Thermal management method of vehicle, medium, equipment and product

Abstract

The embodiment of the invention discloses a thermal management method of a vehicle, a medium, equipment and a product. Dynamic road section information and vehicle real-time information are acquired; determining heat demand information of the vehicle according to at least one of the dynamic road section information and the vehicle real-time information; the heat demand information comprises predicted recoverable heat of one or more heat sources and predicted required heat of the heat load; and a multi-way valve matrix and / or a heater in the vehicle are / is controlled according to the heat demand information and the vehicle real-time information, so that heat of the heat source and / or the heater is conveyed to a heat load and / or a radiator. Heat supply and demand are predicted through dynamic road section information, passive response lag is avoided, heat is dynamically distributed through a multi-way valve matrix, waste heat is preferentially utilized, the heater is supplemented when the heat is insufficient, and the radiator is discharged when the heat is excessive, the energy consumption of the heater is reduced while the heat load requirement is met, and the low-temperature endurance and the energy utilization efficiency are improved.

Description

Technical Field

[0001] This application belongs to the field of vehicle control, and specifically relates to a vehicle thermal management method, storage medium, electronic device and computer program product. Background Technology

[0002] With the increasing popularity of new energy vehicles, the complexity and importance of vehicle thermal management systems are becoming increasingly prominent. Among them, air conditioning is the most power-consuming system besides the power system, and its energy consumption directly affects the driving range.

[0003] Unlike traditional gasoline vehicles that rely on engine waste heat, new energy vehicles require the coordinated management of multiple subsystems, including batteries, motors, electronic controls, and cabin air conditioning. Related technologies passively coordinate and control each circuit based solely on sensor data, resulting in the system needing to respond to heating demands even for short periods of vehicle use, leading to unnecessary energy consumption. This is especially problematic in low-temperature environments, where cabin heating and battery heating drastically reduce driving range.

[0004] Therefore, how to efficiently recover the waste heat generated by the vehicle's heat source and directly use it for heat load is of great significance for improving the energy utilization efficiency of the whole vehicle and extending the driving range in low temperatures. Summary of the Invention

[0005] The purpose of this application is to provide a thermal management method, medium, device, and product for vehicles, which can solve the problem that the need for coordinated control of various circuits based on sensor data in order to respond to heating requirements can lead to unnecessary energy consumption and low driving range.

[0006] In a first aspect, embodiments of this application provide a thermal management method for a vehicle, the method comprising: Obtain dynamic road segment information and real-time vehicle information; The heat demand information of a vehicle is determined based on at least one of the dynamic road segment information and the real-time vehicle information; the heat demand information includes the predicted recoverable heat from one or more heat sources and the predicted heat required for the heat load. Based on the heat demand information and the real-time vehicle information, the multi-way valve matrix and / or heater in the vehicle are controlled to transfer the heat from the heat source and / or the heater to the heat load and / or radiator.

[0007] Optionally, determining the vehicle's heat demand information based on at least one of the dynamic road segment information and the real-time vehicle information includes: The predicted scenario for the vehicle is determined based on the dynamic road segment information; The predicted recoverable heat of the heat source is determined based on the predicted scenario of the vehicle. The predicted heat load required is determined based on the predicted scenario of the vehicle and the real-time information of the vehicle.

[0008] Optionally, determining the predicted recoverable heat of the heat source based on the predicted scenario of the vehicle includes: Obtain the operating parameters of the heat source; The heating efficiency of the heat source in the predicted scenario is determined based on the operating parameters. The predicted recoverable heat of the heat source in the predicted scenario is determined based on the heating efficiency.

[0009] Optionally, the heat load includes the battery and the cabin, and the real-time vehicle information includes the battery's operating temperature, the cabin's environmental parameters, and the user-set temperature. Determining the predicted heat load based on the vehicle's predicted scenario and the real-time vehicle information includes: Obtain the target temperature of the battery; When the operating temperature is lower than the target temperature, the battery preheating requirement required to heat the battery to the target temperature under the predicted scenario is determined based on the difference between the operating temperature and the target temperature. Based on the environmental parameters and the user-set temperature, determine the cabin heating demand required to maintain the cabin temperature at the user-set temperature under the predicted scenario. The predicted heat load required under the predicted scenario is determined based on the battery preheating requirement and the cabin heating requirement.

[0010] Optionally, the real-time vehicle information includes real-time status data of the heat load at the predicted time. The step of controlling the multi-way valve matrix and / or the heater in the vehicle based on the heat demand information and the real-time vehicle information to transfer heat from the heat source and / or the heater to the heat load and / or the radiator includes: Based on the heat demand information of the vehicle at the predicted time, determine whether the predicted recoverable heat of one or more heat sources meets the predicted heat required by the heat load, obtain the supply and demand results, and determine the selected heat source from one or more heat sources based on the supply and demand results and the predicted scenario of the vehicle. An initial control strategy is generated based on the supply and demand results; The multi-way valve matrix and / or the heater are controlled according to the initial control strategy; Based on the heat demand information and the real-time status data, the initial control strategy is modified in real time to generate target control commands; Adjust the multi-way valve matrix and / or the heater according to the target control command.

[0011] Optionally, controlling the multi-way valve matrix and / or the heater according to the initial control strategy includes: When the predicted recoverable heat from the selected heat source is less than the predicted heat required by the heat load, a first initial control strategy is generated to control the multi-way valve matrix and the heater, so that the heat from the selected heat source and the heater is delivered to the heat load through the multi-way valve matrix. When the predicted recoverable heat of the selected heat source is greater than or equal to the predicted heat required by the heat load, a second initial control strategy is generated to control the multi-way valve matrix so that the heat from the selected heat source is delivered to the heat load and the radiator through the multi-way valve matrix.

[0012] Optionally, the step of real-time correction of the initial control strategy based on the heat demand information and the real-time status data, and generation of target control commands, includes: Obtain the predicted state data of the heat load under the predicted required heat; The actual state data is compared with the predicted state data to obtain the state deviation value; The initial control strategy is modified based on the state deviation value to generate the target control command.

[0013] Secondly, embodiments of this application provide a storage medium that stores computer instructions, which, when executed by a computer, are used to perform the steps of the vehicle thermal management method as described in the first aspect.

[0014] Thirdly, embodiments of this application provide an electronic device including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the vehicle thermal management method as described in the first aspect.

