Thermal management system, thermal management control method and vehicle

Abstract

The invention provides a thermal management system, a thermal management control method and a vehicle, the system is applied to the technical field of vehicle thermal management, and the system comprises a battery, a motor and a cabin thermal management assembly which are respectively used for adjusting heat or cold energy resources of corresponding loops; four runner inlets of the multi-runner heat exchanger are respectively connected with the battery, the motor, the cabin heat management assembly and the cooling liquid storage tank; the input end of the multi-way valve is connected with an outlet of the multi-flow-channel heat exchanger, and the output end of the multi-way valve is connected with the battery, the motor and the cabin heat management assembly through a plurality of branch pipes; and the control assembly is used for determining the heat management priority of each loop according to a plurality of heat management requirements of the vehicle and controlling the opening state of the multi-way valve according to the heat management priority so as to distribute the heat or the cold flowing out of the multi-flow-channel heat exchanger to the corresponding loop according to requirements. Therefore, the problems of unreasonable resource allocation and insufficient core function guarantee caused by loose loop coupling and lack of an intelligent arbitration mechanism in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle thermal management technology, and more specifically, to thermal management systems, thermal management control methods, and vehicles in the field of vehicle thermal management technology. Background Technology

[0002] Thermal management of the power battery, drive motor, and cabin environment is crucial for ensuring vehicle safety, range, and ride comfort. An efficient and intelligent thermal management system plays a decisive role in improving overall vehicle energy efficiency, ensuring the lifespan of core components, and optimizing user experience, making it a key area in current new energy vehicle technology research and development.

[0003] In related technologies, each circuit, such as the battery, motor, and cabin, is typically equipped with independent heat exchange components (e.g., battery coolers, motor radiators, air conditioning condensers, etc.), and temporary coupling between circuits is achieved through simple on / off valves or on / off controls. However, the above methods suffer from problems such as unreasonable resource allocation and insufficient protection of core functions due to loose circuit coupling and a lack of intelligent arbitration mechanisms, which urgently need to be addressed. Summary of the Invention

[0004] This application provides a thermal management system, a thermal management control method, and a vehicle. The system constructs a centralized hardware architecture with a multi-channel heat exchanger as the energy hub and a multi-way valve as the path router, permanently coupling the three major thermal management loops of the battery, motor, and cabin to the same heat exchanger. This solves the problems of unreasonable resource allocation and insufficient core function guarantee caused by loose loop coupling and lack of intelligent arbitration mechanism in related technologies. It realizes the integration, intelligence, and efficiency of the vehicle thermal management system, and significantly improves energy utilization efficiency, system response speed, and multi-condition adaptability.

[0005] In a first aspect, a thermal management system is provided, comprising: a battery thermal management component, a motor thermal management component, a cabin thermal management component, a multi-channel heat exchanger, a multi-way valve, and a control component, wherein the battery thermal management component is used to regulate the heat resources and / or cooling resources of the battery thermal management circuit; the motor thermal management component is used to regulate the heat resources and / or cooling resources of the motor thermal management circuit; the cabin thermal management component is used to regulate the heat resources and / or cooling resources of the cabin thermal management circuit; the first channel inlet of the multi-channel heat exchanger is connected to the battery thermal management component, the second channel inlet of the multi-channel heat exchanger is connected to the motor thermal management component, and the third channel inlet of the multi-channel heat exchanger is connected to the battery thermal management component. The cabin thermal management component is connected to the multi-channel heat exchanger, and the fourth channel inlet of the multi-channel heat exchanger is connected to the coolant reservoir. The input end of the multi-way valve is connected to the outlet of the multi-channel heat exchanger, and the output end of the multi-way valve is connected to the battery thermal management component, the motor thermal management component, and the cabin thermal management component through multiple branch pipes. The control component is used to determine the thermal management priority of each thermal management circuit according to the multiple thermal management needs of the vehicle, and to determine the target opening state of the multi-way valve according to the thermal management priority of each thermal management circuit, so as to control the multi-way valve according to the target opening state, thereby realizing the allocation of heat resources and / or cooling resources for at least one thermal management circuit.

[0006] Through the above technical solution, by permanently connecting the battery, motor, and cabin thermal management circuits, as well as the coolant reservoir, to the same multi-channel heat exchanger, and using a multi-way valve to distribute the heat or cold energy at the heat exchanger outlet on demand, bidirectional free flow and flexible scheduling of heat between the circuits are achieved. At the same time, the control component calculates the thermal management priority of each circuit in real time based on the vehicle's multiple thermal management needs, and precisely controls the opening state of the multi-way valve accordingly, thereby ensuring at the hardware level that thermal management resources can prioritize the needs of high-priority circuits.

[0007] In conjunction with the first aspect, in some possible implementations, the thermal management system further includes: an energy storage component, which includes a first three-way valve, a second three-way valve, a primary heat exchanger, a secondary heat exchanger, and a heat storage medium containment area. The outlet of the motor thermal management component is connected to the inlet of the energy storage component via the first three-way valve. The inlet and outlet of the primary heat exchanger are connected to the battery thermal management circuit via pipelines. The inlet and outlet of the secondary heat exchanger are connected to the cabin thermal management circuit via pipelines. The heat storage medium containment area includes a phase change material, and is thermally coupled to both the primary and secondary heat exchangers, allowing heat exchange between the battery thermal management circuit coolant flowing through the primary heat exchanger and / or the cabin thermal management circuit coolant flowing through the secondary heat exchanger and the phase change material.

[0008] Through the above technical solution, the first and second three-way valves are connected in series with the motor circuit to form an independent charging branch. Simultaneously, a primary heat exchanger and a secondary heat exchanger achieve thermal coupling with the battery circuit and the cabin circuit, respectively. Thus, this solution constructs an integrated charging-storage-discharge energy buffer system: when the motor generates excess heat and neither the battery nor the cabin has immediate demand, the excess heat can be absorbed and stored by the phase change material through the energy storage components; when the battery or cabin subsequently generates heat demand, the stored heat can be released to the corresponding circuit through the heat exchanger. This design not only transforms potentially wasted intermittent waste heat into a strategic reserve that can be used for air conditioning over time, significantly improving the overall vehicle energy utilization efficiency, but more importantly, it provides additional thermal capacity buffer for the system to cope with sudden high-load demands such as cold starts and extreme operating conditions. This enhances the robustness and flexibility of the thermal management system throughout its entire lifecycle while ensuring the safety of core components.

[0009] Secondly, a thermal management control method is provided, which is applied to a thermal management system. The method includes: in response to multiple thermal management needs of a vehicle, acquiring thermal state parameters of at least one thermal management loop of the vehicle; determining target thermal comfort parameters for a cabin loop within the at least one thermal management loop based on the thermal state parameters of the at least one thermal management loop, and determining a thermal management priority for each thermal management loop based on the thermal state parameters of the at least one thermal management loop and the target thermal comfort parameters; and allocating heat resources and / or cooling resources to the at least one thermal management loop based on the thermal management priority of each thermal management loop.

[0010] Through the above technical solution, when the system identifies multiple coexisting thermal management needs, it first dynamically generates a cabin thermal comfort target parameter specific to the current user based on the acquired parameters and the embedded personalized learning model, rather than using a fixed universal value. Next, the system uses this personalized comfort target along with the thermal state parameters of other circuits such as the battery and motor as input to calculate the global priority of each circuit. Finally, the system accurately allocates limited heat or cooling resources based on this priority. Thus, the user's real-time, personalized comfort needs are elevated from a passive, isolated control objective to an active, core decision variable that can influence the global resource scheduling strategy. Under the constraint of unavoidable conflicts among multiple needs, the system maximizes and refines the satisfaction of the user's personalized thermal comfort experience while prioritizing the safety of core components such as the battery, achieving intelligent coordination and unification of the three major goals of safety, energy efficiency, and comfort.

[0011] In conjunction with the first aspect, in some possible implementations, determining the target thermal comfort parameter of the cabin circuit in the at least one thermal management loop based on the thermal state parameters of the at least one thermal management loop includes: extracting the thermal state parameters of the cabin circuit from the thermal state parameters of the at least one thermal management loop; obtaining a reference thermal comfort parameter and a current cabin environment parameter from the thermal state parameters of the cabin circuit; and obtaining the target thermal comfort parameter by correcting the reference thermal comfort parameter according to the current cabin environment parameter based on a preset thermal comfort preference mapping strategy.

[0012] The above technical solution extracts a subset of specific parameters directly related to the cabin from the comprehensive thermal state parameters of multiple loops. Then, using benchmark thermal comfort parameters representing the theoretically universal comfort level and real-time environmental parameters reflecting the current objective physical environment of the cabin as input, and combining this with a pre-constructed thermal comfort mapping strategy that characterizes individual user preferences, the benchmark parameters are intelligently and quantitatively offset and corrected using the current environmental parameters as an index. This generates the final target thermal comfort parameters. This not only ensures that the cabin control target is always based on the most relevant real-time environmental data, but more importantly, through strategic correction, it dynamically adapts the static theoretical benchmark representing the "average person" to a personalized target that conforms to the historical preferences and real-time feelings of the current specific user. This provides accurate and reliable personalized input for subsequent global multi-loop resource priority arbitration, laying the foundation for the entire system to achieve "personalized" refined thermal management.

[0013] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, after obtaining the thermal state parameters of at least one thermal management circuit of the vehicle in response to multiple thermal management needs of the vehicle, the method further includes: extracting the current temperature of the motor circuit and the current temperature of the battery circuit from the thermal state parameters of the at least one thermal management circuit; in response to the current temperature of the motor circuit being greater than a first preset temperature threshold, identifying whether the current temperature of the battery circuit is less than a second preset temperature threshold; if the current temperature of the battery circuit is less than the second preset temperature threshold, directing the residual heat of the motor circuit to the battery circuit; otherwise, directing the residual heat of the motor circuit or the heat of the battery circuit to the cabin circuit.

[0014] Through the aforementioned technical solution, by extracting the real-time temperatures of the two key circuits—the motor and the battery—from the comprehensive thermal state parameters, the system first confirms whether the motor is in a recyclable high-temperature state (above the first threshold). If the condition is met, it prioritizes determining whether the battery requires preheating (temperature below the second threshold). It is through this serialized temperature-demand matching logic that the system intelligently selects its path: if the battery needs heat, it performs primary recycling, prioritizing the redirection of waste heat from the motor to the battery; if the battery no longer requires heating, it performs secondary recycling, redirecting the remaining heat or the heat stored in the battery itself to the cabin. This achieves targeted reuse of "safety-level" energy, ensuring not only priority and rapid access to free heat sources to guarantee safety and range when the battery is at low temperatures, but also automatically converting waste heat into energy to enhance cabin comfort after the battery's needs are met. Thus, in multi-demand scenarios, this tiered utilization significantly improves the systemic efficiency of vehicle energy utilization and reduces the load on subsequent, more refined global resource allocation.

[0015] In conjunction with the first aspect and the above-described implementations, in some possible implementations, after directing the residual heat from the motor circuit or the heat from the battery circuit to the cockpit circuit, the method further includes:

[0016] In response to the fact that the cockpit circuit has no heating requirement, the residual heat of the motor circuit is directed to the energy storage module for storage.

