Vehicle thermal management control method, vehicle and storage medium

Abstract

The embodiment of the invention provides a vehicle thermal management control method, a vehicle and a storage medium, and the method comprises the steps: obtaining vehicle state data, road condition data and driving environment data of a target vehicle, the vehicle state data being used for representing driving state information and charge state information of the target vehicle; based on the driving state information and the road condition data, decision analysis is carried out, a demand prediction result corresponding to the target vehicle is obtained, and the demand prediction result is used for representing power demand information of the target vehicle in different driving road sections; a target energy distribution strategy is determined according to the demand prediction result, the driving environment data and the charge state information, and the target energy distribution strategy is used for determining the engine power demand and the battery charging and discharging demand of the target vehicle; and controlling the target vehicle to execute the target energy distribution strategy. The technical problems of poor energy management strategy adaptability, unstable vehicle performance and poor economical efficiency in related technologies are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a vehicle thermal management control method, a vehicle, and a storage medium. Background Technology

[0002] With the rapid development of intelligent driving technology, vehicles face higher demands for efficient energy management and utilization in complex and ever-changing driving environments. Especially when dealing with varying road conditions, ambient temperature, and humidity, precisely controlling the powertrain to ensure safe and stable vehicle operation has become a critical issue that urgently needs to be addressed. Traditional vehicle energy management strategies largely rely on real-time adjustments by the driver, but under the trend of automated driving, these passive management methods are no longer applicable. In related technologies, the failure to effectively couple intelligent driving with the vehicle's thermal management system makes it difficult to guarantee stable engine output at the specified power under heavy load conditions, thus increasing the risk of vehicle stall and overheating, and limiting the vehicle's adaptability and safety under harsh conditions.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a vehicle thermal management control method, a vehicle, and a storage medium to at least solve the technical problems of poor adaptability of energy management strategies, unstable vehicle performance, and poor economy in related technologies.

[0005] According to one aspect of the embodiments of this application, a vehicle thermal management control method is provided, comprising: acquiring vehicle state data, road condition data, and driving environment data of a target vehicle, wherein the vehicle state data is used to represent the driving state information and state of charge information of the target vehicle; performing decision analysis based on the driving state information and road condition data to obtain a demand prediction result corresponding to the target vehicle, wherein the demand prediction result is used to represent the power demand information of the target vehicle in different driving segments; determining a target energy allocation strategy using the demand prediction result, driving environment data, and state of charge information, wherein the target energy allocation strategy is used to determine the engine power demand and battery charging and discharging demand of the target vehicle; and controlling the target vehicle to execute the target energy allocation strategy.

[0006] Optionally, the decision analysis based on driving status information and road condition data to obtain the demand prediction result corresponding to the target vehicle includes: preprocessing the driving status information and road condition data to obtain preprocessed results; determining the road condition prediction information corresponding to the target vehicle based on the preprocessed results; and using the vehicle energy demand model to perform decision analysis on the road condition prediction information to obtain the demand prediction result, wherein the vehicle energy demand model is used to determine the energy demand information of the target vehicle under different driving road conditions.

[0007] Optionally, determining the target energy allocation strategy using demand forecasting results, driving environment data, and state of charge information includes: conducting a risk assessment based on demand forecasting results, driving environment data, and state of charge information to obtain risk assessment results; and determining the target energy allocation strategy based on the risk assessment results.

[0008] Optionally, risk assessment is performed based on demand forecast results, driving environment data, and state of charge information. The risk assessment results include: fusing demand forecast results, driving environment data, and state of charge information to obtain data fusion results; and using preset thermal management risk rules to conduct risk assessment on the data fusion results to obtain risk assessment results.

[0009] Optionally, determining the target energy allocation strategy based on the risk assessment results includes: determining, based on the risk assessment results, that the target vehicle does not have any target risk items, determining a first energy allocation strategy based on demand forecast results, driving environment data, and state of charge information; and determining the first energy allocation strategy as the target energy allocation strategy.

[0010] Optionally, determining the target energy allocation strategy based on the risk assessment results includes: determining that the target vehicle has a target risk item based on the risk assessment results; determining a second energy allocation strategy based on the risk time information and risk location information corresponding to the target risk item; and determining the second energy allocation strategy as the target energy allocation strategy.

[0011] Optionally, the road condition data shall include at least: the slope information, distance information and speed limit information corresponding to the target vehicle's travel path.

[0012] Optionally, the driving environment data may include at least the temperature and humidity information of the target vehicle's driving environment.

