WO2025032809A1 - 情報処理装置、情報処理方法、および、情報処理プログラム - Google Patents

情報処理装置、情報処理方法、および、情報処理プログラム Download PDF

Info

Publication number
WO2025032809A1
WO2025032809A1 PCT/JP2023/029274 JP2023029274W WO2025032809A1 WO 2025032809 A1 WO2025032809 A1 WO 2025032809A1 JP 2023029274 W JP2023029274 W JP 2023029274W WO 2025032809 A1 WO2025032809 A1 WO 2025032809A1
Authority
WO
WIPO (PCT)
Prior art keywords
energy consumption
information processing
consumption amount
vehicle
processing device
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/JP2023/029274
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
令司 松本
慶輔 小川
健太 寺谷
亮 小笠原
誠 土方
憲彦 福島
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Pioneer Corp
Original Assignee
Pioneer Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Pioneer Corp filed Critical Pioneer Corp
Priority to PCT/JP2023/029274 priority Critical patent/WO2025032809A1/ja
Priority to JP2025539071A priority patent/JPWO2025032809A1/ja
Publication of WO2025032809A1 publication Critical patent/WO2025032809A1/ja
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram

Definitions

  • the present invention relates to an information processing device, an information processing method, and an information processing program.
  • the above conventional technology merely displays fuel efficiency information (e.g., how good or bad the fuel efficiency is compared to a reference value) for each travel section based on the correspondence between historical fuel efficiency information for each travel section and the location where the fuel efficiency was measured, but does not take into consideration the comparison of energy consumption between different moving objects. For this reason, the above conventional technology has room for improvement in terms of properly grasping the differences in energy consumption between different moving objects.
  • fuel efficiency information e.g., how good or bad the fuel efficiency is compared to a reference value
  • the present invention has been made in consideration of the above, and proposes an information processing device, an information processing method, and an information processing program that can appropriately grasp the difference in energy consumption between different moving objects.
  • the information processing device of claim 1 is characterized by comprising a first calculation unit that calculates a first energy consumption amount, which is the energy consumption amount when a first moving body moves in a specified area, a second calculation unit that calculates a second energy consumption amount, which is the energy consumption amount when a second moving body moves in the specified area, and a display control unit that displays information based on a comparison between the first energy consumption amount and the second energy consumption amount in the specified area on a map corresponding to the specified area.
  • the information processing method described in claim 15 is an information processing method executed by an information processing device, and is characterized by including a first calculation step of calculating a first energy consumption amount, which is the energy consumption amount when a first moving body moves in a specified area, a second calculation step of calculating a second energy consumption amount, which is the energy consumption amount when a second moving body moves in the specified area, and a display control step of displaying information based on a comparison between the first energy consumption amount and the second energy consumption amount in the specified area on a map corresponding to the specified area.
  • the information processing program described in claim 16 is an information processing program executed by an information processing device, and causes the information processing device to execute a first calculation step of calculating a first energy consumption amount, which is the energy consumption amount when a first moving body moves in a specified area, a second calculation step of calculating a second energy consumption amount, which is the energy consumption amount when a second moving body moves in the specified area, and a display control step of displaying information based on a comparison between the first energy consumption amount and the second energy consumption amount in the specified area on a map corresponding to the specified area.
  • FIG. 1 is a diagram illustrating an example of a system according to an embodiment.
  • FIG. 2 is a diagram illustrating an example of the configuration of the information processing apparatus according to the embodiment.
  • FIG. 3 is a conceptual diagram showing an example of calculation of energy consumption.
  • FIG. 4 is a diagram showing an example of a display mode of the judgment index in the partial region.
  • FIG. 5 is a flowchart showing a procedure of the pre-processing according to the embodiment.
  • FIG. 6 is a flowchart showing the procedure of the energy consumption calculation process according to the embodiment.
  • FIG. 7 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment.
  • a “moving body” will be described as a “vehicle” (automobile). Accordingly, “movement” will be expressed as “traveling.” For example, the expression “energy consumption when a moving body moves in a specified area” can be rephrased as “energy consumption when a vehicle travels in a specified area.”
  • EVs For example, in urban areas, vehicles frequently stop and start in a driving situation known as stop-and-go, making EVs more suitable than GVs (EVs consume less energy than GVs and are more energy efficient).
