Driving information display apparatus and method for providing charging guide information for electric vehicle

Abstract

The invention discloses a driving information display apparatus and method for providing charging guide information for an electric vehicle. The driving information display apparatus may include: a processor configured to perform control to receive driving guide information and position information of an electric vehicle and output a guide screen corresponding to the driving guide information; and a storage unit configured to store the road information and the algorithm executed by the processor. The driving guidance information may include charging guidance information aimed at preventing a battery power level of the electric vehicle from being lower than a target remaining battery power level generated based on internal and external state information of the electric vehicle and information related to a current driver.

Description

[0001] Related Applications

[0002] This application claims priority to Korean Patent Application No. 10-2023-0184066, filed on Dec. 18, 2023, the entire contents of which are incorporated herein by reference for all purposes. Technical Field

[0003] The present disclosure relates to a driving information display device and method for providing charging guidance information for an electric vehicle. Background Art

[0004] Due to the characteristics of electric vehicles, it may be difficult to drive an electric vehicle comfortably because of long charging times and insufficient numbers of charging stations. Therefore, a route guidance service that considers the battery state of an electric vehicle and guides it to a charging station is needed.

[0005] In response to this need, a technique has been disclosed in which, when guiding an electric vehicle along a guiding route, the guiding route is changed by considering the remaining battery charge level at each location, such as the technique disclosed in U.S. Patent No. 9,170,118, entitled “Navigation System for Electric Vehicle”. In this technique, in order to ensure safe driving for the next trip when the electric vehicle reaches its destination, a target remaining battery charge level at the destination is set, and route guidance for traveling via a charging station is provided to achieve the target remaining battery charge level.

[0006] However, when the target remaining battery charge level at the destination is fixed without considering the driving environment, comfortable driving may be interrupted due to unnecessary stops at charging stations, thus requiring a charging guidance method suitable for the situation.

[0007] The information included in this background of the present disclosure is only for enhancing understanding of the general background of the present disclosure, and may not be regarded as an admission or any form of suggestion that this information forms prior art that is publicly known, available, or used. Summary of the Invention

[0008] The present disclosure relates to a driving information display device and method for providing charging guidance information for an electric vehicle, and more particularly, to a device and an operating method thereof for displaying driving guidance information that enables comfortable driving to a destination by considering the battery state of the electric vehicle.

[0009] Embodiments of the present disclosure can provide driving guidance information that enables comfortable driving by considering the remaining battery charge level of an electric vehicle.

[0010] Implementers of the present disclosure can provide driving guidance information suitable for a situation by determining a target remaining battery level according to the situation by using internal and external information of an electric vehicle.

[0011] Implementers of the present disclosure can provide driver-customized driving guidance information by determining a target remaining battery level by considering the driver's tendency.

[0012] Implementers of the present disclosure can provide driving guidance information suitable for a situation even when driver information has not been sufficiently accumulated.

[0013] The problems to be solved as exemplary embodiments of the present disclosure are not necessarily limited to the problems described above, and those skilled in the art can clearly understand solutions to other problems by the embodiments of the present disclosure from the detailed description of the following exemplary embodiments of the present disclosure.

[0014] According to various embodiments of the present disclosure, a driving information display device includes: a processor configured to perform control to receive driving guidance information and position information of an electric vehicle and output a guidance screen corresponding to the driving guidance information; and a storage unit configured to store road information and an algorithm run by the processor, and the driving guidance information can include charging guidance information, which is intended to prevent the battery level of the electric vehicle from falling below a target remaining battery level generated based on the internal state information, the external state information of the electric vehicle, and information related to the current driver.

[0015] The target remaining battery level can be generated based on information obtained by inputting internal and external state information into an artificial intelligence model according to the driver, which is generated by training a driver charging information database that records internal and external state information and the amount of charge each time the current driver has charged the electric vehicle in the past.

[0016] When the artificial intelligence model according to the driver has not been generated, the target remaining battery level can be generated based on information obtained by inputting internal and external state information into a general artificial intelligence model, and the artificial intelligence model according to the driver is generated by training on a general charging information database that records internal and external state information and the amount of charge each time each of all drivers has charged his or her electric vehicle in the past.

[0017] The internal and external state information may include: a normalized value of the number of charging stations within a predetermined distance from the destination; information related to the fatigue of the driver generated based on an image from an internal camera of the electric vehicle; information related to a preferred charging station generated based on brand information of the charging station used by the driver of the electric vehicle; information related to the ancillary facilities of the charging station generated based on information related to whether the ancillary facilities of the charging station used by the driver of the electric vehicle have been used by the driver of the electric vehicle before; and weather information.

[0018] According to various embodiments of the present disclosure, a driving information management server that may be equipped with a central processing unit and a memory, and the driving information management server may include: a connection establishment unit configured to establish a connection to exchange information with driving information display devices of a plurality of electric vehicles; an information receiving unit configured to receive the internal and external state information and current driver information of each of the electric vehicles from the electric vehicles; and an information transmission unit configured to obtain a target remaining battery power level based on the internal and external state information and the current driver information of the electric vehicle, and transmit charging guidance information generated using the target remaining battery power level to the electric vehicle.

[0019] The driving information management server may further include an information storage unit configured to, when the electric vehicle is being charged, receive the internal and external state information and the current driver information of the electric vehicle and store them in a driver charging information database for each driver, and the information transmission unit may obtain the target remaining battery power level based on information obtained by inputting the internal and external state information into an artificial intelligence model for the driver, which is generated by training the information in the driver charging information database corresponding to the current driver.

[0020] The information storage unit may store the received information in a general charging information database that stores the information of all drivers in an integrated manner, and when an artificial intelligence model for the driver has not been generated, the information transmission unit may obtain the target remaining battery power level based on information obtained by inputting the internal and external state information into a general artificial intelligence model, which is generated by training the general charging information database.

