System and method for controlling a battery and an air conditioner

Abstract

The present invention relates to a system and method for controlling a battery and an air conditioner. The method for controlling a battery and an air conditioner includes receiving, by a prediction device, parameters of a plurality of first factors related to a battery thermal management system (BTMS) configured to manage a battery temperature of a vehicle and parameters of a plurality of second factors related to an air conditioning system configured to manage an interior air conditioning of the vehicle. The method for controlling a battery and an air conditioner further includes inputting, by the prediction device, the received parameters of the plurality of first and second factors to a temperature prediction model to predict an interior temperature of the vehicle. The method additionally includes controlling, by an integrated controller, the BTMS and the air conditioning system in an integrated manner based on the predicted interior temperature and one or more set target interior temperatures.

Description

[0001] Cross-references to related applications This application claims the benefit and priority of Korean Patent Application No. 10-2025-0036982, filed on March 24, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to a system and method for controlling batteries and air conditioners, which are used to control battery and air conditioning systems. Background Technology

[0003] The statements in this section are provided only as background information in relation to the present invention and do not constitute prior art.

[0004] The loop-type air conditioning system included in the vehicle is an air conditioning system designed to effectively cool larger spaces, and can maintain a comfortable temperature inside the vehicle while providing a more spacious interior.

[0005] Currently, the air conditioning systems of large vehicles (e.g., buses) are controlled based on uniform, fixed values ​​set during development with worst-case operating conditions (worst-case scenarios). This fails to reflect the various variables of actual operating conditions, leading to unnecessary energy consumption and system inefficiency. For example, the tendency to maintain maximum cooling performance even under conditions that are not the worst results in energy waste and also shortens battery life.

[0006] Furthermore, in air conditioning systems employing a Battery Thermal Management System (BTMS), controlling both air conditioning and battery cooling based on only a single representative temperature reading limits the ability to reflect temperature variations across multiple areas of a spacious vehicle interior. In other words, in large vehicles, optimal collaborative thermal management control for each area is not feasible, potentially leading to uneven perceived temperature and increased discomfort for passengers. Summary of the Invention

[0007] Various aspects of the present invention provide a system for controlling a battery and an air conditioning system, which generates a temperature prediction model by using parameters of control factors for the battery thermal management system and the air conditioning system, predicts the interior temperature of the vehicle in real time by using the temperature prediction model, and integrates the battery thermal management system or the air conditioning system according to the predicted temperature to provide optimal air conditioning performance.

[0008] The technical aspects to be achieved by this invention are not limited to those described above. Other technical aspects not described herein will be more clearly understood by those skilled in the art through the following description.

[0009] According to an embodiment of the present invention, a system for controlling a battery and an air conditioning system is provided. The system includes a predictive device configured to receive parameters of a plurality of first factors related to a battery thermal management system (BTMS) and parameters of a plurality of second factors related to an air conditioning system, the BTMS being configured to manage the battery temperature of a vehicle and the air conditioning system being configured to manage the interior air conditioning of the vehicle. The predictive device is further configured to input the received parameters of the plurality of first and second factors into a temperature prediction model to predict the interior temperature of the vehicle. The system also includes a unified controller configured to control the BTMS and the air conditioning system in a unified manner based on the predicted interior temperature and one or more set target interior temperatures.

[0010] The multiple first and second factors may include factors selected from multiple candidate factors associated with multiple actuators included in the BTMS and air conditioning system. The factors may be selected based on predetermined conditions related to their correlation with the vehicle's average interior temperature.

[0011] The average interior temperature of a vehicle can be the average seat temperature of the vehicle.

[0012] Multiple first and second factors may include two or more of the following: compressor temperature, evaporator temperature, battery cell temperature, battery consumption voltage, valve angle, fan speed, refrigerant pressure, heater temperature, inverter consumption current, inverter temperature, battery cooling sequence, internal air temperature, external air temperature, or state of charge (SOC) change.

[0013] The prediction device can be further configured to receive parameters of a third factor related to the vehicle's driving state and input the parameters into a temperature prediction model.

[0014] The prediction device can be configured to predict the interior temperature of the vehicle for each of multiple regions based on parameters of multiple first and second factors.

[0015] The prediction device can be further configured to determine the average value of the predicted internal temperature for each of the multiple regions.

[0016] Multiple areas may include: the area where the driver's seat of the vehicle is located, the area where one or more passenger seats are located, and the area where one or more air vents are located.

[0017] A temperature prediction model can be trained based on parameters of multiple first and second factors, as well as the actual temperature detected for each of multiple areas of the vehicle.

[0018] The integrated controller can be configured to determine one or more target actuators to be controlled among a plurality of actuators included in the BTMS and air conditioning system, based on a predicted internal temperature and one or more set target internal temperatures. The integrated controller can also be configured to generate control values ​​for each of the target actuators. The integrated controller can further be configured to output the generated control values ​​to the system within the BTMS and air conditioning system that includes the target actuators. The system including the target actuators can be configured to control the operation of the target actuators based on the received control values.

[0019] According to another embodiment of the present invention, a method for controlling a battery and an air conditioning system is provided. The method includes receiving, by a predictive device, parameters of a plurality of first factors related to a battery thermal management system (BTMS) and parameters of a plurality of second factors related to an air conditioning system, the battery thermal management system being configured to manage the battery temperature of a vehicle, and the air conditioning system being configured to manage the interior air conditioning of the vehicle. The method further includes inputting the parameters of the plurality of first and second factors into a temperature prediction model by the predictive device. The method further includes predicting the interior temperature of the vehicle using the temperature prediction model based on the parameters of the plurality of first and second factors. The method further includes controlling the BTMS and the air conditioning system in an integrated manner by an integrated controller based on the predicted interior temperature and one or more set target interior temperatures.

[0020] The multiple first and second factors may include factors selected from multiple candidate factors related to multiple actuators constituting the BTMS and air conditioning system. These factors may be selected based on predetermined conditions relating to the vehicle's average interior temperature.

[0021] The average interior temperature of a vehicle can be the average seat temperature of the vehicle.

[0022] Multiple first and second factors may include two or more of the following: compressor temperature, evaporator temperature, outside air temperature, battery cell temperature, battery consumption voltage, valve angle, fan speed, refrigerant pressure, heater temperature, inverter consumption current, inverter temperature, battery cooling sequence, internal air temperature, outside air temperature, or state of charge (SOC) change.

