Predictive battery temperature control method, device, equipment and medium

Abstract

This application discloses a predictive battery temperature control method, apparatus, device, and medium, relating to the field of battery temperature control technology. The method includes obtaining vehicle driving information, determining whether navigation is enabled based on the vehicle driving information, obtaining a determination result, and predicting the battery temperature based on the determination result and the vehicle driving information. When the predicted battery temperature exceeds a safe temperature threshold, executing a battery temperature control strategy to reduce the temperature. The battery temperature is predicted based on the driving information, and thermal management is dynamically adjusted to reduce energy consumption and ensure safety.

Description

Technical Field

[0001] The present invention relates to the technical field of battery temperature control, and in particular to a predictive battery temperature control method, device, equipment and medium. Background Art

[0002] With the rapid development of the new energy vehicle market, batteries, as core components of electric vehicles, have a significant impact on vehicle range and passenger safety. Battery thermal management, particularly in high-temperature environments, is crucial for ensuring battery life and safe operation. Traditional battery thermal management systems often rely on passive temperature control, initiating cooling only when the battery temperature reaches a fixed threshold. This not only results in delayed thermal management intervention, but also the commonly used compressor cooling method consumes a lot of energy, impacting vehicle energy efficiency and range.

[0003] The battery temperature control currently available on the market predicts the battery temperature rise during driving by analyzing navigation data or big data analysis of typical operating conditions, and controls the closing and opening of the cooling relay based on the prediction results to timely adjust the battery cooling status.

[0004] While current technology has addressed the need for remote parking to a certain extent, it still suffers from several drawbacks. It still relies on AC compressor cooling, which, while effective, consumes a lot of energy and falls short of the energy conservation and emission reduction goals pursued by new energy vehicles. While utilizing navigation data and big data analytics, predictions of driving conditions are not comprehensive and accurate, failing to fully consider more dynamic factors such as real-time traffic conditions and changes in driving habits, limiting the adaptability of thermal management strategies. The system also lacks a mechanism for dynamically adjusting thermal management thresholds, making it unable to optimize thermal management strategies based on real-time battery status, environmental conditions, and driving patterns. This can result in overcooling or inappropriate intervention, impacting battery performance and efficiency. Summary of the Invention

[0005] The main purpose of this application is to provide a predictive battery temperature control method, device, equipment and medium, aiming to solve the technical problem of how to predict the battery temperature and dynamically adjust the temperature.

[0006] To achieve the above objectives, the present application proposes a predictive battery temperature control method, which includes:

[0007] Obtain vehicle driving information;

[0008] Determine whether navigation is turned on for the vehicle based on the vehicle driving information, and obtain a determination result;

[0009] Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information;

[0010] When the predicted battery temperature exceeds a safe temperature threshold, a battery temperature control strategy is executed to perform a temperature reduction process.

[0011] Optionally, before the step of determining whether navigation is turned on for the vehicle based on the vehicle driving information and obtaining the determination result, the following steps are included:

[0012] Get driver information;

[0013] A safety temperature threshold is obtained according to the driver information and the vehicle driving information.

[0014] Optionally, the step of obtaining a safety temperature threshold according to the driver information and the vehicle driving information includes:

[0015] obtaining a driver image and account login information based on the driver information;

[0016] Identify the current driver by matching the driver's image or account login information using facial recognition technology;

[0017] Obtaining historical driving behavior data of the current driver, determining the driver's driving habits, wherein the driving habits include gentle driving and aggressive driving, wherein a preset temperature threshold for gentle driving is higher than a preset temperature threshold for aggressive driving;

[0018] Based on the driving habit, obtaining a first safety temperature threshold;

[0019] obtaining a second safety temperature threshold according to the vehicle driving information;

[0020] The first safety temperature threshold and the second safety temperature threshold are combined to obtain a safety temperature threshold.

[0021] Optionally, before the step of obtaining the predicted battery temperature based on the judgment result and the vehicle driving information, the step includes:

[0022] Obtain vehicle driving sample information and establish an initial temperature prediction model;

[0023] Training an initial temperature prediction model based on the vehicle driving sample information to obtain a preset temperature prediction model;

[0024] Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, further comprising:

[0025] According to the judgment result, the vehicle driving information is passed through a preset temperature prediction model to obtain a predicted battery temperature.