  • GVs are more suitable than EVs (the difference in energy consumption between EVs and GVs is small, and GVs have more advantages than EVs in terms of driving distance and charging time).
  • the proposed technology of the present invention proposes a method of visualizing to users the effect of switching vehicles, taking into account not only driving conditions but also regional characteristics.
  • information comparing the energy consumption of different mobile vehicles, for example GVs and EVs, taking into account regional characteristics is presented for each region on a map, allowing users to properly grasp the difference in energy consumption between GVs and EVs.
  • the first moving body is defined as a "GV” and the second moving body is defined as an "EV”, and information processing is exemplified assuming visualization of the switching effect when switching from a GV to an EV.
  • the proposed technology according to the present invention is also applicable to cases where the first moving body and the second moving body are both "GVs" or where the first moving body and the second moving body are both "EVs”.
  • the proposed technology according to the present invention can also realize visualization of the switching effect from one GV to another type of GV, or visualization of the switching effect from one EV to another type of EV.
  • At least one of the GV and EV can be replaced with an HEV (hybrid electric vehicle), a PHEV (plug-in hybrid electric vehicle), an FCEV (fuel cell electric vehicle), etc.
  • HEV hybrid electric vehicle
  • PHEV plug-in hybrid electric vehicle
  • FCEV fuel cell electric vehicle
  • FIG. 1 is a diagram showing an example of a system according to an embodiment.
  • Fig. 1 shows a system 1 as an example of a system according to an embodiment.
  • Information processing according to the proposed technology of the present invention (hereinafter, abbreviated as "information processing according to an embodiment") is realized in the system 1.
  • the system 1 includes a terminal device 10 and an information processing device 100.
  • the terminal device 10 and the information processing device 100 are connected to each other via a network N so as to be able to communicate with each other via a wired or wireless connection.
  • the system 1 may include any number of terminal devices 10 and any number of information processing devices 100.
  • the terminal device 10 may be an information processing terminal used by a user U who is considering switching to an EV.
  • the terminal device 10 may be a smartphone, a wearable device, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), etc.
  • the terminal device 10 may be a dedicated navigation device that is built into or mounted on a vehicle, i.e., an in-vehicle device.
  • the terminal device 10 as an in-vehicle device may have not only a navigation function but also a recording function (drive recorder function).
  • the terminal device 10 may also be equipped with various sensors.
  • the terminal device 10 may be equipped with various sensors such as a GPS sensor, an acceleration sensor, a gyro sensor, a camera, and an air pressure sensor.
  • the information processing device 100 can obtain various data such as the vehicle's position, speed, acceleration, and gravitational acceleration as sensor data detected by the various sensors. Note that if the vehicle itself is equipped with various sensors, the information processing device 100 may obtain sensor data from sensors that the vehicle has, rather than from sensors that the terminal device 10 has.
  • the information processing device 100 is a central device that handles information processing according to the embodiment. Specifically, the information processing device 100 calculates the amount of gasoline consumed (first energy consumption) when a GV (first mobile body) moves through a specified area, and calculates the amount of electricity consumed (second energy consumption) when an EV (second mobile body) moves through a specified area, and displays information based on a comparison between the amount of gasoline consumed and the amount of electricity consumed in the specified area on a map corresponding to the specified area.
  • the information processing device 100 can be implemented as a cloud computer (server device) that performs processing in the cloud, as opposed to the terminal device 10 being an edge computer that performs processing at the edge.
  • server device that performs processing in the cloud
  • the specified area refers to a partial area sbm obtained by dividing one piece of map data MP (for example, one map area including the entire Japanese archipelago) according to certain rules.
  • the partial area sbm may be called a regional mesh.
  • the partial area sbm may be each area in a heat map defined when visualizing data.
  • the size of the partial area sbm may be changed by a reduced scale according to the display of the map data MP, or by an operation by the user U.
  • Fig. 2 is a diagram showing a configuration example of the information processing device 100 according to an embodiment. As shown in Fig. 2, the information processing device 100 has a communication unit 110, a storage unit 120, and a control unit 130.
  • the communication unit 110 is realized by, for example, a network interface card (NIC) etc.
  • the communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the terminal device 10, for example.