[0017] By continuously monitoring the heating demand status of the cabin circuit through the above technical solution, once it is determined that there is no heating demand in the cabin, an energy storage command is immediately triggered to direct the "remaining" waste heat of the motor to a dedicated energy storage module for storage. This not only solves the problem of mismatch between intermittent waste heat and instantaneous demand, avoiding energy waste, but also transforms the heat that might otherwise be wasted into a strategic reserve resource that can be used for intermittent air conditioning, further improving the overall energy utilization efficiency of the vehicle.

[0018] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, when allocating heat resources and / or cooling resources to the at least one thermal management loop, the method further includes: obtaining the pipeline air resistance status of the at least one thermal management loop; based on the pipeline air resistance status, in response to any thermal management loop having a current air resistance level greater than a preset safety threshold, performing an exhaust operation; based on the pipeline air resistance status, in response to any thermal management loop having a transducer efficiency value less than a preset efficiency threshold, generating a path switching command, and based on the path switching command, switching the fluid path of the thermal management loop having a transducer efficiency value less than the preset efficiency threshold to a preset redundant path.

[0019] Through the above technical solution, while allocating heat / cooling resources, the system executes a fault protection mechanism in parallel. This mechanism involves real-time monitoring of the air resistance status of each loop; if excessive air resistance is detected, automatic venting is immediately initiated to prevent performance degradation. Simultaneously, the energy conversion efficiency is monitored; when efficiency drops to a dangerous threshold, a hardware-level fault is identified, and a command is generated to switch the faulty loop to a preset redundant path. Thus, through preventative venting and post-fault redundancy switching, a system-level reliability guarantee covering both performance degradation and functional failure scenarios is constructed, significantly reducing the risk of thermal management system failure due to fluid malfunctions or single-point hardware failures.

[0020] In combination with the first aspect and the above implementation methods, in some possible implementation methods, obtaining the pipeline air resistance state of the at least one thermal management circuit includes: obtaining the air resistance sensor signal of the at least one thermal management circuit; calculating the bubble ratio of the at least one thermal management circuit based on the air resistance sensor signal and a preset calibration curve; and obtaining the pipeline air resistance state of the at least one thermal management circuit based on the bubble ratio of the at least one thermal management circuit.

[0021] The above technical solution first acquires the raw signal from the sensor, then converts the electrical signal into a bubble ratio value with clear engineering significance based on the pre-calibrated signal-physical quantity correspondence. Finally, this ratio is used to objectively characterize the gas resistance level of the pipeline, transforming the abstract and vague gas resistance state into a physical parameter (bubble ratio) that can be accurately measured, has a clear threshold, and is directly related to the fault mechanism. This provides a scientific, objective, and repeatable data basis for the aforementioned exhaust operation triggering conditions, greatly improving the accuracy and automation level of the fault prediction mechanism.

[0022] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the thermal management priority of each thermal management loop based on the thermal state parameters of the at least one thermal management loop and the target thermal comfort parameters includes: Based on the thermal state parameters of the at least one thermal management loop and the target thermal comfort parameters, the thermal management priority of each thermal management loop is determined according to a preset urgency scoring algorithm. The preset urgency scoring algorithm is as follows: P_n = λ_n × (R_n / S_n); Wherein, P_n is the priority of the nth thermal management loop, λ_n is the weight coefficient of the nth thermal management loop, R_n is the quantification result of the deviation between the current normalized value of at least one state parameter of the nth thermal management loop and the corresponding normalization target threshold, and S_n is the quantification result of at least one constraint factor affecting the thermal management requirements of the nth thermal management loop.

[0023] Through the above technical solution, by comprehensively calculating the priority score of each loop using three core variables—namely, the static weight coefficient representing the inherent importance of the loop, the sum of state deviation values ​​reflecting the urgency of the current demand, and the sum of constraint factors characterizing the difficulty of achieving the demand—the engineering principle of "safety first, comfort second" is transformed into an objective, calculable, and highly interpretable decision rule. This enables the system to abandon empiricism or simple ranking when facing conflicts in demand from multiple loops, and to achieve refined and dynamic arbitration based on quantitative data, thereby ensuring that limited thermal management resources are allocated optimally globally.

[0024] Thirdly, a thermal management control device is provided, which is applied to a thermal management system, wherein the device includes: The acquisition module is used to acquire thermal state parameters of at least one thermal management loop of the vehicle in response to multiple thermal management requirements of the vehicle. The determination module is used to determine the target thermal comfort parameters of the cabin circuit in the at least one thermal management circuit based on the thermal state parameters of the at least one thermal management circuit, and to determine the thermal management priority of each thermal management circuit according to the thermal state parameters of the at least one thermal management circuit and the target thermal comfort parameters. The control module is used to allocate heat resources and / or cold resources to at least one thermal management loop based on the thermal management priority of each thermal management loop.

[0025] In conjunction with the second aspect, in some possible implementations, the determining module is specifically used for: Extract the thermal state parameters of the cabin circuit from the thermal state parameters of the at least one thermal management circuit; Obtain the current cabin environment parameters from the baseline thermal comfort parameters and the thermal state parameters of the cabin circuit; Based on a preset thermal comfort preference mapping strategy, the target thermal comfort parameter is obtained by correcting the baseline thermal comfort parameter according to the current cabin environment parameter.

[0026] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, after acquiring the thermal state parameters of at least one thermal management loop of the vehicle in response to multiple thermal management needs of the vehicle, the acquisition module further includes: The extraction unit is used to extract the current temperature of the motor circuit and the current temperature of the battery circuit from the thermal state parameters of the at least one thermal management circuit. The identification unit is used to identify whether the current temperature of the battery circuit is less than a second preset temperature threshold in response to the current temperature of the motor circuit being greater than a first preset temperature threshold. A first control unit is configured to direct the waste heat of the motor circuit to the battery circuit if the current temperature of the battery circuit is less than the second preset temperature threshold; otherwise, direct the remaining waste heat of the motor circuit or the heat of the battery circuit to the cabin circuit.

[0027] In conjunction with the second aspect and the above implementations, in some possible implementations, after directing the residual heat from the motor circuit or the heat from the battery circuit to the cockpit circuit, the first control unit is further configured to: In response to the fact that the cockpit circuit has no heating requirement, the residual heat of the motor circuit is directed to the energy storage module for storage.

[0028] In conjunction with the second aspect and the above implementation methods, in some possible implementations, when allocating heat resources and / or cooling resources to the at least one thermal management loop, the control module further includes: The acquisition unit is used to acquire the pipeline air resistance status of the at least one thermal management circuit; The second control unit is used to perform an exhaust operation based on the gas resistance status of the pipeline, in response to the current gas resistance level of any thermal management circuit being greater than a preset safety threshold. The third control unit is used to generate a path switching command based on the gas resistance state of the pipeline, in response to the energy conversion efficiency value of any thermal management loop being less than a preset efficiency threshold, and to switch the fluid path of the thermal management loop whose energy conversion efficiency value is less than the preset efficiency threshold to a preset redundant path based on the path switching command.

[0029] In combination with the second aspect and the above implementation methods, in some possible implementations, the acquisition unit is specifically used for: Acquire the air resistance sensor signal of the at least one thermal management loop; Based on the gas resistance sensor signal and the preset calibration curve, the bubble ratio of the at least one thermal management circuit is calculated. Based on the bubble ratio of the at least one thermal management loop, the pipeline air resistance state of the at least one thermal management loop is obtained.

[0030] In combination with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is specifically used for: Based on the thermal state parameters of the at least one thermal management loop and the target thermal comfort parameters, the thermal management priority of each thermal management loop is determined according to a preset urgency scoring algorithm. The preset urgency scoring algorithm is as follows: P_n = λ_n × (R_n / S_n); Wherein, P_n is the priority of the nth thermal management loop, λ_n is the weight coefficient of the nth thermal management loop, R_n is the quantification result of the deviation between the current normalized value of at least one state parameter of the nth thermal management loop and the corresponding normalization target threshold, and S_n is the quantification result of at least one constraint factor affecting the thermal management requirements of the nth thermal management loop.

[0031] Fourthly, a vehicle is provided, including a controller, the controller including a memory, a processor and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the thermal management control method described in the above embodiments.

[0032] Fifthly, a computer program product is provided, comprising: computer program code, which, when executed on a computer, causes the computer to perform the thermal management control method described in the first aspect or any possible implementation thereof.

[0033] In a sixth aspect, a computer-readable storage medium is provided, which stores computer program code that, when executed on a computer, causes the computer to perform the thermal management control method of the first aspect or any possible implementation thereof. Attached Figure Description

[0034] Figure 1 A block diagram of a thermal management system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of the thermal management system provided in an embodiment of this application; Figure 3 A schematic diagram of a multi-way valve provided in an embodiment of this application; Figure 4 A schematic diagram of an energy storage component provided in an embodiment of this application; Figure 5 A schematic flowchart of the thermal management control method provided in the embodiments of this application; Figure 6 A block diagram of a thermal management control device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the vehicle structure according to an embodiment of this application. Detailed Implementation

[0035] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0036] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0037] Figure 1 This is a block diagram of a thermal management system provided in an embodiment of this application.

[0038] For example, such as Figure 1 As shown, the thermal management system 10 includes: a battery thermal management component 100, a motor thermal management component 200, a cabin thermal management component 300, a multi-channel heat exchanger 400, a multi-way valve 500, and a control component 600. The battery thermal management component 100 is used to regulate the heat resources and / or cooling resources of the battery thermal management circuit; the motor thermal management component 200 is used to regulate the heat resources and / or cooling resources of the motor thermal management circuit; the cabin thermal management component 300 is used to regulate the heat resources and / or cooling resources of the cabin thermal management circuit; the first channel inlet of the multi-channel heat exchanger 400 is connected to the battery thermal management component 100, the second channel inlet of the multi-channel heat exchanger 400 is connected to the motor thermal management component 200, and the third channel inlet of the multi-channel heat exchanger 400... The multi-channel heat exchanger 400 is connected to the cabin thermal management component 300, and its fourth channel inlet is connected to the coolant reservoir. The input end of the multi-way valve 500 is connected to the outlet of the multi-channel heat exchanger 400, and the output end of the multi-way valve 500 is connected to the battery thermal management component 100, the motor thermal management component 200, and the cabin thermal management component 300 respectively through multiple branch pipes. The control component 600 is used to determine the thermal management priority of each thermal management circuit according to the multiple thermal management needs of the vehicle, and to determine the target opening state of the multi-way valve 500 according to the thermal management priority of each thermal management circuit, so as to control the multi-way valve 500 according to the target opening state, thereby realizing the allocation of heat resources and / or cooling resources to at least one thermal management circuit.

