Driving assistance system

Abstract

The present invention provides a driving assistance system that enables users to perform efficient charging. [Solution] The system is configured to acquire charging facility information for candidate charging facilities among charging facilities capable of charging the onboard battery 8 that supplies power to the drive source of the vehicle 5, and for charging candidate charging facilities that are suitable for travel to the destination. It also acquires the amount of power that the onboard battery 8 equipped with the vehicle 5 can accept when charging and the vehicle's driving range relative to the remaining charge of the onboard battery 8. Based on this acquired information, the system is configured to determine the charging efficiency when charging the onboard battery 8 at a candidate charging facility along each candidate route from the departure point to the destination.

Description

Technical Field

[0001] The present invention relates to a driving support device that supports charging of an in-vehicle battery.

Background Art

[0002] In recent years, in addition to gasoline vehicles driven by an engine as a driving source, there are electric vehicles driven by a motor driven based on electric power supplied from a battery and hybrid vehicles driven by using both a motor and an engine as driving sources. As a method for charging an in-vehicle battery provided in such electric vehicles and hybrid vehicles, there are a method of charging using regenerative power of a motor generated during deceleration or during driving on a downhill road while the vehicle is running, a method of charging using a generator driven based on an engine, and in addition, a method of charging at home or at a dedicated charging facility.

[0003] Here, when the vehicle charges at the above charging facility, if there are a plurality of candidates for charging facilities where charging is possible, it is difficult to determine which charging facility should be used for charging. Therefore, for example, in International Publication No. 2017 / 183476, a technique for searching for a route from a departure place to a destination using vehicle-dependent data such as fuel consumption as learning data has been proposed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Here, the maximum output of the chargers at the above-mentioned charging facilities varies from facility to facility. On the other hand, the amount of electrical energy that a vehicle's onboard battery can accept also varies from vehicle to vehicle. In order to increase the amount of energy that can be charged in a shorter time, it is ideal to charge at a charging facility equipped with a charger with a high maximum output, but on the other hand, charging with a charger whose maximum output exceeds the amount of electrical energy that the vehicle can accept will not provide any benefit.

[0006] Therefore, in order to improve the charging efficiency (to perform efficient charging) when charging an in-vehicle battery, it is necessary to select a charging facility after considering information from both the charging facility and the vehicle. However, the technology described in Patent Document 1 does not specifically consider such information as training data, which leads to the problem that routes that lead to charging at charging facilities with low charging efficiency are searched for.

[0007] The present invention was made to solve the aforementioned problems of the conventional method, and aims to provide a driving assistance device that can help users perform efficient charging by determining the charging efficiency by considering the maximum output of the charger provided at the charging facility and the amount of power that can be accepted when charging the on-board battery of the vehicle. [Means for solving the problem]

[0008] To achieve the above objective, the driving support device according to the present invention includes: a charging facility information acquisition means that acquires charging facility information, including the location of a charging facility candidate and the maximum output of the charger provided by the charging facility candidate, for charging facility candidates that are candidates for charging during travel to a destination, among charging facilities capable of charging an on-board battery that supplies power to the vehicle's drive source; a battery information acquisition means that acquires maximum power receiving information indicating the amount of power that can be accepted when charging the on-board battery provided by the vehicle; a driving range acquisition means that acquires driving range information indicating the driving range of the vehicle relative to the remaining charge of the on-board battery; and a charging efficiency determination means that determines the charging efficiency when charging the on-board battery at a charging facility candidate located on each candidate route from the departure point to the destination, based on the charging facility information, the maximum power receiving information and the driving range information. [Effects of the Invention]

[0009] According to the driving support device of the present invention having the above configuration, by determining the charging efficiency by considering the maximum output of the charger provided at the charging facility and the amount of power that can be accepted when charging the vehicle's onboard battery, it becomes possible to support the user in performing efficient charging. [Brief explanation of the drawing]

[0010] [Figure 1] This is a schematic diagram showing the driving support system according to this embodiment. [Figure 2] This is a block diagram showing the configuration of the driving support system according to this embodiment. [Figure 3] This diagram shows an example of charging facility information stored in the distribution information database. [Figure 4] This diagram explains the fast charging capacity. [Figure 5] This diagram illustrates the relationship between SOC value and charging output. [Figure 6] This is a schematic block diagram showing the control system of the communication terminal according to this embodiment. [Figure 7]This is a flowchart of the path learning processing program according to this embodiment. [Figure 8] This figure shows a virtual environment for performing path learning in this embodiment. [Figure 9] This diagram shows the mapping of potential charging facilities to route information during route learning for vehicle A. [Figure 10] This diagram shows the mapping of potential charging facilities to route information during route learning for vehicle B. [Figure 11] This diagram shows the possible routes that vehicle A can take from its starting point to its destination. [Figure 12] This diagram shows the possible routes that vehicle B can take from the starting point to the destination. [Modes for carrying out the invention]

[0011] Hereinafter, the driving support device according to the present invention will be described in detail with reference to the drawings, based on one embodiment in which the driving support device according to the present invention is embodied in a communication terminal and an information provision server capable of communicating with the communication terminal. First, the schematic configuration of the driving support system 2 including the communication terminal 1 according to this embodiment will be described with reference to Figure 1. Figure 1 is a schematic configuration diagram showing the driving support system 2 according to this embodiment.