[0015] Fourthly, embodiments of this application provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the vehicle thermal management method as described in the first aspect.

[0016] In this embodiment, by predicting the recoverable heat of the heat source and the predicted heat required by the heat load using dynamic road segment information and real-time vehicle information, the heat supply and demand relationship of the vehicle under future operating conditions can be predicted in advance, avoiding the lag of passively responding based solely on current sensor data. Furthermore, by controlling the multi-way valve matrix and / or heaters based on heat demand information and real-time vehicle information, when there is a difference between the recoverable heat of the heat source and the heat required by the heat load, the heat distribution path can be dynamically adjusted through the multi-way valve matrix, prioritizing the delivery of waste heat to the heat load. This effectively reduces the energy consumption of the vehicle's thermal management system. When the recoverable heat is insufficient, the heaters supplement it as needed; when the recoverable heat is excessive, it is discharged by the radiator. Thus, while meeting the heat load demand, the heater energy consumption is minimized and the system thermal balance is maintained. This not only achieves efficient recovery and utilization of waste heat but also reduces the impact of cabin heating and battery heating on the driving range in low-temperature environments, thereby improving the vehicle's energy utilization efficiency and driving range. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of a vehicle thermal management method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the steps of a thermal management method provided in an embodiment of this application; Figure 3 This is a flowchart illustrating the workflow of a thermal management system provided in an embodiment of this application. Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0020] The vehicle thermal management method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0021] With the widespread adoption of new energy vehicles, especially pure electric vehicles, users are paying close attention to vehicle mileage, which directly translates into sensitivity to vehicle energy consumption. Among these factors, the vehicle's air conditioning system is the largest power consumer besides the powertrain, highlighting the increasing complexity and importance of the vehicle's thermal management system.

[0022] Unlike traditional gasoline vehicles that primarily rely on engine waste heat, the thermal management of new energy vehicles requires the coordinated management of multiple subsystems, including the battery, motor, electronic control system, and cabin air conditioning. Its energy consumption directly impacts the vehicle's driving range. However, in the design of the vehicle's thermal management system, related technologies often passively coordinate and control the motor, battery, and air conditioning circuits based solely on sensor signals from various loops. This passive response mode means that even if the driver uses the vehicle for only a short time, the system still needs to perform heating or cooling control based on sensor data, resulting in unnecessary energy consumption and thus shortening the driving range. Especially in low-temperature environments, the energy consumption of cabin heating and battery heating systems increases significantly, drastically reducing the vehicle's driving range.

[0023] The vehicle thermal management method provided in this application is mainly applied to the thermal management system of pure electric new energy vehicles. The system includes an intelligent prediction and cooperative control module and a dynamic heat flow distribution module. The core of the intelligent prediction and cooperative control module is a multi-objective optimization controller (main controller), which adopts the vehicle's existing controller or a high-performance microcontroller.

[0024] Reference Figure 1 This is a flowchart illustrating the steps of a vehicle thermal management method provided in this application embodiment, specifically including the following steps: Step 101: Obtain dynamic road segment information and real-time vehicle information; In this embodiment, the multi-objective optimization controller is communicatively connected to the vehicle navigation system and the T-BOX (Telematics BOX, vehicle-mounted remote communication terminal). The vehicle navigation system and the T-BOX are communicatively connected to the traffic prediction unit. The multi-objective optimization controller can obtain dynamic road segment information through the traffic prediction unit, including but not limited to one or more of the following: predicted vehicle speed, predicted gradient, predicted traffic flow, and road condition events ahead (such as congestion or accidents). The dynamic road segment information is used to determine the load change trend of the vehicle under future driving conditions.

[0025] Meanwhile, real-time vehicle information (vehicle CAN signal information) from various sensor systems in the vehicle is collected via the CAN (Controller Area Network) bus. The real-time vehicle information includes, but is not limited to, the current status parameters of heat sources and heat loads (all related to thermal management). This real-time information is used to reflect the current thermal state and thermal requirements of the vehicle.

[0026] Step 102: Determine the heat demand information of the vehicle based on at least one of the dynamic road segment information and the real-time vehicle information; the heat demand information includes the predicted recoverable heat from one or more heat sources and the predicted heat required for the heat load. In this embodiment, the multi-objective optimization controller needs to analyze the heat supply and demand balance of the vehicle during future operation based on dynamic road segment information and real-time vehicle information, thereby determining the vehicle's heat demand information. Specifically, when determining the predicted recoverable heat of one or more heat sources, since the heat generation characteristics of the heat source mainly depend on the future operating load (such as motor output torque, speed, etc.), and the future operating load can be effectively characterized by dynamic road segment information (such as predicted vehicle speed, predicted gradient), the recoverable waste heat of the heat source in the future period can be estimated based solely on the dynamic road segment information. When determining the predicted heat required for the heat load, since the heat load demand is affected by both future operating conditions (such as vehicle speed affecting the cabin convective heat transfer intensity) and current conditions (such as current battery temperature, user-set temperature) and environmental parameters (such as ambient temperature, sunlight intensity), in addition to the dynamic road segment information, real-time vehicle information is also required to accurately assess the heat required to maintain the target state of the heat load in the future period.

[0027] Heat demand information is used to characterize the supply and demand relationship between the waste heat resources that each heat source can provide and the heat required by each heat load during the future operation of the vehicle. Among them, the predicted recoverable heat of the heat source refers to the estimate of the recoverable waste heat generated by multiple heat sources during operation based on future changes in operating conditions; the predicted heat required by the heat load refers to the estimate of the heat required to maintain the target state of multiple heat loads within a corresponding time period based on future operating conditions and the current state.

[0028] Traditional waste heat recovery solutions are usually limited to a single heat source (such as using only the motor for preheating), lacking a systematic mechanism for collecting, storing, and distributing waste heat from multiple heat sources throughout the vehicle. There is a lack of a flexible and dynamically reconfigurable heat exchange network between each heat source and the heat load, resulting in insufficient coordination capabilities of the system when facing complex and changing operating conditions, and an inability to achieve optimal heat allocation in time and space.