[0039] Specifically, in this embodiment, the thermal management system is a collaborative hardware topology architecture based on multi-channel heat exchangers (such as a four-channel main heat exchanger) and multi-way valves (such as a dual-drive intelligent four-way valve) to achieve efficient and flexible bidirectional heat exchange and path reconfiguration between the battery, motor, and cabin loops at the physical level. Combined with... Figure 2 As shown, in terms of hardware configuration, this architecture can include a three-loop unit (i.e., the battery thermal management loop of the battery management component 100, the motor thermal management loop of the motor thermal management component 200, and the cabin thermal management loop of the cabin thermal management component 300), a four-channel main heat exchanger 400, a dual-drive intelligent four-way valve 500, and redundant pump sets (such as...). Figure 2 The multiple water pumps shown), control component 600 (i.e., domain controller) and sensor array (such as...) Figure 2 (The temperature sensor, pressure sensor, and flow sensor are shown). The four-channel main heat exchanger 400 can use the SAE J2044 standard flange interface. The flow channel design is optimized using the unsteady Bernoulli equation to control the peak turbulent kinetic energy at 1.85m. 2 / s 2 Within this system, serving as the core heat exchange medium for the three-loop system and coolant replenishment, it achieves a thermal efficiency of ≥95% (ISO17573 test standard). The dual-drive intelligent four-way valve 500, acting as the system's "flow router," integrates electromagnetic drive (response ≤80ms) and stepper motor fine-tuning (accuracy ±3%) dual-mode drive technology, ensuring a total response delay of ≤100ms for heat flow path switching. It also features built-in air resistance detection, enabling automatic degassing when the bubble percentage reaches a certain value. The valve seat undergoes plasma nitriding treatment, forming a hardened layer up to 25μm deep and generating a residual compressive stress of -320MPa, effectively improving wear resistance and crack initiation resistance. This valve is connected in series between the outlet of the four-channel main heat exchanger 400 and the inlet of the three-loop system, serving as a key actuator for switching heat flow paths. The sensor array can collect system status in real time at a period of ≤50ms. The data is transmitted to the control component 600 via CAN FD (Controller Area Network Flexible Data-rate) protocol (delay ≤30ms). The control component 600 has a built-in urgency scoring algorithm to realize closed-loop control of valve group and pump group actions.

[0040] In terms of connectivity, the four inlets of the four-channel main heat exchanger 400 can be connected to the outlets of the three-loop main pumps and the coolant storage tank, respectively. The input end of the dual-drive intelligent four-way valve 500 is rigidly connected to the total outlet of the four-channel main heat exchanger 400, and its three output branches are connected to the return ends of the three loops, forming a closed loop of heat exchanger-valve group-loop. Redundant pumps can be connected in parallel next to each main pump. Temperature sensors, pressure sensors, and bubble sensors can be deployed in each loop, with signals directly connected to the control component 600. Thanks to this topology and the precise control of the four-way valve, the system can support intelligent switching of various heat flow paths. These paths cover everything from simple unidirectional waste heat recovery (such as motor waste heat flowing to the battery for battery preheating) to complex multi-loop coordination (such as three-loop interconnection to cope with multiple conflicting demands for battery preheating, motor cooling, and cabin heating), thereby ensuring the precise execution of a resource allocation strategy that prioritizes safety while also considering comfort at the hardware level.

[0041] In other words, the battery thermal management component 100, the motor thermal management component 200, and the cabin thermal management component 300 are permanently connected to the three inlets of the multi-channel heat exchanger 400 through independent pipelines. The coolant reservoir is also directly connected to the fourth inlet of the multi-channel heat exchanger 400, so that the heat exchange medium of each loop can continuously flow into the multi-channel heat exchanger 400 for heat exchange. After heat exchange, the mixed medium flows out from the outlet of the multi-channel heat exchanger 400 and enters the input end of the multi-way valve 500. The multi-way valve 500 can switch its output end to the branch pipe corresponding to the target loop according to the command of the control component 600, so as to accurately distribute the medium carrying heat or cold to the battery thermal management component 100, the motor thermal management component 200, or the cabin thermal management component 300 that needs to be adjusted. As the decision-making core of the system, the control component 600 acquires multiple thermal management needs of the vehicle in real time, calculates the thermal management priority of each circuit through the built-in algorithm, and generates the target opening state command of the multi-way valve 500 accordingly, so as to realize dynamic and precise control of the heat flow path.

[0042] Furthermore, such as Figure 3 As shown, Figure 3 This is a detailed cross-sectional view of the 500 dual-drive intelligent four-way valve. The valve body integrates a composite drive system consisting of an electromagnetic drive unit and a stepper motor fine-tuning unit. These two units work together to control the valve core's movement: the electromagnetic drive unit enables rapid path switching with a response time ≤80ms; the stepper motor fine-tuning unit precisely corrects the valve opening with an adjustment accuracy of ±3%. The valve core surface undergoes plasma nitriding strengthening treatment, achieving a surface hardness ≥850HV, significantly improving wear resistance and fatigue resistance. An integrated bubble sensor within the valve body monitors the proportion of bubbles in the coolant flowing through the valve chamber in real time. When the detected bubble proportion exceeds 5%, the system automatically opens the vent, directing the accumulated gas to the storage tank, effectively preventing cavitation and ensuring system operational stability.

[0043] Therefore, by permanently connecting the battery, motor, and cabin thermal management circuits, as well as the coolant reservoir, to the same multi-channel heat exchanger, and using a multi-way valve to distribute the heat or cold energy at the heat exchanger outlet on demand, bidirectional free flow and flexible scheduling of heat between the circuits are achieved. At the same time, the control component calculates the thermal management priority of each circuit in real time based on the vehicle's multiple thermal management needs, and precisely controls the opening state of the multi-way valve accordingly, thereby ensuring at the hardware level that thermal management resources can prioritize the needs of high-priority circuits.

[0044] Optionally, in some embodiments, the thermal management system 10 further includes: an energy storage component 700, the energy storage component 700 including a first three-way valve 701, a second three-way valve 702, a primary heat exchanger 703, a secondary heat exchanger 704, and a heat storage medium containing area 705, wherein the outlet of the motor thermal management component 200 is connected to the inlet of the energy storage component 700 through the first three-way valve 701; the inlet and outlet of the primary heat exchanger 703 are connected to the battery thermal management circuit through pipelines; the inlet and outlet of the secondary heat exchanger 704 are connected to the cabin thermal management circuit through pipelines; the heat storage medium containing area 705 includes a phase change material, and the heat storage medium containing area 705 is thermally coupled to the primary heat exchanger 703 and the secondary heat exchanger 704 respectively, so that the battery thermal management circuit coolant flowing through the primary heat exchanger 703 and / or the cabin thermal management circuit coolant flowing through the secondary heat exchanger 704 exchanges heat with the phase change material.

[0045] Specifically, such as Figure 4As shown, the thermal management system 10 further integrates an energy storage component 700 with energy caching function. This component is connected in series to the circuit where the motor thermal management component 200 is located through a first three-way valve 701 and a second three-way valve 702, forming an independent charging branch. Simultaneously, the primary heat exchanger 703 and the secondary heat exchanger 704 inside the energy storage component 700 are connected to the battery thermal management circuit and the cabin thermal management circuit respectively through pipelines, achieving thermal coupling with the two core heat-using circuits. The core heat storage part of the energy storage component 700 is the heat storage medium containment area 705, which is filled with high-performance phase change material, and this containment area maintains thermal coupling with both the primary heat exchanger 703 and the secondary heat exchanger 704. Therefore, when the motor thermal management component 200 generates excess heat and neither the battery nor the cabin has an immediate heat demand, this heat can be introduced into the energy storage component 700 via the first three-way valve 701, and then transferred to the phase change material and stored through the primary heat exchanger 703 and / or the secondary heat exchanger 704. When the battery circuit or cabin circuit subsequently has a heat demand, the stored heat can be released in the reverse direction through the corresponding heat exchanger for use in the corresponding circuit. The anti-aging nanocomposite phase change material can be composed of polyethylene glycol PEG-6000, expanded graphite, and carbon nanotubes in a mass ratio of 75:20:5, and undergoes surface modification treatment with gamma-ray irradiation and silane coupling agent KH-550, exhibiting excellent thermal conductivity and cycle stability.

[0046] Therefore, by connecting the first and second three-way valves in series with the motor circuit, an independent charging branch is formed. Simultaneously, thermal coupling is achieved with the battery circuit and the cabin circuit through a primary heat exchanger and a secondary heat exchanger, respectively. This solution constructs an integrated charging-storage-discharging energy buffer system: when the motor generates excess heat and neither the battery nor the cabin has immediate demand, the excess heat can be absorbed and stored by the phase change material through the energy storage components; when the battery or cabin subsequently generates heat demand, the stored heat can be released to the corresponding circuit through the heat exchanger. This design not only transforms potentially wasted intermittent waste heat into a strategic reserve that can be used for air conditioning over time, significantly improving the overall vehicle energy utilization efficiency, but more importantly, it provides additional thermal capacity buffer for the system to cope with sudden high-load demands such as cold starts and extreme operating conditions. This enhances the robustness and flexibility of the thermal management system throughout its entire lifecycle while ensuring the safety of core components.

[0047] The thermal management control method proposed in the embodiments of this application will be described in detail below. This thermal management control method is applied to a thermal management system (which may be...). Figure 1 The thermal management system in this embodiment can also be a thermal management system with other architectures, which are not specifically limited here.

[0048] For example, such as Figure 5 As shown, the thermal management control method includes the following steps: In step S501, in response to the presence of multiple thermal management requirements in the vehicle, thermal state parameters of at least one thermal management loop of the vehicle are obtained.

[0049] It is understandable that thermal management requirements refer to the objective requirements for temperature regulation (heating or cooling) of various vehicle components due to their own operating conditions or external environment. For example, batteries need to be heated at low temperatures to ensure performance and safety; motors need to be cooled to prevent overheating during high-speed operation; and the cabin needs to maintain a comfortable temperature environment according to occupant preferences. A thermal management loop is an independent closed-loop pipeline established for specific components or areas within the vehicle's thermal management system, used for the cyclical transfer of heat (e.g., through a medium such as coolant). In the embodiments of this application, the thermal management loop mainly includes a battery loop (managing battery pack temperature), a motor loop (managing motor and electronic control temperatures), and a cabin loop (managing in-vehicle air conditioning and thermal comfort). Thermal state parameters refer to the set of key physical quantities used to quantify the current thermal state of each thermal management loop, and may include at least: temperature (e.g., battery pack temperature, motor temperature, cabin air vent temperature), pressure, flow rate, battery SOC, bubble percentage, and cabin-specific thermal comfort-related parameters (e.g., PMV value, air humidity).

[0050] Specifically, when the system detects that the vehicle has multiple thermal management needs simultaneously (for example, in extremely cold weather, the battery needs to be preheated to ensure charging safety, the motor may generate excess heat after starting and needs to be dissipated, and the occupants want the cabin to be heated quickly), the domain controller will be triggered. The domain controller can then collect the thermal status parameters of at least one thermal management loop (i.e., battery loop, motor loop, and cabin loop) in real time and synchronously at a sampling period of 50ms through a sensor network (such as a dense sensing network consisting of 18 sensors) deployed at key nodes of each loop.

