Vehicle thermal management method, vehicle thermal management device, equipment, medium and product

Abstract

The invention provides a vehicle thermal management method, a vehicle thermal management device, equipment, a medium and a product. The method comprises the following steps: firstly, determining a current driving scene according to current driving data of a vehicle; according to the current driving data, determining an initial thermal management sub-strategy in a first thermal management strategy corresponding to the current driving scene; determining a target thermal management sub-strategy according to the current driving data and the initial thermal management sub-strategy; and finally, executing the target thermal management sub-strategy to perform thermal management on the vehicle. The accuracy of thermal management control is improved.

Description

Technical Field

[0001] The present application relates to the field of thermal management technology, and in particular to a vehicle thermal management method, vehicle thermal management device, equipment, medium and product. Background Art

[0002] In the intelligent development of new energy vehicles, in order to ensure the safe operation of the power system, it is necessary to use a thermal management system to control the vehicle's temperature and energy consumption, so that the vehicle can improve energy efficiency at a suitable operating temperature, thereby ensuring the efficient operation of new energy vehicles.

[0003] In the existing technology, environmental data and vehicle status data are obtained during vehicle driving to analyze the vehicle's operating conditions, so that thermal management control of the vehicle's fans, water pumps and other equipment can be carried out in time when the vehicle is in extreme operating conditions, so that the vehicle can run in an ideal state.

[0004] However, the existing technology has the problem of low thermal management control accuracy. Summary of the Invention

[0005] Embodiments of the present application provide a vehicle thermal management method, a vehicle thermal management device, equipment, a medium, and a product to improve the accuracy of thermal management control.

[0006] In a first aspect, an embodiment of the present application provides a vehicle thermal management method, comprising:

[0007] Determine the current driving scenario based on the vehicle's current driving data;

[0008] Determining an initial thermal management sub-strategy in a first thermal management strategy corresponding to the current driving scenario based on the current driving data;

[0009] Determine the target thermal management sub-strategy based on the current driving data and the initial thermal management sub-strategy;

[0010] Execute the targeted thermal management sub-strategy to perform thermal management on the vehicle.

[0011] In one possible implementation, the priority corresponding to each control dimension is determined based on the current driving data and the current driving scenario; the control dimension with the highest priority is determined as the target control dimension; and the thermal management sub-strategy corresponding to the target control dimension in the first thermal management strategy corresponding to the current driving scenario is determined as the initial thermal management sub-strategy.

[0012] In one possible implementation, driving sub-data for each control dimension are determined from the current driving data; preset driving sub-data corresponding to each control dimension are determined based on the current driving scenario; and for each control dimension, the priority corresponding to the control dimension is determined based on the driving sub-data of all control dimensions and the preset driving sub-data.

[0013] In one possible implementation, based on the initial thermal management sub-strategy, the control parameters corresponding to the target control dimension are determined; based on the control parameters corresponding to the target control dimension and the driving sub-data, the driving data of the vehicle at the next moment is predicted; based on the driving data of the vehicle at the next moment, the target driving scenario of the vehicle at the next moment is determined; and the thermal management sub-strategy corresponding to the target control dimension in the second thermal management strategy corresponding to the target driving scenario is determined as the target thermal management sub-strategy.

[0014] In a possible implementation, the current driving data includes driving behavior sub-data, and the current driving data also includes at least one of weather sub-data, driving environment sub-data, and operating condition sub-data.

[0015] In one possible implementation, according to the formula: Calculate the weight corresponding to each control dimension; determine the initial priority corresponding to each control dimension based on the weight corresponding to each control dimension, and the initial priority is proportional to the weight; modify the initial priority based on the current driving scenario to determine the priority corresponding to each control dimension;

[0016] Among them, ω i is the weight corresponding to the control dimension i, β i is the scene sensitivity coefficient of control dimension i, f i is the driving sub-data of control dimension i, f i ref is the preset driving sub-data of control dimension i, β j is the scene sensitivity coefficient of control dimension j, f j is the driving sub-data of control dimension j, f j ref is the preset driving sub-data of control dimension j, and control dimension j is any control dimension among all control dimensions.