[0013] According to another aspect of the embodiments of this application, a vehicle thermal management control device is also provided, comprising: an acquisition module, configured to acquire vehicle state data, road condition data, and driving environment data of a target vehicle, wherein the vehicle state data is used to represent the driving state information and state of charge information of the target vehicle; an analysis module, configured to perform decision analysis based on the driving state information and road condition data to obtain a demand prediction result corresponding to the target vehicle, wherein the demand prediction result is used to represent the power demand information of the target vehicle in different driving segments; a determination module, configured to determine a target energy allocation strategy using the demand prediction result, driving environment data, and state of charge information, wherein the target energy allocation strategy is used to determine the engine power demand and battery charging and discharging demand of the target vehicle; and an execution module, configured to control the target vehicle to execute the target energy allocation strategy.

[0014] Optionally, the analysis module is also used to: preprocess driving status information and road condition data to obtain preprocessing results; determine the road condition prediction information corresponding to the target vehicle based on the preprocessing results; and perform decision analysis on the road condition prediction information using the vehicle energy demand model to obtain demand prediction results, wherein the vehicle energy demand model is used to determine the energy demand information of the target vehicle under different driving road conditions.

[0015] Optionally, the determination module is also used to: conduct a risk assessment based on demand forecast results, driving environment data, and state of charge information to obtain risk assessment results; and determine a target energy allocation strategy based on the risk assessment results.

[0016] Optionally, the determination module is also used to: fuse demand forecast results, driving environment data and state of charge information to obtain data fusion results; and use preset thermal management risk rules to conduct risk assessment on the data fusion results to obtain risk assessment results.

[0017] Optionally, the determining module is further configured to: respond to a risk assessment result that the target vehicle does not have a target risk item, determine a first energy allocation strategy based on demand forecast results, driving environment data and state of charge information; and determine the first energy allocation strategy as the target energy allocation strategy.

[0018] Optionally, the determining module is further configured to: respond to the determination of a target risk item in the target vehicle based on the risk assessment results, determine a second energy allocation strategy based on the risk time information and risk location information corresponding to the target risk item, and determine the second energy allocation strategy as the target energy allocation strategy.

[0019] Optionally, the road condition data shall include at least: the slope information, distance information and speed limit information corresponding to the target vehicle's travel path.

[0020] Optionally, the driving environment data may include at least the temperature and humidity information of the target vehicle's driving environment.

[0021] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0022] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0023] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0024] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0025] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0026] In this embodiment, by acquiring vehicle status data, road condition data, and driving environment data of the target vehicle, and then performing decision analysis based on the driving status information and road condition data, the corresponding demand prediction results for the target vehicle are obtained. Subsequently, the target energy allocation strategy is determined using the demand prediction results, driving environment data, and state of charge information. Finally, the target vehicle is controlled to execute the target energy allocation strategy. This combines real-time data from intelligent driving with the vehicle's thermal management requirements, enabling the prediction of power demand and timely adjustment of energy strategies based on the vehicle's current and future operating conditions and environmental changes. This effectively avoids the risk of engine overheating under high-load conditions and also ensures efficient utilization and extended lifespan of the vehicle's battery. Through refined energy management, this embodiment significantly improves energy efficiency and reduces the workload of the cooling system while ensuring vehicle power performance and driving safety. It greatly improves the vehicle's economy and environmental adaptability, achieving refined energy control and thermal management system optimization. This results in improved vehicle driving safety, power performance, and energy efficiency, enhancing user experience, reducing operating costs, and improving vehicle environmental adaptability. It also solves the technical problems of poor adaptability of energy management strategies, unstable vehicle performance, and poor economy in related technologies. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0028] Figure 1 This is a flowchart of a vehicle thermal management control method according to an embodiment of this application;

[0029] Figure 2 This is a schematic diagram of a vehicle thermal management control system according to an embodiment of this application;

[0030] Figure 3 This is a schematic diagram of a vehicle thermal management control method according to an embodiment of this application;

[0031] Figure 4 This is a structural block diagram of a vehicle thermal management control device according to an embodiment of this application. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] According to an embodiment of this application, a method embodiment of a vehicle thermal management control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This method embodiment can be executed in an electronic device or similar computing device that includes memory and a processor. Taking a computer terminal as an example, the computer terminal may include one or more processors (processors may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field-programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processors (TPUs), artificial intelligence (AI) type processors, etc.) and memory for storing data. Optionally, the computer terminal may also include transmission devices, input / output devices, and display devices for communication functions. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the computer terminal. For example, the computer terminal may include more or fewer components than described above, or have a different configuration than described above.