[0051] In step S502, based on the thermal state parameters of at least one thermal management loop, the target thermal comfort parameters of the cabin loop in at least one thermal management loop are determined, and the thermal management priority of each thermal management loop is determined according to the thermal state parameters and the target thermal comfort parameters of at least one thermal management loop.

[0052] It is understandable that the target thermal comfort parameter refers to the personalized thermal comfort control target value set by the system for the cabin loop to guide the operation of the air conditioning system. It is not a fixed universal value (such as standard PMV=0), but rather the result obtained by dynamically correcting the theoretical universal value based on learning the current user's historical preferences. Thermal management priority refers to the quantitative indicator of the priority order of resource acquisition allocated by the system to the thermal management needs of each loop when multiple loop thermal management needs conflict.

[0053] Specifically, based on the thermal state parameters of at least one thermal management loop obtained in step S501, the system can determine personalized cockpit control targets, i.e., target thermal comfort parameters. Then, the thermal state parameters of each thermal management loop (i.e., battery loop, motor loop, and cockpit loop) are used together with the current target thermal comfort parameters of the cockpit as inputs to output a quantified thermal management priority for each thermal management loop's needs. Thus, the personalized comfort target of the cockpit is no longer isolated but is integrated as a key variable into the decision equation for global resource competition, enabling the system to perform refined and optimized resource allocation for personalized comfort experience while ensuring high-weight safety requirements.

[0054] In step S503, heat resources and / or cold resources are allocated to at least one thermal management loop based on the thermal management priority of each thermal management loop.

[0055] It is understood that, in the embodiments of this application, heat resources refer to the energy sources in the thermal management system that can be used to heat the target, such as heat generated by PTC (Positive Temperature Coefficient) heaters, heat generated in the heating mode of heat pump systems, waste heat generated by motor operation, latent heat of phase change stored in energy storage modules, and usable heat recovered from battery heat dissipation, cabin waste heat, etc. Cooling resources refer to the cooling capacity sources in the thermal management system that can be used to cool the target, such as the cooling capacity generated by compressor-driven air conditioning systems, heat pump systems in cooling mode, etc. Heat resource allocation and / or cooling resource allocation is the process by which the system dynamically divides and directionally distributes limited heating or cooling capacity among different loops according to the thermal management priority of each loop. For example, in winter, if there is only a heating requirement (such as battery preheating and cabin heating), only heat resources can be allocated; in summer, if there is only a cooling requirement (such as battery heat dissipation, motor cooling, and cabin cooling), only cold resources can be allocated; in transitional seasons, there may be complex operating conditions where heating and cooling requirements coexist (such as battery heating, motor heat dissipation, and cabin cooling), and both heat and cold resources can be allocated simultaneously.

[0056] In other words, the domain controller can sort the thermal management priorities of each thermal management loop and generate specific actuator control instruction sets based on the sorting results. These instruction sets can include path switching instructions (i.e., controlling the dual-drive intelligent four-way valve to switch to the corresponding heat flow path, guiding the flow of heat or cold resources to the highest-priority thermal management loop), power adjustment instructions (i.e., adjusting the speed of the main pump in each thermal management loop (to control flow rate), the power output of the PTC (Positive Temperature Coefficient) heater or compressor to control the flow rate allocated to each loop), and composite instructions (i.e., under complex conflicts, the above instructions may be generated simultaneously to achieve multi-path parallelism and proportional flow allocation). Therefore, this embodiment can transform the thermal management priorities of each loop calculated in step S502 into specific hardware execution instructions, achieving precise scheduling and allocation of limited heat / cold resources. The priority value is no longer merely an abstract sorting result, but directly determines the "command" of resource flow; that is, the system can dynamically adjust the path switching state of the dual-drive intelligent four-way valve, the speed of the main pump in each loop, and the power output of the PTC heater or compressor according to the priority level.

[0057] Furthermore, the resource allocation in this application does not simply assign all resources to the highest priority circuit. For example, when both the battery circuit (highest thermal management priority) and the cabin circuit (second highest thermal management priority) require heat and the total heat is insufficient, the system can adopt a strategy of allocating resources proportionally based on priority, rather than completely depriving the cabin of resources. This ensures that comfort is maximized while maintaining absolute safety (battery needs are prioritized). For instance, when the battery has the highest priority, the system can direct more than 80% of the heat flow to the battery circuit while appropriately reducing the heating power of the cabin circuit. When multiple circuits have similar priorities, resources can be allocated proportionally to achieve global optimization.

[0058] Therefore, when the system identifies multiple coexisting thermal management needs, it first dynamically generates a cabin thermal comfort target parameter specific to the current user based on the acquired parameters and the embedded personalized learning model, rather than using a fixed universal value. Next, the system uses this personalized comfort target along with the thermal state parameters of other circuits such as the battery and motor as inputs to calculate the global priority of each circuit. Finally, the system accurately allocates limited heat or cooling resources based on this priority. In this way, the user's real-time, personalized comfort needs are elevated from a passive, isolated control target to an active core decision variable that can influence the global resource scheduling strategy. Thus, under the constraint of unavoidable conflicts of multiple needs, the system maximizes and refines the satisfaction of the user's personalized thermal comfort experience while ensuring the absolute priority of ensuring the safety of core components such as the battery. This achieves intelligent coordination and unification of the three major goals of safety, energy efficiency, and comfort.

[0059] As one possible implementation, in some embodiments, determining the target thermal comfort parameter of the cabin circuit in at least one thermal management loop based on the thermal state parameters of at least one thermal management loop includes: extracting the thermal state parameters of the cabin circuit from the thermal state parameters of at least one thermal management loop; obtaining the current cabin environment parameters from the reference thermal comfort parameters and the thermal state parameters of the cabin circuit; and obtaining the target thermal comfort parameter by correcting the reference thermal comfort parameters according to the current cabin environment parameters based on a preset thermal comfort preference mapping strategy.

[0060] It is understandable that the baseline thermal comfort parameter refers to a theoretical comfort value applicable to a "standard person," calculated based on internationally accepted thermal comfort theoretical models (such as the PMV model) and current objective environmental parameters (temperature, humidity, wind speed, etc.). This parameter is an initial reference value without personalized adjustments. Current cabin environmental parameters are physical environmental data affecting human thermal sensation collected in real time by sensors within the cabin, which may include air temperature, relative humidity, air velocity, and mean radiant temperature. The preset thermal comfort preference mapping strategy refers to a mathematical model (such as the K-nearest neighbor model, a machine learning algorithm based on the idea of ​​"like attracts like," which finds the K most similar historical cases to the current situation and predicts the answer to the current problem based on the results of these cases) that is pre-constructed using machine learning methods to reflect the individual thermal comfort preferences of a specific user. This strategy establishes a mapping relationship between environmental parameters and personalized comfort adjustments.

[0061] Specifically, firstly, the system can extract the thermal state parameters specific to the cabin circuit from the thermal state parameters of all thermal management circuits. Then, the system can acquire two key inputs in parallel: baseline thermal comfort parameters (a theoretical "universal comfort benchmark") and current cabin environment parameters reflecting the objective reality of the cabin. Further, the system can invoke a preset thermal comfort preference mapping strategy, using the current cabin environment parameters as an "index" or "input," to intelligently output a personalized correction amount (e.g., an offset value) for the current user and environment. This correction amount is then used to adjust the baseline thermal comfort parameters in real time and quantitatively, thereby generating the final, dynamic target thermal comfort parameters.

[0062] It should be noted that by continuously collecting user manual adjustment behavior data during driving (e.g., the adjustment range of temperature settings, the frequency of fan speed selection, and the timing of switching between air conditioning cooling / heating / automatic modes), and correlating and aligning this subjective behavior data with corresponding objective environmental parameters (such as ambient temperature inside and outside the vehicle, duration of a single drive, etc.), a time-series dataset can be built using machine learning algorithms such as K-nearest neighbors to train a personalized model that maps environmental conditions to user preferences—that is, a preset thermal comfort preference mapping strategy. This model can dynamically predict the user's most likely preferred comfort settings based on real-time monitored cabin environment parameters during system operation, and automatically fine-tune benchmark control targets such as baseline thermal comfort parameters (for example, when the system recognizes that the user habitually adjusts the set temperature from 22℃ to 24℃ when the cabin environment temperature is X, it will automatically correct the PMV target value by +0.3), enabling the air conditioning system to proactively anticipate and meet the user's comfort needs, thereby significantly reducing the frequency of manual intervention by the user and achieving an intelligent experience upgrade from "human adapting to car" to "car adapting to human".

[0063] Therefore, by extracting a subset of specific parameters directly related to the cabin from the comprehensive thermal state parameters of multiple loops, and then using the benchmark thermal comfort parameters representing the theoretically general comfort level and the real-time environmental parameters reflecting the current objective physical environment of the cabin as inputs, combined with a pre-constructed thermal comfort mapping strategy that can characterize individual user preferences, the benchmark parameters are intelligently and quantitatively offset and corrected using the current environmental parameters as an index, thereby generating the final target thermal comfort parameters. This not only ensures that the cabin control target is always based on the most relevant real-time environmental data, but more importantly, it dynamically adapts the static theoretical benchmark representing the "average person" to a personalized target that conforms to the current specific user's historical preferences and real-time feelings through strategic correction. This provides accurate and reliable personalized input for subsequent global multi-loop resource priority arbitration, laying the foundation for the entire system to achieve "personalized" refined thermal management.

