Thermal management system and operating method for electric vehicles

Abstract

A thermal management system for an electric vehicle and a method for operating the same are provided, which achieves an optimal balance between user comfort and driving range by applying supervised learning. An AI-based control approach considers user requirements regarding driving speed, interior temperature, and conditions of a selected driving route to generate a final control means output that operates the thermal management system with minimum energy consumption and maximum COP. A continuous and highly efficient thermal management system for an electric vehicle is provided, comprising a thermal system including a refrigerant loop and / or a coolant loop and a control means. The control means applies a data-driven supervised learning model in combination with a control optimization that provides optimal control set points, with standard sub-controls.

Description

Cross-reference to related applications

[0001] This application is based on German Patent Application No. 102022115096.8 filed on June 15, 2022, the entire content of which is incorporated herein by reference.

Technical Field

[0002] The present disclosure relates to a thermal management system for an electric vehicle and an operating method thereof.

Background Art

[0003] In battery-driven electric vehicles, a cruising range and driver comfort are required, and further efficiency improvement of the thermal system of electric vehicles is being promoted. For this reason, the thermal management system of electric vehicles and its control are becoming more complex. Therefore, in order to improve the efficiency of the thermal management system of electric vehicles, it is desirable to apply advanced control techniques. The current state-of-the-art thermal systems have many actuators, which are controlled to achieve the target temperatures of the cabin, battery, and electric powertrain. Conventionally, in order to achieve the required heating performance with minimum energy loss, system calibration has been required through a number of tests under various conditions, which greatly increases the labor of system calibration. The discovered calibration values are stored in a lookup table and the control is configured. Despite all calibration efforts, perfect optimal control is not always guaranteed throughout the operating range.

[0004] Citation Document 1 discloses a heat exchange system for cooling purposes in large-scale industrial applications. In order to improve the efficiency of the heat exchange system, a supervised learning model is applied and the operating parameters optimal for a specific cooling application are determined.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] The purpose of this disclosure is to provide an electric vehicle thermal management system and its operation method that can optimize the balance between user comfort and driving range by applying supervised learning.

[0007] The above objectives are addressed by the thermal management system for electric vehicles as disclosed herein.

[0008] The AI-based control method described herein generates a final output of control means that operates the thermal management system with minimum energy consumption and maximum COP, taking into account the user's requirements for driving speed, cabin temperature, and conditions of the selected driving route. A continuous and highly efficient thermal management system for electric vehicles is provided, comprising a thermal system including a refrigerant loop and / or a cooling water loop and control means. The refrigerant system is also known as an air conditioning system or heat pump system (hereinafter also referred to as an H / P system). The heat pump is a critical thermal system for adjusting, heating, or cooling the cabin to a comfortable temperature range and / or adjusting, heating, or cooling the high-voltage battery to an optimal operating state. A cooling water system is required to adjust the electric powertrain and battery to an optimal and robust temperature range. The control means applies a data-driven supervised learning model to standard sub-controls in combination with control optimization that provides optimal control setpoints. The AI-based control means automatically ensures optimal efficiency of the refrigerant or cooling water system under all conditions, extending the range of the electric vehicle. AI-based control systems significantly reduce calibration effort because they can automatically train AI models offline using, for example, neural networks. A digital twin of the thermal plant is trained using supervised learning methods. The data used for training is obtained from an accurate simulation environment that enables efficient data generation through the high-speed and continuous operation of the simulation plant. Alternatively, the above data may be obtained directly from the target vehicle. Once the supervised learning model is trained, it can be used as a function call for the optimal control problem. Both the trained model and the optimization method can be executed in real time to continuously ensure optimality.

[0009] This disclosure has the following advantages: - Guaranteed optimal system performance, i.e., efficiency. - Reduction of calibration effort by replacing the current map-based calibration. - Simple module-level updates by retraining the model with updated datasets.

[0010] For data-driven supervised learning models, artificial neural networks are preferably applied. For optimization in control optimization, group optimization or gradient variance methods are preferably applied.

[0011] This disclosure may be directed to a thermal management system that includes a thermal system, including an HP system, for adjusting, heating, or cooling the cabin to a comfortable temperature range, taking into account the battery charge state, ambient conditions, the state of the HP system, and the state of the vehicle.

[0012] The condenser means includes an inner condenser located within the HVAC flow path and operated by inner condenser power, and / or an outer condenser located outside the HVAC flow path and in communication with a heat core located within the HVAC flow path on the cooling water side, and operated by outer condenser power.

[0013] This disclosure demonstrates how to determine the optimal input parameters for a control means using a data-driven supervised learning model with an optimization algorithm.

[0014] This disclosure may define preferred target power calculation means used as input to a data-driven supervised learning model.

[0015] This disclosure may define the most appropriate optimal control setpoint, which is output by and input to an optimization algorithm / unit.

[0016] This disclosure may determine the most appropriate lower-level control output used for the operation and control of a thermal management system.

[0017] This disclosure relates to a thermal management system comprising a thermal system including an electric powertrain cooling system, which may be directed to a thermal management system for regulating and operating an electric powertrain and battery within an optimal and robust temperature range.

[0018] According to this disclosure, the cooling water loop of the outer condenser may be connected to the cooling water loop of the powertrain / battery.

[0019] This disclosure may specify optimal inputs, intermediate outputs, and optimal control setpoints for a subordinate control unit / algorithm when the H / P system is operating in heating mode to warm the cabin air.