[0017] In a second aspect, an embodiment of the present application provides a vehicle thermal management device, comprising:

[0018] A first determining module is used to determine a current driving scene based on current driving data of the vehicle;

[0019] a second determining module, configured to determine an initial thermal management sub-strategy in the first thermal management strategy corresponding to the current driving scenario based on the current driving data;

[0020] a third determination module, configured to determine a target thermal management sub-strategy based on current driving data and the initial thermal management sub-strategy;

[0021] The control module is used to execute the target thermal management sub-strategy to perform thermal management on the vehicle.

[0022] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0023] Memory stores computer-executable instructions;

[0024] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0025] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0026] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0027] The vehicle thermal management method, vehicle thermal management device, equipment, medium and product provided in the embodiments of the present application first identify the current driving scene through the current driving data of the vehicle. Then, based on the current driving data, a thermal management strategy corresponding to the current driving scene can be selected in the first thermal management strategy as the initial thermal management sub-strategy. Then, based on the current driving data and the initial thermal management sub-strategy, the final target thermal management sub-strategy is determined. The strategy takes into account the current state of the vehicle and selects a suitable strategy in the thermal management strategy based on the driving scene. Finally, the target thermal management sub-strategy is executed to perform thermal management on the vehicle. This improves the accuracy of thermal management control. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0029] Figure 1 A vehicle thermal management system provided in an embodiment of the present application;

[0030] Figure 2 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 1 ;

[0031] Figure 3 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 2 ;

[0032] Figure 4 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 3 ;

[0033] Figure 5 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 4 ;

[0034] Figure 6 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 5 ;

[0035] Figure 7 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 6 ;

[0036] Figure 8 A schematic diagram of the structure of a vehicle thermal management device provided in an embodiment of the present application;

[0037] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0038] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0039] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0040] First, let’s explain the terms involved in this application:

[0041] Thermal management strategy: refers to a series of measures and solutions to optimize the vehicle's overall thermal environment. Its purpose is to ensure that the vehicle's key components (such as the engine, battery, motor and electronic equipment) are within the optimal operating temperature range under different driving conditions and environmental conditions through intelligent control of heat dissipation, heating and heat distribution.

[0042] Figure 1 The vehicle thermal management system provided in the embodiment of the present application. Figure 1As shown, the vehicle thermal management system includes a server 100, which includes a Beidou / Global Positioning System (GPS) dual-mode positioning module 110, a meteorological data communication module 120, a driving behavior collection module 130, a road environment collection module 140, a traditional working condition collection module 150, a central computing unit 160 and a cloud-based pre-control strategy platform 170.

[0043] Among them, the Beidou / GPS dual-mode positioning module 110 is used to collect latitude and longitude, altitude, slope, tunnel and congested road section locations. The meteorological data communication module 120 is used to collect data such as temperature and humidity, air pressure, precipitation probability, and thermal radiation intensity. The driving behavior acquisition module 130 is used to collect rapid acceleration frequency, steering angular velocity, and pedal stroke rate. The road environment acquisition module 140 is used to collect data such as road surface temperature, accumulated water, ice area, and thermal radiation reflectivity. The traditional working condition acquisition module 150 is used to collect battery state of charge (SOC), motor power, electronic control temperature, and air conditioning load. The central computing unit 160 is used to determine the thermal management strategy based on the acquired data.

[0044] The cloud-based pre-control strategy platform 170 interacts with the vehicle via an over-the-air (OTA) update channel 172. A vehicle data feedback interface 171 is included on the cloud-based pre-control strategy platform 170, responsible for feeding back operating data to the platform in real time, forming a closed data loop and enabling the continuous evolution of the system's pre-control capabilities. The cloud-based pre-control strategy platform 170 also includes a cloud-based pre-control strategy module 173, which determines the pre-control thermal management strategy based on operating data.

[0045] As new energy vehicles become more intelligent, thermal management systems are becoming increasingly important as core technologies for ensuring battery safety and improving energy efficiency. The current core industry demand focuses on achieving precise temperature control, optimizing energy consumption, and extending component life in extreme environments through refined energy management.

[0046] In existing technology, thermal management controls analyze the vehicle's operating conditions based on environmental and vehicle status data. When the vehicle's status indicates extreme conditions, the thermal management system controls equipment such as the fan and water pump to maintain optimal vehicle operation.