[0029] Therefore, in this embodiment, the heat source is not limited to a single type of heat-generating component, but covers multiple systems that can generate recoverable waste heat during vehicle operation, including but not limited to electric drive components of the drive system, charging and discharging components of the high-voltage electrical system, active heat-generating components of the thermal management system, and motor braking components. These components generate heat due to energy conversion efficiency loss during normal operation. This heat usually needs to be carried away by coolant and discharged into the environment. By recovering and reusing the waste heat, the energy utilization efficiency of the whole vehicle can be significantly improved.

[0030] Step 103: Control the multi-way valve matrix and / or heater in the vehicle according to the heat demand information and the real-time vehicle information, so as to transfer the heat from the heat source and / or the heater to the heat load and / or radiator.

[0031] In this embodiment, after obtaining the heat demand information, it is necessary to further combine the vehicle's current real-time status (vehicle real-time information) to provide the optimal thermal management system control scheme, and send the instructions of this scheme to the dynamic heat flow distribution module. The dynamic heat flow distribution module controls the multi-way valve matrix and / or heaters. Specifically, the heat demand information can determine the supply and demand relationship between the predicted recoverable heat of the heat source and the predicted heat required by the heat load. Combined with the vehicle real-time information, the current actual state of the heat load can be determined, and then differentiated control can be performed on the multi-way valve matrix and / or heaters. While prioritizing the use of waste heat resources to meet the heat load demand, the optimal allocation of system energy consumption can be achieved.

[0032] A multi-way valve matrix is ​​installed in the coolant circuit, connecting each heat source and each heat load. It is used to reconfigure the connection path of the coolant circuit, enabling directional heat transfer between heat sources, heat loads, and radiators. The multi-way valve matrix consists of multiple electrically controlled valves. Preferably, the multi-way valve matrix consists of multiple electrically controlled two-way valves and electrically controlled three-way valves. The electrically controlled two-way valves are used to control the on / off state of specific branches, realizing the opening and closing of the pipeline; the electrically controlled three-way valves are used to switch the flow direction of the coolant, realizing the selection of connection paths between multiple branches.

[0033] It should be noted that by combining multiple electrically controlled two-way valves and three-way valves, a multi-way valve matrix can construct a topologically variable coolant network at the physical level. The two-way valves act as switches, determining the on / off state of each branch, while the three-way valves act as switches, determining the coolant flow direction at nodes. Their coordinated control allows the coolant circuit connectivity to be adjusted in real time according to actual needs, overcoming the limitations of fixed pipelines or limited mode switching in traditional thermal management systems. This constructs a flexible and dynamically reconfigurable heat exchange network. Based on this, the system can predict and plan heat distribution paths in advance based on future operating conditions in the time dimension, and achieve on-demand heat transfer between heat sources and heat loads at different locations in the spatial dimension. This achieves optimal heat allocation in both time and space, maximizing the utilization efficiency of waste heat resources.

[0034] The heater is located in the coolant circuit and can serve as a supplementary heat source to supplement the required heat when the recoverable heat from the heat source is insufficient to meet the heat load demand.

[0035] The process of transferring heat from the heat source and / or the heater to the heat load and / or the radiator includes: The heat from the heat source is transferred to the heat load; or The heat from the heater is transferred to the heat load; or The heat from the heat source and the heater is transferred to the heat load; or The heat from the heat source is transferred to the radiator; or The heat from the heat source is simultaneously transferred to the heat load and the radiator.

[0036] This application embodiment controls the opening and closing of each valve in the multi-way valve matrix and the heating power of the heater, enabling dynamic distribution of heat among different heat sources and heat loads. When the recoverable heat from the heat source can meet the heat load demand, the heat from the heat source is transferred to the heat load and / or the radiator. When the recoverable heat from the heat source is insufficient to meet the heat load demand, the heater is simultaneously activated, with the heat source and heater jointly supplying heat to the heat load. When the recoverable heat from the heat source exceeds the heat load demand, the excess heat can be discharged through the radiator to maintain system thermal balance.

[0037] The radiator is located in the coolant circuit and exchanges heat with the external environment to dissipate excess heat from the system into the environment and prevent the system from overheating.

[0038] Reference Figure 2 This is a schematic diagram illustrating the steps of a thermal management method provided in an embodiment of this application. It is mainly executed by two modules: an intelligent prediction and collaborative control module and a dynamic heat flow distribution module. Specifically, it includes the following steps: Step 201: The multi-objective optimization controller in the intelligent prediction and collaborative control module obtains dynamic road segment information and real-time vehicle information through the road condition prediction unit, and generates heat demand information and initial control strategy with the minimum energy consumption, optimal battery life and optimal cabin comfort as multi-objective optimization objectives.

[0039] Step 202: The dynamic heat flow distribution module controls the multi-way valve matrix and / or heaters according to the initial control strategy and real-time vehicle information to achieve dynamic distribution of heat between the heat source, heat load and radiator.

[0040] It should be noted that the multi-way valve matrix is ​​connected to the heat source and heat load through the coolant circuit. As the core of the system, the multi-way valve matrix can realize the directional transfer of waste heat between the heat source and heat load, or discharge excess heat through the radiator by changing the pipeline connection method.

[0041] In this embodiment, by predicting the recoverable heat of the heat source and the predicted heat required by the heat load using dynamic road segment information and real-time vehicle information, the heat supply and demand relationship of the vehicle under future operating conditions can be predicted in advance, avoiding the lag of passively responding based solely on current sensor data. Furthermore, by controlling the multi-way valve matrix and / or heaters based on heat demand information and real-time vehicle information, when there is a difference between the recoverable heat of the heat source and the heat required by the heat load, the heat distribution path can be dynamically adjusted through the multi-way valve matrix, prioritizing the delivery of waste heat to the heat load. This effectively reduces the energy consumption of the vehicle's thermal management system. When the recoverable heat is insufficient, the heaters supplement it as needed; when the recoverable heat is excessive, it is discharged by the radiator. Thus, while meeting the heat load demand, the heater energy consumption is minimized and the system thermal balance is maintained. This not only achieves efficient recovery and utilization of waste heat but also reduces the impact of cabin heating and battery heating on the driving range in low-temperature environments, thereby improving the vehicle's energy utilization efficiency and driving range.

[0042] In one embodiment of this application, determining the vehicle's heat demand information based on at least one of the dynamic road segment information and the real-time vehicle information includes: The predicted scenario for the vehicle is determined based on the dynamic road segment information; The predicted recoverable heat of the heat source is determined based on the predicted scenario of the vehicle. The predicted heat load required is determined based on the predicted scenario of the vehicle and the real-time information of the vehicle.

