control algorithm also includes the following features during the prediction process: Passenger compartment comfort constraints, battery temperature constraints, and / or physical boundary constraints of the actuators of the vehicle thermal management system.

5. A control device for a vehicle thermal management system, characterized in that, include: The potential determination module is used to determine the potential at each node in the loop of the vehicle thermal management system based on the detection data at the preset node. The total power loss calculation module is used to determine the total power loss of the vehicle thermal management system based on the resistance of each component and the potential at each node in the equivalent current network of the vehicle thermal management system. The target control quantity control module is used to predict the target control quantity of the vehicle thermal management system based on the optimization objective of minimizing total power loss, and to control the target control quantity based on the predicted control sequence. The equivalent current network of the vehicle thermal management system is established based on the physical connection topology of the vehicle thermal management system. The total power loss calculation module includes: The current calculation unit is used to determine the current of each branch in the vehicle thermal management system based on the resistance of each component and the potential at each node in the equivalent current network of the vehicle thermal management system. The total power loss determination unit is used to determine the total power loss based on the current of each branch and the resistance of each component in the vehicle thermal management system. The flow calculation unit includes: The potential source current calculation subunit is used to determine the current corresponding to the potential source based on the current power of the potential source in the equivalent current network of the vehicle thermal management system and the corresponding physical current. The potential well current calculation subunit is used to determine the current corresponding to the potential well based on the power consumption of the potential well in the equivalent current network of the vehicle thermal management system and the corresponding physical current. The current calculation subunit is used to determine the current of each branch in the vehicle thermal management system based on the current condition satisfied by the current of any node in the equivalent current network flowing into the vehicle thermal management system, and the potential condition satisfied by the potential difference in any closed loop of the vehicle thermal management system. The device further includes: a resistance determination module, specifically used for: The resistance of the three-way valve and / or proportional valve is determined based on the valve opening degree; this resistance is inversely proportional to the valve opening degree. The resistance of the water pump is determined based on the pump speed; this resistance is inversely proportional to the pump speed. The resistance of a plate heat exchanger is determined based on the flow rates on both sides.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the control method of the vehicle thermal management system according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the control method of the vehicle thermal management system according to any one of claims 1-4.

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