[0047] However, if thermal management is applied to a vehicle after it is determined to be in an extreme condition, the vehicle may have already been in an extreme condition for a long time, and passively adjusting the vehicle's related equipment may not be able to prevent the vehicle from an accident. At the same time, thermal management strategies may combine multiple dimensions such as temperature control and energy consumption control. If energy consumption is still prioritized in extreme high temperature conditions, the accuracy of thermal management control may also be reduced. Therefore, the existing technology has the problem of low thermal management control accuracy.

[0048] The inventors believe that the current driving scenario can be used to determine which vehicle devices most need adjustment, and this can serve as the basis for selecting the initial thermal management sub-strategy. Furthermore, selecting a thermal management strategy based on a pre-determined vehicle state can address the issue of passive adjustment. This involves using current driving data as a predictive basis to determine the vehicle's likely future state, and then determining the final target thermal management sub-strategy based on the initial thermal management sub-strategy.

[0049] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0050] Figure 2 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 1 ,like Figure 2 As shown, the method includes:

[0051] S201. Determine a current driving scene based on current driving data of the vehicle.

[0052] The current driving data refers to the data of the vehicle driving at the current moment.

[0053] In a possible implementation, the current driving data includes driving behavior sub-data, and the current driving data also includes at least one of weather sub-data, driving environment sub-data, and working condition sub-data.

[0054] Driving behavior sub-data is used to identify surges in motor / battery thermal load caused by aggressive driving. For example, rapid acceleration frequency, steering angle velocity, and pedal stroke rate can be obtained through the accelerometer and steering wheel angle sensor.

[0055] Meteorological data is used to predict the impact of extreme climate conditions such as high temperature, low temperature, and high humidity on vehicle cooling requirements. For example, data such as temperature, humidity, air pressure, precipitation probability, and thermal radiation intensity within 30 kilometers ahead can be used.

[0056] Driving environment sub-data is used to optimize the cooling system's heat exchange efficiency on specific road surfaces. Examples include road surface temperature, water accumulation, ice areas, and thermal radiation reflectivity. This driving environment sub-data can be collected using infrared sensors, lidar, and other devices, and is not limited in this application.

[0057] Driving environment sub-data can include positioning data to identify thermal load variations caused by geographical features such as plateaus, mountainous areas, and urban canyons. Examples include latitude and longitude, altitude, slope, and the location of tunnels and congested roads, which can be obtained through Beidou or the Global Positioning System (GPS).

[0058] The operating condition sub-data is used to predict component heat generation trends in the next 15 minutes, such as battery SOC, motor power, electronic control temperature, and air conditioning load.

[0059] The current driving scenario refers to the current state of the vehicle. For example, a high-temperature aggressive driving scenario indicates that the vehicle is driving aggressively in a high-temperature environment. An accident may occur at this time, so thermal management is performed on the vehicle.

[0060] In practical applications, the current driving data can be fused to build a scene classification model to identify the current driving scene. For example, using the spatiotemporal attention mechanism, a five-dimensional data fusion model X(t) = [T a (t), P(t), h(t), SOC(t), v(t), θ(t), ρ(t), δ(t), λ(t)], where X(t) is the fused driving data, T a (t) is the ambient temperature, P(t) is the air pressure, h(t) is the altitude, SOC(t) is the battery state of charge, v(t) is the speed, θ(t) is the slope, ρ(t) is the air density, δ(t) is the steering frequency, and λ(t) is the steering angular velocity. The fused data is input into the scene classification model to determine the current driving scenario. During fusion, the collected five-dimensional data is spatially and temporally aligned to ensure a time error of less than 100ms, providing an accurate data foundation for subsequent scene classification and strategy generation.

[0061] In another possible implementation, the current driving scenario can be determined based on the current driving data and a mapping relationship, where the mapping relationship is the relationship between the current driving data and the current driving scenario. For example, a scenario classification model can be constructed using the fuzzy C-means clustering algorithm to accurately classify 20 typical pre-control scenarios. Among them, the judgment condition for the high-temperature aggressive driving scenario is T a (t)>35℃, δ(t)>1 time / minute; for high-altitude low-temperature fast charging scenarios, h(t)>3000 meters, T a(t)<-5℃, SOC(t)<20%.

[0062] In actual applications, the current driving data can be obtained based on hardware devices such as the GPS positioning system, on-board communication module, various sensors (such as temperature sensors, speed sensors, power sensors, etc.), electronic water pumps, fans, air-conditioning systems, and battery thermal management systems installed on the vehicle.