[0012] As shown in Figure 1, the driving support system 2 according to this embodiment basically comprises an information provision server 4 located in the information provision center 3, a navigation device 6 which is an in-vehicle device mounted in the vehicle 5, and a communication terminal 1 held by the user, who is an occupant of the vehicle 5. Furthermore, the information provision server 4 and the communication terminal 1 are configured to send and receive electronic data to and from each other via a communication network 7. In addition, the navigation device 6 and the communication terminal 1 are configured to send and receive electronic data to and from each other via wireless communication such as Bluetooth® or wired communication by connecting a cable. Examples of communication terminals 1 include mobile phones, smartphones, tablet terminals, personal computers, etc.

[0013] Further, the vehicle 5 is a vehicle equipped with at least a motor as a drive source and an in-vehicle battery 8 as means for supplying power to the motor which is the drive source, and is a vehicle capable of charging the in-vehicle battery 8 at a charging facility. Applicable vehicles include electric vehicles (hereinafter referred to as EV vehicles) using only a motor as a drive source, and among hybrid vehicles using both a motor and an engine as drive sources, particularly plug-in hybrid vehicles (hereinafter referred to as PHV vehicles).

[0014] Here, the information providing server 4 is a server device that manages information provided to the communication terminal 1 (that is, the user who holds the communication terminal 1). The information providing server 4 stores information on information providing locations across the country to be provided to the communication terminal 1 in the distribution information DB 9. In the present embodiment, at least information on the charging facility 10 where the in-vehicle battery 8 of the vehicle 5 can be charged is stored in the distribution information DB 9, but information on information providing locations other than the charging facility 10 (for example, commercial facilities, public facilities, parking lots, etc.) may also be stored in the distribution information DB 9. Then, the information providing server 4 provides (distributes) information on the information providing locations stored in the DB to the communication terminal 1 via the communication network 7.

[0015] Here, the charging facility 10 is a facility capable of charging the in-vehicle battery 8 provided in the vehicle 5, and is generally equipped with a parking space for parking the vehicle, and further, a charging facility consisting of an operation panel, a cable connected to the vehicle, etc. is installed around the parking space. In addition to dedicated facilities for charging vehicles, the above charging facility 10 may also be provided in a part of the parking lot of a commercial facility such as a shopping mall or a service area, an automobile sales store, a coin parking lot, etc. In the present embodiment, facilities that can be charged only by specific persons such as at home are excluded from the charging facilities.

[0016] In addition, the information providing server 4 is also communicably connected to a charging facility management server that manages charging facilities 10 across the country. And it can obtain the current availability status of the chargers included in the charging facility 10 and other information regarding the charging facility 10 (for example, the number of chargers, the maximum output of the chargers, the charging upper limit time, available time bands, usage fees, congestion status, compatible vehicle types, etc.) via the charging facility management server.

[0017] In addition, the navigation device 6 is mounted on the vehicle 5, and is an in-vehicle device that displays a map around the vehicle's position based on the map data possessed by the navigation device 6 or map data acquired from outside, displays the current position of the vehicle on the map image, and provides route guidance along the set guidance route. Also, the navigation device 6 is connected to a vehicle control ECU and a battery remaining amount meter mounted on the vehicle 5 via an in-vehicle network such as CAN, and can obtain information regarding the in-vehicle battery 8 (for example, the current remaining energy amount, cruising range information defined for each vehicle, maximum power reception information, etc.), and can transmit such information to the communication terminal 1 using wireless communication such as Bluetooth or wired communication by connecting a cable.

[0018] On the other hand, the communication terminal 1 is possessed by a user who is a passenger of the vehicle, and is an information terminal equipped with a communication function, a navigation function, etc., and for example, a mobile phone, a smartphone, a tablet terminal, a personal computer, etc. are applicable. In particular, when the communication terminal 1 is a terminal capable of executing an application such as a smartphone, an application program that guides a recommended route to the destination and a charging facility for charging is installed as one of the applications when the destination is set. Note that the function of guiding these routes and charging facilities may be a part of the navigation function that provides route guidance to the destination, or may be executed by an application program different from the navigation function.

[0019] Furthermore, the communication network 7 includes numerous base stations located throughout the country and communication companies that manage and control each base station, and is constructed by connecting the base stations and communication companies to each other via wired (optical fiber, ISDN, etc.) or wireless connections. Here, each base station has a transceiver (transceiver) and an antenna that communicates with the communication terminal 1. In addition to conducting wireless communication between communication companies, the base stations also serve as the end of the communication network 7 and have the role of relaying communications from the communication terminal 1 within the range (cell) of the base station's radio waves to the information provision server 4.

[0020] Next, the configuration of the information provision server 4 in the driving support system 2 will be explained in more detail using Figure 2. As shown in Figure 2, the information provision server 4 comprises a server control unit 11, a distribution information DB 9 as an information recording means connected to the server control unit 11, a map information DB 14, and a server-side communication device 15.

[0021] The server control unit 11 is a control unit (MCU, MPU, etc.) that controls the entire information provision server 4, and is equipped with a CPU 21 as an arithmetic unit and control device, RAM 22 which is used as working memory when the CPU 21 performs various arithmetic processing, ROM 23 which stores control programs, etc., and flash memory 24 which stores programs read from ROM 23.

[0022] Furthermore, as mentioned above, the distribution information DB9 is a storage means that stores various information, particularly regarding charging facilities located throughout the country, as locations that are the target of information provision throughout the country. Here, a "charging facility" is a facility that can charge the on-board battery 8 of a vehicle 5, and it is common for it to have a parking space for parking the vehicle, and for a charger consisting of an operation panel and cables that connect to the vehicle to be installed around the parking space. In addition to facilities dedicated to charging vehicles, the above chargers may also be installed in parts of parking lots of commercial facilities such as shopping malls, car dealerships, coin parking lots, etc.