[0064] This application embodiment takes PMV personalized fuzzy PID as the core. After obtaining the thermal state parameters of at least one thermal management loop, the original thermal state parameter data can be cleaned (such as removing outliers and filling missing values) and then normalized by the min-max normalization method to map the original values ​​of each parameter to the [0,1] interval, thereby eliminating the dimensional differences. For example, assuming the current battery temperature is -5℃ and the battery operating temperature range is [-30℃, 60℃], then the normalized current battery temperature = (-5 - (-30)) / (60 - (-30) ≈ 0.278). Subsequently, the system can extract multiple core feature variables from the normalized data, including temperature-related variables (such as average battery pack temperature, maximum motor and electronic control temperature, cabin air outlet temperature, and coolant main channel temperature), pressure and flow-related variables (such as battery circuit pipeline pressure, motor circuit coolant flow rate, and main heat exchanger inlet and outlet pressure difference), thermal comfort-related variables (such as cabin PMV thermal comfort value, user manual adjustment frequency, and three-loop thermal demand status (preheating / heat dissipation / insulation)), and safety-related variables (such as pipeline bubble ratio and redundant pump group standby status). These features can be input into a specially optimized improved decision tree model, which uses the C4.5 algorithm as a framework and achieves high-precision identification through three methods. That is, (1) In the feature selection stage, embedding thermal management demand priority weights (such as the weight of the battery circuit is 1.0, the weight of the motor circuit is 0.8, and the weight of the cabin circuit is 0.6) can enhance the sensitivity to safety key features; (2) Adopting a combination of pre-pruning and post-pruning strategies to control model complexity, namely, pre-pruning: setting the upper limit of tree depth to 6 layers and the minimum number of samples per node to 20 groups to avoid model overfitting; post-pruning: verifying through test sets (such as using a 7:3 ratio to divide the training set and test set (a total of 10,000 multi-condition samples), and optimizing model parameters through 5-fold cross-validation), pruning branches with weak generalization ability, so that the working condition recognition accuracy is improved to ≥99.2%; (3) For ambiguous working conditions (such as "the demand for battery preheating and cabin heating coexist"), setting feature fusion logic in leaf nodes, and combining pressure and flow data for cross-validation to avoid misjudgment of a single feature and improve the recognition accuracy of multi-demand conflict working conditions.Ultimately, the model can accurately identify various scenarios, including: "low-temperature start-up with multiple conflicting demands (e.g., battery temperature ≤ -10℃, motor temperature ≥ 120℃, cabin PMV ≤ -1.0; in winter low-temperature environments, the battery needs preheating, the motor needs cooling, and the cabin needs heating)," "forced cooling during battery fast charging (e.g., battery temperature ≥ 45℃, charging current ≥ 1C, stable pipeline pressure; in DC fast charging scenarios, forced cooling is required to ensure charging safety)," "system fault redundancy (e.g., pipeline air bubble ratio > 5%, main pump efficiency < 65%, abnormal circuit pressure difference; main pump failure or pipeline air resistance; automatic switching of redundant paths to ensure core functions)," and "high-temperature range." The system employs various typical operating conditions, including: "waste heat recovery operation (e.g., motor temperature ≥85℃, battery temperature ≥35℃, energy storage module temperature ≤40℃, during long-distance driving in high summer temperatures, recovering waste heat from the motor for energy storage to reduce the energy consumption of the cooling system)," "normal temperature comfort priority operation (e.g., ambient temperature 15-25℃, cabin PMV value -0.5~+0.5, no high load heat demand, daily commuting in spring and autumn, with personalized thermal comfort as the core control objective)," and "extreme environment tolerance operation (e.g., ambient temperature ≤-30℃ or ≥85℃, three-circuit synchronous heat preservation / heat dissipation, driving in extremely cold / hot areas, requiring the entire system to work together to ensure equipment safety)." These conditions lay the foundation for subsequent dynamic control.

[0065] Based on accurate operating condition identification, the domain controller can distribute the identification results (such as "low-temperature start-up with multiple conflicting demands") to the valve and pump actuators, simultaneously triggering the corresponding preset control strategies. The system can perform dynamic weight allocation to achieve real-time optimization of control objectives. The system presets basic weights centered on safety (e.g., 1.0 for the battery circuit, 0.8 for the motor circuit, and 0.6 for the cabin circuit) and dynamically adjusts them according to different operating conditions. For example, it increases the weight of high-priority targets in multi-demand conflicting conditions and doubles the weight of safety targets in fault redundancy conditions. The weight values ​​can be updated every 150ms to determine the adjustment intensity and resource allocation ratio of the fuzzy PID controller for each thermal management circuit actuator (such as valves and pumps), thereby ensuring that the control actions are always aligned with the global optimal objective.

[0066] Ultimately, control commands that integrate personalized PMV targets (i.e., target thermal comfort parameters), real-time operating information, and dynamic weights are calculated using a fuzzy PID algorithm and transformed into precise adjustment signals for the dual-drive intelligent four-way valve, the main pumps of each thermal management circuit, and the heating / cooling units. This achieves coordinated closed-loop control of the battery circuit, motor circuit, and cabin circuit temperatures, stabilizing temperature fluctuations within ±0.8℃. The entire control cycle is ≤150ms, and it seamlessly integrates with the vehicle's electronic architecture through a standardized interface, enabling plug-and-play intelligent thermal management.

[0067] Before achieving the precise execution layer control described above, the system needs to solve a fundamental decision-making problem: when the thermal management needs of multiple thermal management circuits such as battery circuit, motor circuit, and cabin circuit exist simultaneously and conflict, what criteria should be used to allocate limited heat or cold resources?

[0068] As one possible implementation, in some embodiments, determining the thermal management priority of each thermal management loop based on the thermal state parameters and target thermal comfort parameters of at least one thermal management loop includes: determining the thermal management priority of each thermal management loop based on a preset urgency scoring algorithm, according to the thermal state parameters and target thermal comfort parameters of at least one thermal management loop; wherein the preset urgency scoring algorithm is: P_n = λ_n × (R_n / S_n); Wherein, P_n is the priority of the nth thermal management loop, λ_n is the weight coefficient of the nth thermal management loop, R_n is the quantification result of the deviation between the current normalized value of at least one state parameter of the nth thermal management loop and the corresponding normalization target threshold, and S_n is the quantification result of at least one constraint factor affecting the thermal management requirements of the nth thermal management loop.

[0069] It is understood that, in the embodiments of this application, the state parameter refers to the relevant parameter selected from the thermal state parameters of the thermal management circuit for calculating the deviation value R_n, which may include battery temperature, battery SOC, cabin PMV value, motor temperature, etc. The weighting coefficient refers to a static scaling factor that is pre-set for different thermal management circuits and reflects their inherent importance. This coefficient can reflect the fundamental priority order in the system design. For example, to ensure safety, the weighting coefficient (λ_battery) of the battery circuit can be set to the highest (e.g., 1.0), while the weighting coefficient (λ_cabin) of the comfort-related cabin circuit can be relatively low (e.g., 0.6).

[0070] The quantification result of the deviation value refers to comparing the current normalized value of at least one state parameter (such as battery temperature, battery state of charge, cabin PMV value, etc.) of the nth thermal management loop with its corresponding normalized target threshold, and calculating the weighted sum of the absolute values ​​of all deviations. For example, for the scenario of starting a vehicle in low-temperature winter, given that the normalized current temperature of the battery loop is 0.278, the normalized target residual heat temperature is 0.444, the normalized current battery state of charge is 0.1667, and the normalized target battery state of charge is 0.2778, then the temperature deviation value ΔT = |0.278 - 0.444| = 0.166, and the state of charge deviation value ΔSOC = |0.1667 - 0.2778| = 0.1111, with preset weights (after normalization): ω_T = 0.7, ω_SOC = 0.3. Therefore, we can further calculate R_battery = (0.7 × 0.166) + (0.3 × 0.111) ≈ 0.150.

[0071] The quantification of constraint factors refers to the quantification and summation of various limiting conditions or difficulty factors (i.e., constraint factors) that affect the thermal management requirements of the thermal management loop. These factors may include ambient temperature (affecting the difficulty of heating or cooling), the power of available heat / cold sources, and the current energy state of the system (such as the battery state of charge). This value reflects the cost or resistance to achieving the requirement. For example, for the scenario of starting a vehicle in low-temperature winter conditions, constraint factor 1 for the battery loop is the ambient temperature difference: the lower the ambient temperature, the greater the difficulty in heating to the target temperature. This constraint factor can be quantified as: (target temperature - current ambient temperature) / a certain reference temperature difference. For example, assuming the target temperature is 10℃, the current ambient temperature is -10℃, and the reference temperature difference is 25℃, then constraint factor 1 = (10 - (-10)) / 25 = 0.8. Constraint factor 2 is the proportion of available power: heating the battery requires power, but the total power of the system is limited. This constraint factor can be quantified as: (estimated required power) / (total available power of the system). Assuming the estimated heating battery requires 2kW and the total available power of the system is 6kW, then the constraint factor 2 = 2 / 6 ≈ 0.33. Therefore, S_battery = 0.8 + 0.33 = 1.13.

[0072] Ultimately, the thermal management priority P_n can be determined by these three factors: static weight (λ_n) lays the foundation for importance; dynamic deviation (R_n) expresses the real-time urgency; and constraint factor (S_n) weighs the implementation cost. Through multiplication and division, these three factors are combined to ensure that the thermal management priority calculation simultaneously considers safety assumptions, real-time operating conditions, and resource costs, thus outputting a scientifically sound and highly interpretable quantitative ranking result. This provides a precise and objective basis for subsequent fair and efficient resource allocation.

[0073] Therefore, by comprehensively calculating the priority scores of each loop through three core variables—the static weight coefficient representing the inherent importance of the loop, the sum of state deviation values ​​reflecting the urgency of the current demand, and the sum of constraint factors characterizing the difficulty of achieving the demand—the engineering principle of "safety first, comfort second" is transformed into an objective, calculable, and highly interpretable decision rule. This enables the system to abandon empiricism or simple ranking when facing conflicts in demand from multiple loops, and to achieve refined and dynamic arbitration based on quantitative data, thereby ensuring that limited thermal management resources are allocated optimally globally.

[0074] Optionally, in some embodiments, after obtaining the thermal state parameters of at least one thermal management circuit of the vehicle in response to multiple thermal management needs of the vehicle, the method further includes: extracting the current temperature of the motor circuit and the current temperature of the battery circuit from the thermal state parameters of the at least one thermal management circuit; in response to the current temperature of the motor circuit being greater than a first preset temperature threshold, identifying whether the current temperature of the battery circuit is less than a second preset temperature threshold; if the current temperature of the battery circuit is less than the second preset temperature threshold, directing the waste heat of the motor circuit to the battery circuit; otherwise, directing the remaining waste heat of the motor circuit or the heat of the battery circuit to the cabin circuit.

[0075] It is understood that the first and second preset temperature thresholds can be values ​​pre-set by those skilled in the art, values ​​obtained through a limited number of experiments, or values ​​obtained through a limited number of computer simulations. The first preset temperature threshold serves as the starting temperature point for triggering motor waste heat recovery. When the motor temperature is higher than the first preset temperature threshold (e.g., 75°C), the system can determine that the heat generated by the motor has sufficient quality and capacity for recovery and utilization. The second preset temperature threshold serves as the required temperature point for determining whether the battery needs preheating. When the battery temperature is lower than the second preset temperature threshold (e.g., 25°C), the system determines that the battery has a clear heating requirement to ensure its performance and safety.

[0076] In other words, this application embodiment also sets up a waste heat grading and recovery prediction process. This process can prioritize capturing and utilizing the "free" waste heat generated during motor operation before the system enters complex intelligent arbitration, while simultaneously resolving some non-existent "pseudo-demand conflicts" in advance. For example, the motor generates a large amount of waste heat during operation, which will be wasted if not utilized in time; while the battery has an urgent need for preheating in low-temperature environments. The two coincide in time, resulting in multiple thermal management needs. In this situation, the system can use the simplest and fastest deterministic rules to prioritize guiding the motor's waste heat to the battery circuit, achieving high-value utilization of waste heat, while resolving this non-existent "pseudo-demand conflict." For complex conflicts that still exist after the waste heat grading and recovery prediction process and cannot be resolved by simple rules, an urgency scoring algorithm is then activated for refined arbitration.