[0063] S202 : Determine an initial thermal management sub-strategy in a first thermal management strategy corresponding to a current driving scenario based on current driving data.

[0064] Among them, the first thermal management strategy refers to a set of thermal management strategies set for various driving scenarios. For example, the management strategy for the "high-temperature aggressive driving scenario" is to pre-start the battery liquid cooling system 10 minutes in advance; for the "high-altitude low-temperature fast charging scenario", the "pulse heating + motor waste heat recovery" collaborative strategy is pre-started immediately.

[0065] The initial thermal management strategy is the thermal management strategy determined for the current driving scenario.

[0066] In actual applications, for different driving scenarios, the driving state requirements after the implementation of the thermal management strategy may be different. For example, the vehicle's temperature control effect needs to be improved, or the vehicle's energy loss needs to be low. Therefore, in one possible implementation method, the priority corresponding to each control dimension is first determined based on the current driving data and the current driving scenario. Then, the control dimension with the highest priority is determined as the target control dimension. Finally, the thermal management sub-strategy corresponding to the target control dimension in the first thermal management strategy corresponding to the current driving scenario is determined as the initial thermal management sub-strategy.

[0067] The control dimension refers to the goal achieved after the thermal management strategy is implemented. In one possible implementation, the control dimensions include temperature control, energy consumption control, and safety control, each representing the desired dimension of vehicle control after the thermal management strategy is implemented.

[0068] In practical applications, the objective function of the control dimension can be expressed as minF = ω1·ΔT + ω2·E + ω3·S. This function includes temperature control ΔT, energy consumption control E, and safety control S. For energy consumption control, the goal is to minimize energy consumption. For safety control, it is used to quantify risk indices such as battery thermal runaway.

[0069] The priority of the control dimension is used to determine the priority factor in the thermal management strategy. For example, if temperature control has a high priority (i.e., the target control dimension is temperature control), the thermal management strategy will tend to prioritize temperature control over energy consumption and safety.

[0070] In one possible implementation, driving sub-data for each control dimension is first determined from the current driving data. Then, based on the current driving scenario, preset driving sub-data corresponding to each control dimension is determined. Finally, for each control dimension, the priority corresponding to the control dimension is determined based on the driving sub-data for all control dimensions and the preset driving sub-data.

[0071] The driving sub-data refers to the current driving data related to the control dimension.

[0072] For example, when the control dimension is temperature control, the driving sub-data includes temperature-related data such as road surface temperature, electronic control temperature, and interior temperature. When the control dimension is energy consumption control, the driving sub-data includes energy consumption-related data such as battery state of charge (SOC), motor power, and air conditioning load. When the control dimension is safety control, the driving sub-data includes safety-related data such as rapid acceleration frequency, steering angular velocity, and pedal stroke rate.

[0073] Preset sub-data refers to the value of the driving sub-data under ideal driving conditions. For example, if the driving sub-data is ambient temperature, the preset sub-data is set to 35°C. This means that when the ambient temperature exceeds 35°C, the vehicle is in a high-temperature state and thermal management is required.

[0074] In practical applications, the priority can be determined based on an exponential decay function. First, according to the formula: Calculate the weight corresponding to each control dimension. Then, determine the initial priority corresponding to each control dimension based on the weight corresponding to each control dimension. Based on the current driving scenario, modify the initial priority to determine the priority corresponding to each control dimension.

[0075] Among them, ω i is the weight corresponding to the control dimension i, β i is the scene sensitivity coefficient of control dimension i, f i is the driving sub-data of control dimension i, f i ref is the preset driving sub-data of control dimension i, β j is the scene sensitivity coefficient of control dimension j, f j is the driving sub-data of control dimension j, f j ref is the preset driving sub-data of control dimension j, and control dimension j is any control dimension among all control dimensions.

[0076] The initial priority refers to the order determined by the weight, and the initial priority is proportional to the weight.

[0077] For example, it can be seen that the scenario sensitivity coefficients of temperature control, energy consumption control and safety control are β1=1, β2=0.7, β3=1.5 respectively, and the preset driving sub-data are f1 ref =3℃, The current driving data indicates that its corresponding driving sub-data are f1=5.2℃, f2=8.4kW·h, and f1=0.6, respectively. It can be calculated that ω1=0.107, ω2=0.363, and ω3=0.53. Based on this, it can be seen that safety control has the highest priority, followed by energy consumption control, and finally temperature control, so safety-related equipment should be controlled first.