[0021] The methods and apparatuses of the present disclosure may have other features and advantages, which will be apparent from or can be more particularly set forth in the accompanying drawings incorporated herein and the following detailed description, which together are used to explain certain principles of the exemplary embodiments of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a block diagram showing an internal configuration of a driving information display device according to various exemplary embodiments of the present disclosure;

[0023] Figure 2 is a block diagram showing an internal configuration of a driving information management server according to various exemplary embodiments of the present disclosure;

[0024] Figure 3 is a diagram showing an example of route guidance based on a target remaining battery level in a driving information display device according to various exemplary embodiments of the present disclosure;

[0025] Figure 4 is a diagram showing a process of generating charging guidance information by using an artificial intelligence model in a driving information display device according to various exemplary embodiments of the present disclosure;

[0026] Figure 5 is a diagram showing an example of an artificial intelligence model used in a driving information display device according to various exemplary embodiments of the present disclosure;

[0027] Figure 6 is a flowchart showing a process of a driving information display method according to various exemplary embodiments of the present disclosure; and

[0028] Figure 7 is a flowchart showing a process of a driving information management method according to various exemplary embodiments of the present disclosure.

[0029] It will be understood that the drawings are not necessarily drawn to scale, presenting a somewhat simplified representation of various features illustrative of the basic principles of the exemplary embodiments of the present disclosure. Specific design features of the exemplary embodiments of the present disclosure as included herein (including, for example, specific dimensions, orientations, positions, and shapes) will be determined in part by the particular intended application and use environment.

[0030] In the drawings, reference numerals may refer to the same or equivalent components of the exemplary embodiments of the present disclosure throughout several views of the drawings. DETAILED DESCRIPTION

[0031] Reference will now be made in detail to various exemplary embodiments of the present disclosure, which are illustrated in the accompanying drawings and described below. Although the present disclosure will be described in conjunction with the exemplary embodiments of the present disclosure, it is understood that this specification is not necessarily intended to limit the present disclosure to those exemplary embodiments. On the other hand, the present disclosure is intended to cover not only the exemplary embodiments of the present disclosure, but also various alternatives, modifications, equivalents, and other embodiments that may be included within the spirit and scope of the present disclosure as defined by the appended claims.

[0032] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the following description of the exemplary embodiments of the present disclosure, when a detailed description of any relevant known configuration or function may obscure the gist of the present disclosure, the detailed description may be omitted. In the following description of the exemplary embodiments of the present invention, specific numerical values are merely examples, and the scope of the present disclosure need not be limited thereby.

[0033] In the following description of the components of the exemplary embodiments of the present disclosure, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only used to distinguish the corresponding components from other components, and the nature, sequential position, and / or order of the corresponding components need not be limited by these terms. Unless otherwise defined, the terms used herein (including technical or scientific terms) may include the same meanings as commonly understood by those skilled in the art to which the exemplary embodiments of the present disclosure belong. Terms such as those commonly defined in a dictionary may be interpreted to have meanings consistent with the meanings in the context of the related art, and should not be interpreted to have ideal or overly formal meanings unless expressly defined in this application.

[0034] Embodiments of the present disclosure will be described in detail below with reference to Figures 1 to 7 the accompanying drawings.

[0035] Figure 1 is a block diagram showing the internal configuration of a driving information display device 101 according to various exemplary embodiments of the present disclosure.

[0036] The driving information display device 101 according to an exemplary embodiment may be provided inside a transportation system (such as a vehicle), or may be implemented in a detachable form. The driving information display device 101 may generally be in the form of a vehicle navigation system, an audio, video, and navigation (AVN) system, a head-up display (HUD), etc., and may be implemented, for example, in the form of an application installed on a mobile phone terminal such as a smart phone.

[0037] The driving information display device 101 according to an exemplary embodiment may exist in the form of a server outside a transportation system (e.g., a vehicle). The driving information display device 101 may be implemented to generate driving guidance information by processing determinations while existing outside the transportation system and output the driving guidance information to a display existing inside the transportation system. Different embodiments may be implemented. The scope of rights of the present disclosure need not be limited by the forms of these implementations.

[0038] The driving information display device 101 of the exemplary embodiment may operate in combination with devices for autonomous driving control (such as, an advanced driver assistance system (ADAS), a smart cruise control (SCC) system, a forward collision warning (FCW) system, etc.).

[0039] As shown in the figure, the driving information display device 101 according to an exemplary embodiment may include a processor 110, a storage unit 120, a communication unit 130, and an output unit 140.

[0040] The processor 110 may be configured to control the storage unit 120, the communication unit 130, and the output unit 140 to execute an application program, process data according to an algorithm defined in the application program, communicate with an external module, and provide a processing result to a user.

[0041] The processor 110 may refer to a chip for processing general algorithms (e.g., a central processing unit (CPU) or an application processor (AP)) or a collection of such chips. The processor 110 may refer to a chip optimized for floating-point arithmetic (e.g., general-purpose computing on a graphics processing unit (GPGPU)) to process artificial intelligence algorithms such as deep learning or a collection of such chips. Alternatively, the processor 110 may refer to various types of chips that execute algorithms and process data in a connected and distributed manner.

[0042] The processor 110 may be electrically connected to the storage unit 120 (a storage medium) and the communication unit 130, may electrically control each component, may be a circuit that executes software commands, and may perform various types of data processing and determinations described later. The processor 110 may be, for example, an electronic control unit (ECU), a microcontroller unit (MCU), or another low-level controller installed on a transportation system.

[0043] The storage unit 120 may be a storage medium that may store road information and algorithms executed by the processor. The road information may include map information, road traffic condition information, etc. According to the configuration of the driving information display device 101 of the present disclosure, the form or amount of the road information stored inside the driving information display device 101 may vary.

[0044] In some cases, the storage unit 120 may store road information including map information of all serviceable areas and traffic condition information, and provide services based on the road information. Alternatively, the storage unit 120 may temporarily store only road information related to the location where guidance is being performed, and provide services based on the temporarily stored road information.

[0045] Depending on the form of the driving information display device 101 according to an exemplary embodiment of the present disclosure implemented inside or outside the traffic system, the communication method used, the storage space of the storage unit 120, and / or the input / output speed, this may be implemented in a different form. This is a part that those skilled in the art can autonomously select according to the implementation situation. The scope of the rights of the present disclosure need not be limited by such changes in the implementation.