[0036] The memory can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the vehicle thermal management control method in this embodiment. The processor executes various functional applications and data processing by running the computer program stored in the memory, thereby implementing the aforementioned vehicle thermal management control method. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0037] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0038] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0039] This embodiment provides a vehicle thermal management control method. Figure 1 This is a flowchart of a vehicle thermal management control method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0040] Step S11: Obtain vehicle status data, road condition data, and driving environment data of the target vehicle, wherein the vehicle status data is used to represent the driving status information and charge status information of the target vehicle.

[0041] Step S12: Based on driving status information and road condition data, a decision analysis is performed to obtain the demand prediction result corresponding to the target vehicle. The demand prediction result is used to represent the power demand information of the target vehicle in different driving segments.

[0042] Step S13: Determine the target energy allocation strategy using demand forecast results, driving environment data, and state of charge information. The target energy allocation strategy is used to determine the engine power demand and battery charging and discharging demand of the target vehicle.

[0043] Step S14: Control the target vehicle to execute the target energy distribution strategy.

[0044] The aforementioned vehicle status data can be a series of indicators of the target vehicle's current operating status, including but not limited to vehicle speed, acceleration, direction, and state of charge (SOC). Vehicle status data is the basis for formulating target energy distribution strategies.

[0045] The aforementioned road condition data covers various characteristics of the target vehicle's travel path, such as gradient, curve radius, road surface material, coefficient of friction, and traffic signs ahead. In particular, speed limit and gradient information are crucial for predicting the instantaneous power required by the target vehicle.

[0046] The aforementioned driving environment data refers to the surrounding environmental parameters of the target vehicle during operation, mainly including temperature and humidity. Environmental factors directly affect the heat exchange efficiency of the cooling system, and thus affect the thermal management of the engine and battery.

[0047] The aforementioned driving status information involves the dynamic behavior of the target vehicle, including but not limited to the current vehicle speed, gear, steering angle, accelerator pedal position, etc., which can reflect whether the vehicle is in a state of climbing, accelerating, decelerating or driving at a constant speed.

[0048] The aforementioned state of charge information can specifically refer to the remaining percentage of charge in the target vehicle's battery. This is an important basis for determining the battery's charging and discharging power and a key element for optimizing energy distribution strategies.

[0049] Through intelligent driving systems and onboard sensor networks, real-time vehicle status data, road condition data, and driving environment data of the target vehicle are acquired, providing detailed raw information for subsequent analysis and decision-making.

[0050] After acquiring driving status information and road condition data, decision analysis can be performed based on the driving status information and road condition data, thereby predicting the power demand of the target vehicle on different driving segments that it will be traversed, and generating demand prediction results. The demand prediction results can specifically include the power demand of the engine and battery.

[0051] Furthermore, the demand forecast results are combined with driving environment data and state of charge information for comprehensive analysis, ultimately forming a target energy allocation strategy. This strategy clearly defines engine power requirements and battery charge / discharge requirements to ensure that the vehicle's power and thermal management needs are met under different road conditions and environments. The target energy allocation strategy guides the power output of the engine and battery, ensuring optimal energy distribution. Engine power requirements can be the power output values ​​that the engine needs to produce under different operating conditions, as specified in the target energy allocation strategy, to meet the vehicle's power requirements while considering thermal management constraints. Battery charge / discharge requirements can be the charge / discharge power required by the battery in different driving segments, as determined in the target energy allocation strategy, aiming to balance battery energy and engine output to achieve better thermal management.

[0052] In this application embodiment, the target energy distribution strategy is transformed into actual control commands through the vehicle control unit (VCU), which directly acts on the engine control system and battery management system of the target vehicle to achieve precise energy distribution and efficient thermal management.

[0053] Based on steps S11 to S14 above, by acquiring vehicle status data, road condition data, and driving environment data of the target vehicle, and then conducting decision analysis based on the driving status information and road condition data, the corresponding demand prediction results of the target vehicle are obtained. Subsequently, the target energy allocation strategy is determined using the demand prediction results, driving environment data, and state of charge information. Finally, the target vehicle is controlled to execute the target energy allocation strategy. This combines real-time data of intelligent driving with the vehicle's thermal management requirements, enabling the prediction of power demand and timely adjustment of energy strategies based on the vehicle's current and future operating conditions and environmental changes. This effectively avoids the risk of engine overheating under heavy load conditions, while also ensuring the efficient utilization and extended lifespan of the vehicle's battery. Through refined energy management, the embodiments of this application not only ensure vehicle power performance and driving safety, but also significantly improve energy efficiency, reduce the working pressure of the cooling system, greatly improve the vehicle's economy and environmental adaptability, and realize refined energy control and thermal management system optimization. This achieves the goal of improving vehicle driving safety, power performance and energy efficiency, and realizes the technical effects of enhancing user experience, reducing operating costs and improving vehicle environmental adaptability. In turn, it solves the technical problems of poor adaptability of energy management strategies, unstable vehicle performance and poor economy in related technologies.