[0043] In this embodiment, based on parameters such as predicted vehicle speed, predicted gradient, predicted traffic flow, and road condition events ahead in the dynamic road segment information, the operating scenario of the vehicle in the future can be identified and classified. For example, when the dynamic road segment information shows a low predicted vehicle speed with frequent starts and stops and dense traffic flow, the predicted scenario can be determined as an "urban congestion scenario," which belongs to low-temperature cold start; when the dynamic road segment information shows a high and stable predicted vehicle speed with a gentle road gradient, the predicted scenario can be determined as a "high-speed cruising scenario," where waste heat is abundant; when the dynamic road segment information shows a low predicted vehicle speed but with a continuous downhill gradient, the predicted scenario can be determined as a "long downhill energy recovery scenario," which belongs to energy recovery.

[0044] Different prediction scenarios correspond to different vehicle load characteristics and operating modes, and these characteristics directly determine the heat generation characteristics of the heat source. For example, in the "high-speed cruising scenario," the electric drive system continuously and stably outputs power, generating a large and stable amount of waste heat; in the "urban congestion scenario," the electric drive system frequently starts and stops, resulting in intermittent waste heat generation, but the total amount is limited; in the "long downhill energy recovery scenario," the electric motor generates a large amount of waste heat when recovering energy through braking. Therefore, by identifying the prediction scenarios, the recoverable waste heat of the heat source in the future can be effectively estimated without the need for real-time calculation of complex dynamic models.

[0045] Meanwhile, the demand for heat load is not only affected by the predicted scenario, but also closely related to the current real-time status of the vehicle. The accurate current temperature of the battery and the user's real-time settings cannot be known solely by the predicted scenario. Therefore, it is necessary to combine the real-time information of the vehicle to accurately assess the heat load required to maintain the target state in the future period.

[0046] This application's embodiments transform dynamic road segment information into understandable predictive scenarios, and differentiate the heat generation and heat load demand based on the characteristics of different scenarios. This can reduce computational complexity while ensuring prediction accuracy, making the determination of heat demand information more efficient and reliable, and providing accurate decision-making basis for subsequent heat allocation strategies.

[0047] In one embodiment of this application, determining the predicted recoverable heat of the heat source based on the predicted scenario of the vehicle includes: Obtain the operating parameters of the heat source; The heating efficiency of the heat source in the predicted scenario is determined based on the operating parameters. The predicted recoverable heat of the heat source in the predicted scenario is determined based on the heating efficiency.

[0048] In this application embodiment, for different prediction scenarios, it is necessary to estimate the recoverable waste heat based on the operating characteristics of the heat source in the corresponding scenario. Specifically, the operating parameters of the heat source in the prediction scenario are first obtained. The operating parameters include, but are not limited to, the predicted torque, predicted speed, and predicted power output of the electric drive system. These operating parameters are determined based on the characteristics of the prediction scenario and the vehicle dynamics model. The characteristics of the prediction scenario can be stable high power output in a high-speed cruising scenario, intermittent low power output in an urban congestion scenario, and energy recovery power in a long downhill scenario.

[0049] Thermal efficiency is typically obtained from the efficiency characteristic diagram of a heat source or a pre-calibrated efficiency curve, reflecting the proportion of input energy converted into heat at different operating points (different speeds and torques). For example, when an electric drive system operates in a low-efficiency range, more input energy is converted into waste heat; when operating in a high-efficiency range, relatively less waste heat is generated.

[0050] Finally, based on the heating efficiency and the operating power of the heat source in the predicted scenario, the predicted recoverable heat of the heat source in the predicted scenario can be calculated. This predicted recoverable heat represents the total amount of waste heat resources that the heat source can provide in the future.

[0051] The embodiments of this application determine the operating parameters of the heat source based on the characteristics of the predicted scenario, making the waste heat prediction more consistent with the actual operating state of the vehicle in the future and improving the accuracy of the prediction. By introducing the parameter of heating efficiency, the heat generation capacity of the heat source at different operating points can be accurately quantified, providing a reliable data basis for subsequent heat distribution. At the same time, the above-mentioned method for predicting recoverable heat is applicable to a variety of heat sources, has good versatility and scalability, and can realize unified management and overall utilization of waste heat from multiple heat sources throughout the vehicle.

[0052] In one embodiment of this application, the heat load includes a battery and a cabin, and the real-time vehicle information includes the operating temperature of the battery, environmental parameters of the cabin, and a user-set temperature. Determining the predicted heat required for the heat load based on the predicted scenario of the vehicle and the real-time vehicle information includes: Obtain the target temperature of the battery; When the operating temperature is lower than the target temperature, the battery preheating requirement required to heat the battery to the target temperature under the predicted scenario is determined based on the difference between the operating temperature and the target temperature. Based on the environmental parameters and the user-set temperature, determine the cabin heating demand required to maintain the cabin temperature at the user-set temperature under the predicted scenario. The predicted heat load required under the predicted scenario is determined based on the battery preheating requirement and the cabin heating requirement.

[0053] In this embodiment, the heat required to predict the heat load needs to comprehensively consider two dimensions: the characteristics of the prediction scenario and the current real-time state of the vehicle. To facilitate understanding and clearly demonstrate the specific implementation process of this embodiment, the battery and cabin are used as typical heat loads for illustration.

[0054] For battery thermal load, the target temperature of the battery is first obtained. This target temperature can be a preset value within the battery's optimal operating temperature range (e.g., 25℃-35℃), or an optimized value dynamically determined based on the battery's state of charge (SOC), state of health (SOH), and ambient temperature. When the battery's current operating temperature is lower than the target temperature, it indicates that the battery needs to be heated. At this point, based on the difference between the operating temperature and the target temperature, and combined with the battery's thermal capacity parameters, the total heat required to heat the battery to the target temperature can be calculated. Based on this, and combined with the duration of the predicted scenario (e.g., the estimated duration of the predicted scenario), the required battery preheating demand under the predicted scenario can be further determined (usually expressed as heating power or heat required per unit time).

[0055] For cabin heat load, the cabin heating requirement needs to be determined based on environmental parameters and the user-set temperature. These environmental parameters include ambient temperature, sunlight intensity, and convective heat transfer intensity determined based on the current vehicle speed. These parameters collectively determine the cabin's basic heat load under current operating conditions.