[0046] The road information stored in the storage unit 120 may include not only general road information, but also information related to the location, capacity, and quantity of chargers of charging stations for charging electric vehicles. Based on these types of information, driving information guidance considering the charging situation can be provided.

[0047] The road information stored in the storage unit 120 may include various types of display information to be displayed in the guidance information. The display information may include various types of information to be included in the guidance information displayed in the driving situation, such as intersections, traffic lights, sidewalks, destinations, and main landmarks. The display information included in the road information may include parking positions, entrance positions, ramps for moving between floors, indoor facilities, road information outside the indoor area connected to the exit, etc. for the purpose of guiding inside the indoor area. Such display information may each be composed of a combination of the name of the display information to be displayed as the guidance information and information related to the position where the corresponding display information can be displayed.

[0048] The storage unit 120 may have various forms and may be at least one type of storage medium, such as flash memory, hard disk, micro, card (e.g., Secure Digital (SD) card), Extreme Digital (XD) card, Random Access Memory (RAM), Static RAM (SRAM), Read Only Memory (ROM), Programmable ROM (PROM), Electrically Erasable PROM (EPROM), Magnetic Random Access Memory (MRAM), magnetic disk, or optical disk type storage media, etc. Different types of storage media or combinations of different types of storage media may be selected according to the amount of data to be stored, processing speed, storage time, etc.

[0049] The algorithm stored in the storage unit 120 can be implemented as a computer program in an executable form and can be implemented by being stored in the storage unit 120 and then executed when needed. The algorithm stored in the storage unit 120 can be interpreted as including instructions that are temporarily loaded into the volatile memory and instruct the processor to perform specific operations.

[0050] The communication unit 130 can receive information for driving guidance from outside the driving information display device 101 of the present disclosure through a wired / wireless communication network and transmit necessary information to an external module.

[0051] The communication unit 130 can receive road information stored in the storage unit 120, algorithms executed by the processor 110, etc. from an external module, and can transmit information related to the current state of the traffic system to the outside to obtain necessary information related to the transmitted information. For example, the communication unit 130 can continuously receive traffic information from a traffic information server to check real-time traffic information, and can be configured to transmit the location and route information of the traffic system found by a module such as a global positioning system (GPS) receiver to the outside to obtain real-time traffic information of an area related to the location and route of the traffic system.

[0052] The communication unit 130 can be configured to transmit the internal and external information of the vehicle and receive information required for driving guidance while communicating with the driving information management server 201, where the driving information management server 201 can process and provide information required for driving information guidance.

[0053] The communication unit 130 can be a hardware device implemented using various circuits to send and receive signals through a wireless or wired connection. In an exemplary embodiment of the present disclosure, the communication unit 130 can perform communication within the traffic system using infrastructure traffic system network communication technology and can perform vehicle-to-infrastructure (V2I) communication with a server, infrastructure, another traffic system, etc. outside the traffic system using wireless Internet access or short-range communication technology. Communication within the traffic system can be performed using controller area network (CAN) communication, local interconnect network (LIN) communication, FlexRay communication, etc. as infrastructure traffic system network communication technology. Such wireless communication technologies can include wireless LAN (WLAN), wireless broadband (WiBro), Wi-Fi, worldwide interoperability for microwave access (WiMAX), etc. Short-range communication technologies can include Bluetooth, ZigBee, ultra-wideband (UWB), radio frequency identification (RFID), infrared data association (IrDA), etc.

[0054] The output unit 140 may output augmented reality information controlled by executing an algorithm stored in the storage unit 120 by the processor 110. Augmented reality is a technology that enables relevant information to be provided by adding graphic information to an image or scene of the real world.

[0055] The output unit 140 may be implemented as a head-up display (HUD), an instrument, an audio, video, and navigation (AVN) system, a human-machine interface (HMI), etc. The output unit 140 may include at least one of a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, an active matrix OLED (AMOLED) display, a flexible display, a curved display, and a three-dimensional (3D) display. Some of these displays may be implemented as transparent displays, configured in a transparent or semi-transparent form to enable viewing of the outside. The output unit 140 may be set as a touch screen including a touch panel and may be used as both an input device and an output device.

[0056] In an embodiment of the present disclosure, a vehicle may be described based on a concept including various transportation systems. In some cases, a vehicle may be interpreted based on a concept including not only various land transportation systems (such as cars, motorcycles, trucks, and buses) traveling on roads but also various transportation systems (such as airplanes, drones, ships, etc.).

[0057] Therefore, in an embodiment of the present disclosure, an electric vehicle may be interpreted as one of various transportation means based on a concept including storing electric energy in a secondary battery and using it as a power source in various transportation systems.

[0058] According to the driving guidance information processed by the processor 110, the driving information display device 101 according to an embodiment of the present disclosure may have different exemplary embodiments. Therefore, the generation of the driving guidance information received by the processor 110 and the information included in the driving guidance information will be described below by way of example.

[0059] As described above, the processor 110 may execute control to receive driving guidance information and information related to the position of the electric vehicle and output a guidance screen corresponding to the driving guidance information. The driving guidance information may include various types of information. When the driver sets a destination and wants to receive guidance information related to the destination through the screen, the driving guidance information may include information related to the destination and the route leading to the destination. Various other types of information for guiding the driving of the vehicle may be included in the driving guidance information.

[0060] Specifically, in the case of an electric vehicle, the driving range is short, charging takes a long time, and there is not a sufficient number of charging stations, making it important to provide optimal route guidance passing through charging stations during route guidance. Thus, the processor 110 may be configured to provide guidance on an optimal route along which a driver can safely drive to a destination while charging the electric vehicle at a necessary point by using electric vehicle charging station information included in the map information stored in the storage unit 120.

[0061] The driving guidance information may be information received from the driving information management server 102 through the communication unit 130, or may be information derived by the processor 110 through its own computational processing. The driving guidance information may include charging guidance information providing guidance on a charging station (through which the driver passes when driving an electric vehicle to a destination), and the amount of power required for charging at the charging station.

[0062] The processor 110 may obtain or generate internal and external state information of the electric vehicle and identify information related to the driver currently driving the electric vehicle. The charging guidance information may be generated by considering the state of the electric vehicle and the driver's tendency by using the internal and external state information of the electric vehicle and the information related to the current driver, and thus may be provided as customized information.