[0023] The method for controlling the battery and air conditioning may further include: receiving parameters of a third factor related to the vehicle's driving state, and inputting the parameters of the third factor into a temperature prediction model.

[0024] Predicting the vehicle's interior temperature involves predicting the vehicle's interior temperature for each of multiple regions based on parameters from multiple first and second factors.

[0025] Predicting the interior temperature of a vehicle may include determining the average predicted interior temperature for each of multiple zones.

[0026] Multiple areas may include: the area where the driver's seat of the vehicle is located, the area where one or more passenger seats are located, and the area where one or more air vents are located.

[0027] A temperature prediction model can be trained based on parameters of multiple first and second factors, as well as the actual temperature detected for each of multiple areas of the vehicle.

[0028] Controlling a BTMS and air conditioning system in an integrated manner may include: determining, by an integrated controller, a target actuator to be controlled among multiple actuators included in the BTMS and air conditioning system based on a predicted internal temperature and one or more set target internal temperatures; generating, by the integrated controller, control values ​​for each of the determined target actuators; and outputting the generated control values ​​to the system within the BTMS and air conditioning system that includes the target actuator. The BTMS and air conditioning system may be configured to control the operation of the target actuator based on the received control values.

[0029] According to embodiments of the present invention, multiple control factors can be selected for temperature control of large vehicles (which are wider and have more passenger seats than ordinary passenger vehicles), and passenger comfort can be improved by predicting and controlling the temperature of multiple areas in large vehicles based on the selected multiple control factors.

[0030] According to embodiments of the present invention, the temperature control performance of multiple zones can also be enhanced by applying a temperature prediction model to an existing battery thermal management system (BTMS) or an existing air conditioning control system without installing additional components (e.g., temperature sensors).

[0031] According to an embodiment of the present invention, the BTMS and air conditioning systems applied to environmentally friendly commercial vehicles can be controlled in a unified manner, thereby optimizing the performance and improving the stability of the BTMS and air conditioning systems.

[0032] According to embodiments of the present invention, power consumption can be reduced, energy efficiency improved, and the durability of auxiliary loads enhanced through effective thermal management by predicting the temperature of multiple areas within the vehicle and adaptively controlling the BTMS and air conditioning system.

[0033] The effects that can be obtained from this invention are not limited to those described above. Other effects not described herein will become clearer to those skilled in the art through the following description. Attached Figure Description

[0034] The above and other objects, features and advantages of the present invention will become more apparent to those skilled in the art from the following detailed description taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram illustrating a vehicle according to an embodiment of the present invention; Figure 2 This is a schematic block diagram illustrating a system for controlling a battery and an air conditioner according to an embodiment of the present invention; Figure 3 This is a schematic diagram showing a portion of a cooling processing unit according to an embodiment of the present invention; Figure 4 This is a schematic diagram showing a portion of an air conditioning processing unit according to an embodiment of the present invention; Figure 5 This is a block diagram illustrating an integrated controller according to an embodiment of the present invention; Figure 6A This is a schematic diagram illustrating the operation of selecting the first to third factors from the first to third candidate factors according to an embodiment of the present invention; Figure 6B This is a schematic diagram illustrating the correlation coefficient according to an embodiment of the present invention; Figure 7 This is a block diagram illustrating a prediction device according to an embodiment of the present invention; Figure 8A This is a schematic diagram illustrating multiple areas of a vehicle according to an embodiment of the present invention; Figure 8B This illustrates an embodiment of the present invention. Figure 8A A schematic diagram showing the identification information of multiple regions; Figure 9 This is a schematic diagram illustrating a series of processes for predicting the temperature of multiple regions using a temperature prediction model according to an embodiment of the present invention; Figure 10 This is a schematic diagram illustrating the predicted time-series temperature in H1L across multiple regions according to an embodiment of the present invention; and Figure 11 This is a flowchart illustrating a method for controlling a battery and an air conditioner according to an embodiment of the present invention. Detailed Implementation

[0035] In the following, embodiments of the invention will be described in detail with reference to the accompanying drawings to enable those skilled in the art to implement and practice the invention. However, the invention can be implemented in various different ways and is not limited to the embodiments described herein.

[0036] In the following description, detailed descriptions of known functions or structures will be omitted where it would obscure the spirit of the invention. Identical constituent elements in the drawings are indicated by the same reference numerals, and repeated descriptions of identical elements are omitted.

[0037] In this invention, when an element is simply referred to as “connected to,” “joined to,” or “coupled to” another element, this may mean that the element is “directly connected to,” “directly joined to,” or “directly coupled to” another element, or is connected to, joined to, or coupled to another element in the presence of one or more other elements. Additionally, when an element “comprises” or “has” another element, this means that the element may further include the other element without excluding it, unless otherwise specifically stated herein.

[0038] In this invention, the terms first, second, etc., may be used. These terms are used only to distinguish one element from another and do not limit the order or importance of the elements, unless specifically mentioned herein. Accordingly, without departing from the scope of the invention, a first element in one embodiment may be referred to as a second element in another embodiment, and similarly, a second element in one embodiment may be referred to as a first element in another embodiment.

[0039] In this invention, elements are distinguished from each other for the purpose of clearly describing each feature, but this does not necessarily mean that the elements are separate. For example, multiple elements may be integrated into a single hardware or software unit, or a single element may be distributed and formed in multiple hardware or software units. Therefore, such integrated or distributed implementations are included within the scope of this invention, unless otherwise stated.

[0040] In this invention, the elements described in the various embodiments are not necessarily essential elements, and some elements may be optional. Therefore, embodiments comprising a subset of the elements described in the embodiments are also included within the scope of this invention. Additionally, embodiments including elements other than those described in the various embodiments are also included within the scope of this invention.

[0041] The advantages and features of the invention, as well as methods of implementing them, will become more apparent from the following detailed description of the embodiments in conjunction with the accompanying drawings. However, the invention can be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these described embodiments are provided to complete the invention and to fully convey its scope to those skilled in the art.

[0042] In this invention, each of the phrases such as “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, “at least one of A, B or C”, and “at least one of A, B, C or a combination thereof” can include any or all possible combinations of the items listed together in the corresponding phrase.