[0020] This disclosure may specify optimal inputs, intermediate outputs, and optimal control setpoints for a subordinate control unit / algorithm when the H / P system is operated in a dehumidification mode that reheats and dehumidifies cooled cabin air.

[0021] This disclosure may specify optimal inputs, intermediate outputs, and optimal control setpoints for a lower-level control unit / algorithm when the H / P system is operated in a cooling mode that cools the cabin air. [Brief explanation of the drawing]

[0022] The drawings described herein are for illustrative purposes only and do not limit the scope of this disclosure. In some of the drawings, corresponding parts are indicated by their corresponding reference numbers. [Figure 1] Figure 1 shows the H / P system, which heats and cools cabin air as part of a thermal system managed and controlled by a thermal management system. [Figure 2] Figure 2 shows the electric powertrain / battery cooling system for maintaining the electric powertrain and battery within the desired temperature range. [Figure 3] Figure 3 shows the control means for controlling the thermal system shown in Figures 1 and 2. [Figure 4] Figure 4 shows a first embodiment of the present disclosure that applies when cabin heating is required. [Figure 5] Figure 5 shows a second embodiment of the present disclosure that applies when cabin dehumidification is required. [Figure 6]Figure 6 shows a third embodiment of the present disclosure that applies when cabin cooling is required. [Figure 7] Figure 7 shows a fourth embodiment of the present disclosure, which is a generalized H / P system control. [Figure 8] Figure 8 shows a fifth embodiment of the present disclosure applied to maintain an electric powertrain / battery cooling system within an appropriate temperature range under conditions imposed by the driver, ambient conditions, and vehicle component specifications. [Figure 9] Figure 9 shows a sixth embodiment with a modified H / P system. [Figure 10] Figure 10 shows a seventh embodiment in which an interconnection between the H / P system and the electric powertrain / battery cooling water system is added. [Modes for carrying out the invention]

[0023] Next, exemplary embodiments will be described in more detail with reference to the accompanying drawings. The following description is purely illustrative and is not intended to limit the scope, application, or use of the present disclosure.

[0024] Figure 1 shows a heat pump or H / P system 100 for heating and cooling cabin air, Figure 2 shows an electric powertrain / battery cooling system 200 for maintaining the electric powertrain and battery within a desired temperature range, and Figure 3 shows control means 300 for controlling the thermal management system.

[0025] The H / P system 100 comprises a chiller 2, an inner condenser 102 (an example of condenser means), an evaporator 104, a compressor 106, an outer heat exchanger 108 having fans 110 interconnected via a refrigerant loop 112, and a blower 114 for supplying air as a cooling fluid to the evaporator 104 and the inner condenser 102. Furthermore, the H / P system 100 includes an accumulator 116 for storing liquid refrigerant and separating liquid refrigerant from gaseous refrigerant. The outlet of the compressor 106 is connected to the inlet of the inner condenser 102. The outlet of the inner condenser 102 is connected to an outer heat exchanger expansion valve 118, which is connected to the inlet of the outer heat exchanger 108. The opening degree of the outer heat exchanger expansion valve 118 is EXV OHX This is referred to as (corresponding to the opening of the outer heat exchanger expansion valve). The outlet of the outer heat exchanger 108 is connected via the first check valve 120 to a location between the evaporator expansion valve 122 and the chiller expansion valve 124. The opening of the evaporator expansion valve 122 is EXV EVA (This is referred to as the evaporator expansion valve opening.) The opening of the chiller expansion valve 124 is EXV CHI This is referred to as (corresponding to the chiller expansion valve opening). The chiller expansion valve 124 is connected to the refrigerant inlet of chiller 2. The evaporator expansion valve 122 is connected to the inlet of evaporator 104. The outlet of evaporator 104 is connected to the inlet of accumulator 116 via pressure regulating valve 126. The refrigerant outlet of chiller 2 is also connected to the inlet of accumulator 116. The outlet of the outer heat exchanger 108 is similarly connected to the inlet of accumulator 116 via dehumidification control valve 130 (an example of a dehumidification valve means) and a second check valve 132. A point located between the outer heat exchanger expansion valve 118 and the outlet of the inner condenser 102 is connected via heating control valve 134 (an example of a heating valve means) to a point located between the first check valve 120 and the chiller expansion valve 124 or evaporator expansion valve 122. The internal condenser 102 and evaporator 104 are located in the air conditioning or HVAC channel 136 that enters the vehicle interior.

[0026] The electric powertrain / battery cooling system 200 shown in Figure 2 comprises the cooling water side of a chiller 2, a battery 202, a battery heater 203 (i.e., an electric battery heater), an electric powertrain 204, and a radiator 206, all interconnected via a powertrain / battery cooling water loop 208. The powertrain / battery cooling water loop 208 connects the cooling water outlet of the chiller 2 to the cooling water inlet of the battery heater 203. The cooling water outlet of the battery heater 203 is connected to the cooling water inlet of the battery 202. The cooling water outlet of the battery 202 is connected to the cooling water inlet of the chiller 2 via a two-way valve 210 (an example of valve means) and a first pump 212 (an example of cooling water pump means). The cooling water outlet of the chiller 2 is also connected to the cooling water inlet of the electric powertrain 204 and a second pump 216 (an example of cooling water pump means). The coolant outlet of the electric powertrain 204 is connected to the coolant inlet of the first pump 212 and the radiator 206 via a three-way valve 218 (an example of a valve mechanism). As shown in Figures 1 and 2, the outer heat exchanger 108 is positioned opposite the radiator 206, which is equipped with an air fan 110 and an active grill shutter 4.