[0063] Specifically, in the case of an electric vehicle, when the electric vehicle arrives at a destination or a charging station, the remaining battery power level needs to be maintained at a predetermined value or higher. Thus, the processor 110 may determine the expected remaining battery power level when arriving at the destination or the charging station by considering the driving distance to the destination or the charging station, road conditions, etc., and may provide the driver with driving guidance information including the charging guidance information, where the charging guidance information provides guidance on preventing the determined value from dropping below the target remaining battery power level.

[0064] The target remaining battery power level may not be a fixed value, but may be determined by considering the internal and external state information of the electric vehicle and the information related to the current driver as described above. Thus, customized guidance considering the driving situation and the driver's tendency may be provided.

[0065] For example, when a driver tends to maintain a higher remaining battery power level at a destination, the target remaining battery power level may be set to a value higher than that of other drivers. When it is raining in the current weather, for the purpose of safe driving, the target remaining battery power level may be set to a value higher than usual.

[0066] When the target remaining battery level is set to a higher value, compared to when the target remaining battery level is set to a lower value, guidance can be provided on increasing the amount of charge to be charged at a charging station during driving to a destination. In the opposite case, the driver can be guided to charge only a small amount and start driving quickly again. Thus, charging guidance information suitable for the situation can be provided.

[0067] To generate charging guidance information suitable for the situation, the target remaining battery level can be generated based on information obtained by inputting internal and external state information into an artificial intelligence model according to the driver, where the artificial intelligence model according to the driver is generated by training a driver charging information database, and the driver charging information database records internal and external state information and the amount of charge charged each time the current driver has charged the vehicle in the past.

[0068] An artificial intelligence model such as a machine learning model can be generated by training using different types of context training data. When new situations are input into the generated artificial intelligence model, determination results for the situations can be provided.

[0069] In an embodiment of the present disclosure, when each driver visits a charging station to charge an electric vehicle, the internal and external state information of the electric vehicle and the amount of charge actually charged by the driver are accumulated and stored in the driver charging information database, and the data in the driver charging information database can be used as training data to generate an artificial intelligence model. By doing so, when the driver needs to charge the electric vehicle again, the amount of charge to be charged can be generated as a result value.

[0070] When the driver charging information database is stored separately for each driver using driver information, an artificial intelligence model according to the driver can be generated for each driver. When new environmental information is input into the artificial intelligence model according to the driver, results reflecting the driver's usual tendency in this context can be obtained.

[0071] When the artificial intelligence model according to the driver has not been generated, the target remaining battery level can be generated based on information obtained by inputting internal and external state information into a general artificial intelligence model, where the general artificial intelligence model is generated by training on a general charging information database, and the general charging information database records internal and external state information and the amount of charge charged each time each driver among all drivers has charged his or her vehicle in the past.

[0072] To train an artificial intelligence model, the training may require a minimum amount of training data. When the reference amount of data has not been accumulated, it may not be possible to generate an artificial intelligence model according to the driver. When an artificial intelligence model according to the driver is not generated due to insufficient training data, by storing the information of all drivers in a general charging information database and using a general artificial intelligence model generated based on the general charging information database, charging guidance information can be generated based on the tendencies of general drivers.

[0073] The minimum amount of training data for training may vary according to the algorithm used to generate the artificial intelligence model.

[0074] When information related to the amount of electricity that needs to be charged in the current situation is obtained through the artificial intelligence model, the target remaining battery power level when the driver arrives at the destination can be calculated. For example, in a state where the current remaining battery power level of an electric vehicle is 45%, when the result value of the artificial intelligence model according to the driver can be obtained (the result value indicates that electricity equal to 20% of the total battery capacity needs to be charged at a nearby charging station) and it can be predicted that electricity equal to 15% of the total battery capacity will be consumed to drive from the corresponding charging state to the destination, the target remaining battery power level when the driver arrives at the destination can be 50%.

[0075] When the driver charges electricity equal to 20% of the total battery capacity at the charging station according to the displayed charging information, the electric vehicle can arrive at the destination with a remaining battery power level of 50%.

[0076] The remaining battery power level of the electric vehicle in the range of 30% to 70% is a stable battery output area (platform). Therefore, it is desirable that the remaining battery power level when the electric vehicle arrives at the destination is in the range of 30% to 70%. When the target remaining battery power level calculated by considering the internal and external state information of the electric vehicle and the information related to the current driver is lower than 30% or higher than 70%, adjustments can be made so that the target remaining battery power level can fall within the range of 30% to 70%.

[0077] For example, when the target remaining battery power level calculated based on the amount of electricity to be charged obtained from the artificial intelligence model according to the driver is 20%, it is desirable to generate charging guidance information indicating that the driver charges the electric vehicle with a larger amount of electricity at the charging station than the amount of electricity to be charged obtained from the artificial intelligence model according to the driver, so that the target remaining battery power level can become 30%.

[0078] Various artificial intelligence algorithms can be used to generate an artificial intelligence model according to the driver and a general artificial intelligence model. Regression algorithms such as random forest regressors and neural networks such as deep learning can be used. The present disclosure is not necessarily limited to a specific artificial intelligence algorithm.

[0079] The internal and external state information of the electric vehicle may include information related to charging stations around the destination, information related to the driver's fatigue, information related to preferred charging stations, information related to the ancillary facilities of the charging stations, weather information, etc.

[0080] The information related to charging stations around the destination may include a normalized value of the number of charging stations within a predetermined distance from the destination. When this information is used, the result can reflect the current tendency related to the amount of charge of the driver according to whether there are a sufficient number of charging stations around the destination.

[0081] Information related to the driver's fatigue can be generated based on images from an internal camera of the electric vehicle. By analyzing the images from the internal camera, the driver's fatigue level can be generated on a scale of 1 - 10 using a real-time anomaly detection model. When the information related to the driver's fatigue is used, the result can reflect the current driver's tendency to set the amount of charge differently according to his or her past fatigue.