[0023] Here, Figure 3 shows an example of the information stored in the distribution information DB9. As shown in Figure 3, for charging facilities located throughout the country, information is stored regarding the facility ID, facility name (if it is installed as part of another facility, the name of that other facility), location of the charging facility, maximum output (kW) and number of installed chargers, and current availability. Here, "maximum output of the charger" is indicated in watts, i.e., power, and corresponds to the amount of energy that can be charged per unit time by that charger. A charger with a higher maximum output can charge more energy for the same charging time. However, this is only the maximum output, so the output may be lower depending on the situation. Specifically, a vehicle has an acceptable capacity, which is the amount of electrical energy that can be accepted when charging the on-board battery 8, or more specifically, the upper limit of energy that can be charged per unit time by the on-board battery (determined by the battery voltage of the on-board battery 8, etc.). As shown in Figure 4, even if you charge with a 150kW charger, if the vehicle's fast charging capacity is 30kW, you can only input a maximum of 30kW. Furthermore, during charging of the on-board battery 8, the amount of energy (W) that can be input per unit time gradually decreases as the on-board battery 8 approaches full charge. In particular, once the on-board battery 8 approaches full charge, control is implemented to significantly reduce the amount of energy (W) that can be input per unit time in order to prevent degradation of the on-board battery 8. As a result, for example, if the threshold is set to 80%, as shown in Figure 5, the amount of energy (W) that can be input per unit time decreases significantly when the SOC value is 80% or more of full charge. In other words, considering the same amount of energy to be charged, charging is more efficient when the remaining capacity of the on-board battery 8 is as low as possible. Note that 80% is just an example, and this value is set in various ways depending on the vehicle model. It may also change depending on the charging environment.

[0024] For example, the distribution information DB9 shown in Figure 3 indicates that there are four chargers with a maximum output of 150kW in the parking lot of “○○ Service Area” at location coordinates (x1, y1), and that the chargers are currently in use and are congested. It also indicates that there are two chargers with a maximum output of 20kW in the parking lot of “×× Parking” at location coordinates (x2, y2), and that the chargers are currently available. Furthermore, it indicates that there are four chargers with a maximum output of 90kW at the dedicated charging station “〇× Stand” at location coordinates (x3, y3), and that the chargers are currently available. The information provision server 4 can obtain information on the usage status of charging facilities from the charging facility management server that manages charging facilities 10 throughout the country. The distribution information DB9 may also store other information about charging facilities besides the above (e.g., usage fees, available time slots, maximum charging time, compatible vehicle types, etc.).

[0025] Furthermore, the map information DB14 is a storage means in which map information is stored. Map information consists of various types of information necessary for route searching, route guidance, and map display, including road networks. For example, it consists of link data related to roads (links), node data related to node points, intersection data related to each intersection, location data related to facilities and other points, map display data for displaying maps, search data for searching for routes, and search data for searching for locations.

[0026] Furthermore, when the server control unit 11 receives a route search request from the communication terminal 1, it can also perform a route search from the departure point to the destination using the map information stored in the map information DB 14. Specifically, when a destination is set in the communication terminal 1, the communication terminal 1 sends the information necessary for route search, such as the departure point and destination, to the information provision server 4 along with the route search request. The information provision server 4, upon receiving the route search request, performs a route search using the map information it possesses and identifies a recommended route from the departure point to the destination. It then sends the identified recommended route to the requesting communication terminal 1. The communication terminal 1 then sets the received recommended route as the guidance route and provides travel guidance according to the guidance route. As a result, even if the map information possessed by the communication terminal 1 at the time of route search is an older version, or if the communication terminal 1 does not possess any map information at all, it is possible to set an appropriate guidance route based on the latest version of map information possessed by the information provision server 4.

[0027] Furthermore, the server control unit 11 can also perform route learning to find better routes for the above-mentioned route search. For example, when a user registers frequently visited locations, favorite locations, or locations to be visited in the future in the communication terminal 1, route learning is performed on routes to those locations, using the current location, home, or workplace as the starting point. Also, as mentioned above, it is assumed that the vehicle 5 is equipped with an on-board battery 8, so route learning is performed taking into account the charging efficiency of the on-board battery 8.

[0028] However, if the communication terminal 1 has map information, the above route search processing and route learning can be performed on the communication terminal 1 instead of the information provision server 4. Also, the above route search processing and route learning may be performed on another server that has map information instead of the information provision server 4. In that case, the map information DB 14 is not necessarily required on the information provision server 4.

[0029] On the other hand, the server-side communication device 15 is a communication device for communicating with the communication terminal 1, which is the target of information transmission and reception, via the communication network 7. In addition to the communication terminal 1, it is also possible to receive traffic information consisting of various types of information such as congestion information, regulation information, and traffic accident information transmitted from the Internet network and traffic information centers, such as VICS (registered trademark: Vehicle Information and Communication System) centers.

[0030] Next, the general configuration of the communication terminal 1 owned by the user will be explained using Figure 6. Figure 6 is a schematic block diagram showing the control system of the communication terminal 1 according to this embodiment. In the following explanation, the case in which the communication terminal 1 is a smartphone will be used as an example.

[0031] As shown in Figure 6, the communication terminal 1 is configured by connecting the following to the data bus BUS: a CPU 31, a memory 32 that stores user information (user ID, name, etc.) and application programs related to the user who possesses the communication terminal 1, an input / output unit 35 which is an interface for a microphone 33 and a speaker 34, a display 36 which is made up of a liquid crystal display panel, an input operation unit 37 which is made up of a touch panel and a keyboard, a GPS 38, a transmit / receive circuit unit (RF) 39 which sends and receives signals to and from base stations of the communication network 7, and a BT communication device 40 for Bluetooth communication.