  • NIC network interface card
  • the storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk.
  • the storage unit 120 may store, for example, data and programs related to the information processing according to the embodiment.
  • the storage unit 120 may include a map data storage unit 121, a driving performance data storage unit 122, a performance parameter storage unit 123, a specific parameter storage unit 124, and a consumption amount data storage unit 125.
  • the map data storage unit 121 stores, for example, nationwide map data MP.
  • the map data MP includes road data that represents a road network by a combination of links and nodes, and slope data (road sloping data).
  • a link refers to a section between characteristic points of a road.
  • a node refers to a characteristic point of a road, such as an intersection, a corner, or a dead end.
  • a link refers to a road section that is set based on a specific rule.
  • a link refers to a unit that divides a recorded section of a travel history based on a specific rule.
  • a link may be identified by a link ID.
  • the map data MP may also include facility information and object information around the road.
  • the object information includes information on land features such as signs such as road signs, road markings such as stop lines, road dividing lines such as center lines, and structures along the road, as well as information on temporary obstacles. Obstacles refer to factors that impede the passage of pedestrians and cyclists, such as puddles, sunken parts of the road, fallen objects, and drains (including parts blocked by nets).
  • the object information may also include highly accurate point cloud information of objects for use in estimating the vehicle's position, etc.
  • the map data MP is regularly divided into rectangular areas, and is composed of partial areas sbm that correspond to the divided areas (meshes) formed by the division.
  • the driving history data storage unit 122 stores history data (driving history data) of driving conditions when an arbitrary vehicle Vx, regardless of whether it is a GV or an EV, travels through a predetermined area, i.e., a partial area sbm, included in the map data MP. In other words, the driving history data storage unit 122 stores a driving history when the vehicle Vx travels through the partial area sbm included in the map data MP.
  • the driving performance data includes the position of the vehicle Vx in the partial area sbm, the acceleration when the vehicle Vx drives in the partial area sbm, the gravitational acceleration when the vehicle Vx drives in the partial area sbm, the speed v when the vehicle Vx drives in the partial area sbm, etc.
  • the actual result parameter storage unit 123 stores actual result parameters estimated for each partial region sbm based on the driving actual result data.
  • the unique parameter memory unit 124 stores a first unique parameter that depends on the vehicle type of a GV (hereinafter referred to as "GV vehicle V1”) specified by the user U, and a second unique parameter that depends on the vehicle type of an EV (hereinafter referred to as "EV vehicle V2”) specified by the user U.
  • GV vehicle V1 vehicle type of a GV
  • EV vehicle V2 vehicle type of an EV
  • the consumption data memory unit 125 stores the amount of gasoline consumed (first energy consumption) when a GV vehicle V1 (first moving body) of a type specified by the user U travels through the partial area sbm, and the amount of electricity consumed (second energy consumption) when an EV vehicle V2 (second moving body) of a type specified by the user U travels through the partial area sbm.
  • the control unit 130 is realized by a central processing unit (CPU), a micro processing unit (MPU), or the like executing various programs (e.g., the information processing program according to the embodiment) stored in a storage device inside the information processing device 100 using a RAM as a working area.
  • the control unit 130 is also realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • the control unit 130 has an acquisition unit 131, an estimation unit 132, a reception unit 133, a first calculation unit 134, a second calculation unit 135, a conversion unit 136, and a display control unit 137, and realizes or executes the functions and actions of the information processing described below.
  • the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 2, and may be other configurations as long as they perform the information processing described below.
  • the connection relationships between the processing units of the control unit 130 are not limited to the connection relationships shown in FIG. 2, and may be other connection relationships.
  • the acquisition unit 131 acquires driving performance data corresponding to each of the arbitrary vehicles Vx. For example, the acquisition unit 131 acquires driving performance data based on various sensors as time-series data that changes over time as the vehicle Vx travels. The acquisition unit 131 also stores the acquired driving performance data in the driving performance data storage unit 122.
  • the estimation unit 132 estimates a performance parameter indicating the driving condition of the vehicle Vx in each partial region sbm based on driving performance data when the vehicle Vx drives in the partial region sbm.
  • the performance parameter here is a parameter that is considered to be a parameter in a previous stage of the first energy consumption and does not change (i.e., does not depend on the vehicle type) depending on the vehicle type of the GV vehicle V1 (first moving body) specified by the user U.