[0043] When the components, controllers, devices, elements, equipment, units, etc. of the present invention are described as having a purpose or performing an operation, function, etc., the components, controllers, devices, elements, equipment, units, etc., shall be regarded herein as "configured" to satisfy that purpose or perform that operation or function. Each component, controller, device, element, equipment, unit, etc. may be implemented individually or include a processor and memory as part of the device, such as a non-transitory computer-readable medium.

[0044] In the following, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0045] Figure 1 This is a schematic diagram illustrating a vehicle 1 according to an embodiment of the present invention. Figure 2 This is a block diagram illustrating a system for controlling a battery and an air conditioner according to an embodiment of the present invention.

[0046] refer to Figure 1 and Figure 2 According to an embodiment of the present invention, the vehicle 1 may include: a battery 100, a battery thermal management system (BTMS) 200, an air conditioning system 300, an integrated controller 400, and a prediction device 500.

[0047] Vehicle 1 can be a hybrid vehicle that is driven by electrical energy from battery 100, or by selectively using a fossil fuel-based internal combustion engine and battery 100.

[0048] Vehicle 1 can be a conventional passenger vehicle, commercial vehicle, or purpose-built vehicle (PBV), as well as the bus shown. Vehicle 1 can be a four-wheeled vehicle (e.g., a passenger vehicle, SUV, or minivan), or a vehicle with more than four wheels (e.g., a bus, large truck, container truck, or heavy equipment vehicle).

[0049] Vehicle 1 can be automatically controlled and driven, and the autonomous driving can be implemented as semi-autonomous or fully autonomous driving. Fully autonomous driving can be configured so that the processor of vehicle 1 maintains full control even in uncertain driving conditions, allowing autonomous movement without user intervention. Semi-autonomous driving can be configured so that autonomous movement requires driver intervention in certain specific driving conditions.

[0050] Battery 100 may include multiple battery cells or multiple battery modules. Battery 100 may be a high-voltage battery that stores energy for driving electric vehicle 1. A battery module may include multiple battery cells.

[0051] Battery 100 and BTMS 200 can be integrated into a single battery pack. For example, BTMS 200 and air conditioning system 300 can be mounted on the roof of vehicle 1. However, the positions of BTMS 200 and air conditioning system 300 can be changed without departing from the spirit and scope of the invention.

[0052] The BTMS 200 is responsible for cooling and heating the battery 100 to ensure that the internal or external temperature of the battery 100 is within the optimal operating temperature range. This is because when the temperature of a battery used in an electric or hybrid vehicle exceeds its optimal operating temperature range, battery performance and battery life rapidly decrease, and nearby components are affected.

[0053] Furthermore, BTMS 200 can be used in conjunction with battery management system (BMS) 210 to manage the heat of battery 100. BTMS 200 may include BMS 210, or it may be configured independently of BMS 210. In this invention, a configuration in which BTMS 200 includes BMS 210 has been described as an example, but it will be apparent to those skilled in the art that the invention is not limited thereto.

[0054] like Figure 2 As shown, BTMS 200 may include: BMS 210, cooling processing unit 220, BTMS sensing unit 230 and BTMS controller 240.

[0055] The BMS 210 can collect battery data in real time (e.g., voltage, current, temperature, and state of charge (SOC) of battery 100), analyze the collected battery data to monitor and determine the state of battery 100, and control charging and / or discharging.

[0056] In addition, BMS 210 can send battery data to BTMS controller 240 or integrated controller 400 via CAN communication cable, and can receive response data corresponding to the battery data from BTMS controller 240 or integrated controller 400.

[0057] For example, when it is determined that battery 100 is overheating based on the detected battery temperature, BMS 210 can send the detected battery temperature and a signal requesting battery cooling activation to BTMS controller 240, and BTMS controller 240 can circulate coolant, run the cooling fan, and feed back the coolant temperature to BMS 210. When the detected battery temperature reaches the reference temperature again, BMS 210 can send a signal requesting cooling stop to BTMS controller 240.

[0058] The cooling unit 220 can manage the battery 100 to ensure that the temperature of the battery 100 is kept within an appropriate range.

[0059] Figure 3 This is a schematic diagram showing a portion of a cooling processing unit 220 according to an embodiment of the present invention.

[0060] refer to Figure 3 The cooling unit 220 may include multiple components, such as a radiator, a cooling fan, coolant lines, an electric water pump, a battery cooler, a battery heater, a three-way valve, a coolant reservoir, and an inverter. Hereinafter, the components of the cooling unit 220 will be referred to as actuators of the cooling unit 220.

[0061] For example, radiators and cooling fans dissipate heat or cool the coolant, coolant lines circulate the coolant between battery 100 and cooling unit 220, and an electric water pump pumps the coolant to circulate it in the coolant lines. The battery cooler uses refrigerant from air conditioning system 300 to cool the coolant, and a battery heater generates heat to heat the coolant. A three-way valve provides a path for delivering coolant to battery 100 or for bypassing the coolant, and a reservoir stores the coolant.

[0062] The BTMS sensing unit 230 may be included in the actuator and reservoir of the cooling processing unit 220 to detect cooling data related to the cooling processing unit 220. The BTMS sensing unit 230 includes, for example, a battery temperature sensor included in the battery 100, a coolant temperature sensor included in the coolant line or reservoir, and a coolant level sensor included in the reservoir, and further includes sensors required for detecting cooling data.

[0063] Examples of cooling data may include all values ​​that can be detected by the cooling processing unit 220, including the inlet and outlet temperatures of each actuator, the inlet and outlet voltages of each actuator, the coolant temperature, the coolant level, the refrigerant pressure, and the valve angle.

[0064] Return to reference Figure 2 The BTMS controller 240 can manage or control the operation of the BTMS 200 based on battery data received from the BMS 210 and cooling data received from the BTMS sensing unit 230. Furthermore, the BTMS controller 240 can also handle communication between the BMS 210 and the integrated controller 400. The BTMS controller 240 can be an electronic control unit (ECU) including at least one memory and at least one processor, and can communicate with the BMS 210 and the integrated controller 400 using CAN or LIN communication.

[0065] In the following text, the received battery data and cooling data are referred to as "parameters of the first candidate factor related to BTMS 200". Therefore, BTMS controller 240 can set the received battery data and cooling data as parameters of the first candidate factor and then send this data to integrated controller 400.