[0032] Here, the CPU 31 built into the communication terminal 1 is a control means for the communication terminal 1 that performs various operations according to the operation program stored in the memory 32, and together with the memory 32, constitutes the communication terminal control unit 41. Furthermore, the various processing contents of the communication terminal control unit 41 are displayed on the display 36 as needed. The communication terminal control unit 41, together with the server control unit 11 of the information provision server 4 mentioned above, has various means as processing algorithms. For example, the charging facility information acquisition means acquires charging facility information, including the location of the charging facility candidate and the maximum output of the charger equipped at the charging facility candidate, targeting charging facility candidates that are candidates for charging during travel to the destination, among charging facilities that are capable of charging the on-board battery 8 that supplies power to the vehicle's drive source. The battery information acquisition means acquires maximum power receiving information that indicates the amount of power that can be accepted when charging the on-board battery 8 equipped in the vehicle. The cruising range acquisition means acquires cruising range information that indicates the cruising range of the vehicle in relation to the remaining charge of the on-board battery 8. The charging efficiency determination means determines the charging efficiency when charging the onboard battery 8 at a candidate charging facility along each candidate route from the departure point to the destination, based on charging facility information, maximum power receiving information, and cruising range information. The route learning means uses the determination result of the charging efficiency determination means to set a higher priority for candidate routes with higher charging efficiency among the candidate routes from the departure point to the destination, and performs route learning according to the set priority. In other words, the server control unit 11 and the communication terminal control unit 41 are examples of the charging facility information acquisition means, battery information acquisition means, cruising range acquisition means, charging efficiency determination means, and route learning means.

[0033] Furthermore, the communication terminal 1 can communicate via the transmitting and receiving circuit unit 39, enabling it to perform not only voice calls but also internet communication, receive information about charging facilities from the information provision server 4, and receive traffic information consisting of various types of information such as congestion information, regulation information, and traffic accident information transmitted from traffic information centers, such as VICS (registered trademark) centers and probe centers.

[0034] Furthermore, memory 32 is a storage medium that stores user information (user ID, name, home address, etc.) and map information related to the user who possesses the communication terminal 1, as well as the user's web browsing history, registered locations registered by the user, the user's movement history which is a history of location information detected based on GPS 38 and other sensors, and schedule information. Various application programs, including the route learning processing program (Figure 7) described later, are also stored. Memory 32 may also be configured as a hard disk, memory card, etc.

[0035] Furthermore, memory 32 also stores identification information necessary for communication via Bluetooth (such as the BT address).

[0036] In addition to outputting voice for phone calls, the speaker 34 also outputs voice guidance that directs the user along a designated route (the user's planned travel route) based on instructions from the communication terminal control unit 41 when the navigation function is running.

[0037] Furthermore, the display 36 is mounted on one side of the casing and uses a liquid crystal display or an organic EL display, etc. It displays the top screen for running various applications installed on the communication terminal 1, screens related to the running application (internet screen, email screen, navigation screen, etc.), and various information such as images and videos. In particular, in this embodiment, a screen that guides the user to the destination and charging facilities is also displayed.

[0038] Furthermore, the input operation unit 37 is composed of a touch panel located on the front of the display 36 and hard buttons located on the casing. The communication terminal control unit 41 controls the system to perform various operations based on electrical signals output by pressing the touch panel or hard buttons. The input operation unit 37 can also be composed of various keys such as number / character input keys, cursor keys to move the cursor for selecting displayed content, and a confirmation key to confirm the selection.

[0039] Furthermore, the GPS38 can detect the current location and time of the communication terminal 1 (i.e., the user) by receiving radio waves generated by artificial satellites. In addition to the GPS38, the system may also be configured to include other devices (such as a gyro sensor) for detecting the current location and orientation of the communication terminal 1.

[0040] Furthermore, the transmitting / receiving circuit section 39 is a circuit section for transmitting and receiving signals to and from base stations of the communication network 7 using communication standards such as 3G, 4G, and LTE.

[0041] Furthermore, the BT communication device 40 is a module for performing wireless communication via Bluetooth. The communication terminal 1 communicates with the navigation device 6 and other devices within the communication range via Bluetooth wireless communication through the BT communication device 40.

[0042] Next, the route learning processing program executed in the communication terminal 1 having the above configuration will be described with reference to Figure 7. Figure 7 is a flowchart of the route learning processing program according to this embodiment. Here, the route learning processing program is executed in the communication terminal 1 when a predetermined application program is started, for example when a frequently visited location is registered by the user, when a favorite location is registered, or when a location to be visited in the future is registered in the schedule, and the program performs route learning for the routes to those locations. The program shown in the flowchart in Figure 7 below is stored in the memory 32 of the communication terminal 1 and executed by the CPU 31.