[0082] The information related to preferred charging stations can be set based on statistical information related to the charging stations or charging station brands used by the current driver in the past, such that the charging station with the highest usage rate can be set as the preferred charging station with a value of 1, and other charging stations can be set as non-preferred charging stations with a value of 0. When the information related to preferred charging stations is used, the result can reflect the tendency to set the amount of charge to be filled differently according to whether the current charging station is a preferred charging station.

[0083] The information about the ancillary facilities of the charging station can be quantified by calculating the utilization rate of the ancillary facilities using payment information at the ancillary facilities of the charging stations visited by the current driver in the past. When the information related to the ancillary facilities of the charging station is used, the result reflects the tendency of the user to set the amount of charge to be filled differently according to the ancillary facilities of the charging station.

[0084] The weather information can be the weather information of the area where the electric vehicle is currently located, which can be received through the communication unit 130, or can be information obtained by analyzing the use of the vehicle's windshield washer fluid, the use of the vehicle's windshield wipers, the measurement results of the rain sensor, etc. When the weather information is utilized, the result can reflect the driver's tendency to set the amount of charge differently according to the weather.

[0085] The internal and external state information of an electric vehicle may include various types of data for determining the driver's tendency. It is desirable that the internal and external state information of the electric vehicle includes digital data used as input data to train an artificial intelligence model.

[0086] Although the charging guidance information may be generated by direct operation in the processor 110, it is preferably calculated by the driving information management server 201 and transmitted to the communication unit 130 to smoothly operate the artificial intelligence model.

[0087] Figure 2 It is a block diagram showing the internal configuration of a driving information management server according to various exemplary embodiments of the present disclosure.

[0088] The driving information management server 201 according to various exemplary embodiments of the present disclosure may be a server equipped with a central processing unit (CPU) and a memory, and may include a stand-alone server or a cloud. As long as the driving information management server 201 can obtain the target remaining battery level by processing the information received from the above-mentioned driving information display device 101 through a wired or wireless communication network and transmit the target remaining battery level back to the driving information display device 101 through the communication network, the configuration of the driving information management server 201 is not limited.

[0089] The driving information management server 201 of the embodiments of the present disclosure may process the operations required to generate the charging guidance information displayed on the driving information display device 101. Therefore, the process of generating the charging guidance information in the above-mentioned driving information display device 101 can be executed in the driving information management server 201. Therefore, the description of the process of generating the charging guidance information displayed on the driving information display device 101 can be applied to the driving information management server 201 without significant change.

[0090] As shown in the figure, the driving information management server 201 may include a connection establishment unit 210, an information receiving unit 220, an information storage unit 230, an information sending unit 240, a driver charging information database 250, and a general charging information database 260. Any one, any combination, or all of them may be multiple or may include multiple components thereof.

[0091] The connection establishment unit 210 may establish a connection for exchanging information with the driving information display devices 101 of multiple electric vehicles. The connection establishment unit 210 may establish a connection with the communication unit 130 of the driving information display device 101 through a wired or wireless communication network, and is not limited to a specific type of communication.

[0092] The information receiving unit 220 may receive the internal and external state information of the electric vehicle, the information related to the current driver, and the information related to the destination from the driving information display device 101. As described above, in order to derive the target remaining battery power level through the artificial intelligence model, the internal and external state information of the electric vehicle, the information related to the current driver, and the information related to the destination may be required. Since these information are the information generated by the driving information display device 101 of the electric vehicle, these information may be received through the connection established by the connection establishing unit 210.

[0093] When the electric vehicle is charged, the information storage unit 230 may receive the internal and external state information of the electric vehicle and the information related to the current driver, and store them in the driver charging information database 250 for each driver. The stored information may be stored in association with the information about the actually charged power. As described above, the data obtained by the storage status and the power charged in that status whenever the electric vehicle is charged may become the training data for generating the artificial intelligence model according to the driver, so that the above information can be separated for each driver and stored in the database.

[0094] The information storage unit 230 may store the received information in the general charging information database 260, which stores the information of all drivers. As described above, when there is not enough past charging history for the current driver to accumulate enough training data for generating the artificial intelligence model, the general artificial intelligence model generated by training on the general tendencies of all users may be used to obtain the target remaining battery power level. Therefore, by storing the internal and external state information of the electric vehicle and the information related to the current driver when the electric vehicle is charged in the general charging information database 260 regardless of the driver, and using this information as training data, a general artificial intelligence model can be generated. The stored information may be stored in association with the information about the actually charged power.

[0095] The information sending unit 240 may obtain the target remaining battery power level based on the internal and external state information of the electric vehicle and the information related to the current driver, and may send the target remaining battery power level to the electric vehicle. As described above, the target remaining battery power level may be generated based on the information obtained by inputting the internal and external state information into the artificial intelligence model according to the driver generated by training the information in the driver charging information database corresponding to the current driver. The artificial intelligence model according to the driver may provide the power to be charged when the driver's tendency is reflected in the charging situation as the result value, so that the remaining battery power level when the driver reaches the destination can be obtained using the result value, as described above.

[0096] In the case where the artificial intelligence model according to the driver is not generated, the information sending unit 240 may obtain the target remaining battery power level based on the information obtained by inputting the internal and external state information into the general artificial intelligence model generated based on the training of the general charging information database 260. In the same manner, the information related to the amount of power charged at the charging station can be used to calculate the target remaining battery power level, which is provided as the result value of the general artificial intelligence model.

[0097] Figure 3 FIG. is a diagram showing an example of route guidance based on the target remaining battery power level in a driving information display device according to various exemplary embodiments of the present disclosure.

[0098] In this example, in the case where an electric vehicle charged to 75% of the total battery capacity consumes 40% of the total battery capacity when directly driven to the destination, the route guidance may change according to the target remaining battery power level.

[0099] In the case where the electric vehicle reaches the destination by consuming 40% of the total battery capacity when charged to 75% of the total battery capacity, the remaining battery power level may become 35% of the total battery capacity.

[0100] Therefore, when the target remaining battery power level is set to 30% ( Figure 3 Case (a)), the expected remaining battery power level at the destination may be higher than the target remaining battery power level, so the driver can be guided to directly drive to the destination without passing through a charging station.

