An Adaptive Electric Vehicle Battery Thermal Management Control Method Based on Big Data and Driving Habits

Abstract

This invention provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits, comprising: after user login, obtaining relevant information based on whether the in-vehicle navigation mode is enabled, including actual road condition information and vehicle status information in navigation mode, and vehicle status information and virtual map matrix when navigation mode is not enabled; performing abnormal data cleaning; during driving, intelligently matching the map information in navigation mode or the virtual map matrix in non-navigation mode with the vehicle thermal management model of the cloud user service platform, calling the initial thermal management model of the same or similar road environment and vehicle status to the vehicle; the vehicle obtaining the battery thermal management target optimized temperature matrix and battery thermal management model based on battery information data related to driving habits; executing the battery thermal management model in real time, and simultaneously uploading to the cloud platform. This invention effectively adapts to the thermal management needs arising from different driving habits and road conditions.

Description

Technical Field

[0001] This invention relates to the fields of electric vehicles and intelligent vehicles, and in particular to an adaptive electric vehicle battery thermal management control method based on big data and driving habits. Background Technology

[0002] In the current electric vehicle (EV) field, at least the following problems exist: the discharge of EV batteries is significantly affected by ambient temperature, especially in cold winter weather. At low temperatures, the performance of the battery cells degrades, increasing the battery's thermal requirements to maintain a suitable operating temperature. This necessitates the activation and heating of the battery through thermal management. Current mainstream battery thermal management technologies use a fixed battery temperature threshold as the control for its activation and deactivation. However, this control mode is not flexible enough. Due to the complexity of actual driving environments, road conditions and driver habits place varying demands on vehicle thermal management. For complex environments, heating the battery to a fixed target temperature results in additional energy consumption, leading to reduced driving range and low energy efficiency. Therefore, existing technologies require improvement and development.

[0003] The environment in which the power battery system operates and the battery's own temperature directly affect its output power and battery capacity, especially in winter when low temperatures can lead to a decline in cell performance. To achieve optimal battery system performance and lifespan, a thermal management system is needed to control the battery temperature within a suitable range. The current mainstream battery thermal management control method involves setting a target battery temperature to control the on / off operation of corresponding electric vehicle thermal management heating components, converting electrical energy into heat energy to maintain the power battery within the set target temperature range. However, different road conditions and driver habits result in different vehicle states and thus different thermal management requirements. Maintaining the battery within the set temperature range continuously can lead to high additional energy consumption during prolonged thermal management.

[0004] Because vehicle operating conditions are complex and different drivers have different driving habits, simply using a constant threshold for repeated battery heating often excessively depletes the battery's energy, further reducing the vehicle's range in winter. Especially considering different road conditions and varying driving habits among drivers under the same conditions, the resulting vehicle status differs, leading to varying thermal management needs. A constant thermal management strategy often lacks flexibility. For example, the same heating method might be effective for battery heating on highways but inefficient in congested urban areas. Furthermore, on the same highway in winter, with a speed limit of 120 kph, the driving habits of a driver operating at 130 kph differ from those of a driver operating at 100 kph, resulting in different thermal management requirements for the vehicle.

[0005] Therefore, battery thermal management strategies based on driving habits have emerged, which can effectively solve the problem of insufficient flexibility in thermal management under different driving habits in existing technologies, and can effectively extend the driving range as much as possible while meeting the driver's driving habits. Summary of the Invention

[0006] It should be understood that the general description above and the detailed description below are exemplary and illustrative, and are intended to provide further explanation of this disclosure.

[0007] To address the aforementioned issues, this invention provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits. This method can effectively adapt to the driving habits and road conditions of drivers, thereby improving the flexibility of the thermal management model.

[0008] This invention provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits, characterized by comprising:

[0009] S101, After the user logs in, relevant information is obtained based on whether the in-vehicle navigation mode is enabled. The relevant information includes actual road conditions and vehicle status information under navigation mode, and vehicle status information and virtual map matrix when navigation mode is not enabled.