[0027] The control means 300 comprises a data-driven supervised learning model unit 302, a control optimization unit 304, and a lower control unit 306. The input 318 to the control means is applied to the data-driven supervised learning model unit 302. The data-driven supervised learning model unit 302 uses the data-driven supervised learning model to calculate a cost function in the optimization domain as an intermediate output 320 to the control optimization unit 304. The optimization unit 304 (i.e., the control optimization algorithm) calculates an optimal control setpoint 322 for the lower control unit 306. The lower control unit 306 performs lower control and calculates a final control means output 324 (i.e., lower control output) for operating the thermal management system. The final control means output 324 corresponds to the lower control output.

[0028] The input 318 to the control means 300 and the data-driven supervised learning model unit 302 is selected from a set of parameters that define the target air conditions in the vehicle cabin, ambient conditions, thermal system conditions, and vehicle state. The intermediate output calculated by the data-driven supervised learning model unit 302 includes the thermal system performance coefficient COP and / or the power consumption parameter Pxx of the electrically driven components. Electrically driven components include, for example, pumps, valve actuators, fans, blowers, and compressors. The optimal control setpoint for the lower control unit 306 includes operating parameters such as compressor speed, blower speed, valve operating state, etc., and / or temperature conditions such as vehicle cabin temperature, battery temperature, coolant temperature, and refrigerant temperature.

[0029] Preferably, an artificial neural network is applied to the data-driven supervised learning model unit 302. Preferably, group optimization or gradient variance method is applied to the optimization in the control optimization unit 304.

[0030] Figure 4 shows a first embodiment of the present disclosure when cabin heating is required. An example of a suitable input to the control means 300 for cabin heating is the following input 318: Target T_air_ICDS_out = Target air temperature at the air outlet of the inner condenser (corresponding to the target temperature at the air outlet of the inner condenser), N Blower = Blower rotation speed (corresponding to target blower speed, target blower load / airflow), T_amb = ambient temperature, Target T_coolt_CHI_out = Target coolant temperature at the chiller coolant outlet (corresponding to the target chiller coolant outlet temperature), T_coolt_CHI_in = Chiller inlet cooling water temperature (corresponding to chiller cooling water inflow temperature), Vdot_coolt_CHI_in = Volumetric flow rate of cooling water flowing into the chiller (corresponding to chiller cooling water inflow mass / volume flow rate), H_amb = ambient humidity, V_spd = Vehicle speed, Recycle_ratio = Ventilation rate inside the cabin (corresponding to the recycle rate of indoor air). Fanreq coolt = Fan speed required by general HVAC control (corresponding to the target fan load rate, fan load / speed).

[0031] Among the above inputs, Target T_air_ICDS_out, N Blower , and T_amb are supplied to the inner condenser power calculation means 326 that calculates the target inner condenser power P ICDS,req as an input to the data-driven supervised learning model unit 302. Inputs such as T_coolt_CHI_out, T_coolt_CHI_in, and Vdot_coolt_CHI_in are supplied to the chiller power calculation means 328 that calculates the target chiller power P CHI,req as an input to the data-driven supervised learning model unit 302. The inner condenser power calculation means 326 and the chiller power calculation means 328 may be regarded as part of the data-driven supervised learning model unit 302. Inputs such as T_amb, H_amb, V_spd, Recycle_ratio, and Fanreq coolt are directly input to the data-driven supervised learning model unit 302. The data-driven supervised learning model unit 302 outputs the calculated value P ICDS (u) of the inner condenser power, the calculated value P CHI (u) of the chiller power, and the calculated value COP(u) of the COP as an intermediate output 320. In the control optimization unit 304, a cost function is defined as shown in the following mathematical formulas 1 to 3.

Equation

Equation

Equation

[0032] Here, w1, w2, w3 are the coefficient of performance COP, the chiller power P CHI , and the inner condenser power PICDS This is a weight that adjusts for the effects of COP. COP is the useful heating Q H or Air Conditioning Q C This is the ratio of the work (energy) put into the system to the COP (Coefficient of Performance). In heating, COP is Q H / W in That is the case.

[0033] The appropriate optimal control setting value 322 output by the control optimization unit 304 is: Optimal compressor speed N CMP,opt (corresponding to the rotational speed of compressor 106), Optimal internal condenser subcooling temperature T SC,ICDS,opt (corresponding to the subcooling temperature of the internal condenser 102), and, Optimal chiller superheating temperature T SH,CHI,opt (Includes the superheating temperature of chiller 2)

[0034] In addition, Optimal opening degree EXV of the internal condenser expansion valve 118 and chiller expansion valve 124 xx,opt , and, Optimal fan speed N fan,opt , This may also be selected as the optimal control setting value 322.

[0035] These optimal control setting values ​​322 are input to a common sub-control unit 306 that generates the final control means output 324.

[0036] Figure 5 shows a second embodiment of the present disclosure when cabin dehumidification is required. The following input 318 is shown as a preferred input to the control means 300 for cabin dehumidification: Target T_air_ICDS_out = Target air temperature at the air outlet of the internal condenser. N Blower = Blower rotation speed, T_amb = ambient temperature, Target T_air_EVA_out = Target air temperature at the evaporator air outlet (corresponding to the target temperature at the evaporator air outlet), H_amb = ambient humidity, V_spd = Vehicle speed, Recycle_ratio = Ventilation rate inside the cabin, and Fanreq coolt = Fan speed required by typical HVAC control.