[0066] The first candidate factors can be items indicating the state of BTMS 200 or battery 100, and include items that can be detected by battery 100 and BTMS sensing unit 230 or calculated (or otherwise determined) by BMS 210 or BTMS controller 240. For example, the first candidate factors include not only the inlet and outlet temperatures, inlet and outlet voltages, coolant temperature, coolant level, refrigerant pressure, and valve angle of each actuator included in cooling processing unit 220, but also all items that can be detected by BTMS sensing unit 230.

[0067] Furthermore, the BTMS controller 240 can receive control values ​​from the integrated controller 400 for controlling the cooling processing unit 220 of the BTMS 200, and can generate control signals for driving the actuators of the cooling processing unit 220 based on the received control values. For example, when the received control values ​​are related to a cooling fan, the BTMS controller 240 can generate a control signal for driving the cooling fan and control the cooling fan so that the cooling fan operates according to the generated control signal.

[0068] The BTMS controller 240 may be an ECU and may include a first interface module, a first memory, and a first processor.

[0069] The first interface module may include a communication interface and a sensor interface. The communication interface is used to communicate with the BMS 210, the integrated controller 400 and the air conditioning system 300. The sensor interface receives battery data from the BMS 210, receives cooling data from the sensing unit, and uses an AD converter to convert the received data into signals when needed.

[0070] The first memory may store program code (e.g., in the form of computer-readable instructions) required to control the BTMS 200, various types of data and setting values, battery data received from the BMS 210, and cooling data received from the cooling processing unit 220. The first memory may include volatile memory and / or non-volatile memory.

[0071] The first processor can control the BTMS 200 based on received battery data or cooling data. Furthermore, the first processor can send the received battery data or cooling data (i.e., parameters of multiple first candidate factors) to the integrated controller 400. Additionally, the first processor can generate control signals for driving each actuator of the cooling processing unit 220 based on control values ​​received from the integrated controller 400, and can control the driving of the cooling processing unit 220 based on the generated control signals.

[0072] The air conditioning system 300 can be a system that manages the interior air conditioning (heating, cooling, ventilation) of the vehicle 1. The air conditioning system 300 may include: an air control panel (ACP) 310, an air conditioning processing unit 320, an air conditioning sensing unit 330, and an air conditioning controller 340.

[0073] The ACP 310 can receive the desired operating mode or target interior temperature from the user and send a signal corresponding to the input operating mode or target interior temperature to the air conditioning controller 340. Examples of operating modes include various modes such as cooling mode, heating mode, ventilation mode, and cleaning mode. In addition, the ACP 310 can display the interior and exterior temperatures of the vehicle 1.

[0074] The air conditioning unit 320 can maintain the interior temperature of the vehicle 1 at a target interior temperature.

[0075] Figure 4 This is a schematic diagram showing a portion of the air conditioning processing unit 320 according to the embodiment.

[0076] refer to Figure 4 The air conditioning processing unit 320 includes: a converter, an electric compressor (ECOMP), a condenser, an evaporator, a blower fan, a four-way valve, EXV and SOL valves, a heater, and an inverter, and individual components can be added or removed. Hereinafter, the converter, electric compressor (ECOMP), condenser, evaporator, blower fan, four-way valve, EXV and SOL valves, heater, and inverter are referred to as components or actuators of the air conditioning processing unit 320.

[0077] For example, in cooling mode, the converter can convert power from a high-voltage battery (e.g., DC 600 V to 800 V) to low voltage (e.g., DC 24 V) and supply this voltage to the air conditioning system 300 and electrical components. An electric compressor compresses the refrigerant to convert it to a high-temperature, high-pressure state, then delivers the converted refrigerant to the condenser. The condenser condenses the compressed refrigerant to convert it to a liquid state, and the condensed refrigerant expands through the EXV and SOL valves to reach a low-temperature, low-pressure state. A four-way valve provides a flow path for the cooled refrigerant to move to at least one of the evaporator and BTMS 200, where the evaporator evaporates the cooled refrigerant, and the evaporated refrigerant is supplied to the interior via a fan to cool the cabin air. When the interior temperature reaches a target temperature, the electric compressor adjusts its output to regulate cooling capacity. The EXV and SOL valves switch the flow of either refrigerant or coolant to switch between heating and cooling modes. For example, in cooling mode, refrigerant is delivered to the evaporator, and in heating mode, refrigerant is delivered to the heater.

[0078] In heating mode, the EXV and SOL valves provide a flow path for the refrigerant to move to the heater, which heats the refrigerant. The heater core uses coolant or refrigerant heated by the engine to heat the air, and the heated air is supplied to the vehicle interior via a blower fan.

[0079] The air conditioning sensing unit 330 may be included in the actuator of the air conditioning processing unit 320, and may detect air conditioning data related to the air conditioning processing unit 320. The air conditioning sensing unit 330 may include, for example, sensors required for detecting air conditioning data, such as an outside air temperature sensor for detecting the outside air temperature of the vehicle 1, a refrigerant temperature sensor, a refrigerant pressure sensor, temperature sensors disposed at the inlet and outlet of the electric compressor, an angle sensor disposed at the valve, and an electric fan speed sensor, etc.

[0080] Examples of air conditioning data may include all values ​​that can be detected by the air conditioning processing unit 320, including the inlet and outlet temperatures of each actuator, the inlet and outlet voltages of each actuator, the refrigerant temperature, the refrigerant pressure, and the valve angle.

[0081] Return to reference Figure 2 The air conditioning controller 340 can be an HVAC control unit (HCU) that controls the air conditioning system 300, and can manage or control the operation of the air conditioning system 300 based on air conditioning data received from the air conditioning sensing unit 330. Furthermore, the air conditioning controller 340 can communicate with the integrated controller 400 using CAN communication, LIN communication, or similar methods. For example, the air conditioning controller 340 can send air conditioning data to the integrated controller 400 and receive control values ​​from the integrated controller 400.

[0082] In the following text, the detected air conditioning data is referred to as "parameters of the second candidate factor related to the air conditioning system 300". Therefore, the air conditioning controller 340 can determine the air conditioning data as parameters of the second candidate factor and then send the air conditioning data to the integrated controller 400.