[0010] S102, perform abnormal data cleaning, retain real-time data that is relevant to the thermal management control method and has timeliness;

[0011] S103, During driving, the map information in the navigation mode or the virtual map matrix in the non-navigation mode is intelligently matched with the vehicle thermal management model of the cloud user service platform, and the initial thermal management model with the same or similar road environment and vehicle status is called to the vehicle. The vehicle obtains the battery thermal management target optimized temperature matrix and battery thermal management model based on battery information data related to driving habits.

[0012] S104, executes the battery thermal management model in real time and uploads the data to the cloud platform until the vehicle reaches its destination.

[0013] Preferably, the present invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits, characterized in that step S103 further includes:

[0014] Step S301: Collect and process data in navigation mode and non-navigation mode to form matching physical parameters;

[0015] Step S302, intelligent matching of the cloud service platform based on the physical parameters and the vehicle's own state information, including inputting the physical parameters into the intelligent matching model, filtering and matching the thermal management model library of each road segment information based on machine learning, thereby predicting and locking the specific thermal management model in the cloud that is suitable for each road segment, forming the initial model of the current battery thermal management;

[0016] Step S303: Based on the battery information data in the vehicle's own status information, a reverse cell database lookup is performed to select the lowest temperature value in the suitable cell temperature range, forming the battery thermal management target temperature optimization matrix 1 for the road segment; when a road segment is identified where the brake pedal accelerates rapidly, the braking frequency is high, and the braking travel is deep, a reverse cell database lookup is performed to determine the cell target temperature range suitable for the current braking and charging state, and the lowest value in the cell temperature range is taken to form the battery thermal management target temperature optimization matrix 2 for the braking road segment;

[0017] The cell database is a calibration data matrix of the cells used in the vehicle. The cell database determines the cell temperature output range to maintain the current battery output power and SOC state.

[0018] The thermal management target temperature optimization matrix 1 includes a smooth driving road scenario, and the thermal management target temperature optimization matrix 2 includes a multi-braking road segment or a predicted traffic intersection or special road segment scenario.

[0019] Step S304: The latest thermal management model is matched and stored.

[0020] Preferably, the present invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits, characterized in that, after step S103, it further includes:

[0021] The system performs a self-check and determines whether the discharge capacity is below the battery's low-temperature discharge threshold. If so, it prioritizes heating the battery to a suitable operating temperature range.

[0022] Preferably, the present invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits. The method is characterized in that, in step S302, the cloud service platform includes the physical parameter classification and an indexable battery thermal management model library. The cloud-based intelligent matching should, when the thermal management model and the physical parameters are input into the intelligent matching model, perform cloud-based screening and matching of thermal management model library information for each road segment based on machine learning, in order to predict and lock the thermal management model of each road segment that is compatible with the cloud service platform.

[0023] Preferably, the present invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits. The method is characterized in that, in step S301, the data in the navigation mode includes the origin, destination, current time, current vehicle position, current vehicle model, current weather conditions, and current air resistance, kinetic energy, potential energy, regenerative braking energy, current power demand, and battery information obtained by processing vehicle state information such as vehicle speed, battery status, model parameters, brake pedal depth, and acceleration. The data in the non-navigation mode includes all or part of the parameters from a virtual map of the vehicle, representing a navigation-like state.

[0024] Preferably, the present invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits. The virtual map matrix is ​​formed by collecting the vehicle's own state information in non-navigation mode to assist in vehicle state determination and environmental perception. The virtual map matrix only expands a road segment based on the current road segment prediction, and expands the next predicted road segment after completing the journey.

[0025] Preferably, this invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits, characterized in that the thermal management model is implemented in the following ways:

[0026] The battery can be heated by any one of the following methods: pulse current, heat pump, waste heat recovery, or water cooling system.

[0027] Preferably, the present invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits, characterized in that the map information in the navigation mode comes from the navigation map module, and the map information in the non-navigation mode is the virtual map matrix obtained from the vehicle's own state information.