[0078] Correcting the initial priorities means adjusting them based on the current driving scenario. For example, if the initial priorities are energy consumption control > temperature control > safety control, and the current driving scenario is "aggressive driving in high temperatures," requiring urgent temperature control, then the corrected priorities for each control dimension will be temperature control > energy consumption control > safety control.

[0079] In actual applications, for high-temperature aggressive driving scenarios, the weight corresponding to temperature control is automatically increased to 60%, ensuring that telecommunications temperature control is within an accuracy range of ±1.2°C, thereby correcting the initial priority.

[0080] In actual applications, more than 200 typical scenario strategies can be stored based on the pre-adjusted strategy cache library, and scenario recognition and strategy generation can be completed quickly within 200ms.

[0081] S203 : Determine a target thermal management sub-strategy based on current driving data and the initial thermal management sub-strategy.

[0082] Among them, the target thermal management sub-strategy refers to the thermal management strategy that is ultimately implemented on the vehicle and is used to perform thermal management of the vehicle in the future.

[0083] It should be understood that the target thermal management sub-strategy is a strategy selected after predicting the future scenario based on current data.

[0084] Therefore, in one possible implementation, the control parameters corresponding to the target control dimension are first determined based on the initial thermal management sub-strategy. The vehicle's driving data at the next moment is then predicted based on the control parameters corresponding to the target control dimension and the driving sub-data. The target driving scenario for the vehicle at the next moment is then determined based on the vehicle's driving data at the next moment. Finally, the thermal management sub-strategy corresponding to the target control dimension in the second thermal management strategy corresponding to the target driving scenario is determined as the target thermal management sub-strategy.

[0085] Control parameters are parameters used to control devices associated with the target control dimension. They represent the corresponding parameters in the strategy selected based on the current data. For example, if the target control dimension is temperature control, control parameters might include air conditioning power, which affects the vehicle's interior temperature, or coolant flow, which affects engine temperature.

[0086] Predicting the vehicle's driving data at the next moment refers to the driving data reflected by the vehicle after the initial thermal management sub-strategy is executed on the vehicle in the current driving state, that is, after the control parameters are used to control the equipment corresponding to the target control dimension. This can be used as a basis for evaluating whether the control parameters are accurate.

[0087] In practical applications, the vehicle's driving data at the next moment is calculated according to the formula: x(k+1)=Ax(k)+Bu(k)+w(k).

[0088] Among them, x(k+1) is the driving data of the vehicle at the next moment, x(k) is the driving sub-data, u(k) is the control parameter corresponding to the target control dimension, w(k) is the process noise, and A and B are both correlation coefficients.

[0089] It should be understood that when the target control dimension is temperature control, the driving sub-data may include temperature-related data such as battery temperature, motor temperature, and cabin temperature, and the control parameters may include parameters affecting temperature such as coolant flow and air conditioning power.

[0090] The target driving scenario refers to a driving scenario determined based on the predicted driving data. The determination method is the same as determining the current driving scenario based on the current driving data of the vehicle, and will not be repeated here.

[0091] The second thermal management strategy is a set of thermal management strategies set for different target driving scenarios.

[0092] The target thermal management sub-strategy refers to the thermal management strategy selected in the second thermal management strategy based on the target control dimension.

[0093] For example, when the target driving scenario is determined to be a high-temperature aggressive driving scenario, the second thermal management strategy can be determined as "(1) Start the battery liquid cooling system 8 minutes in advance, adjust the flow rate to 15L / min, and reduce the battery cell temperature to 28°C; (2) Adjust the air-conditioning outlet angle 5 minutes in advance and increase the facial air supply ratio by 30%; (3) Turn on the economic mode and reduce the compressor power by 15%; (4) During driving, once a sudden acceleration event is detected, immediately increase the cooling flow rate to 20L / min, and the response time is ≤100ms". This includes thermal management strategies in three dimensions: temperature control, energy consumption control, and safety control.