[0043] In this embodiment, the route learning processing program uses the vehicle's current location, home location, or workplace as the starting point and performs route learning for the route from the starting point to the destination. The destination can be, for example, a place the user frequently visits, a favorite place, or a place scheduled to be visited in the future, as registered in the communication terminal 1. However, any location entered by the user may be used as both the starting point and the destination. Route learning may be performed for multiple routes by changing the starting point and destination. Furthermore, in this embodiment, an example of performing the above route learning using the virtual environment shown in Figure 8 will be described. In this virtual environment, as shown in Figure 8, there are a total of 128 links (8x8 + 8x8) that the vehicle can travel on from the starting point to the destination. It is assumed that all links are the same length (for example, 5km). It is also assumed that there are charging facilities at the intersections where each link intersects (except for the starting point and destination). That is, these 79 charging facilities become candidate charging facilities for the journey to the destination. Furthermore, the maximum output of the chargers at each candidate charging facility varies. There are four candidate charging facilities with a maximum output of 150kW, eight with a maximum output of 90kW, twelve with a maximum output of 50kW, and fifty-five with a maximum output of 20kW. In the following explanation, the intersections of each link containing a candidate charging facility are indicated by the combinations of A to I for left and right, and 0 to 8 for up and down. That is, the starting point is A0 and the destination is I8. In addition, unless otherwise specified, the following explanation will generally ignore the charging limit time and available time set at each charging facility, and will also ignore waiting times at charging facilities. In other words, it will be assumed that it is always possible to charge to the maximum limit without waiting at each candidate charging facility.

[0044] First, in step 1 (hereinafter abbreviated as S), the CPU 31 acquires information about the vehicle 5 in which the user is riding and information about potential charging facilities, which are necessary for route learning. However, the user does not necessarily need to be riding in vehicle 5 when route learning is performed. The information about vehicle 5 includes maximum power receiving information, which indicates the amount of electrical energy that can be accepted when charging the onboard battery 8 equipped in vehicle 5, i.e., the upper limit of energy that can be charged per unit time by the onboard battery 8 (hereinafter referred to as the acceptable capacity), and driving range information, which indicates the driving range of the vehicle based on the remaining charge of the onboard battery 8. The driving range information is information that indicates, for example, how much energy the vehicle can travel by consuming how far (corresponding to fuel efficiency). On the other hand, the information about potential charging facilities is acquired, including the location of the potential charging facility and the maximum output of the charger equipped in the potential charging facility.

[0045] Furthermore, "vehicle information 5" will be obtained from the navigation device 6 via wireless communication such as Bluetooth. On the other hand, "charging facility candidate information" will be obtained from the information provision server 4.

[0046] Subsequently, in S2, the CPU 31 calculates the minimum number of charges required to travel to the destination (hereinafter referred to as the minimum number of charges) based on the cruising range information acquired in S1 and the distance from the departure point to the destination. It is assumed that the onboard battery 8 is fully charged at the start of departure. For example, if a vehicle (hereinafter referred to as vehicle A) has a drivable distance equivalent to 6 links shown in Figure 8 when the onboard battery 8 is fully charged, it will need to travel a distance equivalent to 16 links from the departure point to the destination, so the minimum number of charges will be 2. It is assumed that the links are one-way from the departure point to the destination, and that travel in the direction of returning to the departure point is not performed. On the other hand, if a vehicle (hereinafter referred to as vehicle B) has a drivable distance equivalent to 3 links shown in Figure 8 when the onboard battery 8 is fully charged, the minimum number of charges will be 5.

[0047] Next, in S3, the CPU 31, based on the maximum power receiving information and charging facility information acquired in S1, limits the target charging facilities to only those capable of the most efficient charging for the vehicle, and maps the charging facility candidates to the route information to the destination. For example, for vehicle A, which has an acceptable capacity of 150kW, the most efficient charging is possible at a charging facility candidate equipped with a charger with a maximum output of 150kW. As shown in Figure 9, only charging facility candidates equipped with a charger with a maximum output of 150kW are mapped to the route information to the destination. On the other hand, for vehicle B, which has an acceptable capacity of 30kW, as shown in Figure 4, the charging efficiency does not change as long as the charger has a maximum output exceeding the acceptable capacity. Therefore, the most efficient charging is possible at a charging facility candidate equipped with a charger with a maximum output greater than 30kW. As shown in Figure 10, charging facility candidates equipped with a charger with a maximum output of 50kW, a charger with a maximum output of 90kW, and a charger with a maximum output of 150kW are mapped to the route information to the destination.

[0048] Next, in S4, the CPU 31 uses the route information mapped in S3 to calculate all possible route combinations that the vehicle can take from the departure point to the destination. Note that only the charging station candidates mapped in S3 are considered to be available for charging. It is also assumed that the onboard battery 8 is fully charged at the start of departure, and that it will be fully charged when stopping at a charging station candidate.

[0049] As a result, for vehicle A, which has a driving range of 6 links on a full charge (e.g., 60kWh) and a receiving capacity of 150kW, six possible routes are calculated as shown in Figure 11 (a) to (f). For example, to explain route (a), first travel 5 links from A0 (starting point) to B4 and charge to full at B4. Then travel 4 links from B4 to D6 and charge to full at D6. Then travel 3 links from D6 to E8 and charge to full at E8. After that, travel 4 links from E8 to I8 (destination). Similarly, to explain route (d), first travel 5 links from A0 (starting point) to E1 and charge to full at E1. Then travel 5 links from E1 to H3 and charge to full at H3. After that, the route involves traveling across six links from H3 to I8 (the destination). The other routes are as shown in Figure 11. As shown in Figure 11, the number of charging stops required to reach the destination differs for each route, with some routes requiring two stops and others requiring three.