[0028] Preferably, the present invention further provides an adaptive electric vehicle battery thermal management control method based on big data and driving habits, characterized in that the vehicle's own state information includes, but is not limited to, battery state parameters, GPS positioning, pedal displacement depth, pedal acceleration, waiting time at traffic lights, radar sensors, and laser sensors.

[0029] Compared with the prior art, the present invention has the following advantages: it can effectively adapt to the thermal management needs generated by different driving habits and road conditions, improve the flexibility of the thermal management model, reduce unnecessary energy consumption in winter, and extend its winter driving range as much as possible while meeting driving needs and habits. Attached Figure Description

[0030] Embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. Preferred embodiments of the present disclosure will now be described in detail, examples of which are illustrated in the drawings. Wherever possible, the same reference numerals will be used in all the drawings to denote the same or similar parts. Furthermore, although the terminology used in this disclosure is selected from commonly known and used terminology, some terms referenced in this disclosure may have been chosen by the applicant at his or her judgment, and their detailed meanings are explained in the relevant sections of the description herein. Moreover, this disclosure should be understood not only by the actual terms used, but also by the meaning implied by each term.

[0031] The above and other objects, features and advantages of the present invention will become apparent to those skilled in the art from the detailed description thereof, with reference to the accompanying drawings.

[0032] Figure 1 The flowchart shows the adaptive electric vehicle battery thermal management control method based on big data and driving habits according to the present invention.

[0033] Figure 2 yes Figure 1 Further specific process steps in step S105. Detailed Implementation

[0034] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0035] First, the application scenarios of this invention are described. This invention can be applied to vehicle battery heating scenarios in low-temperature environments (such as winter). In such scenarios, the low ambient temperature causes a decline in the performance of the battery cells, potentially affecting discharge power and capacity. Therefore, in current technology, a specific heating method is generally used. Heating is activated when the battery temperature is detected to be below a predetermined threshold, and then turned off after the power battery is heated to a fixed target temperature, thereby increasing the battery temperature. This heating method can be repeatedly activated throughout the entire driving range to maintain the battery's temperature.

[0036] Compared to the traditional approach of repeatedly heating the battery using a constant threshold, this invention enables a more precise thermal management strategy that meets driving needs while minimizing the additional energy consumption of thermal management heating, thus helping to improve the overall efficiency of vehicle operation in winter.

[0037] The following is a detailed explanation with reference to the attached diagram.

[0038] Figure 1 This is a flowchart of the adaptive electric vehicle battery thermal management control method based on big data and driving habits according to the present invention.

[0039] S100, User Login, Enter User Information. The user information should include the current user ID and information such as whether the user is a frequently used user.

[0040] S101, Determine whether the user has enabled the in-vehicle navigation mode. This operation is completed by the user information acquisition module. It is usually done by the driver after entering the car and starting the vehicle, logging into the user page, and determining whether navigation needs to be turned on.

[0041] S102, when the user selects navigation mode, in the control method adopted by this invention, after the user inputs the destination in the system, based on the possible route planning and road conditions involved in the target trip, the driver selects a specific route. Simultaneously, the map module connects with the intelligent interconnection system through a communication network and sends the collected road condition information to the intelligent interconnection system. The intelligent interconnection system segments the navigation route, parses the road condition information, extracts the actual road condition information, and sends the extracted road condition information to the vehicle's control unit via the CAN bus for preprocessing. At the same time, it acquires the vehicle's own status information and synchronously feeds it back to the vehicle control unit for processing.

[0042] The extracted actual road condition information should include: visibility data of the current road segment where the vehicle is located, the speed limit range of the current road segment, the vehicle model, traffic flow information of the current road segment, the estimated distance and time to reach the destination, whether there are congested sections, the estimated time for congested sections, and the estimated distance and time to reach the nearest charging station, etc. These signals are obtained from the intelligent navigation system's big data platform, and the relevant navigation information is displayed in real time on the central control screen.