[0054] The vehicle thermal management control method in the embodiments of this application will be further described below.

[0055] In an optional embodiment, in step S12, decision analysis is performed based on driving status information and road condition data to obtain the demand prediction result corresponding to the target vehicle, including:

[0056] Step S121: Preprocess the driving status information and road condition data to obtain the preprocessing result;

[0057] Step S122: Determine the road condition prediction information corresponding to the target vehicle based on the preprocessing results;

[0058] Step S123: Use the vehicle energy demand model to perform decision analysis on the road condition prediction information to obtain the demand prediction result. The vehicle energy demand model is used to determine the energy demand information of the target vehicle under different driving road conditions.

[0059] Specifically, the driving status information and road condition data are preprocessed to obtain preprocessed results. Through preprocessing, such as noise removal, missing value filling, and data format standardization, the embodiments of this application ensure the quality and consistency of the input data, providing a reliable foundation for subsequent road condition prediction and decision analysis. Thus, the preprocessing improves the accuracy of analysis, reduces the risk of misjudgment, and enhances the robustness and effectiveness of the entire energy management strategy.

[0060] Furthermore, through predictive algorithms in intelligent driving systems, it is possible to predict in advance the road conditions that the target vehicle will encounter, such as continuous uphill climbs, frequent turns, or traffic congestion. Utilizing preprocessed data, a more detailed and forward-looking description of road conditions is provided, thereby achieving accurate prediction of vehicle energy demand and significantly improving the targeting and predictability of thermal management strategies.

[0061] The aforementioned road condition prediction information is a description of the road conditions that the target vehicle will encounter in the future, based on preprocessed driving status information and road condition data, and obtained through model prediction or algorithm analysis. Examples include the predicted average gradient, turning frequency, and degree of traffic congestion.

[0062] The aforementioned vehicle energy demand model can be a mathematical model or a machine learning model, used to estimate the energy consumption of a target vehicle under specific conditions, including the load on the engine and battery. It can be dynamically adjusted based on different operating conditions and environmental parameters. Based on extensive historical data and complex mathematical models, the vehicle energy demand model can accurately calculate the energy demands of the engine and battery under different driving conditions based on preprocessed road condition prediction information. The model not only considers the vehicle's own state but also incorporates the influence of the external environment, such as temperature and humidity, making the demand prediction results closer to actual operating conditions and providing strong support for developing reasonable and efficient energy allocation strategies. By combining road condition predictions obtained from intelligent driving with the energy demand model, refined prediction of vehicle energy demand is achieved, providing a scientific basis for the optimal allocation of energy.

[0063] This application embodiment first preprocesses the acquired driving status information and road condition data, including data cleaning, format conversion, and standardization, to ensure the integrity and usability of all input data. Next, based on the preprocessing results, this application embodiment applies advanced data mining and machine learning techniques to predict the specific road conditions the target vehicle will encounter on the upcoming route, forming detailed road condition prediction information. Finally, the road condition prediction information is input into the vehicle energy demand model. Through the calculation and analysis of the vehicle energy demand model, the demand prediction results for engine power and battery charging and discharging power of the target vehicle under different operating conditions are obtained. The demand prediction results will serve as an important basis for formulating the target energy allocation strategy. For example, if the road ahead has a steep gradient and a high speed limit, the predicted power demand for that section is high, and the energy allocation strategy needs to be adjusted in advance.

[0064] Based on the above optional embodiments, by preprocessing driving status information and road condition data, combining road condition prediction information, and using a vehicle energy demand model for decision analysis, accurate prediction of the target vehicle's energy demand can be achieved, thereby ensuring efficient energy utilization and effective thermal management. The introduction of preprocessing improves data quality and analytical accuracy, while road condition prediction based on preprocessing results further enhances predictive capabilities, especially in the face of complex road conditions and variable environments. Through decision analysis using the vehicle energy demand model, this application embodiment can plan the engine and battery operating modes in advance, avoiding performance degradation or thermal management failure due to energy supply and demand imbalances, thereby improving vehicle operation safety and economy.

[0065] In an optional embodiment, step S13, determining the target energy allocation strategy using demand forecasting results, driving environment data, and state of charge information includes:

[0066] Step S131: Conduct a risk assessment based on the demand forecast results, driving environment data, and state of charge information to obtain the risk assessment results;

[0067] Step S132: Determine the target energy allocation strategy based on the risk assessment results.