[0056] Specifically, convective heat transfer intensity is positively correlated with vehicle speed. The higher the vehicle speed, the faster the heat exchange rate between the vehicle body surface and the air, resulting in faster heat loss from the cabin and a corresponding increase in the heating power required to maintain the set temperature. Therefore, by using the convective heat transfer intensity determined based on the current vehicle speed as one of the environmental parameters, the impact of vehicle operating status on cabin heat load can be fully reflected when calculating cabin heating demand, thereby accurately determining the cabin heating demand required to maintain the cabin temperature at the user's set temperature under the predicted scenario.

[0057] Finally, based on the battery preheating demand and cabin heating demand, the predicted heat load required under the predicted scenario is determined. The predicted heat load is used to characterize the total heat that the vehicle needs to provide to each heat load in the future.

[0058] In practical applications, the method for predicting the required heat can be flexibly designed according to the complexity and priority of the control strategy, and the embodiments in this application are not limited thereto.

[0059] In one embodiment, the battery preheating requirement and the cabin heating requirement can be directly added together to obtain the total predicted heat required. For example, if the battery preheating requirement is 2kW and the cabin heating requirement is 3kW, then the predicted heat required is 5kW, indicating that the system needs to provide a total of 5kW of heat to the heat load under the predicted scenario.

[0060] In another embodiment, considering the potential differences in the importance of different heat loads, the battery preheating demand and cabin heating demand can be weighted and summed according to a preset priority. For example, in low-temperature environments, battery heating is crucial for ensuring vehicle power performance and safety. Therefore, the battery preheating demand can be assigned a higher weight (e.g., 0.7), and the cabin heating demand a lower weight (e.g., 0.3). The predicted heat demand is then calculated through weighted summation. If the battery preheating demand is 2kW and the cabin heating demand is 3kW, the weighted predicted heat demand is 2×0.7 + 3×0.3 = 2.3kW, reflecting the control logic of prioritizing battery heating under limited heat resources.

[0061] It should be noted that the heat load in the embodiments of this application is not limited to the battery and cabin, but may also include other vehicle components that need to absorb heat under specific operating conditions, such as transmission oil preheating, fluid reservoirs that need to be insulated in low-temperature environments, and sensor modules that need to quickly reach operating temperature. The battery and cabin are only typical heat loads that are widely used and account for a high proportion of energy consumption in the current thermal management system of new energy vehicles. The technical solution of the embodiments of this application is also applicable to the prediction and management of heat demand for other types of heat loads.

[0062] This application's embodiments differentiate between two different types of heat loads—battery and cabin—and employ different calculation methods for each, making heat demand prediction more accurate and targeted. The difference between the target temperature and the operating temperature is introduced in the calculation of battery preheating demand, while environmental parameters and user-set temperatures are introduced in the calculation of cabin heating demand, fully considering the actual state of the heat load and the user's subjective needs.

[0063] In one embodiment of this application, the real-time vehicle information includes real-time status data of the heat load at the predicted time. The step of controlling the multi-way valve matrix and / or the heater in the vehicle based on the heat demand information and the real-time vehicle information to transfer heat from the heat source and / or the heater to the heat load and / or the radiator includes: Based on the heat demand information of the vehicle at the predicted time, determine whether the predicted recoverable heat of one or more heat sources meets the predicted heat required by the heat load, obtain the supply and demand results, and determine the selected heat source from one or more heat sources based on the supply and demand results and the predicted scenario of the vehicle. An initial control strategy is generated based on the supply and demand results; The multi-way valve matrix and / or the heater are controlled according to the initial control strategy; Based on the heat demand information and the real-time status data, the initial control strategy is modified in real time to generate target control commands; Adjust the multi-way valve matrix and / or the heater according to the target control command.

[0064] In this embodiment of the application, in order to realize prediction-based waste heat recovery and utilization, it is necessary to combine prediction information with real-time feedback to form a complete control closed loop. Specifically, firstly, based on the heat demand information at the prediction time, the supply and demand relationship between the predicted recoverable heat of the heat source and the predicted heat required by the heat load is judged to obtain the supply and demand result. This supply and demand result is used to determine whether the waste heat that the heat source can provide is sufficient to meet the heat load demand during the prediction period.

[0065] Building upon this, it is also necessary to select a suitable heat source from multiple heat sources based on the predicted vehicle scenario. Specifically, different predicted scenarios correspond to different heat generation characteristics and spatial distributions of heat sources. For example, in urban congestion scenarios, the electric drive system generates limited heat but at a relatively high temperature, making it suitable as a priority heat source; in high-speed cruising scenarios, the electric drive system continuously generates heat with stable temperatures, making it a primary heat source; in long downhill scenarios, the electric motor generates instantaneous high-power waste heat during braking energy recovery, making the motor braking components a short-term high-power heat source; in charging scenarios, the on-board charger and the battery itself generate heat, serving as heat sources. The multi-objective optimization controller, based on the characteristics of the predicted scenario and considering factors such as the predicted recoverable heat of each heat source, the geographical distribution of the heat load, and energy losses along the heat transport path, selects the most suitable heat source from one or more heat sources for the current predicted scenario, ensuring maximum efficiency in waste heat recovery and utilization.

[0066] Subsequently, an initial control strategy is generated based on the supply and demand results. This strategy is then used to initially control the multi-way valve matrix and / or heaters, enabling the system to plan heat distribution paths in advance and avoid the lag of passive response. Specifically, the initial control strategy is a pre-defined plan based on forecast information, guiding the multi-way valve matrix and / or heaters to operate within the forecast period. For example, if the forecast indicates sufficient recoverable heat from the heat source, the initial control strategy can plan to transfer heat solely through the multi-way valve matrix; if the forecast indicates insufficient recoverable heat, the initial control strategy can plan to simultaneously activate the heaters as a supplement.

[0067] To more clearly demonstrate the logic and execution method of the initial control strategy in the embodiments of this application, the following description is provided in conjunction with specific scenarios.