[0050] On the other hand, for vehicle B, which has a driving range of 3 links on a full charge (e.g., 30kWh) and a receiving capacity of 30kW, 28 possible routes are calculated as shown in Figure 12 (a) to (β). For example, to explain route (a), first, travel 3 links from A0 (starting point) to C1 and charge to full at C1. Then, travel 2 links from C1 to D2 and charge to full at D2. Then, travel 2 links from D2 to D4 and charge to full at D4. Then, travel 2 links from D4 to D6 and charge to full at D6. Then, travel 1 link from D6 to D7 and charge to full at D7. Then, travel 2 links from D7 to E8 and charge to full at E8. Then, travel 2 links from E8 to G8 and charge to full at G8. After that, the route involves traveling two links from G8 to I8 (destination). Similarly, for route (q), first, the vehicle travels three links from A0 (starting point) to C1, where it charges to full capacity. Then, it travels two links from C1 to D2, where it charges to full capacity. After that, it travels two links from D2 to F2, where it charges to full capacity. Then, it travels three links from F2 to H3, where it charges to full capacity. After that, it travels three links from H3 to I5, where it charges to full capacity. Finally, the route involves traveling three links from I5 to I8 (destination). The other routes are as shown in Figure 12. As shown in Figure 12, the number of charging stops to the destination differs for each route, with routes requiring 5 stops, 6 stops, and 7 stops.

[0051] In the above example, the route is calculated assuming that the vehicle will fully charge at each candidate charging station it visits. However, in reality, it is not necessary to fully charge the vehicle as long as it charges enough to travel to the next candidate charging station or destination. The routes that the vehicle can take from the starting point to the destination, calculated in S4 (Figures 11(a)~(f), 12(a)~(β)), are candidate routes (candidate paths) for the vehicle to travel from the starting point to the destination.

[0052] Next, in S5, the CPU 31 calculates a priority for each route calculated in S4. The priority is determined by the charging efficiency (hereinafter simply referred to as charging efficiency) of the route when charging the vehicle battery 8 at the candidate charging facilities along the route. Specifically, in S5, the charging efficiency is judged based on the number of charging cycles, and the fewer the number of charging cycles required to reach the destination, the more likely it is to be a candidate route with high charging efficiency, and the higher the priority is determined.

[0053] For example, when calculating the priority of the six routes shown in Figure 11 for vehicle A, which has a driving range of 6 links and an accepting capacity of 150kW when fully charged (e.g., 60kWh), routes (c) to (e), which require 2 charges, are calculated to have the highest priority, followed by routes (a), (b), and (f), which require 3 charges.

[0054] On the other hand, when calculating the priority of the 28 possible routes shown in Figure 12 for vehicle B, which has a driving range equivalent to three links and an accepting capacity of 30kW when fully charged (e.g., 30kWh), routes (q)~(s), (w), and (z)~(β) with 5 charging cycles are calculated to have the highest priority, followed by routes (d)~(p), (t)~(v), (x), and (y) with 6 charging cycles, and finally route (a)~(c) with 7 charging cycles, which has the lowest priority.

[0055] Subsequently, in S6, the CPU 31 determines whether or not to include candidate charging facilities that offer the next most efficient charging for the vehicle as targets for charging. For example, if the number of routes (number of candidate routes) calculated in S4 is less than a threshold, it is expected that there will be insufficient learning material and that recommended candidate routes may be missed. Therefore, in order to increase the number of candidate routes, it is decided to include candidate charging facilities that offer the next most efficient charging for the vehicle as targets for charging. The more candidate charging facilities that are targeted for charging, the more candidate routes will be calculated in S4. It is expected that increasing the number of candidate routes will increase the amount of learning and enable the search for a more optimal route. However, if the number of candidate routes increases too much, the processing load related to the learning process will increase and the processing time will also increase. Therefore, it is desirable to set the threshold considering the processing load and processing time of the control unit. For example, set it to 5.

[0056] Then, if it is determined that the next most efficient charging facility candidate for the vehicle should also be included as a charging target (S6:YES), based on the maximum power receiving information and charging facility information obtained in S1, the next most efficient charging facility candidate for the vehicle should also be included as a charging target and mapped to the route information to the destination (S7). For example, in S3, for vehicle A, which has an acceptable capacity of 150kW, only charging facility candidates equipped with a charger with a maximum output of 150kW were included as charging targets, but in S7, charging facility candidates equipped with a 90kW charger, which is the next best option for charging, will also be included as a charging target. On the other hand, in S3, for vehicle B, which has an acceptable capacity of 30kW, charging facility candidates equipped with a 50kW charger, a 90kW charger, and a 150kW charger were included as charging targets, but in S7, charging facility candidates equipped with a 20kW charger, which is the next best option for charging, will also be included as a charging target.

[0057] Then, after increasing the number of candidate charging facilities in S7, the system recalculates all possible route combinations from the starting point to the destination, including the newly added candidate charging facilities (S4), and then calculates a priority for each of the calculated routes (S5). After that, the judgment process in S6 is performed again, and if necessary, the system also adds the next most efficient candidate charging facility to the list of candidates charging facilities.

[0058] On the other hand, if it is determined that there is no need to include the next most efficient charging facility for the vehicle in the charging target (S6:NO), the process proceeds to S8.

[0059] In S8, CPU31 reads all the routes calculated in S4 up to that point and determines them as candidate routes from the starting point to the destination to be studied.

[0060] Subsequently, in S9, the CPU 31 compares the priority calculated in S5 for each candidate path determined in S8. If there are multiple candidate paths with the same priority, the CPU 31 determines the priority using conditions other than the number of charge cycles. Examples of these other conditions include (1) to (4) below. (1) The more candidate routes there are, the higher the priority will be calculated. (2) Candidate routes with shorter travel times to the destination are given higher priority. (3) Candidate routes with less variation in arrival time to the destination will be given a higher priority. (4) Candidate routes with a lower SOC value (the ratio of remaining energy to a fully charged vehicle battery 8) during charging are given a higher priority.