[0043] In addition, the vehicle collects its own status information to assist in vehicle status determination and environmental perception. This status information should not be limited to the current state of technological development, but includes, but is not limited to, battery status parameters, GPS positioning (if applicable), pedal displacement depth, pedal acceleration, waiting time at traffic lights, radar sensors (if applicable), laser sensors (if applicable), and other advanced vehicle status, vehicle-to-vehicle connectivity, or environmental perception technologies that may emerge in the future.

[0044] S103, when the user does not activate navigation, there will be a lack of data transmission between the intelligent connectivity system and the cloud map module. Instead, the system will only collect vehicle status information to assist in vehicle status determination and environmental perception, forming a virtual map matrix in the current vehicle controller. Since the destination is unknown, this virtual map matrix will only predict and expand a section of the road based on the current route. After completing this journey, the next predicted road segment will be expanded. Similar to the situation when navigation is enabled, the vehicle's status information in the no-navigation mode is not limited to current technological advancements, but includes, but is not limited to, battery status parameters, GPS positioning (if applicable), pedal displacement depth, pedal acceleration, waiting time at traffic lights, radar sensors (if applicable), laser sensors (if applicable), and other advanced vehicle status, vehicle-to-vehicle connectivity, or environmental perception technologies that may emerge in the future.

[0045] S104, perform abnormal data cleaning;

[0046] In navigation mode, acquired road data is converted into valid electric vehicle controller-related data and anomaly cleaning is performed to effectively prevent errors in the vehicle's perception of its surroundings. Similarly, in non-navigation mode, the vehicle's own state information needs to be converted into valid electric vehicle controller-related data and anomaly cleaning is performed to prevent unreasonable parameters from appearing when forming the virtual map matrix. Removing anomaly data is necessary in this step; other data that is not related to thermal management control methods or is outdated is deleted to maintain the current vehicle thermal management strategy state, ensure the system can function normally, and also obtain a new real-time database.

[0047] S105, Cloud-based thermal management model matching and invocation, local optimization, and subsequent storage. This step applies to cloud-based access thermal management strategies.

[0048] This invention defaults to a cloud-based user service platform, which can access and interact with the vehicle's local network. During and at the start of vehicle operation, the vehicle controller intelligently matches information collected from the navigation map module with the cloud-based vehicle thermal management model. In non-navigation mode, the map module information should originate from the virtual map matrix formed by the controller in step S103. Through interaction between the local vehicle and the cloud-based user service platform, it is possible to determine if a thermal management model with the same or similar road environment and vehicle status is available for use.

[0049] Furthermore, since different driver styles and preferences can influence thermal management strategies, even on the same road, different thermal management strategies may be applied. Therefore, intelligent matching and model optimization are necessary. This step considers both big data from the cloud-based user service platform and driving habits for optimization.

[0050] Figure 2 The detailed implementation of step S105 is given below, with reference to the diagram:

[0051] Step S301: Collect and process data to form matching physical parameters;

[0052] In navigation mode, relevant physical parameters include: origin, destination, current time, current vehicle position, current vehicle model, current weather conditions, and current air resistance, kinetic energy, potential energy, regenerative braking energy, current power demand, and battery information, all derived from vehicle status information such as speed, battery status, model parameters, brake pedal depth, and acceleration. In non-navigation mode, all or some of these parameters are lacking; the physical parameters in this step are virtualized using the vehicle virtual map in step S103.

[0053] Step S302 involves intelligent matching with the cloud service platform, specifically:

[0054] The core of the intelligent matching system is a physical model, which is composed of multiple main variables, including road information, air resistance, vehicle kinetic energy, vehicle potential energy, vehicle regenerative braking energy, battery information, and vehicle current power demand, which are obtained based on the above navigation state parameters and vehicle's own state information. These are input into the intelligent matching model by the local vehicle controller through the cloud service platform.

[0055] In this invention, the cloud service platform should possess a battery thermal management model library that is categorized based on the aforementioned key parameters and is indexable. This battery thermal management model library should be referable, downloadable, and compatible with the vehicle's local infotainment system. The number of key parameters may differ between navigation and non-navigation modes; therefore, the intelligent model matching should be compatible to identify the initially applicable thermal management model for subsequent model matching.