[0037] Of the above inputs, Target T_air_ICDS_out, N Blower , and T_amb are the target internal condenser power P as inputs to the data-driven supervised learning model unit 302. ICDS,req The target T_air_EVA_out, N is supplied to the internal condenser power calculation means 326 for calculation. Blower And T_amb is the input to the data-driven supervised learning model unit 302, target evaporator power P EVA,req The following are input to the evaporator power calculation means 330, which calculates T_amb, H_amb, V_spd, Recycle_ratio, and Fanreq. The inner condenser power calculation means 326 and the evaporator power calculation means 330 may be considered as part of the data-driven supervised learning model unit 302. coolt Such inputs are directly input to the data-driven supervised learning model unit 302. The data-driven supervised learning model unit 302 outputs the calculated internal condenser power P of the H / P system 100 as an intermediate output 320. ICDS (u), Calculated value of evaporator power P EVA (u) and the calculated COP value COP(u) are output. In the control optimization unit 304, the cost function is defined as shown in the following equations 4 to 6.

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number

[0038] Here, w1, w2, and w3 are the performance coefficients COP and chiller power P, respectively. CHI , internal condenser power P ICDSThis is a weight that adjusts for the influence of [something].

[0039] The appropriate optimal control setting value 322 output by the control optimization unit 304 is: Optimal compressor speed N CMP,opt , Optimal evaporator superheating temperature T SH,EVA,opt (corresponding to the superheating temperature of evaporator 104), Dehumidification control valve 134 status (condition): 2WV Dehum,opt , and, Status of heating control valve 130: 2WV Heat,opt Includes.

[0040] In addition, Optimal internal condenser subcooling temperature T SC,ICDS,opt , Optimal opening degree EXV of the inner condenser expansion valve 118 and the evaporator expansion valve 122 xx,opt , and, Optimal fan speed N fan,opt , This may also be selected as the optimal control setting value 322.

[0041] These optimal control setting values ​​322 are input to a common sub-control unit 306 that generates the final control means output 324.

[0042] Figure 6 shows a third embodiment of the present disclosure when cabin cooling is required. A preferred example of input to the control means 300 for cabin dehumidification is the following input 318: Target T_air_EVA_out = Target air temperature at the evaporator air outlet. N Blower = Blower rotation speed, T_amb = ambient temperature, Target T_coolt_CHI_out = target cooling water temperature at chiller cooling water outlet, T_coolt_CHI_in = Cooling water temperature at chiller inlet, Vdot_coolt_CHI_in = Amount of cooling water flowing into the chiller, H_amb = ambient humidity, V_spd = Vehicle speed, Recycle_ratio = Ventilation rate inside the cabin, and Fanreq coolt = Fan speed required by typical HVAC control.

[0043] Target T_air_EVA_out, N Blower The above inputs, such as T_amb, are used as inputs to the data-driven supervised learning model unit 302 as the target evaporator power P EVA,reqを The inputs T_coolt_CHI_out, T_coolt_CHI_in, and Vdot_coolt_CHI_in are inputs to the evaporator power calculation means 330. The target chiller power P is input to the data-driven supervised learning model unit 302. CHI,reqを The following are supplied to the chiller power calculation means 328: T_amb, H_amb, V_spd, Recycle_ratio and Fanreq. coolt Such inputs are directly input to the data-driven supervised learning model unit 302. The data-driven supervised learning model unit 302 outputs the calculated evaporator power P of the H / P system 100 as an intermediate output 320. EVA (u), Chiller power calculation value P CHI (u) and the calculated COP value COP(u) are output. In the control optimization unit 304, the cost function is defined as shown in the following equations 7 to 9.

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[0044] The appropriate optimal control setting value 322 output by the control optimization unit 304 is: Optimal compressor speed N CMP,opt , Optimal external heat exchanger subcooling temperature T SC,OHX,opt(corresponding to the subcooling temperature of the outer heat exchanger 108), and, Optimal chiller superheating temperature T SH,CHI,opt That is the case.

[0045] In addition, Optimal opening degree EXV of the outer heat exchanger expansion valve 118 and the chiller expansion valve 124 xx,opt , and Optimal fan speed N fan,opt , This may also be selected as the optimal control setting value 322.

[0046] These optimal control setting values ​​322 are input to a common sub-control unit 306 that generates the final control means output 324.

[0047] Figure 7 shows a generalized H / P system control, which is essentially a combination of the first, second, and third embodiments. An example of a suitable input to the control means 300 for cabin heating is the following input 318: Target T_air_ICDS_out = Target air temperature at the air outlet of the internal condenser. N Blower = Blower rotation speed (corresponding to blower load / airflow), T_amb = ambient temperature, Target T_air_EVA_out = Target air temperature at the evaporator air outlet. Target T_coolt_CHI_out = target cooling water temperature at chiller cooling water outlet, T_coolt_CHI_in = Cooling water temperature at chiller inlet, Vdot_coolt_CHI_in = Amount of cooling water flowing into the chiller, H_amb = ambient humidity, V_spd = Vehicle speed, Recycle_ratio = Ventilation rate inside the cabin, and Fanreq coolt = Fan speed required by typical HVAC control.