[0068] Specifically, based on a comprehensive analysis of demand forecast results, driving environment data, and state of charge information, this application embodiment evaluates the potential risks of vehicle thermal management in the face of different driving conditions and environmental changes, such as overheating risk and insufficient battery power risk, to ensure the safety and reliability of the thermal management strategy.

[0069] By combining demand forecasting results with real-time driving environment data and state of charge information, this approach not only considers future power demands but also assesses the impact of the current environment on thermal management and battery capacity limitations. This enables precise identification of thermal management risks, providing a scientific basis for energy allocation strategies under complex operating conditions and variable environments. It effectively prevents the risks of thermal management failure and battery overuse, significantly improving vehicle safety and stability. For example, in high-temperature and high-humidity environments, the upper limit of battery charging and discharging power can be appropriately reduced, while engine operating parameters, such as ignition advance angle and fuel injection quantity, can be adjusted to adapt to environmental changes.

[0070] Next, a target energy allocation strategy was developed based on the risk assessment results, clarifying the power output requirements of the engine and battery under different operating conditions to effectively address potential thermal management risks. The development of the target energy allocation strategy fully considered the guiding significance of the risk assessment results, enabling the optimization of thermal management while ensuring vehicle power performance, thus guaranteeing the safe operation of the vehicle under complex conditions.

[0071] Based on predicted power demand, environmental assessment results, and battery state of charge (SOC), the VCU can adjust battery charging and discharging power and engine operating mode in real time. For example, when SOC is high and power demand is low, engine power is reduced and battery discharge power is increased; when SOC is low and power demand is high, engine power output is increased while battery discharge power is reasonably controlled to ensure vehicle power performance; if SOC is too low and it is predicted that the engine alone cannot meet the wheel-end demand under the upcoming operating conditions, posing a risk of stalling, engine power output can be increased while battery charging power is reasonably controlled to ensure vehicle power performance.

[0072] Based on the above optional embodiments, proactive and intelligent control of vehicle thermal management is achieved through risk assessment and the formulation of energy allocation strategies based on the risk assessment results. Obtaining the risk assessment results ensures the effective identification and prediction of thermal management risks under different driving conditions, providing a reliable basis for subsequent energy allocation strategies. The target energy allocation strategy determined based on the risk assessment results not only optimizes the power output of the engine and battery but also flexibly adjusts energy allocation according to real-time environmental changes and vehicle status, effectively avoiding thermal management failures and battery overload.

[0073] In an optional embodiment, in step S131, a risk assessment is performed based on the demand forecast results, driving environment data, and state of charge information, and the risk assessment results include:

[0074] The demand forecast results, driving environment data, and state of charge information are fused to obtain the data fusion result.

[0075] Risk assessment results are obtained by using preset thermal management risk rules to evaluate the data fusion results.

[0076] Specifically, demand forecasting results, driving environment data, and state of charge information are deeply integrated and processed to form a comprehensive data fusion result, which is used to more comprehensively assess the overall energy and thermal management needs of the vehicle. Through complex mathematical models and algorithms, the inherent relationships and influence mechanisms between various data can be uncovered, thus providing a more accurate and comprehensive analytical foundation for subsequent risk assessment. This not only enhances the scientific nature and applicability of thermal management strategies but also greatly improves the efficiency and accuracy of energy management, ensuring rapid response under complex operating conditions and environmental changes, effectively controlling thermal management risks, and guaranteeing the safe and stable operation of the vehicle.

[0077] The aforementioned preset thermal management risk rules can be predefined algorithms or rule sets used to assess the thermal management risk of a target vehicle, covering risk thresholds and control strategies under different operating conditions, environmental states, and battery SOC levels. These preset thermal management risk rules are constructed based on extensive experimental data and professional expertise, enabling quantitative analysis of the impact of various parameters in the data fusion results on thermal management risk. Through the application of these preset thermal management risk rules, this embodiment of the application can accurately assess the thermal management risk level of the target vehicle under specific operating conditions, thereby guiding the formulation of energy allocation strategies to avoid or reduce thermal management risks. Combining data fusion results with thermal management risk assessment achieves a leap from information integration to quantitative risk analysis, providing data support for the optimization of energy management strategies. Furthermore, the rule-based risk assessment mechanism can quickly respond to changes in the environment and operating conditions, improving the flexibility and real-time performance of thermal management, and ensuring the safety and economy of the vehicle's powertrain under complex conditions.

[0078] The risk assessment process not only considers immediate power demand, but also takes into account the impact of environmental conditions and battery status on thermal management, which can generate a comprehensive risk assessment report that clarifies the thermal management risk level that the vehicle may face at different driving stages, as well as the corresponding warning and control thresholds.