[0101] On the other hand, if the target remaining battery power level is set to 50% ( Figure 3 Case (b)), the expected remaining battery power level at the destination is lower than the target remaining battery power level. Therefore, in the case where the driver reaches the destination after passing through a charging station, the driver can be guided to maintain the remaining battery power level equal to the target remaining battery power level.

[0102] In the related art, the target remaining battery power level is directly fixed or set by the driver, so route guidance is provided without considering the driving situation or the driver.

[0103] When the driver drives an electric vehicle with the target remaining battery power level set to 50%, regardless of the driver or the internal and external state information of the electric vehicle, the driver can be guided to charge the electric vehicle at the charging station with 20% of the total battery capacity and move to the destination, as Figure 3 shown in Case (b).

[0104] Therefore, it is possible to guide the driver to charge the electric vehicle at a location other than the preferred charging station, or it is possible to guide the driver to stop at the charging station unnecessarily, which may cause inconvenience to driving even when it is determined based on the driver's preference that sufficient remaining battery power level can be maintained at the destination.

[0105] Therefore, in an embodiment of the present disclosure, the target remaining battery power level may not be set to a fixed value. When the logic for driving past the charging station is activated on the route of the electric vehicle, the amount of charge at the corresponding charging station can be determined by inputting the internal and external state information of the electric vehicle and the information related to the current driver into the artificial intelligence model, and the target remaining battery power level at the destination can be obtained based on this information. Therefore, guidance customized for the driver's preference or situation can be provided.

[0106] For example, in the case where driver A is a driver who charges the electric vehicle with a large amount of power at the charging station along his or her route to maintain a high remaining battery power level upon arrival, even when the same electric vehicle is driven by a different driver, the target remaining battery power level can be set to a high value, and thus, driver A can be guided to drive past the charging station, as Figure 3 shown in case (a).

[0107] On the contrary, in the case where driver B has a tendency to charge the electric vehicle at the charging station along his or her route infrequently but to charge the electric vehicle after arriving at the destination when there are multiple charging stations at the destination, the target remaining battery power level can be set to a low value, and thus, driver B can be guided to drive directly to the destination without passing through the charging station.

[0108] When the target remaining battery power level is set to reflect the preference of each driver, the driver can drive comfortably according to his or her usual driving manner.

[0109] Figure 4 is a diagram showing the process of generating charging guidance information in a driving information display device by using an artificial intelligence model according to various exemplary embodiments of the present disclosure.

[0110] As described above, when the driver charges the electric vehicle using electricity at the charging station, the internal and external state information of the vehicle and the information related to the current driver can be identified and stored in the driver charging information database 250. To generate training data, information related to the amount of charge actually charged can be stored in the driver charging information database 250 in association with the internal and external state information of the vehicle.

[0111] The driver charging information database 250 can store the internal and external status information of the vehicle and information related to the amount of electricity actually charged by each driver, and this information can also be stored in the general charging information database 260. However, the general charging information database 260 may not be separated for each driver but can store information related to all drivers.

[0112] In the training step, when the data stored in the driver charging information database 250 has been sufficiently accumulated to form training data, this data can be used as training data to generate an artificial intelligence model according to the driver.

[0113] A general artificial intelligence model can be generated by training the data stored in the general charging information database 260, which can store the charging information of all drivers.

[0114] The artificial intelligence model according to the driver and the general artificial intelligence model can be configured to be continuously updated as data accumulates.

[0115] Thereafter, when the electric vehicle is actually driven and the logic for driving through a charging station on the route is activated, the amount of electricity to be charged at the charging station and the target remaining battery power level to be set can be determined by considering the current driver's tendency.

[0116] First, information related to the current driver can be identified, and it can be determined whether there is an artificial intelligence model according to the driver. When there is an artificial intelligence model according to the driver, the charging guidance information for each driver at the corresponding charging station can be derived by inputting the internal and external status information of the vehicle into the artificial intelligence model according to the driver generated in the previous training step. The charging guidance information can include information related to the amount of electricity to be charged at the corresponding charging station, and the target remaining battery power level can be determined based on the information related to the amount of electricity to be charged.

[0117] When there is no artificial intelligence model according to the driver, general charging guidance information according to the tendency of general drivers can be generated by inputting the internal and external status information of the vehicle into the general artificial intelligence model. The general charging guidance information can also include information related to the amount of electricity to be charged at the corresponding charging station, and the target remaining battery power level can be determined based on the information related to the amount of electricity to be charged.

[0118] Figure 5 A diagram showing an example of an artificial intelligence model used in a driving information display device according to various exemplary embodiments of the present disclosure.

[0119] In the present exemplary embodiment, for example, five types of information (i.e., weather, driver fatigue, preferred charging stations, information related to charging stations near the destination, and ancillary facilities of the charging stations) are used as training and input parameters. As described above, it is desirable to quantify and store each piece of information.

[0120] In this way, an artificial intelligence model generated by training training data with five parameters can provide the amount of electricity to be discharged as an output value when receiving the five parameters as input values.

[0121] The above-mentioned artificial intelligence model according to the driver and the general artificial intelligence model may have the same structure except that different types of training data may be used therein.

[0122] When using the amount of electricity to be charged output as an output value by the artificial intelligence model, the remaining battery power level obtained at the destination after charging a specific amount of electricity at the charging station can be predicted, and the predicted remaining battery power level becomes the target remaining battery power level. The target remaining battery power level can be set based on the result obtained by analyzing the output value of the artificial intelligence model rather than using the output value of the artificial intelligence model without change. For example, as described above, in order to keep the target remaining battery power level within the range of 30% to 70%, when charging an amount of electricity equal to the output value of the artificial intelligence model, if the remaining battery power level at the destination is lower than 30% or higher than 70%, correction can be made.

[0123] When the amount of electricity to be charged does not exceed a predetermined reference value, it can be set so that the driver is guided to the destination without driving past the corresponding charging station.

[0124] Figure 6 It is a flowchart showing the process of a driving information display method according to various exemplary embodiments of the present disclosure.

[0125] In the present exemplary embodiment, the driving information display method according to the present disclosure may be a method executed by a driving information display device 101 including a processor 110 and a storage unit 120. The components described above in connection with the operation of the driving information display device 101 can be applied to the driving information display method without significant change. Therefore, by applying the foregoing description of the driving information display device 101, those skilled in the art can even implement components not specifically described in combination with the following driving information display method.