[0048] The target internal condenser power P is calculated using the internal condenser power calculation means 326, chiller power calculation means 328, and evaporator power calculation means 330, which are not shown in Figure 7. ICDS,req Target chiller power P CHI,req Target evaporator power P EVA,req The following are calculated and input to the data-driven supervised learning model unit 302. Inputs such as T_amb, H_amb, and V_spd are directly input to the data-driven supervised learning model unit 302. Furthermore, Recycle_ratio and Fanreq coolt This may be selected as input 318 to the control means and input to the data-driven supervised learning model unit 302.

[0049] The data-driven supervised learning model unit 302 outputs the calculated internal condenser power P of the H / P system 100 as an intermediate output 320. ICDS (u), Calculated value of evaporator power P EVA (u), Chiller power calculation value P CHI (u) and the calculated COP value COP(u) are output. The control optimization unit 304 defines the cost function as shown in the following equation.

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number

[0050] In addition, Optimal opening degrees EXV for the outer heat exchanger expansion valve 118, chiller expansion valve 124, and evaporator expansion valve 122. xx,opt , and Optimal fan rotation speed N Blower,opt , This may also be selected as the optimal control setting value 322.

[0051] These optimal control setting values ​​322 are input to a common sub-control unit 306 that generates the final control means output 324.

[0052] Figure 8 shows a fifth embodiment of the present disclosure applied to maintain an electric powertrain / battery cooling system within a suitable temperature range under conditions imposed by the driver, ambient conditions, and vehicle component specifications. An example of a suitable input to the control means 300 for cabin dehumidification is the following input 318: Ambient temperature T_amb, Vehicle speed V_spd, Battery temperature T_battery, Electric powertrain temperature T_ePT, Target chiller power P CHI,req (i.e., required chiller power), Target power P for electric powertrains ePT,req (Power requirements for the electric powertrain), and, Target battery power P battery,req (i.e., required battery power).

[0053] The data-driven supervised learning model unit 302 analyzes the power consumption P of the electric powertrain 204. ePT (u) is a calculated value, and the output power P of battery 202 is also calculated. battery The calculated value of (u) (i.e., the calculated power supply), and the power consumption P of the electric drive auxiliary component. aux Output the calculated value of (u). Here P auxThis represents the power consumption of all electrically driven components, such as fans, blowers, water pumps, and valve actuators. In the control optimization unit 304, a cost function is defined as shown in the following equations 13 to 15.

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[0054] The appropriate optimal control setting value 322 output by the control optimization unit 304 is: Optimal valve control setting value MCVe ctrl (corresponding to optimal valve mechanism control parameter), Optimal battery heater control PTC ctrl (corresponds to optimal electric heater control parameters), Optimal load of the first water pump 212 eWP1 duty, The optimal load eWP2 duty of the second water pump 216, and The optimal fan control is Fan ctrl.

[0055] In addition, Optimal electric powertrain oil load ePT oil pump, Optimal Active Grill Shutter Control (AGS) grill shutter, Optimal auxiliary component control parameters: Aux ctrl, and Optimal chiller power P CHI,opt , This may also be selected as the optimal control setting value 322.

[0056] The electric powertrain / battery cooling system shown in Figure 2 is fitted with a two-way valve 210 and a three-way valve 218. A multi-control valve arrangement may be used instead of the single valves described above.

[0057] In any embodiment, the control optimization unit 304 calculates optimal control setpoints that allow the thermal management system to operate with minimum energy consumption and maximum COP, taking into account the user's requirements regarding driving speed, in-vehicle temperature, and conditions of the selected driving route.

[0058] Figure 9 shows a sixth embodiment of a modified H / P system in which an external condenser 138 (an example of condenser means) located outside the HVAC channel 136 is applied instead of the internal condenser 102, and a heat core 140 located inside the HVAC channel 136 is applied. The external condenser 138 and the heat core 140 are interconnected via an external condenser cooling water loop 142 equipped with a cooling water pump 144. The heat core 140 inside the HVAC channel 136 is a liquid / air heat exchanger for warming the room air with heat from the external condenser 138.

[0059] When using the outer condenser 138, the control parameters corresponding to the control parameters of the inner condenser 102 are as follows: P OCDS,req = Target value for the external condenser power, P OCDS (u) = Calculated value of the outer condenser power, T_coolt_OCDS_out = target outer condenser cooling water outlet temperature, T SC,OCDS = Outer condenser subcooling temperature (corresponding to the subcooling temperature of outer condenser 138), The volumetric flow rate of the cooling water in the outer condenser cooling water loop 142 (i.e., the target volumetric flow rate of the cooling water passing through the outer condenser 138), and the cooling water pump load factor.

[0060] These parameters need to be input to the control means 300 and optimized. Target T_air_OCDS_out, T_amb, and the cooling water flow rate through the outer condenser 138 are input to the target outer condenser power P as input to the data-driven supervised learning model unit 302. OCDS,req It is supplied to the external condenser power calculation means that calculates the power.

[0061] Figure 10 shows a seventh embodiment in which the heat core 140 is provided in addition to the inner condenser 102, rather than replacing it with the heat core 140. Furthermore, the outer condenser coolant loop 142 is connected to the powertrain / battery coolant loop 208. The heat core 140 heats the cabin air with heat from the outer condenser and heat from the electric powertrain 204 and / or battery 202. However, in a bypass with a bypass valve 220, the powertrain / battery coolant loop 208 and the outer condenser coolant loop 142 may be separated.