[0079] Based on the above optional embodiments, by fusing demand forecast results, driving environment data and state of charge information, a data fusion result is obtained. Then, a risk assessment is performed on the data fusion result using preset thermal management risk rules to obtain a risk assessment result. This can accurately identify and quantify the thermal management risks that vehicles may encounter, providing precise guidance for the formulation of energy distribution strategies.

[0080] In an optional embodiment, step S132, determining the target energy allocation strategy based on the risk assessment results includes:

[0081] The response determines that the target vehicle does not have any target risk items based on the risk assessment results, and determines the first energy allocation strategy based on the demand forecast results, driving environment data and state of charge information;

[0082] The first energy allocation strategy is determined as the target energy allocation strategy.

[0083] Specifically, the target risk item can be a thermal management risk, such as engine overheating, insufficient battery charge, or decreased cooling system efficiency. These target risks pose a direct threat to the normal operation of the vehicle. Risk assessment is used as a prerequisite for formulating the target energy allocation strategy. That is, when the target vehicle does not have high-risk thermal management problems under specific driving conditions, a first energy allocation strategy can be determined based on demand forecasting results, driving environment data, and state of charge information. The formulation of the first energy allocation strategy comprehensively considers the target vehicle's power demand, environmental impact, and battery status, aiming to achieve efficient energy utilization and stable operation of the thermal management system. This ensures that the vehicle can operate in a better condition under no-risk or low-risk conditions, thereby avoiding overly conservative energy management under no-risk or low-risk conditions and improving the vehicle's operating economy and power performance.

[0084] Based on the above optional embodiments, by responding to the risk assessment results to determine that the target vehicle does not have a target risk item, and determining the first energy allocation strategy based on the demand forecast results, driving environment data and state of charge information, and then determining the first energy allocation strategy as the target energy allocation strategy, it can make full use of the energy of the vehicle power system and battery, improve the vehicle's operating efficiency and economy, while reducing unnecessary energy consumption and improving the efficiency of the thermal management system.

[0085] In an optional embodiment, step S132, determining the target energy allocation strategy based on the risk assessment results includes:

[0086] The response determines a target risk item for the target vehicle based on the risk assessment results, and then determines a second energy allocation strategy based on the risk time information and risk location information corresponding to the target risk item.

[0087] The second energy allocation strategy is determined as the target energy allocation strategy.

[0088] When a target vehicle presents a target risk, this application embodiment uses risk time information and risk location information as key parameters for formulating a second energy allocation strategy. This allows for advance planning and implementation of corresponding energy management measures, effectively reducing the likelihood and impact of the risk. Compared to traditional passive risk response, this application embodiment significantly enhances the initiative and flexibility of the strategy, substantially improving the efficiency of the thermal management system and the safety of vehicle operation. It avoids engine or cooling system failures caused by improper thermal management, and the resulting degradation in vehicle performance.

[0089] Upon identifying a target risk item, the energy allocation strategy can be quickly adjusted, and a second energy allocation strategy can be directly applied to the target vehicle to address and mitigate the risk. This simplifies the decision-making process, avoids risk escalation due to decision delays, and ensures the timeliness and effectiveness of thermal management measures.

[0090] The prediction system identifies potential risks along the target vehicle's travel path. By collecting signals from the acquisition module, it predicts the time period and location where these risks will occur. Combined with the vehicle's State of Charge (SOC), it controls engine power and preemptively activates the cooling system to increase cooling capacity, preventing engine overheating and ensuring the engine outputs the specified power to meet the vehicle's power demands under heavy load conditions, thus preventing stalling. For example, if the predicted travel time is 30 minutes, and a 5-minute high-risk, high-load condition is predicted at 20 minutes, leading to overheating, the risk prediction system can determine the maximum engine power for this phase. If the engine power is lower than the power limit for that condition, the battery needs to discharge to maintain vehicle power. If the initial SOC is too low, indicating a stall risk during that time period, the system can preemptively increase engine power under low load conditions to charge the battery, ensuring sufficient battery charge under heavy load conditions.

[0091] Based on the above optional embodiments, by responding to the risk assessment results to determine that the target vehicle has a target risk item, and determining the risk time information and risk location information corresponding to the target risk item, a second energy allocation strategy is determined, and then the second energy allocation strategy is determined as the target energy allocation strategy. This ensures that when facing complex driving conditions and changing environments, the target vehicle can effectively avoid or mitigate thermal management risks by adjusting the working state of the engine and battery in a timely manner, thereby ensuring the safety and stability of vehicle operation.

[0092] In one optional embodiment, the road condition data includes at least: slope information, distance information, and speed limit information corresponding to the target vehicle's travel path.