[0126] In the target remaining battery power level information generation step S601, information related to the target remaining battery power level can be generated based on the internal and external state information of the electric vehicle and information related to the current driver.

[0127] In the charging guidance information generation step S602, charging guidance information can be generated that aims to prevent the battery power level of the electric vehicle from falling below the generated target remaining battery power level.

[0128] In the driving information display step S603, driving information including the generated charging guidance information can be displayed.

[0129] The target remaining battery power level can be generated based on information obtained by inputting internal and external state information into an artificial intelligence model according to the driver, which is generated by training a driver charging information database that records internal and external state information and the amount of electricity charged by the current driver for the vehicle each time in the past.

[0130] When the artificial intelligence model according to the driver has not been generated, the target remaining battery power level can be generated based on information obtained by inputting internal and external state information into a general artificial intelligence model, where the general artificial intelligence model is generated by training on a general charging information database that records internal and external state information and the amount of electricity charged each time when each of all drivers charged their vehicles in the past.

[0131] Figure 7 It is a flowchart showing the process of a driving information management method according to various exemplary embodiments of the present disclosure.

[0132] In the present exemplary embodiment, the driving information management method according to the present disclosure is a method executable by a driving information management server 201 including a CPU and a memory. The components described above in conjunction with the operation of the driving information management server 201 can be applied to the driving information management method without significant change. Therefore, those skilled in the art can even implement components not specifically described in the following driving information management method by applying the above description of the driving information management server 201.

[0133] In the connection establishment step S701, a connection can be established to exchange information with the driving information display devices of multiple electric vehicles.

[0134] In the information reception step S702, internal and external state information and current driver information of each electric vehicle can be received from the electric vehicle.

[0135] In the information storage step S703, when the electric vehicle is charged, the internal and external state information and current driver information of the electric vehicle are received and stored in the driver charging information database 250 for each driver.

[0136] In the information storage step S703, the received information may also be stored in the general charging information database 260, where the general charging information database stores information of all drivers.

[0137] In the information sending step S704, a target remaining battery power level may be obtained based on the internal and external state information of the electric vehicle and the current driver information, and the charging guidance information generated using the target remaining battery power level may be sent to the electric vehicle.

[0138] In the information sending step S704, the target remaining battery power level may be generated based on the information obtained by inputting the internal and external state information into the artificial intelligence model according to the driver generated by training the information in the driver charging information database corresponding to the current driver. When the artificial intelligence model according to the driver has not been generated, the target remaining battery power level may be obtained based on the information obtained by inputting the internal and external state information into the general artificial intelligence model generated by training on the general charging information database.

[0139] Embodiments of the present disclosure can achieve the advantage of providing guidance information that enables comfortable driving by considering the remaining battery power level of the electric vehicle.

[0140] Embodiments of the present disclosure can achieve the advantage of providing driving guidance information suitable for the situation by determining the target remaining battery power level according to the situation based on the use of the internal and external information of the electric vehicle.

[0141] Embodiments of the present disclosure can achieve the advantage of providing driver-customized driving guidance information by determining the target remaining battery power level based on the consideration of the driver's tendency.

[0142] Embodiments of the present disclosure can achieve the advantage of providing driving guidance information suitable for the situation even when the driver information has not been fully accumulated.

[0143] Throughout this specification, various advantages that can be directly or indirectly understood by those skilled in the art can be provided.

[0144] Although the present disclosure has been described with reference to exemplary embodiments, however, without departing from the spirit and scope of the present disclosure described in the appended claims, those skilled in the art may make various modifications and changes to the embodiments of the present disclosure.

[0145] The control device may be at least one microprocessor operated by a predetermined program, and the predetermined program may include a series of commands for executing the methods included in the above various exemplary embodiments of the present disclosure.

[0146] In various exemplary embodiments of the present disclosure, each of the above operations may be performed by a control device, and the control device may be configured by a plurality of control devices or an integrated single control device.

[0147] In various exemplary embodiments of the present disclosure, the memory and the processor may be provided as one chip or as separate chips.

[0148] In different exemplary embodiments of the present disclosure, the scope of the present disclosure may include software or machine-executable commands (e.g., operating systems, applications, firmware, programs, etc.) for enabling the operations of methods according to different embodiments to be performed on a device or computer, and non-transitory computer-readable media storing such software or commands and executable on the device or computer.

[0149] In various exemplary embodiments of the present disclosure, the control device may be implemented in the form of hardware or software, or may be implemented in a combination of hardware and software.

[0150] Terms such as "unit", "module", etc. included in the specification may be units for processing at least one function or operation, which may be implemented by hardware, software, or a combination thereof.

[0151] In an exemplary embodiment of the present disclosure, a vehicle may be referred to based on the concept including various transportation systems. In some cases, a vehicle may be interpreted as based on various transportation systems including not only various land transportation systems (such as cars, motorcycles, trucks, and buses) traveling on roads but also various transportation systems such as airplanes, drones, ships, etc.

[0152] For the convenience of interpretation and accurate definition in the appended claims, terms such as "above", "below", "inside", "outside", "upward", "downward", "upward", "downward", "front", "rear", "rear", "inside", "outside", "inward", "outward", "internal", "external", "internal", "external", "forward", and "backward" may be used to describe the features of exemplary embodiments with reference to the positions of such features as shown in the drawings. It can be further understood that the term "connected" or its derivatives may refer to direct connection and indirect connection.

[0153] The term "and / or" may include combinations of multiple related listed items or any of the multiple related listed items. For example, "A and / or B" may include all three cases, such as "A", "B", and "A and B".

[0154] In this specification, unless otherwise stated, the singular may express including the plural expression, unless the context clearly indicates otherwise.

[0155] In an exemplary embodiment of the present disclosure, "at least one of A and B" may refer to "at least one of A or B" or "at least one of a combination of at least one of A and B". Further, "one or more of A and B" may refer to "one or more of A or B" or "one or more of a combination of one or more of A and B".