[0026] Optionally, the step of applying the vehicle driving information to a preset temperature prediction model according to the judgment result to obtain a predicted battery temperature includes:

[0027] When the result of the determination is that the vehicle is navigating, inputting vehicle driving sample information into the initial temperature prediction model to construct a first preset temperature prediction model, wherein the vehicle driving sample information includes at least destination information, real-time updates of road conditions, estimated driving distance, estimated arrival time, and weather forecast data;

[0028] The vehicle driving information is passed through a first preset temperature prediction model according to the judgment result to obtain a predicted battery temperature.

[0029] Optionally, the step of applying the vehicle driving information to a preset temperature prediction model according to the judgment result to obtain a predicted battery temperature further includes:

[0030] When it is determined that navigation is not enabled for the vehicle, a date category, a first duration, a second duration, and a third duration are obtained based on the vehicle driving sample information, wherein the date category includes weekdays and weekends, the first duration is a time during which the speed continuously exceeds a preset speed, the second duration is a time during which the vehicle is continuously driven on a highway, and the third duration is a time during which the vehicle is driven uphill;

[0031] Performing classification statistics according to the date categories to obtain the frequencies and distribution patterns of the first duration, the second duration, and the third duration under different date categories;

[0032] Constructing a second preset temperature prediction model according to the frequency and distribution law;

[0033] The vehicle driving information is passed through a second preset temperature prediction model according to the judgment result to obtain a predicted battery temperature.

[0034] Optionally, when the predicted battery temperature exceeds a safety temperature threshold, triggering a battery temperature control strategy to perform a temperature reduction process includes:

[0035] When the battery temperature is predicted to exceed the safety temperature threshold, the coolant circulation is regulated by controlling the fan and water pump to work according to the preset duty cycle, and heat exchange is performed between the environment and the coolant to cool the battery.

[0036] In addition, to achieve the above objectives, the present application also proposes a predictive battery temperature control device, the predictive battery temperature control device comprising:

[0037] An acquisition module is used to obtain vehicle driving information;

[0038] A judgment module is used to judge whether the vehicle is turned on for navigation according to the vehicle driving information and obtain a judgment result;

[0039] The acquisition module is further configured to obtain a predicted battery temperature based on the judgment result and the vehicle driving information;

[0040] The execution module is used to execute the battery temperature control strategy to perform a temperature reduction process when the predicted battery temperature exceeds a safe temperature threshold.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the steps of the predictive battery temperature control method as described above are implemented.

[0042] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the predictive battery temperature control method as described above.

[0043] This application obtains vehicle driving information, determines whether navigation is enabled based on the vehicle's driving information, and then predicts the battery temperature based on the prediction and the vehicle's driving information. When the predicted battery temperature exceeds a safe temperature threshold, the battery temperature control strategy is implemented to reduce the temperature. Based on the predicted battery temperature from driving information, dynamic thermal management is used to reduce energy consumption and ensure safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a flow chart of the first embodiment of the predictive battery temperature control method of the present application;

[0046] Figure 2 This is a flow chart of a second embodiment of the predictive battery temperature control method of the present application;

[0047] Figure 3 This is a schematic diagram of the module structure of the predictive battery temperature control device according to an embodiment of the present application;

[0048] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the predictive battery temperature control method in the embodiment of the present application.

[0049] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] The main solution of the embodiment of the present application is: by obtaining vehicle driving information, judging whether the vehicle has turned on navigation based on the vehicle driving information, obtaining a judgment result, and obtaining a predicted battery temperature based on the judgment result and the vehicle driving information. When the predicted battery temperature exceeds the safe temperature threshold, the battery temperature control strategy is executed to perform cooling processing.

[0053] Based on this, the embodiment of the present application provides a predictive battery temperature control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the predictive battery temperature control method of the present application.

[0054] In this embodiment, the predictive battery temperature control method includes steps S10 to S40:

[0055] Step S10: Acquire vehicle driving information.

[0056] It's important to note that acquiring vehicle driving information is fundamental to implementing intelligent thermal management strategies. This process involves the integrated utilization of the vehicle's sensor network, onboard electronic systems, and external data sources. First, a variety of sensors throughout the vehicle, such as GPS, speed, ambient temperature, and battery temperature sensors, collect key data such as the vehicle's location, speed, ambient temperature, and current battery temperature in real time. The GPS module not only provides precise geographic location information but also, when combined with map data, predicts upcoming road conditions, such as whether the vehicle is about to enter a mountainous area or enter a highway. This information is crucial for predicting changes in battery load. Vehicle driving information also includes the selected driving mode (e.g., Economy or Sport). The differences in motor power output in different modes directly affect battery heat generation. Furthermore, the vehicle's internal intelligent system records the driver's operating habits, such as the frequency and intensity of acceleration and braking. These habits also affect the battery's operating state and heat generation.