[0083] The second candidate factors are items that indicate the state of the air conditioning system 300, and further include items that can be detected by the air conditioning sensing unit 330 or calculated (or otherwise determined) by the air conditioning controller 340 to control the air conditioning system 300. For example, the second candidate factors may further include all items that can be detected by the air conditioning sensing unit 330, such as the inlet and outlet temperatures, inlet and outlet voltages, fan speeds, heater temperatures, inverter current consumption, inverter temperatures, outside air temperatures, refrigerant temperatures, and refrigerant pressures of each actuator included in the air conditioning processing unit 320.

[0084] Furthermore, the air conditioning controller 340 can receive control values ​​from the integrated controller 400 for controlling the air conditioning processing unit 320, and generate control signals for driving the actuators of the air conditioning processing unit 320 based on the received control values. For example, when the received control values ​​are related to a converter, the air conditioning controller 340 can generate control signals for driving the converter and control the converter so that the converter operates according to the generated control signals.

[0085] The air conditioner controller 340 may include a second interface module, a second memory, and a second processor.

[0086] The second interface module may include a communication interface and a sensor interface. The communication interface is used to communicate with the integrated controller 400 or BTMS 200, and the sensor interface receives air conditioning data from the air conditioning sensing unit 330 and converts the received data into a signal using an AD converter when necessary.

[0087] The second memory may store program code (e.g., in the form of computer-readable instructions) required to control the air conditioning system 300, various types of data, setting values, and air conditioning data, and may include volatile memory and non-volatile memory.

[0088] The second processor can control the air conditioning processing unit 320 based on the detected air conditioning data, so that the air conditioning system 300 maintains the target temperature. Furthermore, the second processor can send the received air conditioning data (i.e., parameters of multiple received second candidate factors) to the integrated controller 400. Additionally, the second processor can generate control signals for driving each actuator of the air conditioning processing unit 320 based on the control values ​​received from the integrated controller 400.

[0089] The integrated controller 400 can control the BTMS 200 and the air conditioning system 300 in an integrated or coordinated manner, so that the battery 100 and the interior of the vehicle 1 can maintain optimal temperatures simultaneously or in parallel. The integrated controller 400 may be, for example, a vehicle control unit (VCU).

[0090] For example, when it is determined that the battery 100 is overheating based on the temperature of the battery 100 in the parameters of the first candidate factors received from BTMS 200, the integrated controller 400 can generate control values ​​through cooperation between the cooling processing unit 220 and the air conditioning processing unit 320 to maintain the temperature of the battery 100 at an appropriate temperature, and send the generated control values ​​to the cooling processing unit 220 and the air conditioning processing unit 320. Accordingly, the water pump of the cooling processing unit 220 can circulate the coolant, the three-way valve can provide a flow path for delivering the coolant to the battery, and the cooling fan can cool the coolant, thereby cooling the battery 100. In addition, when cooperative control is required, the air conditioning system 300 can use refrigerant to cool the battery 100 more quickly. On the other hand, the air conditioning system 300 can also use the heat from the battery 100 to heat the interior.

[0091] Figure 5 This is a block diagram illustrating an integrated controller 400 according to an embodiment of the present invention.

[0092] refer to Figure 5 According to an embodiment of the present invention, the integrated controller 400 may include: an input and output unit 410, a communication unit 420, a third memory 430, and a third processor 440.

[0093] The input and output unit 410 can receive driving state data from a driving sensor (not shown) related to the driving state of vehicle 1, and convert the received driving state data into a form that can be processed by the third processor 440. The driving state related data are parameters of a third candidate factor related to the driving state of vehicle 1.

[0094] The third candidate factor is items related to the driving status of the vehicle 1, and includes wheel speed, tilt, driving direction, vehicle wheel speed, driving direction, vehicle attitude, vehicle tilt, vehicle weight, vehicle fuel quantity, tire pressure, steering angle, vehicle interior temperature and humidity, pedal position, engine temperature and path information.

[0095] The communication unit 420 can send and receive data with the battery 100, BTMS 200, air conditioning system 300, or prediction device 500 via CAN or LIN communication protocols. For example, the communication unit 420 can receive parameters of a first candidate factor from BTMS 200 and parameters of a second candidate factor from air conditioning system 300.

[0096] In addition, the communication unit 420 can send the parameters of the first factor, the second factor, or the third factor to the prediction device 500, and can receive the predicted internal temperature of the vehicle 1 from the prediction device 500.

[0097] The third memory 430 may store program code (e.g., in the form of computer-readable instructions) required by the integrated control controller 400, various types of data, set values, parameters of the first to third candidate factors, parameters of the first to third factors, and one or more target internal temperatures. The third memory 430 may include volatile memory and / or non-volatile memory.

[0098] The third processor 440 can collect and analyze parameters (e.g., battery data, cooling data, air conditioning data, vehicle driving status data, etc.) received from the BTMS 200, the air conditioning system 300, and driving sensors (not shown) of the first to third candidate factors to determine the state of the battery 100, the BTMS 200, and the air conditioning system 300. Based on the determination results, the third processor 440 can generate control values ​​for controlling the battery 100, BTMS 200, or air conditioning system 300, and then perform processing to send the control values ​​to the battery 100, BTMS 200, or air conditioning system 300. For example, the third processor 440 can coordinate the operation of the BTMS 200 and the air conditioning system 300 (e.g., RPM distribution) to maximize energy efficiency and maintain interior comfort.

[0099] Furthermore, the third processor 440 can select parameters for the first to third factors from the received parameters of the first to third candidate factors, and perform processing to send the selected parameters of the first to third factors to the prediction device 500. The parameters are the actual detected values ​​or actual measured values ​​of all factors.

[0100] The first factor is a factor selected from all first candidate factors indicating the state of BTMS 200 or battery 100. The first factor can be predetermined. For example, BTMS controller 240 can receive parameters of multiple first candidate factors related to BTMS 200 from BMS 210, cooling processing unit 220, and BTMS sensing unit 230, and can send the received parameters of multiple first candidate factors to integrated controller 400. The parameters of the multiple first candidate factors related to BTMS 200 may include battery data received from BMS 210 and cooling data obtainable from the actuators and reservoir of cooling processing unit 220.