[0068] Scenario 1: Traffic congestion in the city, cold start due to low temperature In this scenario, dynamic road information indicates a low predicted vehicle speed and frequent starts and stops, while real-time vehicle information shows a low ambient temperature and a cold start state for the battery. Heat demand information indicates that the waste heat generated by the electric drive system is limited but valuable, while the battery pack needs to be heated rapidly to ensure activity and charging safety. Based on this supply and demand result, the initial control strategy is: waste heat is prioritized for battery preheating. Specifically, the multi-objective optimization controller controls the multi-way valve matrix, closing the valve leading to the main radiator from the electric drive system coolant while opening the valve leading to the battery pack cooling circuit; and by adjusting the water pump duty cycle, all the limited waste heat generated by the electric drive system is diverted to the battery pack for rapid and stable heating. In this mode, cabin heating is still provided by the existing independent PTC (Positive Temperature Coefficient) heater to ensure passenger cabin comfort.

[0069] Scenario 2: High-speed cruising, excess waste heat In this scenario, dynamic road information indicates a high and stable predicted vehicle speed, with sufficient recoverable heat from the heat source. Heat demand information shows that the electric drive system continuously generates a large amount of waste heat, while the battery pack needs to be kept warm and the cabin needs continuous heating. Based on this supply and demand result, the initial control strategy is to simultaneously meet battery insulation and cabin heating needs with waste heat; if insufficient, this is supplemented by a PTC heater, and excess heat is discharged through the radiator. In practice, the multi-objective optimization controller adjusts the multi-way valve matrix to divide the high-temperature coolant into two paths: one continues to insulate the battery pack; the other is introduced into the heat exchanger in the cabin heater, where the cold air flowing through the heater is heated by the waste heat and then sent into the cabin. If the electric drive's heat generation is insufficient to simultaneously meet both needs, the heat generated by the PTC heater is introduced into the high-temperature circuit for supplementation through valve control; if there is still excess waste heat, some of the hot liquid is returned to the radiator to prevent system overheating. In this mode, the power consumption of the original cabin PTC heater can be significantly reduced or even completely shut down, resulting in significant energy savings.

[0070] Scenario 3: Long downhill slope, energy recovery In this scenario, dynamic road information indicates an upcoming continuous downhill section, where the motor braking will generate a surge of instantaneous high-power waste heat. Heat demand information indicates a significant amount of waste heat will be generated in the short term, which can be recovered and utilized. Based on this supply and demand result, the initial control strategy is to utilize the anticipated waste heat in advance, planning to use the waste heat generated by the motor braking components for battery heating. Specifically, before entering the downhill section, the multi-objective optimization controller pre-sets the valve states of the multi-way valve matrix based on the predicted information, ensuring that the instantaneous high-power waste heat generated by the motor braking can be directly guided to the battery pack and cabin heater for storage and utilization through the connected loops, preventing heat loss. Simultaneously, by maintaining the battery temperature within the optimal operating range, the energy recovery system can be ensured to operate at its highest efficiency.

[0071] However, due to factors such as prediction errors, environmental disturbances, or component response delays that may occur during actual operation, relying solely on the initial control strategy is insufficient to guarantee control accuracy. Therefore, this embodiment further modifies the initial control strategy in real time based on heat demand information and real-time status data of the heat load at the predicted time, generating target control commands. This effectively compensates for the impact of prediction errors and external disturbances, improving not only control accuracy but also optimizing heat exchange efficiency. Specifically, the real-time status data reflects the actual state of the heat load at the current moment, such as the actual heating rate of the battery and the actual temperature change of the cabin. By monitoring and analyzing this real-time data, it is possible to determine whether the execution effect of the initial control strategy has achieved the expected results.

[0072] Finally, the multi-way valve matrix and / or heaters are adjusted according to the target control commands to achieve precise control of heat distribution.

[0073] The embodiments of this application, through the above-described scheme, can realize a series of closed-loop control processes including predictive planning, preliminary execution, real-time feedback, and dynamic correction. The system can fully utilize the advantages of predictive information while taking into account changes in real-time status, and can achieve optimal allocation of heat resources under complex and ever-changing operating conditions, ensuring control accuracy and robustness.

[0074] In one embodiment of this application, controlling the multi-way valve matrix and / or the heater according to the initial control strategy includes: When the predicted recoverable heat from the selected heat source is less than the predicted heat required by the heat load, a first initial control strategy is generated to control the multi-way valve matrix and the heater, so that the heat from the selected heat source and the heater is delivered to the heat load through the multi-way valve matrix. When the predicted recoverable heat of the selected heat source is greater than or equal to the predicted heat required by the heat load, a second initial control strategy is generated to control the multi-way valve matrix so that the heat from the selected heat source is delivered to the heat load and the radiator through the multi-way valve matrix.

[0075] In the embodiments of this application, the specific content of the initial control strategy depends on the supply and demand relationship between the recoverable heat of the heat source and the heat required by the heat load, forming two typical control modes.

[0076] The first mode corresponds to the situation of insufficient heat, i.e., the predicted recoverable heat from the selected heat source is less than the predicted heat required by the heat load. In this case, the system generates a first initial control strategy, which simultaneously controls the multi-way valve matrix and the heater. Specifically, the multi-way valve matrix is ​​configured to reconfigure the coolant circuit, establishing a transport path connecting the heat source and the heat load; simultaneously, the heater is activated, injecting additional heat into the coolant as a supplementary heat source; the waste heat generated by the selected heat source and the supplementary heat provided by the heater are jointly transported to the heat load through the multi-way valve matrix to meet the heat load's requirements. In this mode, the system prioritizes the use of waste heat resources, activating the heater only when waste heat is insufficient, thus maximizing the utilization of waste heat.

[0077] The second mode corresponds to situations where there is sufficient or excessive heat, i.e., the predicted recoverable heat from the selected heat source is greater than or equal to the predicted heat required by the heat load. In this case, the system generates a second initial control strategy, which only controls the multi-way valve matrix while the heater remains closed. The multi-way valve matrix is ​​configured to transfer waste heat generated by the selected heat source to the heat load to meet its heat demand. When the recoverable heat from the selected heat source exceeds the heat required by the heat load, the excess heat is transferred through the multi-way valve matrix to the radiator, which then discharges it into the environment to maintain the system's thermal balance.

[0078] By differentiating and switching between the two modes mentioned above, the embodiments of this application realize the overall management of waste heat from the selected heat source and energy consumption of the heater. Both initial control strategies use a multi-way valve matrix as the core actuator and achieve directional heat transfer by reconstructing the coolant circuit.