[0094] When the target control dimension is temperature control, the target thermal management sub-strategies can be determined as "(1) start the battery liquid cooling system 8 minutes in advance, adjust the flow rate to 15L / min, and reduce the battery cell temperature to 28°C; (2) adjust the air outlet angle of the air conditioner 5 minutes in advance and increase the face air supply ratio by 30%" for temperature control.

[0095] S204: Execute the target thermal management sub-strategy to perform thermal management on the vehicle.

[0096] The target thermal management sub-strategy is used to generate control instructions for the corresponding equipment. For example, an electronic water pump with a flow adjustment range of 0-25L / min and an accuracy of ±0.1L / min can be selected to adjust the coolant flow in real time according to the control instructions. For another example, a three-flow reversing valve that supports any combination of switching between the "battery cooling-motor heat dissipation-cabin heating" circuits can significantly improve waste heat recovery efficiency. For another example, a variable-frequency air-conditioning compressor with a power adjustment accuracy of ±5% can adjust the air-conditioning power according to the thermal management strategy to achieve energy consumption optimization. For another example, a battery pulse heating module with a power range of 0-30kW can be selected to preheat the battery in low-temperature scenarios to improve battery performance.

[0097] It should be understood that the target thermal management sub-strategy can be set to be issued within a certain period of time in the future, so as to achieve the effect of thermal management of the vehicle in advance.

[0098] In practice, a 15-minute prediction horizon can be set, with rolling optimization triggered every 200ms based on real-time feedback, outputting air conditioning control commands via the Controller Area Network Bus (CAN Bus). When the temperature sensor error is less than ±2°C, the coolant flow curve accuracy is ±0.1L / min, or the power regulation capability is ±5%, the adaptive anti-interference algorithm is triggered, recalculating the control sequence for the next three minutes to ensure the thermal management system is always operating optimally.

[0099] The vehicle thermal management method provided in this embodiment uses the vehicle's current driving data to identify the driving scenario, determine an initial thermal management sub-strategy from the first thermal management strategy corresponding to that scenario, further determine a target thermal management sub-strategy based on the current driving data, and finally execute this target strategy to achieve effective thermal management of the vehicle. This method integrates dynamic analysis of the driving environment and vehicle status, improving the accuracy of thermal management control.

[0100] In practical applications, current driving data can be based on millions of vehicle data, using a hybrid architecture of deep reinforcement learning (DRL) and model predictive control (MPC) to train the pre-control strategy generation model, so that the model can predict the required thermal management strategy based on the driving scenario.

[0101] In addition, the optimized scenario classification thresholds (such as redefining the traffic flow density threshold of "congestion"), weight distribution coefficients (such as updating the temperature sensitivity coefficients of different areas), and model prediction control parameters (such as altitude-heat dissipation efficiency correction coefficient) are pushed to the edge end every week through OTA technology to achieve continuous evolution of the system's pre-control capabilities.

[0102] Figure 3 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 2 ,like Figure 3 As shown, this embodiment Figure 3 Based on the embodiment, a vehicle thermal management method is described in detail, and the method includes:

[0103] S301, obtaining current driving data of the vehicle;

[0104] S302. Optionally, build a cloud-based pre-control strategy platform to assist in analyzing current driving data;

[0105] S303: Determine the current driving scene based on the current driving data and the scene classification model;

[0106] S304, calculating the weight corresponding to each control dimension in the current driving data;

[0107] S305: Determine the priority corresponding to each control dimension based on the weight and the current driving scenario;

[0108] S306: Determine a target thermal management sub-strategy based on the current driving data and the thermal management sub-strategy corresponding to the highest priority;

[0109] S307, executing the target thermal management sub-strategy to perform thermal management on the vehicle;

[0110] S308: Modify the parameters of the scene classification model according to the execution result.

[0111] Figure 4 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 3 ,like Figure 4 As shown, the method includes:

[0112] S401, obtaining driving environment sub-data, weather sub-data, driving behavior sub-data, and operating condition sub-data;

[0113] S402, fusing the acquired data;

[0114] S403. Observe the prediction curve reflecting the heat load trend based on the fusion result.

[0115] Figure 5 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 4 ,like Figure 5 As shown, the method includes:

[0116] S501, obtaining the current driving data of the vehicle;

[0117] S502: Determine the current driving scene corresponding to the vehicle's current driving data using a scene classification model, wherein the scene classification model is constructed based on a fuzzy C-means clustering algorithm and can accurately classify 20 typical pre-control scenes;

[0118] S503. Calculate the weight corresponding to each control dimension in the current driving data according to the current driving scenario, where the objective function of the control dimension is minF=ω1·ΔT+ω2·E+ω3·S, and the weights ω1, ω2, and ω3 are calculated based on an exponential decay function.