[0101] The second factor is a factor selected from all second candidate factors indicating the state of the air conditioning system 300. The second factor can be predetermined. For example, the air conditioning controller 340 can receive parameters of multiple second candidate factors related to the air conditioning system 300 from the BMS 210, the cooling processing unit 220, and the BTMS sensing unit 230, and can send the received parameters of the multiple second candidate factors to the integrated controller 400. The parameters of the multiple second candidate factors related to the air conditioning system 300 may include air conditioning data received from the air conditioning sensing unit 330.

[0102] The third factor is selected from all third candidate factors related to the driving state. The third factor can be predetermined.

[0103] The first, second, and third factors are selected from a pool of first to third candidate factors obtained from the plurality of actuators 220 and 320 constituting the BTMS 200 and the air conditioning system 300, as well as driving sensors (not shown), based on the fact that their correlation with the average interior temperature of the vehicle 1 meets predetermined conditions. The first, second, and third factors can be factors that have a high influence on the average interior temperature of the vehicle. The average interior temperature of the vehicle 1 can be the average temperature of the seats of the vehicle 1 or multiple areas described in more detail below.

[0104] Figure 6A This is a schematic diagram illustrating the operation of selecting the first, second, and third factors from the first, second, and third candidate factors respectively, according to the implementation scheme. Figure 6B This is a schematic diagram showing the correlation coefficients according to the implementation scheme.

[0105] refer to Figure 6A The selection module (not shown) can receive, for example, 67 first to third candidate factors as raw data. The selection module (not shown) can calculate or otherwise determine the correlation coefficient between the raw data (i.e., each of the first, second, and third candidate factors) and the mean internal temperature. The correlation coefficient represents the effect of each candidate factor on an increase or decrease in the mean internal temperature.

[0106] refer to Figure 6B The selection module (not shown) can use the Python heatmap library to determine the Pearson correlation coefficient. When the influence of each candidate factor on the mean internal temperature is close to ±1, the selection module (not shown) can determine the correlation as very high, while when the influence is close to 0, the selection module (not shown) can determine the correlation as very low or zero.

[0107] The selection module (not shown) can initially select a first, second, and third factor from 67 calculated or otherwise determined correlation coefficients whose absolute values ​​are equal to or greater than a baseline value (e.g., 0.6, which may be changed). The number of first to third factors initially selected can be, for example, 24. The selection module (not shown) can further remove one factor with overlapping properties from each of the initially selected first to third factors to finally select the first to third factors. Factors with overlapping properties include, for example, the compressor's inlet and outlet temperatures. Figure 6A In this case, the compressor outlet temperature is removed. The selection module (not shown) can ultimately select 16 factors by removing factors with repetitive properties. Figure 6A In the case of excluding third factors and further including average internal temperature.

[0108] The selection module (not shown) may be included in the integrated controller 400 or may be implemented as a separate computing device. When the selection module is implemented as a separate device, it may provide the integrated controller 400 with identification information for the first to third factors to be finally selected. The integrated controller 400 may then send parameters of the first to third factors, containing the received identification information, to the prediction device 500.

[0109] The third processor 440 can generate control values ​​for integrated control of the BTMS 200 or the air conditioning system 300 based on the predicted interior temperature of the vehicle 1 received from the prediction device 500 and one or more target interior temperatures, and can perform processing to send the generated control values ​​to the BTMS 200 or the air conditioning system 300. When the interior of the vehicle 1 is divided into multiple zones, the one or more target interior temperatures can be target temperatures set for each zone.

[0110] In this implementation, the third processor 440 can determine one or more target actuators to be controlled among a plurality of actuators constituting the BTMS 200 and the air conditioning system 300 based on a predicted internal temperature and one or more target internal temperatures, and can generate control values ​​for controlling one or more determined target actuators. The third processor 440 can output the generated control values ​​to a system in the BTMS 200 and the air conditioning system 300 that includes at least one target actuator. Therefore, the system in the BTMS 200 and the air conditioning system 300 that receives the control values ​​can control the operation of the target actuators based on the received control values.

[0111] Figure 7 This is a block diagram illustrating a prediction device 500 according to an embodiment of the present invention.

[0112] The prediction device 500 can be an on-board device installed in the vehicle 1 to collect and process data. The prediction device 500 can predict the interior temperature of the vehicle 1 while it is in motion based on a temperature prediction model and send the predicted interior temperature to the integrated controller 400. For example, the prediction device 500 can use a feature importance scheme to train the model. A feature importance scheme is an algorithm that selects factors highly correlated with the output as inputs for learning in order to improve the generalization performance and complexity of the prediction model.

[0113] refer to Figure 7 The prediction device 500 may include: a communication unit 510, a database (DB) 520, a memory 530, and a processor 540.

[0114] The communication unit 510 can communicate with the integrated controller 400 via wired or wireless communication. The communication unit 510 may include at least one of various modules, such as a cellular communication module, a Wi-Fi module, a Bluetooth module, a vehicle-to-everything (V2X) communication module, a CAN gateway, and an Ethernet communication module.

[0115] DB 520 can store parameters of the first to third factors received from the integrated controller 400, as well as the interior temperature data of vehicle 1. The interior temperature of the vehicle can be included in the first to third factors, or it can be received separately from the temperature sensor in vehicle 1.

[0116] Memory 530 may store at least one program (e.g., operating system, software, firmware, middleware, or application) for controlling the prediction device 500, various types of data, and at least one instruction or computer-readable instruction, and may load the program, read or write data, or perform operations corresponding to the instructions in response to a request from processor 540. Memory 530 may include volatile memory and / or non-volatile memory. Memory 530 may store a program (e.g., in the form of computer-readable instructions) for generating a temperature prediction model, and the generated temperature prediction model.

[0117] Processor 540 can perform overall control of prediction device 500 according to input instructions. Instructions can be input to processor 540 from memory 530 or communication unit 510. For example, processor 540 can execute programs or instructions stored in memory 530 to perform data processing and calculations.

[0118] In addition, the processor 540 can load instructions or data received from other components into volatile memory, process the instructions or data stored in volatile memory, and store the processing results in non-volatile memory.

[0119] In an embodiment of the present invention, processor 540 can learn parameters of first to third factors collected during a certain period of vehicle 1's operation, as well as temperature data of multiple areas of vehicle 1 detected at time points with the same or within the error range as the parameters of the first to third factors, to generate a temperature prediction model capable of predicting time-series temperatures. Furthermore, processor 540 can use the generated temperature prediction model to predict near-future temperatures in time-series format.