[0079] This application's embodiments generate differentiated initial control strategies based on the heat supply and demand relationship, enabling the system to adopt targeted control methods in different scenarios, thus improving the adaptability and flexibility of control. Simultaneously, when heat is insufficient, waste heat is prioritized for utilization, with only a small amount of heater energy consumption supplemented, minimizing additional energy consumption. When heat is sufficient, heat is transferred solely through a multi-way valve matrix, achieving zero-cost utilization of waste heat. Furthermore, when heat is excessive, excess heat is dissipated through radiators, ensuring the system operates in a safe thermal equilibrium state.

[0080] In one embodiment of this application, the step of real-time correction of the initial control strategy based on the heat demand information and the real-time status data to generate a target control command includes: Obtain the predicted state data of the heat load under the predicted required heat; The actual state data is compared with the predicted state data to obtain the state deviation value; The initial control strategy is modified based on the state deviation value to generate the target control command.

[0081] In this embodiment, the real-time correction mechanism achieved through heat demand information and real-time status data is a key link in realizing refined control. The core lies in dynamically adjusting the initial control strategy based on the deviation between the actual and expected heat load states, so that the actual control effect approaches the expected target.

[0082] Specifically, the first step is to obtain the predicted state data of the heat load under the predicted heat demand. The predicted state data is the state that the heat load should reach after receiving the predicted heat, which is derived in advance based on the heat demand information. For example, the expected temperature of the battery after receiving the preset heating amount, the expected temperature change curve of the cabin after receiving the preset heating amount, etc. These predicted state data are the theoretical benchmarks for measuring the control effect.

[0083] The actual state data is collected by sensors in the vehicle's real-time information and truly reflects the actual state of the heat load at the current moment. Therefore, by comparing the actual state data of the heat load with the predicted state data, the obtained state deviation value can reflect the gap between theoretical expectations and actual effects. For example, the actual battery temperature is 2°C lower than the expected temperature, or the actual cabin heating rate is 20% slower than the expected rate.

[0084] In some embodiments, the step of modifying the initial control strategy based on the state deviation value and generating a target control command includes: When the dynamic road segment information remains unchanged and the state deviation value meets the preset conditions, the initial control strategy is modified according to the state deviation value, and a target control command is generated.

[0085] Specifically, dynamic road segment information is a set of information characterizing the road environment features that a vehicle will face during future driving, including but not limited to predicted vehicle speed, predicted gradient, predicted traffic flow, and upcoming road condition events. When the dynamic road segment information remains unchanged, it means that the external conditions faced by the vehicle are consistent with the prediction. If a significant state deviation still occurs at this point, it indicates that the deviation mainly originates from internal system factors, such as model errors, component aging, and actuator response deviations. Under this premise, if the state deviation value meets preset conditions, a correction mechanism is triggered. These preset conditions refer to the quantitative thresholds that must be met to trigger the correction mechanism, such as the deviation exceeding a set threshold or the deviation persisting for a set duration.

[0086] The correction mechanism mainly involves dynamically adjusting the initial control strategy based on the state deviation value to generate a target control command. The adjustments can include fine-tuning the opening of relevant valves in the multi-way valve matrix to change the coolant flow rate, adjusting the heating power of the heater to make up for the heat gap, or adjusting the heat distribution ratio among various heat loads. The target control command obtained after the adjustment will replace the initial control strategy.

[0087] Through the aforementioned real-time correction mechanism, the system can continuously adjust its control actions based on actual feedback during operation, gradually bringing the actual control effect closer to the expected goal. When similar operating conditions recur, the system can also use the experience accumulated during the correction process to optimize future predictive models, forming a continuously evolving control capability.

[0088] This application embodiment can promptly detect control errors by comparing the deviation between the predicted state and the actual state. Based on the deviation, the initial control strategy is dynamically adjusted so that the actual control effect approaches the expected target, improving the control accuracy and robustness of the system. Correction is only triggered when the dynamic road segment information has not changed and the deviation meets the preset conditions, avoiding frequent adjustments caused by changes in external operating conditions or minor fluctuations, and ensuring the stability of control.

[0089] Reference Figure 3 This is a workflow logic diagram of a thermal management system provided in an embodiment of this application, illustrating a complete intelligent control closed loop from information input and predictive decision-making to dynamic execution and feedback learning, specifically including the following steps: Step 301: The road condition prediction unit acquires dynamic road segment information and real-time vehicle information, and the multi-objective optimization controller analyzes the heat demand information through the dynamic road segment information and real-time vehicle information.

[0090] Step 302: The multi-objective optimization controller will further determine the supply and demand results based on the heat demand information, and generate a control strategy based on the supply and demand results.

[0091] Step 303: Control the multi-way valve matrix and / or heaters based on the control strategy to control the heat distribution of the battery pack, cabin, and radiator.

[0092] Step 304: The system continuously monitors the actual state data of the heat load and compares it with the predicted data. Based on the deviation, the control strategy and prediction model are dynamically corrected and optimized.

[0093] The vehicle thermal management method provided in this application does not rely on expensive heat pump systems. Instead, it maximizes the utilization of waste heat by defining heat flow through software. Through predictive control and dynamic fluid network reconfiguration, it improves the overall vehicle thermal management efficiency at a very low cost, making it particularly suitable for cost-sensitive economical electric vehicle platforms.

[0094] In one embodiment of this application, long-term operational data is fed back to a multi-objective optimization controller to continuously train and refine the predictive model used in the controller to predict recoverable and required heat. Specifically, the system records the deviation between the actual and predicted heat load data during each trip and uses these deviations as training samples to input into the predictive model, continuously optimizing the model's internal parameters so that the model's prediction accuracy gradually improves with increasing operating time. In this way, the system can achieve continuous evolution of control performance, making the determination of heat demand information increasingly accurate and better adapting to the actual operating characteristics of the vehicle.

[0095] Simultaneously, the system generates personalized thermal management control strategies through statistical analysis of drivers' long-term driving behavior. For example, the system can learn drivers' preferences for air conditioning settings under different ambient temperatures, driving styles under different road conditions (such as aggressive or mild driving), and personalized needs for cabin comfort. Based on these learning results, the multi-objective optimization controller actively matches the driver's habitual characteristics when formulating thermal management strategies. For instance, under the same predicted scenario, drivers who prefer comfort reserve more cabin heating redundancy, while drivers who prefer energy conservation tend to prioritize the use of waste heat resources. Through the generation of the aforementioned personalized strategies, the embodiments of this application can achieve optimal control of the thermal management system while meeting the personalized needs of drivers.