[0077] Specifically, firstly, the system can extract the current temperatures of the motor circuit and battery circuit from the thermal state parameters of the thermal management circuit, and determine whether the current temperature of the motor circuit is higher than a first preset temperature threshold (e.g., 75°C). If the condition is met, it indicates that the motor is operating under high load, generating excess heat that can be recovered. Next, the system can further determine whether the current temperature of the battery circuit is lower than a second preset temperature threshold (e.g., 25°C). This determination can establish the priority level of the recovered heat application; that is, if the battery temperature is low, the system can determine that the battery preheating demand (related to safety and energy efficiency) has the highest priority, and then control the valve group to directly guide the waste heat of the motor circuit to the battery circuit, completing the first-level recovery and realizing the first high-value utilization of "waste heat". If the battery temperature has reached the standard (i.e., not lower than the second preset temperature threshold), it indicates that the battery does not have an urgent heating demand, and the system can execute the second-level recovery logic, that is, to guide the remaining waste heat of the motor circuit (or, when the battery temperature itself is high, even the excess light and heat stored in the battery) to the cabin circuit for cabin heating.

[0078] In the case of primary heat recovery operation, the dual-drive intelligent four-way valve first completes path switching within 80ms under electromagnetic drive, directing the heat flow to the path of motor circuit - four-channel main heat exchanger - battery circuit. To accelerate heat transfer, the power of the main pump in the motor circuit can be simultaneously increased to 80%, and the power of the main pump in the battery circuit can be adjusted to 70% to increase the circulation rate. At the same time, the four-channel main heat exchanger dynamically adjusts the channel opening to match the sudden increase in heat flow and strictly controls the peak turbulent kinetic energy of the internal flow field at 1.85m. 2 / s 2 Within this range, flow losses are minimized. Throughout the process, redundant pump units are in hot standby mode, and pressure sensors continuously monitor pipeline status to ensure rapid replenishment. Primary recycling terminates when the battery temperature reaches the target value of 25°C (or the motor temperature drops below 75°C).

[0079] Subsequently, the system seamlessly transitions to secondary recovery operation. The valve assembly receives algorithmic commands, and its stepper motor performs fine-tuning (accuracy ±3%), precisely switching the heat flow path to the motor loop (and / or battery loop) – four-channel main heat exchanger – cabin loop. This can increase the cabin loop main pump power to 60%, driving the cabin evaporator / condenser to initiate heat exchange, converting the transferred waste heat into a heating source. Simultaneously, a personalized thermal comfort unit intervenes, dynamically adjusting the heating rate based on a real-time monitored user preference model, striving to stabilize the cabin PMV value within the comfort range (-0.5~0). Secondary recovery operation continues until the cabin PMV value rises above 0 or the motor temperature drops below 75°C. The entire hardware collaborative execution process, from path switching, pump power adjustment, flow field optimization to safety monitoring and personalized control, constitutes a highly efficient, stable, and intelligent closed-loop energy transfer system.

[0080] Therefore, by extracting the real-time temperatures of the two key circuits—the motor and the battery—from the comprehensive thermal state parameters, the system first confirms whether the motor is in a recyclable high-temperature state (above the first threshold). If the condition is met, it prioritizes determining whether the battery needs preheating (temperature below the second threshold). Through this serialized temperature-demand matching logic, the system intelligently selects a path: if the battery needs heat, it performs primary recycling, prioritizing the redirection of waste heat from the motor to the battery; if the battery no longer needs heating, it performs secondary recycling, redirecting the remaining heat or the heat stored in the battery itself to the cabin. This achieves the targeted reuse of "safety-level" energy, ensuring not only priority and rapid access to free heat sources to guarantee safety and range when the battery is at low temperatures, but also automatically converting waste heat into energy to improve cabin comfort after the battery's needs are met. Thus, in multi-demand scenarios, the system significantly improves the overall efficiency of vehicle energy utilization through tiered utilization, and reduces the load on subsequent more refined global resource allocation.

[0081] Furthermore, in some embodiments, after directing the residual heat from the motor circuit or the heat from the battery circuit to the cabin circuit, the method further includes: in response to the cabin circuit having no heating requirement, directing the residual heat from the motor circuit to an energy storage module for storage.

[0082] In other words, after the secondary recovery operation, the system does not treat any remaining residual heat from the motor that may not be immediately consumed by the cabin as waste heat emission. Instead, it continuously monitors and determines that the cabin circuit has no heating requirement (for example, the cabin is already warm enough or the user has turned off the heating). Once this condition is met, the system immediately generates a control command to drive the dual-drive intelligent four-way valve to switch paths, directing the residual heat continuously generated in the motor circuit to a dedicated energy storage module. The module is internally filled with a specially modified nanocomposite phase change material (such as a PEG-6000 / expanded graphite / carbon nanotube composite material, with a ratio of PEG-6000:expanded graphite:carbon nanotube = 75:20:5). This material undergoes a unique process of surface modification using gamma-ray irradiation (50kGy) and a silane coupling agent (KH-550), achieving a uniform nanoscale composite (i.e., an average particle size of 946-982nm). This results in a high latent heat of phase change of 21.59-59.81J / g, while achieving a thermal conductivity as high as 2.08~3.13W / (m·K), three times higher than traditional materials. It also ensures excellent cycle stability (latent heat decay rate of only 1.87% after 1000 cycles), efficiently absorbing and locking in this heat. This energy storage module can be placed in the spare space of the vehicle chassis and connected to a pre-defined thermal management topology via flanges and piping systems. For example, the energy storage module can be connected in series downstream of a dedicated flow channel of a four-channel main heat exchanger, rigidly connected to the energy storage branch of a dual-drive intelligent four-way valve, forming an independent closed-loop circuit of heat exchanger-valve assembly-energy storage module-heat exchanger. The energy storage module has built-in temperature and pressure sensors and can communicate with the domain controller in real time via the CAN FD protocol.

[0083] In actual operation, the application of the energy storage module is deeply integrated with the system control. After the primary and secondary waste heat recovery is completed, if the motor still has continuous waste heat (i.e., temperature > 5℃), the domain controller can instruct the four-way valve to switch to the energy storage path. The waste heat is introduced into the energy storage module through the heat exchanger, triggering the material phase change to store energy. Its charging trigger threshold is dynamically set according to the ambient temperature (38℃ for low temperature, 40℃ for normal temperature, and 42℃ for high temperature). When the system enters a low-temperature operating condition and lacks an immediate heat source (such as when the battery needs preheating or the cabin needs heating), the domain controller instructs the valve group to switch to the heat release path. The energy storage module releases the latent heat of phase change, which is preferentially supplied to the high-priority battery circuit or supplemented to the cabin circuit through the heat exchanger. The entire charging and discharging process strictly follows the priority set by the urgency scoring algorithm and monitors the material status through sensors. When the phase change cycle approaches 1000 times, the system will prompt maintenance, thereby controlling the material performance degradation to within 5% throughout the entire life cycle, ensuring the long-term, reliable, and efficient operation of the energy storage system. The heat utilization rate can still be maintained above 85% after 24 hours of rest.

[0084] Thus, by converting intermittent and wasteful surplus heat energy into a stable and time-separated strategic reserve for air conditioning, not only is the overall energy utilization rate of the system maximized, but more importantly, it provides an "energy buffer" to cope with future sudden high heat demands (such as rapid preheating of the battery when the vehicle is cold-started again, or compensating for cabin heating in extreme environments), significantly enhancing the flexibility and reliability of the thermal management system in dealing with complex and dynamic operating conditions.

[0085] Optionally, in some embodiments, after obtaining the thermal state parameters of at least one thermal management circuit of the vehicle in response to multiple thermal management needs of the vehicle, the method further includes: determining the temperature rise rate and voltage consistency parameters of the battery circuit based on the thermal state parameters of at least one thermal management circuit; generating a thermal runaway warning signal in response to the temperature rise rate being greater than a first warning threshold and the voltage consistency parameter being greater than a second warning threshold; and controlling the thermal management system to enter a preset forced cooling mode based on the thermal runaway warning signal.

[0086] Understandably, the rate of temperature rise is the change in the average temperature of the battery pack per unit time, used to characterize the intensity of heat generation inside the battery. Voltage consistency parameters refer to indicators used to quantify the degree of voltage difference between individual cells or modules within the battery pack. Specifically, they can be expressed as maximum voltage difference, voltage standard deviation, or voltage variation coefficient. This parameter is a key electrical indicator reflecting electrochemical imbalances between cells, the presence of internal short circuits, or the risk of localized overcharging / over-discharging. The preset forced cooling mode refers to an emergency cooling plan that is automatically triggered and executed by the system when it determines an extreme safety risk (such as thermal runaway), exceeding the scope of conventional control logic. This mode can prioritize maximizing heat dissipation for the battery pack, utilizing all available cooling resources (such as maximum compressor power and maximum cooling pump speed), and can temporarily sacrifice other non-safety objectives such as cabin comfort.

[0087] Specifically, after obtaining the thermal state parameters of at least one thermal management loop, two core early warning indicators for battery safety can be extracted and calculated in real time from the thermal state parameters: the rate of temperature rise and the voltage consistency parameter. The system sets clear early warning thresholds for these two parameters (i.e., the first early warning threshold and the second early warning threshold). Only when both the rate of temperature rise and the voltage consistency parameter exceed their respective thresholds (i.e., the rate of temperature rise is greater than the first early warning threshold, and the voltage consistency parameter is greater than the second early warning threshold) does the system determine that the risk of battery thermal runaway has reached a critical level. Once the conditions are met, the system immediately generates the highest-level thermal runaway early warning signal. This signal will trigger the system to enter a preset forced cooling mode that cannot be overridden by conventional priority arbitration (i.e., the preset forced cooling mode). In this mode, the thermal management system will temporarily suspend all optimization goals related to comfort and energy efficiency, and prioritize and maximize all available cooling capacity and cooling flow to the battery loop, aiming to suppress the abnormal rise in battery temperature as quickly as possible and block potential thermal runaway chain reactions.

[0088] Therefore, after acquiring multi-loop thermal state parameters, two core indicators strongly correlated with battery thermal runaway are precisely calculated: the temperature rise rate, characterizing the intensity of physical heat generation in the cell, and the voltage consistency parameter, reflecting the imbalance of the cell's electrochemical state. Only when both the temperature rise rate and the voltage consistency parameter simultaneously exceed their respective warning thresholds is the thermal runaway risk deemed to have reached a critical level, and the highest-level warning signal is generated. Based on this signal, the system immediately overrides all conventional resource allocation and priority arbitration logic, forcibly activating a preset emergency mode designed to maximize heat dissipation. Thus, through multi-dimensional evidence fusion, the risk of system malfunction due to false alarms from a single sensor or noise interference is significantly reduced, minimizing the false alarm rate. Simultaneously, it defines the highest-priority response path under extreme safety events, ensuring that the system can intervene with the fastest speed and highest resource priority in the early stages of a thermal runaway chain reaction, significantly improving the overall vehicle safety level.

[0089] Optionally, in some embodiments, when allocating heat resources and / or cold resources to at least one thermal management loop, the method further includes: obtaining the pipeline air resistance status of at least one thermal management loop; based on the pipeline air resistance status, performing an exhaust operation in response to the current air resistance level of any thermal management loop being greater than a preset safety threshold; based on the pipeline air resistance status, generating a path switching command in response to the energy conversion efficiency value of any thermal management loop being less than a preset efficiency threshold, and switching the fluid path of the thermal management loop whose energy conversion efficiency value is less than the preset efficiency threshold to a preset redundant path based on the path switching command.