[0061] Furthermore, regarding the conditions for calculating priority in S9, (1) to (3) differ from S5 in that priority is calculated based on criteria other than charging efficiency. First, regarding (1), when actually driving, if it becomes necessary to change the route, for example, if a planned charging facility is closed or the planned road is closed and driving is impossible, the more options there are, the more flexibility there is in changing the route, which is advantageous for the user. Therefore, candidate routes with more options are assigned a higher priority. For example, when calculating the priority of the six routes shown in Figure 11 for vehicle A, which has a driving range of 6 links and an accepting capacity of 150kW when fully charged (e.g., 60kWh), using condition (1), after the first charge is completed, routes (a) to (c) have two options: the next charge will be at either D6 or G5, while routes (d) to (f) have three options: the next charge will be at either H2, G5 or I1. In other words, route (d) to (f) has more options ahead of it than route (a) to (c), and is therefore calculated to have a higher priority. Similarly, the number of options after the second and third charging cycles are compared, and routes with more options ahead are calculated to have a higher priority.

[0062] Next, regarding (2), generally, the faster the arrival time to the destination, the more advantageous it is for users who are in a hurry. Therefore, candidate routes with shorter travel times to the destination are given higher priority. The travel time to the destination is calculated taking into account the time required for charging and the time required to start charging. For example, this can be estimated from the congestion status and the number of chargers at candidate charging facilities, and this information can be obtained from Information Center 3 (Figure 3). Furthermore, it is desirable to estimate the arrival time to the destination by obtaining traffic information from an external server and taking into account road congestion.

[0063] Next, regarding (3), generally speaking, the less variation there is in the arrival time to the destination, the more advantageous it is for users who seek stable travel to their destination (i.e., users who want to avoid problems such as significant delays in arriving at their destination). Therefore, the less variation there is in the arrival time to the destination, the higher the priority should be set. The amount of variation in arrival time to the destination can be estimated, for example, from the past travel results of other vehicles or the changes in congestion status at potential charging facilities for each time of day. This information can be obtained from Information Center 3. Furthermore, it is desirable to estimate the amount of variation in arrival time to the destination by also considering past road congestion status obtained from external servers.

[0064] On the other hand, (4) is a condition for calculating priority that takes into account charging efficiency other than the number of charges. As explained using Figure 5, charging is more efficient when the SOC value (the ratio of remaining energy to a fully charged vehicle battery 8) is low, so candidate routes with a lower SOC value at the time of charging are given a higher priority. The SOC value at the time of charging can be determined from Figures 11 and 12. For example, comparing routes (a) and (f) shown in Figure 11, the SOC value for route (a) is 30kW at the time of the third charge, while the SOC value for route (f) is 20kW at the time of the third charge. It should be noted that in performing the above calculation, it is assumed that the maximum capacity of the battery is 60kWh, that 10kWh is consumed during driving on one link, and that the battery is fully charged on the second charge. In other words, for the third charge, route (f) has a lower SOC value at the time of charging, so it is given a higher priority.

[0065] Furthermore, the user can select and set which of the conditions (1) to (4) to use when calculating the priority of S9. For example, the user can select their preferred priorities (e.g., 'prioritize options', 'prioritize arrival time') in advance on the route learning settings screen. As a result, route learning tailored to the user's preferences becomes possible.

[0066] Furthermore, the device may automatically set which of conditions (1) to (4) to use based on the vehicle's driving history. Specifically, the user's driving history is stored in memory 32, etc., and the user's preferences (for example, a tendency to rush to reach the destination, a tendency to prioritize charging efficiency, etc.) are estimated from that driving history. Then, the device decides which of conditions (1) to (4) to use according to the estimated user preferences.

[0067] Furthermore, between the priority calculated in S5 and the priority calculated in S9, the priority calculated in S5 takes precedence. That is, first, the priority is calculated for each candidate route in S5, and if there are multiple candidate routes with the same priority, the priority calculated in S9 is used to rank those candidate routes in descending order of priority. However, the priority calculated in S9 may also be given priority.

[0068] Next, in S10, the CPU 31 performs route learning from the starting point to the destination, according to the candidate routes determined in S8 and the priority levels calculated for those candidate routes in S5 and S9. If, for example, the process in S10 is skipped and route learning is performed without performing the processes in S1 to S9, the number of routes to be learned from A0 to I8 in the virtual environment shown in Figure 8 would be 32,768 (2 to the power of 15). These are all routes that can reach the destination, ignoring charging efficiency and user preferences, and include many routes that are clearly not recommended for the user. Therefore, the learning process becomes extremely inefficient, and the processing load and processing time related to the learning process also increase.

[0069] On the other hand, in the driving support system 2 of this embodiment, a number of routes are narrowed down to candidate routes based on the vehicle's information and charging facility information, and then priority is calculated considering charging efficiency and user preferences. This improves the efficiency of route learning. In other words, it becomes possible to efficiently learn which routes are recommended when a user travels from the starting point to the destination. In addition to charging efficiency and user preferences, the route learning in S10 may also consider the necessary travel fees, road traffic conditions, and the user's past driving history to learn which routes are recommended for the user. The results of the route learning in S10 are stored in memory or elsewhere. By repeating route learning, it becomes possible to search for routes recommended for the user with greater accuracy.

[0070] Subsequently, in S11, the CPU 31, if the user wishes to travel to a destination, proposes a route to the user based on the route learning results in S10. For example, it displays information on recommended routes to the destination (locations frequently visited by the user, favorite locations, or locations scheduled to be visited in the future, registered on the communication terminal 1) and information on charging facilities to stop at for charging on the display 36 of the communication terminal 1. Basically, the system proposes candidate routes with the highest priority calculated in S5 and S9 to the user, but depending on the situation, the candidate route with the highest priority calculated may not necessarily be proposed to the user. The system may propose more than one route. The system may also incorporate the user's reaction to the proposed routes (such as which routes the user showed interest in) into the learning results. Alternatively, the processing from S11 onwards may be omitted, and the system may simply save the learning results and terminate.