[0096] In one embodiment of this application, the thermal management system further includes a phase change material (PCM) heat storage module for storing waste heat. Specifically, when the predicted recoverable heat from the heat source exceeds the predicted heat required by the heat load, the excess heat can be guided to the PCM heat storage module for storage, rather than being directly discharged into the environment through a radiator. When the predicted recoverable heat from the heat source is insufficient to meet the heat load demand, especially in scenarios where heaters are inconvenient to start or energy consumption is limited, the heat stored in the PCM heat storage module can be released and transported to the heat load along with the waste heat from the heat source, achieving secondary utilization of the waste heat.

[0097] By introducing a phase change material heat storage module, the embodiments of this application can further extend the time dimension of waste heat utilization, transferring heat from surplus periods to shortage periods, meeting more diverse vehicle usage scenarios, such as cabin insulation in parked conditions and rapid preheating during cold starts, thereby further improving the energy utilization efficiency and thermal management flexibility of the vehicle.

[0098] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0099] This application also provides a storage medium that stores computer instructions. When the computer executes the computer instructions, it is used to perform various processes of the above-described vehicle thermal management method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0100] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0101] This application also provides an electronic device, including a processor 4010, a memory 409, and a program or instructions stored in the memory 409 and executable on the processor 4010. When the program or instructions are executed by the processor 4010, they implement the various processes of the above-described vehicle thermal management method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0102] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0103] Figure 4 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0104] The electronic device 400 includes, but is not limited to, components such as: radio frequency unit 401, network module 402, audio output unit 403, input unit 404, sensor 405, display unit 406, user input unit 407, interface unit 408, memory 409, and processor 4010.

[0105] Those skilled in the art will understand that the electronic device 400 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 4010 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 4The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0106] This application also provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the various processes of the above-described vehicle thermal management method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0107] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0108] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0109] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A thermal management method for a vehicle, characterized in that, include: Obtain dynamic road segment information and real-time vehicle information; The vehicle's heat demand information is determined based on at least one of the dynamic road segment information and the real-time vehicle information; The heat demand information includes the predicted recoverable heat from one or more heat sources and the predicted heat required by the heat load. Based on the heat demand information and the real-time vehicle information, the multi-way valve matrix and / or heater in the vehicle are controlled to transfer the heat from the heat source and / or the heater to the heat load and / or radiator.

2. The method according to claim 1, characterized in that, Determining the vehicle's heat demand information based on at least one of the dynamic road segment information and the real-time vehicle information includes: The predicted scenario for the vehicle is determined based on the dynamic road segment information; The predicted recoverable heat of the heat source is determined based on the predicted scenario of the vehicle. The predicted heat load required is determined based on the predicted scenario of the vehicle and the real-time information of the vehicle.

3. The method according to claim 2, characterized in that, Determining the predicted recoverable heat of the heat source based on the predicted scenario of the vehicle includes: Obtain the operating parameters of the heat source; The heating efficiency of the heat source in the predicted scenario is determined based on the operating parameters. The predicted recoverable heat of the heat source in the predicted scenario is determined based on the heating efficiency.

4. The method according to claim 2, characterized in that, The heat load includes the battery and the cabin. The real-time vehicle information includes the battery's operating temperature, the cabin's environmental parameters, and the user-set temperature. Determining the predicted heat load based on the vehicle's predicted scenario and the real-time vehicle information includes: Obtain the target temperature of the battery; When the operating temperature is lower than the target temperature, the battery preheating requirement required to heat the battery to the target temperature under the predicted scenario is determined based on the difference between the operating temperature and the target temperature. Based on the environmental parameters and the user-set temperature, determine the cabin heating demand required to maintain the cabin temperature at the user-set temperature under the predicted scenario. The predicted heat load required under the predicted scenario is determined based on the battery preheating requirement and the cabin heating requirement.

5. The method according to claim 2, characterized in that, The real-time vehicle information includes real-time status data of the heat load at the predicted time. The step of controlling the multi-way valve matrix and / or heater in the vehicle based on the heat demand information and the real-time vehicle information to transfer heat from the heat source and / or the heater to the heat load and / or radiator includes: Based on the heat demand information of the vehicle at the predicted time, determine whether the predicted recoverable heat of one or more heat sources meets the predicted heat required by the heat load, obtain the supply and demand results, and determine the selected heat source from one or more heat sources based on the supply and demand results and the predicted scenario of the vehicle. An initial control strategy is generated based on the supply and demand results; The multi-way valve matrix and / or the heater are controlled according to the initial control strategy; Based on the heat demand information and the real-time status data, the initial control strategy is modified in real time to generate target control commands; Adjust the multi-way valve matrix and / or the heater according to the target control command.

6. The method according to claim 5, characterized in that, The step of controlling the multi-way valve matrix and / or the heater according to the initial control strategy includes: When the predicted recoverable heat from the selected heat source is less than the predicted heat required by the heat load, a first initial control strategy is generated to control the multi-way valve matrix and the heater, so that the heat from the selected heat source and the heater is delivered to the heat load through the multi-way valve matrix. When the predicted recoverable heat of the selected heat source is greater than or equal to the predicted heat required by the heat load, a second initial control strategy is generated to control the multi-way valve matrix so that the heat from the selected heat source is delivered to the heat load and the radiator through the multi-way valve matrix.

7. The method according to claim 5, characterized in that, The step of real-time correction of the initial control strategy based on the heat demand information and the real-time status data, and generation of target control commands, includes: Obtain the predicted state data of the heat load under the predicted required heat; The actual state data is compared with the predicted state data to obtain the state deviation value; The initial control strategy is modified based on the state deviation value to generate the target control command.

8. A storage medium, characterized in that, The storage medium stores computer instructions, which, when executed by the computer, are used to perform a vehicle thermal management method as described in any one of claims 1-7.

9. An electronic device, characterized in that, Includes at least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform a thermal management method for a vehicle as described in any one of claims 1-7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement a thermal management method for a vehicle as described in any one of claims 1-7.

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