[0057] Step S20: determining whether navigation is enabled on the vehicle based on the vehicle driving information, and obtaining a determination result.

[0058] It should be noted that while the vehicle is in motion, the system monitors various vehicle status information in real time through the integrated connected vehicle platform, including whether the vehicle's navigation function is enabled. This determination process involves the interaction record of the vehicle's central control system and the coordinated operation of the data communication module. If the driver activates the in-vehicle navigation system and sets a destination, the system receives a corresponding signal, indicating not only the activation of route planning but also a clear direction and purpose for the upcoming journey. Before determining whether the vehicle's navigation function is enabled, driver information is first obtained; based on this driver information and vehicle driving information, a safety temperature threshold is determined.

[0059] Specifically, once navigation is enabled, the system uses detailed route information provided by the navigation system, such as destination type, road complexity, estimated driving distance, driving pattern, and altitude changes, to comprehensively analyze historical data on battery temperature rise under similar driving conditions and predict the battery temperature trend for the current trip. This prediction model relies on big data analysis and machine learning algorithms, and can be continuously optimized as more driving data accumulates, improving prediction accuracy. When navigation is disabled, the system relies on learning driving patterns on weekdays and non-weekdays, using pattern recognition algorithms to analyze the match between current driving behavior and historical typical driving conditions, indirectly inferring driving conditions and adjusting thermal management strategies. In either case, the present invention aims to use an intelligent prediction mechanism to pre-schedule the front-end cooling module, effectively controlling battery temperature in a low-energy manner and avoiding frequent compressor activation. This improves the energy efficiency and range of electric vehicles while also ensuring battery safety and lifespan.

[0060] Step S30: Obtain the predicted battery temperature based on the judgment result and the vehicle driving information.

[0061] It should be understood that before obtaining the predicted battery temperature, vehicle driving sample information is obtained and an initial temperature prediction model is established. The initial temperature prediction model is trained based on the vehicle driving sample information to obtain a preset temperature prediction model. Based on the judgment result, the vehicle driving information is passed through the preset temperature prediction model to obtain the predicted battery temperature.

[0062] If the vehicle's navigation is enabled, the system inputs sample vehicle driving information into an initial temperature prediction model to construct a first preset temperature prediction model. The sample vehicle driving information includes at least destination information, real-time road condition updates, estimated driving distance, estimated arrival time, and weather forecast data. Based on the judgment result, the vehicle driving information is passed through the first preset temperature prediction model to obtain a predicted battery temperature. Specifically, after determining whether the vehicle has navigation enabled, the system combines this information with other vehicle driving data for in-depth analysis and processing to predict the battery temperature changes during the upcoming trip. If navigation is enabled, the system carefully considers key factors in the navigation data, such as the characteristics of the destination (such as a hospital or shopping mall), the road conditions along the route (highway, national highway, bumpy roads), expected traffic congestion, driving distance, driving mode (such as economy or sport mode), and altitude changes. This information is input into the temperature prediction model, which, based on historical data learning, can identify typical battery temperature rise patterns under different operating conditions, thereby predicting the expected battery temperature changes during the trip.

[0063] If the vehicle's navigation is not enabled, the system determines the date category, first duration, second duration, and third duration based on the vehicle's driving sample information. The date categories include weekdays and weekends. The first duration is the time the vehicle's speed continuously exceeds a preset speed, the second duration is the time spent driving continuously on a highway, and the third duration is the time spent climbing a hill. Classification and statistics are performed based on the date category to determine the frequency and distribution of the first, second, and third durations for each date category. Based on the frequency and distribution patterns, a second preset temperature prediction model is constructed. Based on the determination result, the vehicle's driving information is applied to the second preset temperature prediction model to obtain a predicted battery temperature. Specifically, when navigation is disabled, the system relies on daily learning of the vehicle's driving behavior, particularly focusing on driving habits on weekdays and non-weekdays. For example, the system records and analyzes the vehicle's behavior patterns of continuous highway driving, speeds above 80 km / h, and climbing hills in mountainous areas. The frequency and duration of these behaviors are then input into the temperature prediction model to categorize the battery into corresponding typical operating conditions. By statistically analyzing the distribution patterns of these operating conditions on different dates and then inputting them into the temperature prediction model, we can predict the driving conditions most likely to be encountered on this trip and estimate the battery temperature changes accordingly.