[0093] In one optional embodiment, the driving environment data includes at least: temperature information and humidity information in the driving environment of the target vehicle.

[0094] Figure 2 This is a schematic diagram of a vehicle thermal management control system according to an embodiment of this application, as shown below. Figure 2 As shown, the vehicle thermal management control system mainly includes an intelligent driving information acquisition module, an environmental information acquisition module, a battery management system, an engine control system, a vehicle control unit, and a risk prediction system. The intelligent driving information acquisition module can acquire information such as slope, distance, and speed limit of the vehicle's travel path through onboard sensors (such as cameras, millimeter-wave radar, and lidar), vehicle-to-everything (V2X) communication equipment, and high-precision maps, and transmit this information to the risk prediction system and the vehicle control unit. The environmental information acquisition module uses temperature and humidity sensors to measure the ambient temperature and humidity around the vehicle, and the data is also transmitted to the risk prediction system. The battery management system is responsible for monitoring the battery's state of charge (SOC) and sending the SOC data to the risk prediction system. The risk prediction system is responsible for predicting the overall vehicle thermal management risks, formulating thermal management strategies based on the collected data, determining the engine power limit, and outputting the results to the vehicle control unit. The engine control system can control the engine's operating mode and power output, and receive control commands from the vehicle control unit. The vehicle control unit, as the core control unit, comprehensively processes information from various modules, formulates energy management strategies, and sends corresponding control commands to the battery management system and the engine control system.

[0095] Figure 3 This is a schematic diagram of a vehicle thermal management control method according to an embodiment of this application, as shown below. Figure 3As shown, vehicle status data, road condition data, and driving environment data of the target vehicle are acquired. Vehicle status data represents the target vehicle's driving state and state of charge information. Decision analysis is performed based on the driving state and road condition data to obtain the corresponding demand forecast result for the target vehicle. The demand forecast result represents the target vehicle's power demand information in different driving segments. A risk assessment is performed based on the demand forecast result, driving environment data, and state of charge information to obtain the risk assessment result. The response determines that the target vehicle does not have a target risk item based on the risk assessment result. A first energy allocation strategy is determined based on the demand forecast result, driving environment data, and state of charge information, and is designated as the target energy allocation strategy. The response determines that the target vehicle has a target risk item based on the risk assessment result. A second energy allocation strategy is determined based on the risk time and risk location information corresponding to the target risk item, and is designated as the target energy allocation strategy.

[0096] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0097] According to an embodiment of this application, an apparatus embodiment for a vehicle thermal management control method is provided. It should be noted that the apparatus can be used to execute the above-described vehicle thermal management control method.

[0098] Figure 4 This is a structural block diagram of a vehicle thermal management control device according to an embodiment of this application, such as... Figure 4 As shown, the device includes:

[0099] The acquisition module 401 is used to acquire vehicle status data, road condition data, and driving environment data of the target vehicle, wherein the vehicle status data represents the driving status information and charge status information of the target vehicle; the analysis module 402 is used to perform decision analysis based on the driving status information and road condition data to obtain the demand prediction result corresponding to the target vehicle, wherein the demand prediction result represents the power demand information of the target vehicle in different driving segments; the determination module 403 is used to determine the target energy allocation strategy using the demand prediction result, driving environment data, and charge status information, wherein the target energy allocation strategy is used to determine the engine power demand and battery charging and discharging demand of the target vehicle; and the execution module 404 is used to control the target vehicle to execute the target energy allocation strategy.

[0100] Optionally, the analysis module 402 is further configured to: preprocess the driving status information and road condition data to obtain preprocessing results; determine the road condition prediction information corresponding to the target vehicle based on the preprocessing results; and perform decision analysis on the road condition prediction information using the vehicle energy demand model to obtain demand prediction results, wherein the vehicle energy demand model is used to determine the energy demand information of the target vehicle under different driving road conditions.

[0101] Optionally, the determining module 403 is also used to: perform a risk assessment based on the demand forecast results, driving environment data and state of charge information, and obtain a risk assessment result; and determine a target energy allocation strategy based on the risk assessment result.

[0102] Optionally, the determination module is also used to: fuse demand forecast results, driving environment data and state of charge information to obtain data fusion results; and use preset thermal management risk rules to conduct risk assessment on the data fusion results to obtain risk assessment results.

[0103] Optionally, the determining module 403 is further configured to: respond to a risk assessment result that the target vehicle does not have a target risk item, determine a first energy allocation strategy based on demand forecast results, driving environment data and state of charge information; and determine the first energy allocation strategy as the target energy allocation strategy.