[0090] It is understandable that the air resistance state in the pipeline refers to the increased fluid flow resistance caused by air mixing (forming bubbles) in the coolant circulation pipeline. The energy conversion efficiency value refers to the overall efficiency of the entire thermal management loop in converting the electrical energy consumed by the pump into effective heating or cooling. It is calculated as follows: Within a fixed period (e.g., 10 seconds), the actual heat carried by the coolant due to temperature changes (c×m×ΔT_actual) is taken as the effective output; the total work done by the main pump within the same period is subtracted from the estimated pipeline heat dissipation loss (P_pump×t). The ratio of P_loss to the total energy input is the energy conversion efficiency value (η). The preset redundant path refers to a backup fluid channel pre-designed in the system hardware topology and connected in parallel with the main loop. When the main loop cannot function properly due to a fault (such as pump failure or valve jamming), this path can be activated by switching valves to maintain basic thermal management functions.

[0091] Specifically, while performing resource allocation, the system runs a concurrent fault monitoring and fault-tolerant processing mechanism in parallel. This mechanism continuously monitors a key indicator that characterizes the health of the system, namely the air resistance status of the pipelines in each thermal management loop. Based on this state, the system can execute a two-tiered response: The first tier is active protection. When the gas resistance level (e.g., bubble percentage) of any thermal management loop is detected to exceed a preset safety threshold (e.g., 5%), the system immediately and automatically triggers an exhaust operation. That is, the main pump power is reduced by 15% and switched to the bubble removal path. The vacuum pump of the energy replenishment tank is used for gas-liquid separation. If the gas resistance level is still high after 10 seconds, the redundant pump group can be started and the heat exchanger opening can be adjusted for emergency exhaust until the gas resistance level is below 3% for 5 consecutive seconds before the process is terminated. The second tier is passive fault tolerance. When any thermal management loop is detected to have its energy exchange efficiency value drop below a preset efficiency threshold (e.g., 65%) due to an irreversible fault (e.g., pump efficiency reduction or heat exchanger blockage), the system determines that the thermal management loop has suffered a functional failure. At this time, the system will not attempt to repair it, but will immediately generate a path switching command, control the corresponding valve action, switch the fluid path of the faulty loop from the main loop to the preset redundant path, and simultaneously start the backup pump and other redundant equipment.

[0092] Furthermore, in this embodiment, the system can also construct a leakage prediction model based on historical data such as three-dimensional confocal microscopy imaging, using a gradient boosting tree algorithm as its core. This model takes six dimensions as input: normalized pipeline pressure (i.e., real-time pressure values ​​and pressure fluctuation amplitudes of the three thermal management loops), temperature gradient (i.e., temperature difference between the heat exchanger inlet / outlet and pump inlet / outlet), bubble ratio (i.e., compensated data from the air resistance detection sensor), coolant flow deviation (i.e., the deviation rate between the real-time and rated flow values ​​of the thermal management loops), valve opening (i.e., the real-time opening of the dual-drive intelligent four-way valve), and runtime (i.e., the cumulative system runtime). It outputs in real-time leakage risk levels of 0 (no risk, leakage probability < 0.1%), 1 (low risk, leakage probability 0.1%-1%), 2 (medium risk, leakage probability 1%-5%), 3 (high risk, leakage probability 5%-10%), and 4 (fault warning, leakage probability > 10%). When training the leak prediction model, 1000 sets of historical data can be collected, including 800 sets of normal operating conditions and 200 sets of leak conditions with different levels of leakage. These are divided into training and testing sets in a 7:3 ratio. Five-fold cross-validation is used to optimize the model's hyperparameters (tree depth can be set to 8, learning rate 0.1), ensuring a prediction accuracy of ≥98%. The trained model undergoes pruning and compression to adapt to the computing power limitations of the vehicle's domain controller. In real-world vehicle applications, the system can take progressive responses based on the risk level output by the model: Level 0-1 involves normal operation and continuous monitoring; Level 2 triggers instrument panel alerts for inspection; Level 3 initiates leak localization and reduces power by 20% to minimize leakage; Level 4 directly triggers the highest level of emergency protection, switching to redundant circuits and closing faulty valves, while simultaneously sending an alarm to the backend. The entire model supports OTA iterative upgrades to ensure the leakage rate consistently meets the requirement of ≤5×10⁻⁶. -10 Pa·m 3 The stringent automotive-grade standards (ISO 4414) of / s enable comprehensive, forward-looking, system-level safety protection, from "bubble prevention" to "leakage warning".

[0093] Under the passive fault-tolerant mechanism, in addition to switching the fluid path of the faulty circuit from the main circuit to a preset redundant path when the energy conversion efficiency value drops below a preset efficiency threshold (e.g., 65%), the fluid path of the faulty circuit can also be switched from the main circuit to a preset redundant path when the valve group fails. For valve group failures, a comprehensive judgment can be made by monitoring multiple indicators such as response delay (e.g., >200ms), opening deviation (e.g., continuously exceeding ±3%), pressure fluctuation rate (e.g., >8%), and abnormal power supply voltage. To further ensure diagnostic accuracy (≥99%) and avoid false triggers, the system employs a multi-sensor fusion algorithm. First, it preprocesses data from six types of sensors—valve position, pressure, flow, temperature, voltage, and communication status—using methods such as cleaning, normalization, and synchronization. Then, it extracts key features like "response delay" and assigns them different weights (e.g., response delay weight 0.3, opening deviation weight 0.4, differential pressure fluctuation weight 0.2, and the rest weight 0.1). Finally, it calculates the fault probability corresponding to each feature using a Bayesian inference algorithm, and then sums the weighted probabilities to obtain the total fault probability. A true fault is confirmed only when the overall fault probability P ≥ 95%. If the overall fault probability P < 30%, it is judged as sensor interference. A secondary verification is triggered when 30% ≤ overall fault probability P < 95%, thereby reducing the false positive rate from 5% to below 0.5%.

[0094] Once a fault is confirmed, the system can automatically switch to a preset redundant path within ≤500ms. If the fault originates from the main pump or low energy conversion efficiency, the domain controller can shut down the main pump, start redundant pumps with ≥80% main pump power, switch the four-way valve to the redundant flow path, and adjust the flow distribution according to the urgency scoring algorithm. If the fault originates from the dual-drive intelligent four-way valve itself, the system can lock the faulty valve, open the bypass redundant valve with a response ≤80ms, and switch to a fixed priority allocation strategy of "battery > motor > cabin" to ensure core safety. After switching, the system can continuously monitor the redundant energy conversion efficiency to ensure it is not lower than 60%. After fault resolution, the system supports both automatic and manual recovery modes. In automatic mode, when the fault indicators return to normal for 30 consecutive seconds (e.g., valve group response ≤100ms, efficiency ≥75%), the system can automatically shut down redundant equipment, switch back to the main path and intelligent algorithm, and monitor for 5 minutes to confirm recovery. If automatic recovery fails, it can be manually confirmed and restored via OBD (On-Board Diagnostics). After restoration, the system will perform a 10-minute "trial run verification." If the fault recurs, it will automatically switch back to redundant mode and lock. This entire mechanism constructs a complete closed loop from intelligent diagnosis and rapid fault tolerance to safe recovery, ensuring extremely high reliability and availability of the thermal management system throughout its lifecycle.

[0095] Therefore, by simultaneously allocating heat / cooling resources, the system executes a fault protection mechanism in parallel. This mechanism involves real-time monitoring of the air resistance status of each loop, automatically venting air upon detecting excessive air resistance to prevent performance degradation; and monitoring the energy conversion efficiency value. When the efficiency drops to a dangerous threshold, a hardware-level fault is identified, and a command is generated to switch the faulty loop to a preset redundant path. Thus, through preventative venting and post-fault redundancy switching, a system-level reliability guarantee covering both performance degradation and functional failure scenarios is constructed, significantly reducing the risk of thermal management system failure due to fluid malfunctions or single-point hardware failures.

[0096] In one possible implementation, in some embodiments, obtaining the pipeline air resistance status of at least one thermal management loop includes: obtaining the air resistance sensor signal of at least one thermal management loop; calculating the bubble ratio of at least one thermal management loop based on the air resistance sensor signal and a preset calibration curve; and obtaining the pipeline air resistance status of at least one thermal management loop based on the bubble ratio of at least one thermal management loop.

[0097] It is understandable that the air resistance sensor signal refers to the raw electrical signal (such as voltage or current) output by a dedicated sensor (such as an ultrasonic bubble sensor, optical sensor, or capacitive sensor) deployed at key locations in the coolant pipeline (such as the pump inlet, high point of the heat exchanger). The amplitude, frequency, and other characteristics of this signal are related to the bubble content in the fluid flowing through the sensor. A preset calibration curve refers to a relationship curve or lookup table established in advance during system assembly or calibration through laboratory testing, mapping the air resistance sensor output signal (i.e., voltage, such as 0-5V) to a known bubble percentage. Temperature compensation can be introduced to eliminate environmental interference. The bubble percentage is the percentage of volume or area occupied by the gas phase (air / bubbles) in the coolant flow cross-section; it is a dimensionless physical quantity (such as 0%-100%) that can directly quantify the severity of air resistance.

[0098] Specifically, in determining the air resistance status of the thermal management loop, the system first acquires the raw electrical signal output by the air resistance sensor. Then, the system calls a preset calibration curve, substitutes the real-time air resistance sensor signal into this curve, and calculates the actual proportion of air bubbles in the coolant flowing through the monitoring point (e.g., a signal voltage of 0.22V corresponds to a 5% air bubble proportion) through interpolation or table lookup. Finally, the system can define and judge the air resistance status based on the calculated air bubble proportion value; for example, when the proportion is >5%, it is determined that the air resistance level exceeds the standard.

[0099] Therefore, by first acquiring the raw signal from the sensor, and then converting the electrical signal into a bubble ratio value with clear engineering significance based on the signal-physical quantity correspondence pre-calibrated through experiments, and finally using this ratio to objectively characterize the air resistance level of the pipeline, the abstract and vague air resistance state is transformed into a physical parameter (bubble ratio) that can be accurately measured, has a clear threshold, and is directly related to the fault mechanism. This provides a scientific, objective, and repeatable data basis for the aforementioned exhaust operation triggering conditions, greatly improving the accuracy and automation level of the fault prediction mechanism.

[0100] Figure 6 This is a schematic diagram of the structure of a thermal management control device provided in an embodiment of this application.

[0101] For example, such as Figure 6 As shown, the thermal management control device 20 may include: an acquisition module 800, a determination module 900, and a control module 1000.

[0102] The acquisition module 800 is used to acquire the thermal state parameters of at least one thermal management loop of the vehicle in response to the presence of multiple thermal management requirements of the vehicle. The determination module 900 is used to determine the target thermal comfort parameters of the cabin circuit in at least one thermal management circuit based on the thermal state parameters of at least one thermal management circuit, and to determine the thermal management priority of each thermal management circuit according to the thermal state parameters and the target thermal comfort parameters of at least one thermal management circuit. The control module 1000 is used to allocate heat resources and / or cooling resources to at least one thermal management loop based on the thermal management priority of each thermal management loop.