[0056] The cloud-based intelligent matching system should, when inputting relevant models and parameters into the intelligent matching model, use machine learning to filter and match thermal management model libraries for each road segment information in the cloud, thereby predicting and locking the specific cloud-based thermal management model suitable for each road segment, thus forming the current initial battery thermal management model.

[0057] Step S303: After referencing the initial thermal management model to the local vehicle via cloud-to-local vehicle interaction, optimize the thermal management strategy based on local vehicle information.

[0058] In this step, different driver styles and preferences will affect different vehicle power requirements and thermal management requirements. Therefore, even on the same road, there may still be different thermal management strategies. Thus, this step requires an evaluation of the battery's own power and discharge capacity to obtain thermal management performance that is more in line with local battery characteristics and driver habits.

[0059] The battery management system (BMS) retrieves battery information data from the vehicle's own status information, such as output power, battery temperature, battery SOC, and vehicle speed, and performs a reverse lookup in the cell database. This cell database is a calibration data matrix of the cells used in the vehicle. Based on the current battery output power and SOC, the database determines the cell temperature output range needed to maintain that state. The lowest temperature value within this suitable cell temperature range is then taken to form the target matrix 1 for optimizing the battery thermal management target temperature for a specific road segment.

[0060] Furthermore, in order to more effectively identify the frequent charging and discharging states of vehicles caused by different braking energy demands resulting from different driving habits, when a road segment is identified where the brake pedal accelerates rapidly, the braking frequency is high, and the braking travel is deep, the battery BMS should perform a reverse lookup of the cell database using the vehicle information obtained, such as charging power, vehicle SOC, battery temperature, and vehicle speed. This database is also the calibration charging data matrix of the telecommunications company used by the vehicle. The target temperature range of the cells suitable for the current braking and charging state is determined from this database, and the lowest value of the cell temperature range is taken to form the battery thermal management target optimization temperature matrix 2 for that braking segment.

[0061] In a preferred embodiment, the braking energy recovery road segments are often closely related to road conditions and the location of traffic intersections. Therefore, during the optimization of the current initial thermal management model, it is necessary to divide the road segments. Thermal management target temperature optimization matrix 1 is used in the driving road, while thermal management target temperature optimization matrix 2 is used in multi-braking road segments, predicted traffic intersections, or special road segments. Based on this, a local thermal management optimization model 1 can be formed.

[0062] In navigation-free mode, the braking section should be judged and predicted by combining local virtual maps with brake pedal travel, acceleration, and frequency. In the future, related environmental perception technologies can assist in judging road conditions to better determine the needs of turning, traffic intersections, and special road sections.

[0063] In a preferred embodiment, to prevent the thermal management target temperature from fluctuating excessively due to overly dense detection, it is necessary to divide the State of Charge (SOC) into intervals and determine the cyclical temperature adaptation value for the SOC that varies within a certain interval. A threshold for the thermal management target temperature variation interval is determined, forming a thermal management target temperature optimization matrix 2 based on the SOC segments within a road segment. Subsequently, the thermal management model is optimized based on the thermal management target temperature matrix of the road segment and SOC segmentation, further dividing the model to obtain the optimized thermal management model 2.

[0064] The aforementioned thermal management model matching and optimization process should involve real-time optimization and storage of the local model, and upon completion, a thermal management model applicable to the current trip and driver should be generated.

[0065] Step S304: Matching and storing the latest thermal management model.

[0066] The development of a new thermal management model requires parameterization and labeling. The model parameters should reference the input parameters in model matching (S303) to facilitate future calls to the thermal management model. To improve calling efficiency, road segments with a certain number of repetitions should be recorded as commuter routes in the thermal management model. In subsequent calls, these routes can be accurately referenced by identifying GPS vehicle locations in navigation-free mode.