[0064] Select a temperature prediction model based on the judgment results. Consider the real-time ambient temperature, vehicle speed, and the temperature prediction model corresponding to the current battery temperature input. Use advanced algorithms, such as neural networks or machine learning, to comprehensively analyze all the above information to obtain the predicted battery temperature.

[0065] Step S40: When the predicted battery temperature exceeds the safety temperature threshold, the battery temperature control strategy is executed to perform a temperature reduction process.

[0066] It should be understood that when the battery temperature is predicted to exceed the preset safety threshold, the system immediately triggers the battery temperature control strategy to cool the battery in an efficient and energy-saving manner. When the battery temperature is predicted to exceed the safety temperature threshold, the coolant circulation is regulated by controlling the fan and water pump to work according to the preset duty cycle, and the environment and the coolant are used for heat exchange to achieve battery cooling. Specifically, the control unit will first adjust the working mode of the front-end module cooling system, and guide the coolant into the electric drive circuit by precisely controlling the opening of the water valve. The airflow generated by the fan in the electric drive circuit is used to perform efficient heat exchange with the coolant. Compared with traditional compressor refrigeration, this process greatly reduces energy consumption and has a significant effect on improving vehicle endurance.

[0067] At the same time, the system will dynamically adjust the operating duty cycle of the fan and water pump based on multiple parameters such as the current battery temperature, coolant temperature, ambient temperature, and vehicle speed. This means that the cooling system's operating intensity will be closely matched to actual needs, avoiding resource waste caused by overcooling and ensuring that the battery can be maintained within the ideal temperature range under various operating conditions. In addition, to further optimize cooling efficiency, the system will also consider the vehicle's real-time driving status, such as climbing, high-speed driving, or low-speed urban driving, as well as external environmental conditions such as extreme weather or high altitude environments, and adjust the cooling strategy in a timely manner. For example, when driving at high speed in the hot summer, the system may increase the fan speed and coolant circulation rate, while when the temperature is lower or driving smoothly, the cooling intensity will be reduced accordingly to achieve the best state of energy saving and maintaining battery temperature balance.

[0068] Through such an intelligent temperature control strategy, the present invention can not only effectively prevent battery overheating, protect battery health and extend its service life, but also minimize energy consumption during battery thermal management without affecting vehicle performance, thereby improving the overall energy efficiency and driving experience of electric vehicles.

[0069] This embodiment provides a predictive battery temperature control method. This method obtains vehicle driving information, determines whether navigation is enabled based on the vehicle's driving information, and then uses the vehicle's driving information to predict the battery temperature. When the predicted battery temperature exceeds a safe temperature threshold, a battery temperature control strategy is implemented to reduce the temperature. By dynamically adjusting thermal management based on the predicted battery temperature based on driving information, energy consumption is reduced and safety is ensured.

[0070] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 The vehicle wake-up automatic test method further includes steps S201 to S202 before step S20:

[0071] Step S201, obtaining driver information.

[0072] It's important to note that by integrating advanced biometric technologies such as Face ID or account login systems, the vehicle can quickly identify individual drivers, a crucial step for a customized driving experience. Each driver's driving habits (such as acceleration and braking frequency, and preferred speed) vary significantly, directly impacting the vehicle's energy consumption and battery thermal load. Based on the driver's identity information, the system accesses a database of their driving behavior history. For example, a driver who frequently accelerates hard and drives at high speeds may be more susceptible to battery overheating. Therefore, the system automatically lowers the threshold for thermal management intervention, ensuring cooling is initiated before battery temperatures reach dangerous levels. Conversely, for drivers with a more moderate driving style and who prefer to use economy mode, the system can appropriately raise the threshold for thermal management intervention, reducing unnecessary battery cooling operations and thus saving energy. Furthermore, the system can combine the driver's daily routes and habits with real-time traffic information to provide more accurate battery temperature predictions. For example, if a driver often drives on congested urban roads during rush hour in the morning and evening, the system will predict the frequent start-stop and low-speed driving conditions that the battery may face, and adjust the cooling strategy in advance to deal with possible battery heat accumulation.