[0062] The embodiments shown in Figures 9 and 10 are based on the HP system 100 and the electric powertrain / battery cooling system 200. Figures 9 and 10 basically show only the additions and modifications. Components not shown in Figures 9 and 10 are elements of the sixth and seventh embodiments.

[0063] The control means and methods described in this application can be fully implemented by a special-purpose computer configured with a processor programmed to perform one or more specific functions embodied in a computer program.

Claims

1. A thermal management system for an electric vehicle with a cabin, The thermal system (100, 200) includes sensor means for detecting ambient environmental parameters, driving parameters, and the state of the thermal system and the electric vehicle, and control means (300) configured to generate a lower control output (324) for operating the thermal management system (100, 200) based on a control means input (318) including user requests and parameters detected by the sensor means. The thermal system (100, 200) includes a cooling or heating component equipped with an electrically driven component and an electrically driven auxiliary component, and a cooling or heating component not equipped with an electrically driven component and an electrically driven auxiliary component. The control means (300) is Data-driven supervised learning modeling section (302), Control optimization unit (304), and It includes a lower control unit (306), The input (318) to the control means (300) is applied to the data-driven supervised learning model unit (302), The data-driven supervised learning model unit (302) is configured to calculate the cost function in the optimization domain of the control optimization unit (304) and generate the calculated intermediate output (320). The control optimization unit (304) is configured to calculate the optimal control set value (322) for the lower control unit (306). The lower control unit (306) is configured to calculate the lower control output (324) for the operation of the thermal management system. The input (318) to the control means is selected from a set of parameters that define the target air conditions inside the vehicle cabin, ambient conditions, thermal system conditions, and vehicle state. The intermediate output (320) calculated by the data-driven supervised learning model unit (302) is the coefficient of performance (COP) of the thermal system (100, 200) and / or the power consumption parameter (P) of the electrical components. xx ) including, The optimal control setpoint (322) for the lower control unit (306) includes the operating parameters and / or temperature conditions of the thermal system, in a thermal management system.

2. A thermal management system according to claim 1, The thermal system comprises an H / P system (100) interconnected via a refrigerant loop (112), having chillers (2), condenser means (102, 138), an evaporator (104) located within an HVAC (136), a compressor (106), an outer heat exchanger (108) with a fan (110), a blower (114) for air as a cooling fluid for the evaporator (104) and the inner condenser (102), a heating valve means (134), and a dehumidifying valve means (130). The intermediate output (320) calculated by the data-driven supervised learning model unit (302) is: The coefficient of performance (COP) of the aforementioned HP system, The following parameter list includes two parameters selected from the following parameter list: Calculated condenser power (P ICDS (u), P OCDS (u)) Chiller power calculation value (P CHI (u)), and, Calculated evaporator power value according to capacity (P EVA (u)) The optimal control setting value (322) for the lower control unit (306) is the rotational speed of the compressor (N CMP A thermal management system including ) and one temperature parameter.

3. A thermal management system according to claim 2, The condenser means is arranged within the HVAC flow path (136), and the inner condenser power (P ICDS An inner condenser (102) having ) and / or an outer condenser power (P) located outside the HVAC flow path (136). OCDS A thermal management system comprising a heat core (140) for heating the in-vehicle air, which is located within the HVAC flow path (136), and an outer condenser (138) connected on the coolant side.

4. A thermal management system according to claim 3, The input to the control means (318) is the following set of parameters: Ambient temperature (T_amb), Ambient humidity (H_amb), Vehicle speed (V_spd), Indoor air recycling rate (Recycle_ratio), Target fan load ratio (Fanreq coolt ) Target inner condenser air outlet temperature (T_air_ICDS_out), Target outer condenser cooling water outlet temperature (T_coolt_OCDS_out), Target evaporator air outlet temperature (T_air_EVA_out), Target blower speed (N) Blower ), Target chiller cooling water outlet temperature (T_coolt_CHI_out), Chiller cooling water inflow temperature (T_coolt_CHI_in), and, Chiller cooling water inflow mass / volume flow rate (Vdot_coolt_CHI_in), A thermal management system can be selected from the following options.

5. the target inner condenser air outlet temperature (T_air_ICDS_out), the ambient temperature (T_amb), and the target blower speed (N Blower Based on this, the target inner condenser power (P ICDS,req The thermal management system according to claim 4, further comprising an internal condenser power calculation means (326) configured to calculate ).

6. the target evaporator air outlet temperature (T_air_EVA_out), the ambient temperature (T_amb), and the target blower speed (N Blower Based on this, the target evaporator power (P EVA，req The thermal management system according to claim 4, further comprising evaporator power calculation means (330) configured to calculate ).

7. Based on the target outer condenser cooling water outlet temperature (T_coolt_OCDS_out), the ambient temperature (T_amb), and the cooling water flow rate through the outer condenser (138), OCDS，req The thermal management system according to claim 4, further comprising an outer condenser power calculation means configured to calculate ).

8. The target chiller power (P CHI，req The thermal management system according to claim 4, further comprising chiller power calculation means (328) configured to calculate ).