[0067] Through the above-mentioned intelligent matching and optimized thermal management model, different vehicle states caused by different driving habits and road conditions can be effectively identified, and appropriate thermal management models can be matched to them. This effectively ensures that different drivers with different driving habits can be matched with appropriate thermal management strategies under different road conditions in winter.

[0068] In other words, different drivers on the same road segment will produce different driving states, and even different drivers on the same road segment may produce the same driving states. Furthermore, in urban conditions, the complexity of roads and the diversity of driving habits require a more precise and flexible thermal management system to reduce additional energy consumption. Therefore, road data and the vehicle states generated by drivers are digitized and modeled, and the data is processed to derive a thermal management mode that matches driving habits.

[0069] Optionally, the thermal management model can be implemented in the following ways:

[0070] Preferably, the battery is heated by a pulsed current;

[0071] Alternatively, the battery is heated by a heat pump;

[0072] Preferably, the battery is heated by waste heat recovery;

[0073] Alternatively, the battery can be heated using a water-cooling system.

[0074] S106: The thermal management model obtained in step S105 is a thermal management matching model adapted to the driver's driving needs. However, it is necessary to consider the possibility that the release of energy decreases at extremely low SOC and low temperatures, leading to a decrease in driving range. Therefore, a judgment step is needed to set a battery low-temperature discharge threshold and a database of temperatures. This database contains the battery charge retention values ​​under different ambient temperatures. When the cumulative released energy reaches the threshold at this temperature, the thermal management model loop exits and proceeds to the next step.

[0075] In steps S107 and S106, if the self-test result indicates that the battery's discharge level is within the low-temperature operating range, heating the battery to a suitable operating temperature range under low temperature is the first priority within this SOC range, and a thermal management strategy matching driving habits is the second priority. Full-power battery heating is initiated to heat the battery to a range where its discharge level is unaffected by the ambient temperature, ensuring maximum discharge and preventing driver range anxiety. This step avoids extreme environments: low temperature, low charge, and low output occurring simultaneously, and high output at high vehicle speeds, which would cause the battery to discharge excessively in traffic jams, thus preventing temperature increases.

[0076] S108, if step S106 determines that the battery's discharge capacity is not within the affected low-temperature operating range, then the current suitable thermal management mode for driving habits is determined and formed and executed in real time, and simultaneously uploaded to the cloud platform.

[0077] S109, the thermal management control module controls the relevant components to execute the battery thermal management strategy until the vehicle reaches its destination.

[0078] After the above journey is completed, the vehicle controller performs system identification and inductive self-learning on the thermal management model, identifies high-frequency road segments in the system at the same time, determines the driver's braking depth frequency and speed acceleration preferences, etc., and can realize the rapid calling and optimization of subsequent models.

[0079] The hardware modules involved in this implementation should include a vehicle controller, a cloud server, and a battery thermal management controller for interacting and processing information with the cloud server and the vehicle thermal management controller, and for the battery thermal management controller to determine and execute thermal management strategies and for related thermal management components to perform the final execution.

[0080] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0081] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0082] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0083] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0084] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0085] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0086] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0087] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0088] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0089] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0090] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0091] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0092] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0093] Although this application has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of this application. Therefore, any changes or modifications to the above embodiments within the essential spirit of this application will fall within the scope of the claims of this application.