[0156] In an exemplary embodiment of the present disclosure, it is understood that terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, operations, elements, components, or combinations thereof described in the specification, and do not preclude the possibility of adding or existing one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0157] For purposes of illustration and description, the foregoing description of specific exemplary embodiments of the present disclosure has been presented. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teachings. The exemplary embodiments were chosen and described to explain certain principles of the present disclosure and its practical application so that others skilled in the art may make and utilize various exemplary embodiments of the present disclosure and its various alternatives and modifications. The scope of the present disclosure is intended to be defined by the appended claims and their equivalents.

Claims

1. A driving information display device, comprising: one or more processors; as well as A storage medium configured to store road information and an algorithm configured to be executed by the one or more processors, and storing computer readable instructions that, when executed by the one or more processors, enable the one or more processors to: executing control to receive position information and driving guidance information of the electric vehicle, and outputting a guidance picture corresponding to the driving guidance information; and The driving guidance information includes charging guidance information intended to prevent the battery power level of the electric vehicle from falling below a target remaining battery power level, wherein the target remaining battery power level is generated based on internal and external status information of the electric vehicle and current driver information related to the current driver.

2. The driving information display device according to claim 1, wherein: The target remaining battery power level is generated based on driver-specific artificial intelligence model information, wherein the driver-specific artificial intelligence model information is obtained by inputting the internal and external state information into the driver-specific artificial intelligence model, and the driver-specific artificial intelligence model is generated by training on a driver charging information database, which records the internal and external state information and the amount of power charged each time the current driver previously charged the electric vehicle within a first past period.

3. The driving information display device according to claim 2, wherein: In response to the driver-specific artificial intelligence model not being generated for the current driver, generating the target remaining battery power level based on general artificial intelligence model information, wherein the general artificial intelligence model information is obtained by inputting the internal and external state information into a general artificial intelligence model, the general artificial intelligence model being generated by training on a general charging information database that records the internal and external state information and the amount of power charged each time the driver in the large set of drivers previously charged the driver's electric vehicle during a second past period.

4. The driving information display device according to claim 1, wherein: The internal and external status information includes one or any combination of the following: normalized values ​​for a plurality of charging stations within a predetermined distance from a destination; current driver fatigue information, information related to a current driver's fatigue level generated based on an image from an internal camera of the electric vehicle; preferred charging station information, information related to preferred charging stations generated based on brand information of a group of previously used charging stations previously used by a current driver of the electric vehicle; incidental facility information, information related to incidental facilities of a given charging station generated based on whether the incidental facilities of the given charging station were previously used by a current driver of the electric vehicle; as well as Weather information.

5. A driving information management server, comprising: one or more processors; as well as A storage medium configured to store computer-readable instructions that, when executed by the one or more processors, enable the one or more processors to: establishing a connection to exchange information with a plurality of driving information display devices of electric vehicles; receiving current driver information and internal and external status information for each of the plurality of electric vehicles; as well as Based on the current driver information and the internal and external state information of the plurality of electric vehicles, a target remaining battery level for a given electric vehicle of the plurality of electric vehicles is obtained, and charging guidance information generated using the target remaining battery level is sent to the given electric vehicle.

6. The driving information management server according to claim 5, wherein: The computer readable instructions further enable the one or more processors to: In response to the given electric vehicle being charged, receiving the current driver information and the internal and external state information of the given electric vehicle, and storing the internal and external state information and the current driver information in a driver charging information database for each driver; as well as The target remaining battery power level is obtained based on the driver-specific artificial intelligence model information obtained by inputting the internal and external state information into the driver-specific artificial intelligence model, wherein the driver-specific artificial intelligence model is generated by training on the current driver information of the driver charging information database corresponding to the current driver.

7. The driving information management server according to claim 6, wherein: The computer readable instructions further enable the one or more processors to: storing the internal and external state information and the current driver information in a universal charging information database, the universal charging information database storing the current driver information and the internal and external state information of a large set of drivers in an integrated manner; as well as In response to the driver-specific artificial intelligence model not being generated for the current driver, the target remaining battery power level is obtained based on general artificial intelligence model information, wherein the general artificial intelligence model information is obtained by inputting internal and external state information of the current driver into a general artificial intelligence model, and the general artificial intelligence model is generated by training on the general charging information database.

8. A driving information display method, comprising: generating a target remaining battery charge level of the electric vehicle based on internal and external state information of the electric vehicle and current driver information related to a current driver; generating charging guidance information intended to prevent a battery power level of the electric vehicle from falling below a generated target remaining battery power level; as well as Driving information including the generated charging guidance information is displayed.

9. The driving information display method according to claim 8, wherein: Generating the target remaining battery power level is based on driver-specific artificial intelligence model information obtained by inputting the internal and external state information into a driver-specific artificial intelligence model, wherein the driver-specific artificial intelligence model is generated by training on a driver charging information database, wherein the driver charging information database records the internal and external state information and the amount of power charged each time the current driver previously charged the electric vehicle within a first past period.

10. The driving information display method according to claim 9, wherein: In response to the driver-specific artificial intelligence model not having been generated for the current driver, the generation of the target remaining battery power level is based on general artificial intelligence model information, wherein the general artificial intelligence model information is obtained by inputting the internal and external state information into a general artificial intelligence model, the general artificial intelligence model being generated by training on a general charging information database, the general charging information database recording the internal and external state information and the amount of power charged each time the driver's electric vehicle was previously charged by each driver in a large set of drivers within a second past period.

11. The driving information display method according to claim 8, wherein: The internal and external status information includes one or any combination of the following: normalized values ​​for a plurality of charging stations within a predetermined distance from a destination; current driver fatigue information, information related to a current driver's fatigue level generated based on an image from an internal camera of the electric vehicle; preferred charging station information, information related to preferred charging stations generated based on brand information of a group of previously used charging stations previously used by a current driver of the electric vehicle; incidental facility information, information related to incidental facilities of a given charging station generated based on whether the incidental facilities of the given charging station were previously used by a current driver of the electric vehicle; as well as Weather information. 12 . A non-transitory computer-readable storage medium having a program stored thereon, which, when executed by a processor, causes the processor to execute the driving information display method of claim 8 .

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