[0073] Step S202: Obtain a safety temperature threshold based on the driver information and vehicle driving information.

[0074] It's important to understand that the system obtains a driver image and account login information based on driver information; identifies the current driver by matching the driver image or account login information using facial recognition technology; obtains historical driving behavior data based on the current driver to determine the driver's driving habits, which include both gentle and aggressive driving, with the preset temperature threshold for gentle driving being higher than the preset temperature threshold for aggressive driving; obtains a first safety temperature threshold based on driving habits; obtains a second safety temperature threshold based on vehicle driving information; and obtains a safety temperature threshold by combining the first and second safety temperature thresholds. Specifically, it first identifies the driver's identity and then uses Face ID or account login information to match each driver's unique driving habit database. For drivers who frequently adopt an aggressive driving style, the system may lower the threshold for thermal management intervention to ensure pre-cooling of the battery before high-intensity driving. For drivers with a more moderate driving style, the threshold may be appropriately raised to reduce unnecessary system activation, thereby optimizing energy consumption. The first safety temperature threshold is determined based on the driver information. Taking into account the vehicle's real-time speed, ambient temperature, battery state of charge (SOC), and the current driving mode (e.g., Economy or Sport), the system intelligently analyzes historical data for cases similar to the current operating conditions to calculate a second safety temperature threshold. By combining the first and second safety temperature thresholds to determine the final safety temperature threshold, this approach allows for more flexible response to the battery's thermal management needs under varying driving conditions, avoiding energy waste due to overcooling.

[0075] This embodiment obtains driver information and obtains a safe temperature threshold based on the driver information and the vehicle driving information, ensuring that the battery always operates in the most suitable temperature range in various complex environments, thereby extending battery life and improving vehicle performance and endurance.

[0076] This application also provides a predictive battery temperature control device, please refer to Figure 3 , the device comprises:

[0077] An acquisition module 10 is used to acquire vehicle driving information;

[0078] A judgment module 20 is used to judge whether the vehicle is turned on for navigation according to the vehicle driving information and obtain a judgment result;

[0079] The acquisition module 10 is further configured to obtain a predicted battery temperature based on the judgment result and the vehicle driving information;

[0080] The execution module 30 is configured to execute a battery temperature control strategy to perform a temperature reduction process when the predicted battery temperature exceeds a safety temperature threshold.

[0081] The predictive battery temperature control device provided in this application, utilizing the predictive battery temperature control method described in the aforementioned embodiments, can address the technical problem of predicting battery temperature and dynamically adjusting it. Compared to the prior art, the predictive battery temperature control device provided in this application achieves the same beneficial effects as the predictive battery temperature control method described in the aforementioned embodiments. Other technical features of the predictive battery temperature control device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0082] In one embodiment, the acquisition module 10 is further configured to acquire driver information; and obtain a safety temperature threshold value based on the driver information and vehicle driving information.

[0083] In one embodiment, the acquisition module 10 is also used to obtain a driver image and account login information based on the driver information; identify the current driver by matching the driver image or account login information through facial recognition technology; obtain historical driving behavior data based on the current driver to determine the driver's driving habits, which include gentle driving and aggressive driving, where the preset temperature threshold for gentle driving is higher than the preset temperature threshold for aggressive driving; obtain a first safety temperature threshold based on the driving habits; obtain a second safety temperature threshold based on the vehicle driving information; and obtain a safety temperature threshold by combining the first safety temperature threshold and the second safety temperature threshold.

[0084] In one embodiment, the acquisition module 10 is also used to obtain vehicle driving sample information and establish an initial temperature prediction model; the initial temperature prediction model is trained according to the vehicle driving sample information to obtain a preset temperature prediction model; the predicted battery temperature is obtained according to the judgment result and the vehicle driving information, and also includes: according to the judgment result, the vehicle driving information is passed through the preset temperature prediction model to obtain the predicted battery temperature.

[0085] In one embodiment, the acquisition module 10 is also used to input the vehicle driving sample information into the initial temperature prediction model when the judgment result is that the vehicle starts navigation, and construct a first preset temperature prediction model. The vehicle driving sample information includes at least destination information, real-time updates of road conditions, estimated driving distance, estimated arrival time and weather forecast data; according to the judgment result, the vehicle driving information is passed through the first preset temperature prediction model to obtain the predicted battery temperature.