[0119] S504: Determine the priority corresponding to each control dimension based on the weight and the current driving scenario;

[0120] S505 . Determine a target thermal management sub-strategy based on the current driving data and the thermal management sub-strategy corresponding to the highest priority, and perform feedback adjustment based on the execution results within 15 minutes after the strategy is executed.

[0121] Figure 6 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 5 ,like Figure 6 As shown, the method includes:

[0122] S601: The central computing unit obtains the current driving data sent by the preview perception layer;

[0123] S602: The pre-control execution layer executes the pre-control instruction determined by the target thermal management sub-strategy to perform thermal management on the vehicle;

[0124] S603, the central computing unit receives execution feedback after thermal management;

[0125] S604, the central computing unit uploads the execution feedback to the cloud pre-control strategy platform;

[0126] S605. The cloud-based pre-control strategy platform can use OTA technology to update the thermal management strategy.

[0127] Figure 7 Schematic diagram of the process of the vehicle thermal management method provided in the embodiment of the present application Figure 6 ,like Figure 7 As shown, the method includes:

[0128] S701: After determining the thermal management strategy, the central computing unit generates control instructions and sends them to the electronic water pump, variable frequency air conditioning compressor, three-energy flow reversing valve, and battery pulse heating module;

[0129] S702: The electronic water pump regulates the coolant flow, the variable frequency air conditioning compressor adjusts the air conditioning power, the three-energy flow reversing valve switches the three circuits, and the battery pulse heating module preheats or dissipates heat from the battery.

[0130] Figure 8 This is a schematic diagram of the structure of the vehicle thermal management device provided in the embodiment of the present application, as shown in FIG. Figure 8 As shown, the vehicle thermal management device 80 provided in this embodiment includes:

[0131] A first determining module 801 is configured to determine a current driving scene based on the current driving data of the vehicle;

[0132] A second determining module 802 is configured to determine an initial thermal management sub-strategy in the first thermal management strategy corresponding to the current driving scenario based on the current driving data;

[0133] A third determination module 803 is configured to determine a target thermal management sub-strategy based on the current driving data and the initial thermal management sub-strategy;

[0134] The control module 804 is configured to execute the target thermal management sub-strategy to perform thermal management on the vehicle.

[0135] In one possible implementation, the second determination module 802 is further used to determine the priority corresponding to each control dimension based on the current driving data and the current driving scenario; determine the control dimension with the highest priority as the target control dimension; and determine the thermal management sub-strategy corresponding to the target control dimension in the first thermal management strategy corresponding to the current driving scenario as the initial thermal management sub-strategy.

[0136] In one possible implementation, the second determination module 802 is further used to determine the driving sub-data of each control dimension from the current driving data; determine the preset driving sub-data corresponding to each control dimension based on the current driving scenario; and for each control dimension, determine the priority corresponding to the control dimension based on the driving sub-data of all control dimensions and the preset driving sub-data.

[0137] In one possible implementation, the third determination module 803 is further used to determine the control parameters corresponding to the target control dimension based on the initial thermal management sub-strategy; predict the driving data of the vehicle at the next moment based on the control parameters and driving sub-data corresponding to the target control dimension; determine the target driving scenario of the vehicle at the next moment based on the driving data of the vehicle at the next moment; and determine the thermal management sub-strategy corresponding to the target control dimension in the second thermal management strategy corresponding to the target driving scenario as the target thermal management sub-strategy.

[0138] In a possible implementation, the second determining module 802 is further configured to determine the value of the following formula: Calculate the weight corresponding to each control dimension; determine the initial priority corresponding to each control dimension based on the weight corresponding to each control dimension, and the initial priority is proportional to the weight; modify the initial priority based on the current driving scenario to determine the priority corresponding to each control dimension;

[0139] Among them, ω i is the weight corresponding to the control dimension i, β i is the scene sensitivity coefficient of control dimension i, f i is the driving sub-data of control dimension i, f i ref is the preset driving sub-data of control dimension i, β j is the scene sensitivity coefficient of control dimension j, f j is the driving sub-data of control dimension j, f j ref is the preset driving sub-data of control dimension j, and control dimension j is any control dimension among all control dimensions.