[0103] Optionally, in one embodiment of this application, the determining module 900 is specifically used for: Extract the thermal state parameters of the cabin circuit from the thermal state parameters of at least one thermal management circuit; Obtain the current cabin environment parameters from the baseline thermal comfort parameters and the thermal state parameters of the cabin circuits; Based on a preset thermal comfort preference mapping strategy, the target thermal comfort parameters are obtained by correcting the baseline thermal comfort parameters according to the current cabin environment parameters.

[0104] Optionally, in one embodiment of this application, after obtaining the thermal state parameters of at least one thermal management loop of the vehicle in response to multiple thermal management needs of the vehicle, the acquisition module 800 further includes: The extraction unit is used to extract the current temperature of the motor circuit and the current temperature of the battery circuit from the thermal state parameters of at least one thermal management circuit. The identification unit is used to identify whether the current temperature of the battery circuit is less than a second preset temperature threshold in response to the current temperature of the motor circuit being greater than a first preset temperature threshold. The first control unit is used to direct the waste heat of the motor circuit to the battery circuit when the current temperature of the battery circuit is less than a second preset temperature threshold; otherwise, it directs the remaining waste heat of the motor circuit or the heat of the battery circuit to the cabin circuit.

[0105] Optionally, in one embodiment of this application, after directing the residual heat from the motor circuit or the heat from the battery circuit to the cabin circuit, the first control unit is further configured to: Since there is no heating requirement in the cabin circuit, the residual heat from the motor circuit is directed to the energy storage module for storage.

[0106] Optionally, in one embodiment of this application, when allocating heat resources and / or cooling resources to at least one thermal management loop, the control module 1000 further includes: Acquisition unit, used to acquire the gas resistance status of at least one thermal management loop; The second control unit is used to perform an exhaust operation based on the gas resistance status of the pipeline, in response to the current gas resistance level of any thermal management circuit being greater than a preset safety threshold. The third control unit is used to generate a path switching command based on the pipeline air resistance status and in response to the energy conversion efficiency value of any thermal management loop being less than a preset efficiency threshold, and to switch the fluid path of the thermal management loop whose energy conversion efficiency value is less than the preset efficiency threshold to a preset redundant path based on the path switching command.

[0107] Optionally, in one embodiment of this application, the acquiring unit is specifically used for: Acquire the air resistance sensor signal of at least one thermal management loop; Based on the gas resistance sensor signal and the preset calibration curve, calculate the bubble ratio of at least one thermal management loop; Based on the bubble percentage of at least one thermal management loop, the pipeline air resistance state of at least one thermal management loop is obtained.

[0108] Optionally, in one embodiment of this application, the determining module 900 is specifically used for: Based on the thermal state parameters and target thermal comfort parameters of at least one thermal management loop, and using a preset urgency scoring algorithm, the thermal management priority of each thermal management loop is determined. The preset urgency scoring algorithm is as follows: P_n = λ_n × (R_n / S_n); Wherein, P_n is the priority of the nth thermal management loop, λ_n is the weight coefficient of the nth thermal management loop, R_n is the quantification result of the deviation between the current normalized value of at least one state parameter of the nth thermal management loop and the corresponding normalization target threshold, and S_n is the quantification result of at least one constraint factor affecting the thermal management requirements of the nth thermal management loop.

[0109] In summary, the thermal management control device according to the embodiments of this application introduces a cabin thermal comfort target based on personalized learning and dynamic correction, and uses it as one of the core inputs for system-level resource conflict arbitration. This solves the problem in related technologies where the use of fixed and universal comfort target values ​​leads to an inability to effectively balance safety and personalized comfort experience when multiple demands conflict, thereby simultaneously improving system safety, energy efficiency and user satisfaction.

[0110] Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0111] It should be understood that the methods described above can be applied to... Figure 7 In the vehicle with the structure shown.

[0112] like Figure 7 As shown, the vehicle includes a controller, which may include a memory 701 and a processor 702. The memory 701 stores executable program code, and the processor 702 is used to call and execute the executable program code to perform the thermal management control method provided in the embodiments of this application.

[0113] Furthermore, the controller also includes a communication interface 707 for communication between the memory 701 and the processor 702.

[0114] This embodiment can divide the vehicle into functional modules based on the above method example. For example, each module can correspond to a separate function module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0115] It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0116] It should be understood that the vehicle provided in this embodiment is used to execute the above-described thermal management control method, and therefore can achieve the same effect as the above-described implementation method.

[0117] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement a thermal management control method provided in the above embodiment.

[0118] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement a thermal management control method provided in the above embodiment.

[0119] In this embodiment, the vehicle, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0120] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0121] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0122] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A thermal management system, characterized in that, include: A battery thermal management component, wherein the battery thermal management component is used to regulate the heat resources and / or cooling resources of the battery thermal management circuit; A motor thermal management component, wherein the motor thermal management component is used to regulate the heat resources and / or cooling resources of the motor thermal management circuit; A cockpit thermal management component, wherein the cockpit thermal management component is used to regulate the heat resources and / or cooling resources of the cockpit thermal management loop; A multi-channel heat exchanger, wherein the first channel inlet of the multi-channel heat exchanger is connected to the battery thermal management component, the second channel inlet of the multi-channel heat exchanger is connected to the motor thermal management component, the third channel inlet of the multi-channel heat exchanger is connected to the cabin thermal management component, and the fourth channel inlet of the multi-channel heat exchanger is connected to the coolant storage tank. A multi-way valve, the input end of which is connected to the outlet of the multi-channel heat exchanger, and the output end of which is connected to the battery thermal management component, the motor thermal management component and the cabin thermal management component respectively through multiple branch pipes; A control component is configured to determine the thermal management priority of each thermal management circuit based on multiple thermal management requirements of the vehicle, and to determine the target opening state of the multi-way valve based on the thermal management priority of each thermal management circuit, so as to control the multi-way valve according to the target opening state, thereby realizing the allocation of heat resources and / or cooling resources to at least one thermal management circuit.

2. The thermal management system according to claim 1, characterized in that, Also includes: The energy storage component includes a first three-way valve, a second three-way valve, a primary heat exchanger, a secondary heat exchanger, and a heat storage medium containment area. The outlet of the motor thermal management component is connected to the inlet of the energy storage component through the first three-way valve; The inlet and outlet of the primary heat exchanger are connected to the battery thermal management circuit via pipelines. The inlet and outlet of the secondary heat exchanger are connected to the cabin thermal management circuit via pipelines. The heat storage medium containment area includes a phase change material, and the heat storage medium containment area is thermally coupled to the primary heat exchanger and the secondary heat exchanger respectively, so that the battery thermal management circuit coolant flowing through the primary heat exchanger and / or the cabin thermal management circuit coolant flowing through the secondary heat exchanger exchanges heat with the phase change material.

3. A thermal management control method, characterized in that, The method is applied to a thermal management system, and the method includes the following steps: In response to multiple thermal management requirements of the vehicle, thermal state parameters of at least one thermal management loop of the vehicle are obtained; Based on the thermal state parameters of the at least one thermal management loop, the target thermal comfort parameters of the cabin loop in the at least one thermal management loop are determined, and the thermal management priority of each thermal management loop is determined according to the thermal state parameters of the at least one thermal management loop and the target thermal comfort parameters. Based on the thermal management priority of each thermal management loop, heat resources and / or cold resources are allocated to the at least one thermal management loop.

4. The method according to claim 3, characterized in that, Determining the target thermal comfort parameters of the cabin circuit within the at least one thermal management circuit based on the thermal state parameters of the at least one thermal management circuit includes: Extract the thermal state parameters of the cabin circuit from the thermal state parameters of the at least one thermal management circuit; Obtain the current cabin environment parameters from the baseline thermal comfort parameters and the thermal state parameters of the cabin circuit; Based on a preset thermal comfort preference mapping strategy, the target thermal comfort parameter is obtained by correcting the baseline thermal comfort parameter according to the current cabin environment parameter.

5. The method according to claim 3, characterized in that, After obtaining the thermal state parameters of at least one thermal management loop of the vehicle in response to multiple thermal management needs of the vehicle, the method further includes: Extract the current temperature of the motor circuit and the current temperature of the battery circuit from the thermal state parameters of the at least one thermal management circuit. In response to the current temperature of the motor circuit being greater than a first preset temperature threshold, it is determined whether the current temperature of the battery circuit is less than a second preset temperature threshold. If the current temperature of the battery circuit is less than the second preset temperature threshold, the waste heat of the motor circuit is directed to the battery circuit; otherwise, the remaining waste heat of the motor circuit or the heat of the battery circuit is directed to the cabin circuit.

6. The method according to claim 5, characterized in that, After directing the residual heat from the motor circuit or the heat from the battery circuit to the cockpit circuit, the method further includes: In response to the fact that the cockpit circuit has no heating requirement, the residual heat of the motor circuit is directed to the energy storage module for storage.

7. The method according to claim 3, characterized in that, When allocating heat resources and / or cold resources to the at least one thermal management loop, the method further includes: Obtain the piping air resistance status of the at least one thermal management loop; Based on the gas resistance status of the pipeline, in response to the current gas resistance level of any thermal management circuit being greater than a preset safety threshold, an exhaust operation is performed. Based on the gas resistance state of the pipeline, in response to the energy conversion efficiency value of any thermal management loop being less than a preset efficiency threshold, a path switching command is generated, and based on the path switching command, the fluid path of the thermal management loop whose energy conversion efficiency value is less than the preset efficiency threshold is switched to a preset redundant path.

8. The method according to claim 7, characterized in that, The step of obtaining the gas resistance status of the piping in the at least one thermal management circuit includes: Acquire the air resistance sensor signal of the at least one thermal management loop; Based on the gas resistance sensor signal and the preset calibration curve, the bubble ratio of the at least one thermal management circuit is calculated. Based on the bubble ratio of the at least one thermal management loop, the pipeline air resistance state of the at least one thermal management loop is obtained.

9. The method according to claim 3, characterized in that, The step of determining the thermal management priority of each thermal management loop based on the thermal state parameters of the at least one thermal management loop and the target thermal comfort parameters includes: Based on the thermal state parameters of the at least one thermal management loop and the target thermal comfort parameters, the thermal management priority of each thermal management loop is determined according to a preset urgency scoring algorithm. The preset urgency scoring algorithm is as follows: P_n = λ_n × (R_n / S_n); Wherein, P_n is the priority of the nth thermal management loop, λ_n is the weight coefficient of the nth thermal management loop, R_n is the quantification result of the deviation between the current normalized value of at least one state parameter of the nth thermal management loop and the corresponding normalization target threshold, and S_n is the quantification result of at least one constraint factor affecting the thermal management requirements of the nth thermal management loop.

10. A vehicle comprising a controller, the controller including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the program to implement the thermal management control method as described in any one of claims 3-9.

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