9. A thermal management system according to claim 2, The optimal control setting value (322) of the control optimization unit (304) is one of the following groups of operating parameters of the thermal management system: The rotational speed of the compressor (N CMP ), The superheating temperature of the evaporator (T SH,EVA ), The subcooling temperature (T) of the inner condenser SC,ICDS ), Subcooling temperature of the outer condenser (T SC,OCDS ), The subcooling temperature of the outer heat exchanger (T SC,OHX ), The chiller's superheating temperature (T SH,CHI ), Chiller expansion valve opening (EXV CHI ), and Target Fan Load Rate (Fanreq coolt ), A thermal management system can be selected from the following options.

10. A thermal management system according to claim 3, The lower control output (324) of the lower control unit (306) is one of the following groups of operating parameters of the thermal management system: State of the aforementioned heating valve means (2WV heat ), State of the dehumidifying valve means (2WV dehum ), Evaporator expansion valve opening (EXV EVA ), External heat exchanger expansion port opening (EXV) OHX ), Chiller expansion valve opening (EXV CHI ), Target blower speed (N) Blower ), The target volumetric flow rate of the cooling water passing through the outer condenser (138), and Fan load / speed (Fanreq coolt ), A thermal management system can be selected from the following options.

11. A thermal management system according to claim 1, The thermal system comprises an electric powertrain / battery coolant system (200) interconnected via a powertrain / battery coolant loop (208), which includes an electric powertrain (204), a battery (202), valve means (210, 218), coolant pump means (212, 216), an electric battery heater (203), a cooler (2), and a radiator (206) with a fan (110). The input to the control means (318) is, Ambient temperature (T_amb), Vehicle speed (V_spd), Battery temperature (T_battery), Electric powertrain temperature (T_ePT), Target chiller power (P CHI,req ), Target power of electric powertrain (P ePT,req ), and, Target battery power (P battery,req ) including, The intermediate output (320) calculated by the data-driven supervised learning model unit (302) is: Calculated power consumption of electric powertrain (P ePT (u)) Calculated value of battery output power (P battery (u)), and, Calculated power consumption of electric drive auxiliary components (P aux (u) includes, The optimal control set value (322) for the lower control unit (306) is: Optimal valve mechanism control parameter (MCVe ctrl), Optimal electric heater control parameters (PTC ctrl), Optimal auxiliary component control parameters (Aux ctrl), and, Optimal chiller power (P CHI,opt A thermal management system, including )

12. A thermal management system according to claim 11, The powertrain / battery coolant loop (208) is connected to the outer condenser coolant loop (142), A thermal management system in which the cooling water inlet of the heat core (140) is connected to the cooling water outlet of the outer condenser (138).

13. A method for operating a thermal management system according to any one of claims 1 to 12, The data-driven supervised learning model calculates a cost function in the optimization domain to generate the optimal control setpoint (322) for lower control (306). The data-driven supervised learning model generates an intermediate output (320) that forms the input to the control optimization unit (304) based on the input (318). The control optimization unit (304) generates an optimal control setpoint (322) used to generate a lower control output (324) as an operating parameter of the thermal management system. The input (318) to the control means (300) is selected from a set of parameters that define the target air conditions in the vehicle cabin, ambient conditions, thermal system conditions, and vehicle state. The intermediate output (320) calculated by the data-driven supervised learning model includes the coefficient of performance (COP) of the thermal system (100, 200) and / or power consumption parameters of the electrical components. A method wherein the optimal control setpoint (322) for the lower control (306) includes the operating parameters and / or temperature conditions of the thermal system.

14. The method according to claim 13, The thermal system is an H / P system (100), The intermediate output (320) calculated by the data-driven supervised learning model includes at least the calculated performance coefficient (COP(u)) of the HP system and two parameters selected from the following list of parameters: Calculated value of the internal condenser power (P ICDS (u)) Calculated value of the external condenser power (P OCDS (u)) Chiller power calculation value (P CHI (u)), and Calculated value of evaporator power (P EVA (u)) The optimal control setting value (322) for the lower control is the rotational speed of the compressor (N CMP A method comprising ) and one temperature parameter.

15. The method according to claim 14, The input (318) to the aforementioned data-driven supervised learning model consists of the following set of parameters: Ambient temperature (T_amb), Ambient humidity (H_amb), Vehicle speed (V_spd), Indoor air recycling rate (Recycle_ratio), Target Fan Load Rate (Fanreq coolt ), Target inner condenser air outlet temperature (T_air_ICDS_out), Target outer condenser cooling water outlet temperature (T_coolt_OCDS_out), Target evaporator air outlet temperature (T_air_EVA_out), Target blower speed (N) Blower ), Target chiller cooling water outlet temperature (T_coolt_CHI_out), Chiller cooling water inflow temperature (T_coolt_CHI_in), and, Chiller cooling water inflow mass / volume flow rate (Vdot_coolt_CHI_in), The method of selection.

16. The optimal control setting value (322) of the control optimization unit (304) is one of the following groups of operating parameters of the thermal management system: The rotational speed of the compressor (N CMP ), Evaporator superheating temperature (T SH,EVA ), Subcooling temperature of the internal condenser (T SC,ICDS ), Subcooling temperature of the outer heat exchanger (T SC,OHX ), Chiller overheating temperature (T SH,CHI ), Chiller expansion valve opening (EXV CHI ), and Target Fan Load Rate (Fanreq coolt ), The method according to claim 14, selected from among.