[0086] In one embodiment, the acquisition module 10 is further used to obtain a date category, a first duration, a second duration, and a third duration based on the vehicle driving sample information when the judgment result is that the navigation is not turned on for the vehicle, wherein the date category includes weekdays and weekends, the first duration is the time when the speed continuously exceeds the preset speed, the second duration is the time of continuous driving on the highway, and the third duration is the time of climbing. Classification statistics are performed according to the date category to obtain the frequency and distribution pattern of the first duration, the second duration, and the third duration under different date categories; a second preset temperature prediction model is constructed based on the frequency and distribution pattern; and the vehicle driving information is passed through the second preset temperature prediction model based on the judgment result to obtain the predicted battery temperature.

[0087] In one embodiment, the execution module 30 is further configured to control the coolant circulation by controlling the fan and the water pump to operate according to a preset duty cycle when the battery temperature is predicted to exceed a safety temperature threshold, thereby utilizing heat exchange between the environment and the coolant to cool the battery.

[0088] The present application provides a predictive battery temperature control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the predictive battery temperature control method in the above-mentioned embodiment 1.

[0089] Reference below Figure 4 , which shows a schematic diagram of the structure of a predictive battery temperature control device suitable for implementing embodiments of the present application. The predictive battery temperature control device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The predictive battery temperature control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0090] like Figure 4As shown, the predictive battery temperature control device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the predictive battery temperature control device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. Communication device 1009 may allow the predictive battery temperature control device to communicate with other devices wirelessly or by wire to exchange data. While the figure illustrates a predictive battery temperature control device with various systems, it should be understood that implementation or presence of all illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0091] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0092] The predictive battery temperature control device provided in this application, utilizing the predictive battery temperature control method described in the aforementioned embodiment, can address the technical problem of predicting battery temperature and dynamically adjusting it. Compared to the prior art, the predictive battery temperature control device provided in this application achieves the same beneficial effects as the predictive battery temperature control method described in the aforementioned embodiment. Other technical features of this predictive battery temperature control device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0093] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0094] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0095] The present application provides a computer-readable medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the predictive battery temperature control method in the above-mentioned embodiment.

[0096] The computer-readable medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0097] The computer-readable medium may be included in the predictive battery temperature control device, or may exist independently without being incorporated into the predictive battery temperature control device.

[0098] The computer-readable medium carries one or more programs that, when executed by the predictive battery temperature control device, enable the predictive battery temperature control device to write computer program code for performing the operations of the present application in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0099] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0100] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0101] The computer-readable medium provided in this application is a computer-readable medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned predictive battery temperature control method. This computer-readable medium addresses the technical problem of predicting battery temperature and dynamically adjusting it. Compared to the prior art, the computer-readable medium provided in this application offers the same beneficial effects as the predictive battery temperature control method provided in the aforementioned embodiment, and will not be further elaborated upon here.

[0102] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned predictive battery temperature control method when executed by a processor.

[0103] The computer program product provided in this application can solve the technical problem of predicting battery temperature and dynamically adjusting it. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the predictive battery temperature control method provided in the above embodiment, and will not be elaborated here.

[0104] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A predictive battery temperature control method, characterized in that: The method comprises: Obtain vehicle driving information; Determine whether navigation is turned on for the vehicle based on the vehicle driving information, and obtain a determination result; Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information; Before the step of obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, the method includes: Obtain vehicle driving sample information and establish an initial temperature prediction model; Training an initial temperature prediction model based on the vehicle driving sample information to obtain a preset temperature prediction model; Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, further comprising: According to the judgment result, the vehicle driving information is passed through a preset temperature prediction model to obtain a predicted battery temperature; The step of applying the vehicle driving information to a preset temperature prediction model according to the judgment result to obtain a predicted battery temperature includes: When the result of the determination is that the vehicle is navigating, inputting vehicle driving sample information into the initial temperature prediction model to construct a first preset temperature prediction model, wherein the vehicle driving sample information includes at least destination information, real-time updates of road conditions, estimated driving distance, estimated arrival time, and weather forecast data; According to the judgment result, the vehicle driving information is passed through a first preset temperature prediction model to obtain a predicted battery temperature; The step of applying the vehicle driving information to a preset temperature prediction model according to the judgment result to obtain a predicted battery temperature further includes: When it is determined that navigation is not enabled for the vehicle, a date category, a first duration, a second duration, and a third duration are obtained based on the vehicle driving sample information, wherein the date category includes weekdays and weekends, the first duration is a time during which the speed continuously exceeds a preset speed, the second duration is a time during which the vehicle is continuously driven on a highway, and the third duration is a time during which the vehicle is driven uphill; Performing classification statistics according to the date categories to obtain the frequencies and distribution patterns of the first duration, the second duration, and the third duration under different date categories; Constructing a second preset temperature prediction model according to the frequency and distribution law; According to the determination result, the vehicle driving information is passed through a second preset temperature prediction model to obtain a predicted battery temperature; When the predicted battery temperature exceeds a safe temperature threshold, a battery temperature control strategy is executed to perform a temperature reduction process.