Claims

1. An adaptive electric vehicle battery thermal management control method based on big data and driving habits, characterized in that, include: S101, After the user logs in, relevant information is obtained based on whether the in-vehicle navigation mode is enabled. The relevant information includes actual road conditions and vehicle status information under navigation mode, and vehicle status information and virtual map matrix when navigation mode is not enabled. S102, perform abnormal data cleaning, and retain real-time data that is relevant to the thermal management control method and has timeliness; S103, During driving, the map information in the navigation mode or the virtual map matrix in the non-navigation mode is intelligently matched with the vehicle thermal management model of the cloud user service platform, and the initial thermal management model with the same or similar road environment and vehicle status is called to the vehicle. The vehicle obtains the battery thermal management target optimized temperature matrix and battery thermal management model based on battery information data related to driving habits. S104, execute the battery thermal management model in real time and upload it to the cloud platform until the vehicle reaches its destination; Step S103 further includes: Step S301: Collect and process data in navigation mode and non-navigation mode to form matching physical parameters; Step S302, intelligent matching of the cloud service platform based on the physical parameters and the vehicle's own state information, including inputting the physical parameters into the intelligent matching model, filtering and matching the thermal management model library of each road segment information based on machine learning, thereby predicting and locking the specific thermal management model in the cloud that is suitable for each road segment, forming the initial model of the current battery thermal management; Step S303: Based on the battery information data in the vehicle's own status information, a reverse cell database lookup is performed to select the lowest temperature value in the suitable cell temperature range, forming the battery thermal management target temperature optimization matrix 1 for the road segment; when a road segment is identified where the brake pedal accelerates rapidly, the braking frequency is high, and the braking travel is deep, a reverse cell database lookup is performed to determine the cell target temperature range suitable for the current braking and charging state, and the lowest value in the cell temperature range is taken to form the battery thermal management target optimization matrix 2 for the braking road segment; The cell database is a calibration data matrix of the cells used in the vehicle. The cell database determines the cell temperature output range to maintain the current battery output power and SOC state. The thermal management target temperature optimization matrix 1 includes a smooth driving road scenario, and the thermal management target temperature optimization matrix 2 includes a multi-braking road segment or a predicted traffic intersection or special road segment scenario. Step S304: Match and store the latest thermal management model.

2. The adaptive electric vehicle battery thermal management control method based on big data and driving habits according to claim 1, characterized in that, Step S103 is followed by: The system performs a self-check and determines whether the discharge capacity is below the battery's low-temperature discharge threshold. If so, it prioritizes heating the battery to a suitable operating temperature range.

3. The adaptive electric vehicle battery thermal management control method based on big data and driving habits according to claim 2, characterized in that, In step S302, the cloud service platform includes the physical parameter classification and an indexable battery thermal management model library. The cloud intelligent matching should, when the thermal management model and the physical parameters are input into the intelligent matching model, perform cloud screening and matching of the thermal management model library for each road segment based on machine learning, so as to predict and lock the thermal management model of each road segment that is compatible with the cloud service platform.

4. The adaptive electric vehicle battery thermal management control method based on big data and driving habits according to claim 3, characterized in that, In step S301, the data in the navigation mode includes the origin, destination, current time, current vehicle position, current vehicle model, current weather conditions, and current air resistance, kinetic energy, potential energy, regenerative braking energy, current power demand, and battery information of the vehicle, which are obtained by processing vehicle state information such as vehicle speed, vehicle battery status, vehicle model parameters, brake pedal depth, and acceleration. The data in the non-navigation mode includes all or part of the parameters from the navigation-like state formed in the vehicle virtual map.

5. The adaptive electric vehicle battery thermal management control method based on big data and driving habits according to claim 4, characterized in that, The virtual map matrix is ​​formed by collecting the vehicle's own status information in non-navigation mode to assist in vehicle status determination and environmental perception. The virtual map matrix only expands a section of road based on the current road segment prediction, and expands the next predicted road segment after completing the journey.

6. The adaptive electric vehicle battery thermal management control method based on big data and driving habits according to claim 5, characterized in that, The thermal management model is implemented in the following ways: The battery can be heated by any one of the following methods: pulse current, heat pump, waste heat recovery, or water cooling system.

7. The adaptive electric vehicle battery thermal management control method based on big data and driving habits according to claim 6, characterized in that, The map information in the navigation mode comes from the navigation map module, while the map information in the non-navigation mode is the virtual map matrix obtained from the vehicle's own status information.

8. The adaptive electric vehicle battery thermal management control method based on big data and driving habits according to claim 7, characterized in that, The vehicle's own status information includes, but is not limited to, battery status parameters, GPS positioning, pedal displacement depth, pedal acceleration, waiting time at traffic lights, radar sensors, and laser sensors.

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