[0140] The vehicle thermal management device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and will not be described in detail in this embodiment.

[0141] Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 9 As shown, the electronic device 90 provided in this embodiment includes: at least one processor 901 and a memory 902. Optionally, the electronic device 90 further includes a communication component 903. The processor 901, the memory 902, and the communication component 903 are connected via a bus 904.

[0142] The electronic device 90 may be implemented as Figure 1 Server 100 in.

[0143] During the specific implementation process, at least one processor 901 executes the computer-executable instructions stored in the memory 902, so that the at least one processor 901 performs the above method.

[0144] The specific implementation process of the processor 901 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0145] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules in the processor.

[0146] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0147] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0148] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0149] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0150] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0151] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0152] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0153] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0154] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0156] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0157] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A vehicle thermal management method, characterized in that: include: Determine the current driving scenario based on the vehicle's current driving data; determining, based on the current driving data, an initial thermal management sub-strategy in a first thermal management strategy corresponding to the current driving scenario; determining a target thermal management sub-strategy based on the current driving data and the initial thermal management sub-strategy; The target thermal management sub-strategy is executed to perform thermal management on the vehicle.

2. The method according to claim 1, characterized in that The determining, based on the current driving data, an initial thermal management sub-strategy in the first thermal management strategy corresponding to the current driving scenario includes: Determining a priority corresponding to each control dimension based on the current driving data and the current driving scenario; Identify the control dimension with the highest priority as the target control dimension; The thermal management sub-strategy corresponding to the target control dimension in the first thermal management strategy corresponding to the current driving scenario is determined as the initial thermal management sub-strategy.

3. The method according to claim 2, characterized in that The determining, based on the current driving data and the current driving scenario, the priority corresponding to each control dimension includes: Determining driving sub-data of each control dimension from the current driving data; Determining preset driving sub-data corresponding to each control dimension according to the current driving scenario; For each control dimension, the priority corresponding to the control dimension is determined according to the driving sub-data of all control dimensions and the preset driving sub-data.

4. The method according to claim 2 or 3, characterized in that The determining of a target thermal management sub-strategy based on the current driving data and the initial thermal management sub-strategy includes: Determining control parameters corresponding to the target control dimension according to the initial thermal management sub-strategy; Predicting the driving data of the vehicle at the next moment based on the control parameters corresponding to the target control dimension and the driving sub-data; determining a target driving scenario for the vehicle at the next moment based on the driving data of the vehicle at the next moment; The thermal management sub-strategy corresponding to the target control dimension in the second thermal management strategy corresponding to the target driving scenario is determined as the target thermal management sub-strategy.

5. The method according to any one of claims 1 to 3, characterized in that The current driving data includes driving behavior sub-data, and the current driving data also includes at least one of weather sub-data, driving environment sub-data, and working condition sub-data.

6. The method according to claim 3, characterized in that For each control dimension, determining the priority corresponding to the control dimension according to the driving sub-data of all control dimensions and the preset driving sub-data includes: According to the formula: Calculate the weight corresponding to each control dimension; Determine an initial priority corresponding to each control dimension according to a weight corresponding to each control dimension, wherein the initial priority is proportional to the weight; Modifying the initial priority according to the current driving scenario to determine the priority corresponding to each control dimension; Among them, ω i is the weight corresponding to the control dimension i, β i is the scene sensitivity coefficient of control dimension i, f i is the driving sub-data of control dimension i, f i ref is the preset driving sub-data of control dimension i, β j is the scene sensitivity coefficient of control dimension j, f j is the driving sub-data of control dimension j, f j ref is the preset driving sub-data of control dimension j, and control dimension j is any control dimension among all control dimensions.

7. A vehicle thermal management device, characterized in that: include: A first determining module is used to determine a current driving scene based on current driving data of the vehicle; a second determining module, configured to determine, based on the current driving data, an initial thermal management sub-strategy in the first thermal management strategy corresponding to the current driving scenario; a third determining module, configured to determine a target thermal management sub-strategy based on the current driving data and the initial thermal management sub-strategy; The control module is configured to execute the target thermal management sub-strategy to perform thermal management on the vehicle.

8. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

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