[0120] Figure 8A This is a schematic diagram showing multiple areas of vehicle 1 according to the implementation scheme.

[0121] refer to Figure 8A The multiple zones defined for real-time temperature measurement inside vehicle 1 may include head, foot, vents, and return air vents. The head is the upper part of the seat, the foot is the bottom of the seat where the measurement is performed, the vents are located at the rear of vehicle 1 to exhaust air, and the return air vents are located at the front of vehicle 1 to draw in air.

[0122] Figure 8B The implementation scheme is shown. Figure 8A The identification information for the multiple regions shown.

[0123] refer to Figure 8B H1R is the headroom of the right-hand seat in the first row, and H1L is the headroom of the left-hand seat in the first row. V8R is the right-hand air vent in the eighth row, and RVR is the right-hand return air vent. AVG_H is the average temperature of the seat (headroom), and AVG_V is the average temperature of the air vent and return air vent.

[0124] The following section describes in more detail the operation of generating a temperature prediction model according to the implementation plan.

[0125] The processor 540 can map the parameters of the first to third factors collected during the actual driving of vehicle 1, along with temperature data from multiple areas within the same time period, and store the parameters and temperature data in DB 520. When large amounts of data are obtained by collecting data over a certain period, the processor 540 can input the collected (e.g., 16) parameters of the first to third factors, the parameter of the average seat temperature AVG_H, and the actual detected temperature data from multiple areas into an artificial intelligence model to perform machine learning. This allows the generation of a temperature prediction model capable of predicting the near-future temperature of vehicle 1 during actual driving.

[0126] Furthermore, the processor 540 can use test data to determine the accuracy of the temperature prediction model, and when the accuracy is higher than a benchmark value, the temperature prediction model can be stored in the memory 530. Then, while the vehicle 1 is in motion, the processor 540 can input the parameters of the first to third factors received in real time into the temperature prediction model to predict the near-future interior temperature of the vehicle 1 in the form of a time series.

[0127] Figure 9 This is a schematic diagram illustrating a series of processes for predicting the temperature of multiple regions using a temperature prediction model according to the implementation plan. Figure 10 This is a schematic diagram illustrating the predicted time-series temperatures in H1L across multiple regions according to the implementation scheme.

[0128] refer to Figure 9 The processor 540 can input parameters from 16 first through third factors into a temperature prediction model to predict time-series temperatures and two average temperatures for 19 regions. These numbers are illustrative and not limiting. Reference Figure 10 As can be seen, for the seat's H1L, the predicted temperature and the actual temperature are basically the same.

[0129] Figure 11 This is a flowchart illustrating a method for controlling a battery and an air conditioner according to an embodiment of the present invention.

[0130] refer to Figure 11 In step S1100, the prediction device 500 can collect parameters of multiple first factors related to the BTMS 200 that manages the battery temperature of the vehicle 1, parameters of multiple second factors related to the air conditioning system 300 that manages the interior air conditioning of the vehicle 1, and the measured interior temperature for each of the multiple areas.

[0131] In step S1110, for example, when big data is collected, the prediction device 500 can learn the parameters of the first and second factors collected and the internal temperature of multiple regions to generate a temperature prediction model.

[0132] The prediction device 500 can input test data into the generated temperature prediction model to calculate (or otherwise determine) the accuracy, and when the accuracy is ensured (yes in step S1120), the temperature prediction model can be activated.

[0133] In step S1130, the prediction device 500 may receive one or more target interior temperatures of vehicle 1 (which may be set by the user). In step S1140, the prediction device 500 may input parameters of the first to third factors into the temperature prediction model to predict the temperature of multiple areas and the average interior temperature. In step S1140, parameters of the first to third candidate factors selected from the first to third candidate factors may be received from the integration controller 400; for example, parameters of candidate factors whose correlation coefficients are equal to or less than the baseline value may not be received.

[0134] The predictive device 500 can send the predicted internal temperature to the integrated controller 400. In step S1150, the integrated controller 400 can generate control values ​​(i.e., integrated control information) for integrated control of the BTMS 200 and the air conditioning system 300.

[0135] In step S1160, the integrated controller 400 can control the BTMS 200 or the air conditioning system 300 so that the BTMS 200 or the air conditioning system 300 operates based on the generated integrated control information.

[0136] BTMS 200 or air conditioning system 300 can control the operation of cooling processing unit 220 or air conditioning processing unit 320 based on the received integrated control information, and can feed back the parameters of the first or second candidate factor updated due to operation to integrated controller 400. In step S1170, prediction device 500 can input the parameters of the first or second factor selected by integrated controller 400 and re-inputted from integrated controller 400 into temperature prediction model to predict the temperature of multiple areas.

[0137] The operation of the methods or algorithms described in conjunction with the embodiments disclosed in this specification can be implemented directly in hardware, software modules, or a combination of both. Software modules can reside in storage media (e.g., memory), such as random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, or optical disc (CD)-ROMs. Example storage media can be coupled to a processor, and the processor can read information from and write information to the storage media. Alternatively, the storage media can be integrated with the processor. The processor and storage media can reside within an application-specific integrated circuit (ASIC). The ASIC can reside within a device. Alternatively, the processor and storage media can reside as separate components within the device. For example, the device can be at least one of BMS 210, BTMS controller 240, air conditioning controller 340, integrated controller 400, or predictive device 500.

[0138] Additionally, for example, the processor disclosed in this invention may be implemented as at least one of a central processing unit (CPU), a digital signal processor (DSP), a programmable logic device (PLD), a field-programmable gate array (FPGA), a microcontroller, and / or a microprocessor, or may include at least one of these.

[0139] Although the exemplary methods of the present invention described above are represented as a series of operations for clarity of description, they are not intended to limit the order of execution of the steps, and the steps may be performed simultaneously or in different orders as needed. To implement the methods according to embodiments of the present invention, the described steps may further include other steps, including steps in addition to some steps, or may include additional steps in addition to some steps.

[0140] The various embodiments of the present invention are not a list of all possible combinations, but are intended to describe representative aspects of the invention, and the contents described in the various embodiments may be applied independently or in combination of two or more.

[0141] Furthermore, various embodiments of the present invention can be implemented in hardware, firmware, software, or a combination thereof. When implementing the present invention in hardware, it can be implemented using application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, etc.