2. The method according to claim 1, wherein Before the step of determining whether the vehicle is turned on for navigation based on the vehicle driving information and obtaining the determination result, the method includes: Get driver information; A safety temperature threshold is obtained according to the driver information and the vehicle driving information.

3. The method according to claim 2, wherein The step of obtaining a safety temperature threshold based on the driver information and the vehicle driving information includes: obtaining a driver image and account login information based on the driver information; Identify the current driver by matching the driver's image or account login information using facial recognition technology; Obtaining historical driving behavior data of the current driver, determining the driver's driving habits, wherein the driving habits include gentle driving and aggressive driving, wherein a preset temperature threshold for gentle driving is higher than a preset temperature threshold for aggressive driving; Based on the driving habit, obtaining a first safety temperature threshold; obtaining a second safety temperature threshold according to the vehicle driving information; The first safety temperature threshold and the second safety temperature threshold are combined to obtain a safety temperature threshold.

4. The method according to claim 1, wherein The step of triggering a battery temperature control strategy to perform a temperature reduction process when the predicted battery temperature exceeds a safety temperature threshold comprises: When the battery temperature is predicted to exceed the safety temperature threshold, the coolant circulation is regulated by controlling the fan and water pump to work according to the preset duty cycle, and heat exchange is performed between the environment and the coolant to cool the battery.

5. A predictive battery temperature control device, characterized in that: The device comprises: An acquisition module is used to obtain vehicle driving information; A judgment module is used to judge whether the vehicle is turned on for navigation according to the vehicle driving information and obtain a judgment result; The acquisition module is further configured to obtain a predicted battery temperature based on the judgment result and the vehicle driving information; The acquisition module is also used to obtain vehicle driving sample information and establish an initial temperature prediction model; Training an initial temperature prediction model based on the vehicle driving sample information to obtain a preset temperature prediction model; Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, further comprising: According to the judgment result, the vehicle driving information is passed through a preset temperature prediction model to obtain a predicted battery temperature; The acquisition module is further configured to input vehicle driving sample information into the initial temperature prediction model to construct a first preset temperature prediction model when the result of the determination is that the vehicle is navigating, wherein the vehicle driving sample information includes at least destination information, real-time updates of road conditions, estimated driving distance, estimated arrival time, and weather forecast data; According to the judgment result, the vehicle driving information is passed through a first preset temperature prediction model to obtain a predicted battery temperature; The acquisition module is further configured to, when a result of determining that navigation is not enabled for the vehicle, obtain a date category, a first duration, a second duration, and a third duration based on the vehicle driving sample information, wherein the date category includes weekdays and weekends, the first duration is a time during which a speed continuously exceeds a preset speed, the second duration is a time during which the vehicle is driven continuously on a highway, and the third duration is a time during which the vehicle is driven uphill; Performing classification statistics according to the date categories to obtain the frequencies and distribution patterns of the first duration, the second duration, and the third duration under different date categories; Constructing a second preset temperature prediction model according to the frequency and distribution law; According to the determination result, the vehicle driving information is passed through a second preset temperature prediction model to obtain a predicted battery temperature; The execution module is used to execute the battery temperature control strategy to perform a temperature reduction process when the predicted battery temperature exceeds a safe temperature threshold.

6. A predictive battery temperature control device, characterized in that: The device includes: a memory, a processor, and a predictive battery temperature control program stored in the memory and executable on the processor, wherein the predictive battery temperature control program is configured to implement the steps of the predictive battery temperature control method according to any one of claims 1 to 4.

7. A medium, characterized in that The medium stores a predictive battery temperature control program, which, when executed by a processor, implements the steps of the predictive battery temperature control method according to any one of claims 1 to 4.

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