[0104] Optionally, the determining module 403 is further configured to: respond to the determination that the target vehicle has a target risk item based on the risk assessment results, determine a second energy allocation strategy based on the risk time information and risk location information corresponding to the target risk item, and determine the second energy allocation strategy as the target energy allocation strategy.

[0105] Optionally, the road condition data shall include at least: the slope information, distance information and speed limit information corresponding to the target vehicle's travel path.

[0106] Optionally, the driving environment data may include at least the temperature and humidity information of the target vehicle's driving environment.

[0107] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0108] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0109] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0110] S1, acquire vehicle status data, road condition data and driving environment data of the target vehicle, wherein the vehicle status data is used to represent the driving status information and charge status information of the target vehicle.

[0111] S2, based on driving status information and road condition data, performs decision analysis to obtain the demand prediction results corresponding to the target vehicle. The demand prediction results are used to represent the power demand information of the target vehicle in different driving segments.

[0112] S3, using demand forecast results, driving environment data and state of charge information to determine the target energy allocation strategy, wherein the target energy allocation strategy is used to determine the engine power demand and battery charging and discharging demand of the target vehicle.

[0113] S4 controls the target vehicle to execute the target energy distribution strategy.

[0114] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0115] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0116] S1, acquire vehicle status data, road condition data and driving environment data of the target vehicle, wherein the vehicle status data is used to represent the driving status information and charge status information of the target vehicle.

[0117] S2, based on driving status information and road condition data, performs decision analysis to obtain the demand prediction results corresponding to the target vehicle. The demand prediction results are used to represent the power demand information of the target vehicle in different driving segments.

[0118] S3, using demand forecast results, driving environment data and state of charge information to determine the target energy allocation strategy, wherein the target energy allocation strategy is used to determine the engine power demand and battery charging and discharging demand of the target vehicle.

[0119] S4 controls the target vehicle to execute the target energy distribution strategy.

[0120] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0121] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0122] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0123] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0127] If the integrated unit is implemented as 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 this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0128] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle thermal management control method, characterized in that, include: Acquire vehicle status data, road condition data, and driving environment data of the target vehicle, wherein the vehicle status data is used to represent the driving status information and charge status information of the target vehicle; Decision analysis is performed based on the driving status information and the road condition data to obtain the demand prediction result corresponding to the target vehicle, wherein the demand prediction result is used to represent the power demand information of the target vehicle in different driving segments. A target energy allocation strategy is determined using the demand forecast results, the driving environment data, and the state of charge information, wherein the target energy allocation strategy is used to determine the engine power demand and battery charging and discharging demand of the target vehicle; Control the target vehicle to execute the target energy distribution strategy.

2. The method according to claim 1, characterized in that, Based on the driving status information and the road condition data, a decision analysis is performed to obtain the demand prediction result corresponding to the target vehicle, including: The driving status information and the road condition data are preprocessed to obtain the preprocessing result; Based on the preprocessing results, the road condition prediction information corresponding to the target vehicle is determined; The road condition prediction information is analyzed using a vehicle energy demand model to obtain the demand prediction result, wherein the vehicle energy demand model is used to determine the energy demand information of the target vehicle under different driving road conditions.

3. The method according to claim 1, characterized in that, Determining the target energy allocation strategy using the demand forecast results, the driving environment data, and the state of charge information includes: A risk assessment is performed based on the demand forecast results, the driving environment data, and the state of charge information to obtain the risk assessment results. The target energy allocation strategy is determined based on the risk assessment results.

4. The method according to claim 3, characterized in that, Based on the demand forecast results, the driving environment data, and the state of charge information, a risk assessment is performed, and the risk assessment results include: The demand forecast results, the driving environment data, and the state of charge information are fused to obtain the data fusion result. The risk assessment results are obtained by using preset thermal management risk rules to evaluate the data fusion results.

5. The method according to claim 3, characterized in that, The target energy allocation strategy determined based on the risk assessment results includes: The response determines that the target vehicle does not have any target risk items based on the risk assessment results, and determines a first energy allocation strategy based on the demand forecast results, the driving environment data, and the state of charge information; The first energy allocation strategy is determined as the target energy allocation strategy.

6. The method according to claim 3, characterized in that, The target energy allocation strategy determined based on the risk assessment results includes: The response determines that the target vehicle has a target risk item based on the risk assessment results, and determines a second energy allocation strategy based on the risk time information and risk location information corresponding to the target risk item. The second energy allocation strategy is determined as the target energy allocation strategy.

7. The method according to claim 1, characterized in that, The road condition data includes at least the following: slope information, distance information, and speed limit information corresponding to the target vehicle's travel path.

8. The method according to claim 1, characterized in that, The driving environment data includes at least the temperature and humidity information of the target vehicle's driving environment.

9. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 8.

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