[0142] The scope of this invention includes software or machine-executable commands or computer-readable instructions (e.g., operating systems, applications, firmware, programs, etc.) for enabling the operation of methods according to various embodiments to be executed on a device or computer, and non-transitory computer-readable media having such software or commands stored thereon and executable on a device or computer.

Claims

1. A system for controlling a battery and an air conditioner, comprising: The prediction device is configured as follows: The system receives parameters of multiple first factors related to a battery thermal management system and parameters of multiple second factors related to an air conditioning system, wherein the battery thermal management system is configured to manage the battery temperature of the vehicle and the air conditioning system is configured to manage the interior air conditioning of the vehicle. The parameters of multiple first factors and multiple second factors received are input into the temperature prediction model to predict the interior temperature of the vehicle. and An integrated controller is configured to control the battery thermal management system and the air conditioning system in an integrated manner based on the predicted internal temperature and one or more target internal temperatures.

2. The system for controlling the battery and air conditioner according to claim 1, wherein, The plurality of first and second factors include factors selected from a plurality of candidate factors associated with a plurality of actuators included in the battery thermal management system and the air conditioning system, wherein the factors are selected based on predetermined conditions of correlation with the average interior temperature of the vehicle.

3. The system for controlling the battery and air conditioner according to claim 2, wherein, The average interior temperature of the vehicle is the average seat temperature of the vehicle.

4. The system for controlling a battery and air conditioner according to claim 1, wherein, The plurality of first factors and plurality of second factors include two or more of the following: compressor temperature, evaporator temperature, battery cell temperature, battery consumption voltage, valve angle, electric fan speed, refrigerant pressure, heater temperature, inverter consumption current, inverter temperature, battery cooling sequence, internal air temperature, external air temperature, or state of charge change.

5. The system for controlling a battery and air conditioner according to claim 1, wherein, The prediction device is further configured as follows: Receive parameters from a third factor related to the vehicle's driving status; The parameters of the third factor are input into the temperature prediction model.

6. The system for controlling a battery and air conditioner according to claim 1, wherein, The prediction device is configured to predict the interior temperature of the vehicle for each of multiple regions based on parameters of multiple first factors and parameters of multiple second factors.

7. The system for controlling a battery and air conditioner according to claim 6, wherein, The prediction device is further configured to determine an average value of the predicted internal temperature for each of the multiple regions.

8. The system for controlling a battery and air conditioner according to claim 6, wherein, The multiple areas include: the area where the driver's seat of the vehicle is located, the area where one or more passenger seats are located, and the area where one or more air vents are located.

9. The system for controlling a battery and air conditioner according to claim 6, wherein, The temperature prediction model is trained based on parameters of multiple first factors and multiple second factors, as well as the actual temperature detected for each of multiple regions of the vehicle.

10. The system for controlling a battery and an air conditioner according to claim 1, wherein, The integrated controller is configured as follows: The target actuator to be controlled is determined based on the predicted internal temperature and the target internal temperature of one or more settings, including the multiple actuators in the battery thermal management system and the air conditioning system. For each of the one or more target actuators, generate control values ​​for controlling the target actuator; The generated control value is output to the battery thermal management system and the air conditioning system, which includes the target actuator. The system including the target actuator is configured to control the operation of the target actuator based on the received control value.

11. A method for controlling a battery and an air conditioner, comprising: The predictive device receives parameters of multiple first factors related to the battery thermal management system and parameters of multiple second factors related to the air conditioning system, wherein the battery thermal management system is configured to manage the battery temperature of the vehicle and the air conditioning system is configured to manage the interior air conditioning of the vehicle. The predictive device inputs parameters of multiple first factors and multiple second factors into the temperature prediction model; The predictive device uses a temperature prediction model to predict the interior temperature of a vehicle based on parameters of multiple first factors and multiple second factors. The integrated controller controls the battery thermal management system and the air conditioning system in an integrated manner based on the predicted internal temperature and one or more set target internal temperatures.

12. The method for controlling a battery and an air conditioner according to claim 11, wherein, The plurality of first factors and the plurality of second factors include factors selected from a plurality of candidate factors associated with a plurality of actuators included in the battery thermal management system and the air conditioning system, wherein the factors are selected based on predetermined conditions of correlation with the average interior temperature of the vehicle.

13. The method for controlling a battery and an air conditioner according to claim 12, wherein, The average interior temperature of the vehicle is the average seat temperature of the vehicle.

14. The method for controlling a battery and an air conditioner according to claim 11, wherein, The plurality of first factors and plurality of second factors include two or more of the following: compressor temperature, evaporator temperature, outside air temperature, battery cell temperature, battery consumption voltage, valve angle, electric fan speed, refrigerant pressure, heater temperature, inverter consumption current, inverter temperature, battery cooling sequence, internal air temperature, outside air temperature, or state of charge change.

15. The method for controlling a battery and an air conditioner according to claim 11, further comprising: Receive parameters from a third factor related to the vehicle's driving status; The parameters of the third factor are input into the temperature prediction model.

16. The method for controlling a battery and an air conditioner according to claim 11, wherein, Predicting the vehicle's interior temperature involves predicting the vehicle's interior temperature for each of multiple regions based on parameters of multiple first factors and multiple parameters of multiple second factors.

17. The method for controlling a battery and an air conditioner according to claim 16, wherein, Predicting the interior temperature of a vehicle involves determining the average predicted interior temperature for each of multiple zones.

18. The method for controlling a battery and an air conditioner according to claim 16, wherein, The multiple areas include: the area where the driver's seat of the vehicle is located, the area where one or more passenger seats are located, and the area where one or more air vents are located.

19. The method for controlling a battery and an air conditioner according to claim 16, wherein, The temperature prediction model is trained based on parameters of multiple first factors and multiple second factors, as well as the actual temperature detected for each of multiple regions of the vehicle.

20. The method for controlling a battery and an air conditioner according to claim 11, wherein, Control in an integrated manner includes: The integrated controller determines one or more target actuators to be controlled among multiple actuators included in the battery thermal management system and air conditioning system, based on the predicted internal temperature and the target internal temperature of one or more settings. The integrated controller generates control values ​​for controlling each of one or more target actuators. The integrated controller outputs the generated control values ​​to the battery thermal management system and the air conditioning system, including the target actuator. The system including the target actuator is configured to control the operation of the target actuator based on the received control values.

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