[0071] Furthermore, if the user selects one of the one or more routes proposed by the communication terminal 1 in S11 and begins driving, a determination process is performed from the start of driving until arrival at the destination to determine whether or not the destination can be reached by driving according to the selected route. For example, if an event occurs such as the charging facility candidate that the user was planning to use being closed, or the road that the user was planning to use being closed and therefore unable to drive, it may be determined that the destination cannot be reached. In such cases, it is desirable to repeat the process from S2 onwards, using the vehicle's current location as the starting point, and propose a newly recommended route to the user.

[0072] As described in detail above, the communication terminal 1, information provision server 4, and computer programs executed on the communication terminal 1 and information provision server 4 according to this embodiment acquire charging facility information, including the location of the charging facility candidate and the maximum output of the charger provided by the charging facility candidate, targeting charging facility candidates that are candidates for charging during travel to the destination (S1), acquire maximum power receiving information indicating the amount of power that can be accepted when charging the onboard battery 8 equipped in the vehicle 5 (S1), acquire driving range information indicating the driving range of the vehicle relative to the remaining charge of the onboard battery 8 (S1), and based on the charging facility information, maximum power receiving information and driving range information, determine the charging efficiency when charging the onboard battery 8 at a charging facility candidate along each candidate route from the departure point to the destination (S5), thereby enabling support for efficient charging for the user. Furthermore, by using the results of the charging efficiency determination, candidate routes with higher charging efficiency are given a higher priority among the candidate routes from the origin to the destination (S5), and route learning is performed according to the set priority (S10). This makes it possible to narrow down the number of candidate routes in advance compared to conventional methods, and it is also possible to improve the learning efficiency by considering the priority. Furthermore, among the candidate routes from the starting point to the destination, the candidate route that allows the vehicle to reach the destination with the fewest number of charges is determined to be the candidate route with the highest charging efficiency (S5). This makes it possible to provide support based on the recommended route that minimizes the number of charges during travel to the destination. Furthermore, if there are multiple candidate routes that can reach the destination with the same number of charges, the charging efficiency is determined based on other conditions (S9). These other conditions can be selected and set by the user from among multiple candidates, or they can be set based on the vehicle's driving history. This allows for support based on recommended routes that also take the user's preferences into account.

[0073] It should be noted that the present invention is not limited to the embodiments described above, and various improvements and modifications are possible without departing from the spirit of the invention. For example, in this embodiment, the charging efficiency of charging the on-board battery 8 at a candidate charging facility along each candidate route from the starting point to the destination is determined, and route learning to the destination is performed based on the determination result (S10). However, route learning may be omitted, and the system may simply guide the system to prioritize routes according to the charging efficiency of each candidate route.

[0074] Furthermore, although this embodiment describes an example where the communication terminal 1 is applied to a smartphone, it can also be applied to other types of communication terminals as long as they have communication functions with the vehicle and the information provision server 4. For example, it can be applied to mobile phones, tablet terminals, personal computers, etc. Also, the in-vehicle navigation device 6 may also function as the communication terminal 1, or other in-vehicle devices or vehicle control ECUs may perform processing in place of the communication terminal 1.

[0075] Furthermore, in this embodiment, although the communication terminal 1 was the main execution unit for the route calculation and charging efficiency determination processes (S1 to S10) in the route learning processing program shown in Figure 7, the information provision server 4 may also perform some or all of these processes. That is, the information provision server 4 may be the driving support device of the present invention, or the present invention can be applied to a system including the communication terminal 1 and the information provision server 4. [Explanation of Symbols]

[0076] 1...Communication terminal (driving support system), 2...Information provision system, 4...Information provision server (driving support system), 5...Vehicle, 6...Navigation system, 8...On-board battery, 9...Distribution information DB, 10...Charging facility, 11...Server control unit, 31...CPU, 32...Memory, 41...Communication terminal control unit

Claims

1. A charging facility information acquisition means acquires charging facility information, including the location of a candidate charging facility and the maximum output of the charger provided by the candidate charging facility, for a group of charging facilities that are candidates for charging during travel to the destination, among charging facilities that are capable of charging the vehicle's onboard battery, which supplies power to the vehicle's drive source. A battery information acquisition means that acquires maximum power receiving information indicating the amount of power that can be accepted when charging the vehicle's onboard battery, A means for acquiring driving range information that indicates the remaining driving range of a vehicle in relation to the remaining charge of the onboard battery, A driving support device having a charging efficiency determination means that determines the charging efficiency when charging the on-board battery at the charging facility candidate located along each candidate route from the departure point to the destination, based on the charging facility information, the maximum power receiving information, and the cruising range information.

2. The driving support device according to claim 1, further comprising a route learning means that uses the determination result of the charging efficiency determination means to set a higher priority for candidate routes from the starting point to the destination, with the candidate route having a higher charging efficiency, and performs route learning according to the set priority.

3. The driving support device according to claim 1 or 2, wherein the charging efficiency determination means determines that among the candidate routes from the starting point to the destination, the candidate route that allows the destination to be reached with fewer charging cycles is a candidate route with high charging efficiency.

4. The charging efficiency determination means, in cases where there are multiple candidate routes that can reach the destination with the same number of charges, determines the charging efficiency based on other conditions. The driving support device according to claim 3, wherein the other conditions can be selected and set by the user from among several candidates, or are set based on the vehicle's driving history.

Images