17. A method for operating a thermal management system according to claim 14, The lower control output (324) of the lower control is the following group of operating parameters of the thermal management system: Condition of the heating valve means (2W) heat ), Dehumidifying valve mechanism status (2W) heat ), Evaporator expansion valve opening (EXV EVA ), External heat exchanger expansion port opening (EXV) OHX ), Chiller expansion valve opening (EXV CHI ), Target blower load / speed (N Blower ), and, Fan load / speed (Fanreq coolt ), The method of selection.

18. A method for operating the thermal management system described in claim 14 in heating mode, The input (318) to the aforementioned data-driven supervised learning model is: Target inner condenser air outlet temperature (T_air_ICDS_out), Target blower speed (N) Blower ), Target chiller cooling water outlet temperature (T_coolt_CHI_out), Chiller cooling water inlet temperature (T_coolt_CHI_in), Chiller cooling water inflow mass / volume flow rate (Vdot_coolt_CHI_in), Ambient temperature (T_amb), Ambient humidity (H_amb), Vehicle speed (V_spd), Indoor air recycling rate (Recycle_ratio), and Target Fan Load Rate (Fanreq coolt ) including, The intermediate output (320) of the aforementioned data-driven supervised learning model is: Calculated value of the internal condenser power (P ICDS (u)) Calculated value of chiller power ( PCHI (u)), and This includes the calculated value of the performance coefficient (COP) of the aforementioned H / P system, The optimal control setting value (322) for the lower-level control is, The rotational speed of the compressor (N CMP ), Evaporator superheating temperature (T SH,EVA ), and, Subcooling temperature of the internal condenser (T SC,ICDS A method that includes )

19. A method for operating the thermal management system described in claim 14 in dehumidification mode, The input (318) to the aforementioned data-driven supervised learning model is: Target inner condenser air outlet temperature (T_air_ICDS_out), Target evaporator air outlet temperature (T_air_EVA_out), Target blower speed (N) Blower ), Ambient temperature (T_amb), Ambient humidity (H_amb), and, Includes vehicle speed (V_spd), The intermediate output (320) of the data-driven supervised learning model is Calculated value of the internal condenser power (P ICDS (u)) Calculated value of evaporator power (P EVA (u)), and, This includes the calculated value of the performance coefficient (COP) of the aforementioned H / P system, The optimal control setting value (322) for the lower-level control is at least, The rotational speed of the compressor (N CMP ), Evaporator superheating temperature (T SH,EVA ), Condition of the heating valve means (2WV dehum ), and, Dehumidifying valve mechanism status (2W) heat A method that includes )

20. A method for operating the thermal management system described in claim 14 in cooling mode, The input (318) to the aforementioned data-driven supervised learning model is: Target evaporator air outlet temperature (T_air_EVA_out), Target blower speed (N) Blower ), Target chiller cooling water outlet temperature (T_coolt_CHI_out), Chiller cooling water inlet temperature (T_coolt_CHI_in), Chiller cooling water inflow mass / volume flow rate (Vdot_coolt_CHI_in), Ambient temperature (T_amb), Ambient humidity (H_amb), and, Includes vehicle speed (V_spd), The intermediate output (320) of the data-driven supervised learning model is Calculated value of chiller power (P CHI (u)) Calculated value of evaporator power (P EVA (u)), and, This includes the calculated value of the performance coefficient (COP) of the aforementioned H / P system, The optimal control setting value (322) for the lower-level control is, The rotational speed of the compressor (N CMP ), Superheat temperature of the outer heat exchanger (T) SH,OHX ), and び, Chiller overheating temperature (T SH,CHI A method that includes )

21. The method according to claim 14, Target inner condenser power (P ICDS,req ) is the target inner condenser air outlet temperature (T_air_ICDS_out), ambient temperature (T_amb), and blower speed (N Blower A method calculated based on ).

22. The method according to claim 14, Target outer condenser power (P OCDS,req The method is calculated based on the target outside condenser cooling water outlet temperature (T_coolt_OCDS_out), the ambient temperature (T_amb), and the cooling water flow rate through the outside condenser (138).

23. The method according to claim 14, Target evaporator power (P EVA,req ) is determined by the target evaporator air outlet temperature (T_air_EVA_out), ambient temperature (T_amb), and blower speed (N Blower A method calculated based on ).

24. The method according to claim 14, Target chiller power (P CHI,req The method is calculated based on the target chiller coolant outlet temperature (T_coolt_CHI_out), chiller coolant inlet temperature (T_coolt_CHI_in), and chiller coolant inlet mass / volume flow rate (Vdot_coolt_CHI_in).

25. A method for operating the thermal management system according to claim 10, which has an electric powertrain / battery cooling system (200), in electric powertrain cooling system mode, The input (318) to the aforementioned data-driven supervised learning model is: Ambient temperature (T_amb), Includes vehicle speed (V_spd), Battery temperature (T_battery), Electric powertrain temperature (T_ePT), Target chiller power (P CHI,req ), Target power of electric powertrain (P ePT,req ), and Target battery power (P battery,req ) including, The intermediate output (320) calculated by the data-driven supervised learning model is: Calculated power consumption of electric powertrain (P ePT (u)) Calculated value of battery power consumption (P battery (u)), and Calculated power consumption of electric drive auxiliary components (P aux (u) includes, The optimal control setting value (322) for lower-level control is: Optimal valve mechanism control parameter (MCVe ctrl), Optimal electric heater control parameters (PTC ctrl), Optimal auxiliary component control parameters (Aux ctrl), and, Optimal chiller power (P CHI A method that includes )

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