  • the performance parameter is also a parameter that is considered to be a parameter in a previous stage of the second energy consumption and does not change (i.e., does not depend on the vehicle type) depending on the vehicle type of the EV vehicle V2 (second moving body) specified by the user U.
  • the estimation unit 132 may estimate, for each partial area sbm, a performance parameter indicating the driving conditions of the vehicle Vx in a specific road link included in the partial area sbm, based on the travel performance data when the vehicle Vx travels on the specific road link included in the partial area sbm.
  • the specific road link here may be a road link that constitutes a search result route between two points arbitrarily determined in the partial area sbm.
  • the estimation unit 132 also estimates in advance vehicle-type-independent performance parameters that are independent of either the vehicle type of the GV vehicle V1 specified by the user U or the vehicle type of the EV vehicle V2 specified by the user U, and stores the estimated performance parameters in the performance parameter storage unit 123.
  • the performance parameters are estimated in advance as a pre-processing before the user U specifies the GV vehicle V1 and the EV vehicle V2, and are held in the performance parameter storage unit 123.
  • the performance parameters are used in the subsequent information processing according to the embodiment (processing for calculating the first energy consumption amount and the second energy consumption amount).
  • the parts that are not dependent on either the type of GV vehicle V1 specified by the user U or the type of EV vehicle V2 specified by the user U are estimated and stored in advance, so there is no need to estimate actual parameters until the stage of calculating the energy consumption according to the information specified by the user U.
  • the information processing device 100 can reduce the calculation resources at the stage of calculating the energy consumption, and can shorten the time required to calculate the energy consumption.
  • the reception unit 133 receives a designation of a GV before the transfer from the user U. For example, the reception unit 133 receives a designation of a vehicle model that identifies the GV before the transfer from the user U. The reception unit 133 also receives a designation of an EV after the transfer from the user U. For example, the reception unit 133 receives a designation of a vehicle model that identifies the EV after the transfer from the user U.
  • First calculation unit 134 When the user U specifies a GV before the transfer (e.g., the model of the GV before the transfer), the first calculation unit 134 calculates the gasoline consumption (first energy consumption) when the specified GV vehicle V1 travels through the partial area sbm for each partial area sbm. For example, the first calculation unit 134 calculates the gasoline consumption of the GV vehicle V1 based on a first inherent parameter, which is a model-dependent parameter that changes according to the model of the GV vehicle V1 specified by the user U (i.e., depends on the model), and a model-independent actual parameter that has been estimated in advance.
  • the first inherent parameter used here is stored in the inherent parameter storage unit 124, for example, for each model of the GV vehicle V1.
  • the second calculation unit 135 calculates the power consumption (second energy consumption) when the specified EV vehicle V2 travels through each partial region sbm. For example, the second calculation unit 135 calculates the power consumption of the EV vehicle V2 based on a second inherent parameter that is a model-dependent parameter that changes according to the model of the EV vehicle V2 specified by the user U (i.e., depends on the model) and a model-independent actual parameter that has been estimated in advance.
  • the second inherent parameter used here is stored in the inherent parameter storage unit 124, for example, for each model of the EV vehicle V2.
  • the conversion unit 136 performs a conversion process for each combination of gasoline consumption (first energy consumption) and power consumption (second energy consumption) calculated for each partial region sbm to match the units between the gasoline consumption and power consumption included in the combination. For example, the conversion unit 136 converts the gasoline consumption and power consumption into carbon dioxide emissions, heat, or the price of consumed energy. In the case of power consumption, the price of consumed energy indicates the gasoline cost for the amount of gasoline consumed, and in the case of power consumption, indicates the electricity cost for the amount of power consumed.
  • the display control unit 137 displays information based on a comparison between the gasoline consumption and power consumption in the partial region sbm on the map data MP corresponding to the partial region sbm. Specifically, the display control unit 137 displays information based on a comparison between the gasoline consumption calculated for each partial region sbm and the power consumption calculated for each partial region sbm on the map data MP for each partial region sbm. More specifically, the display control unit 137 displays information based on a comparison between the gasoline consumption and power consumption for each combination of gasoline consumption and power consumption calculated for mutually common partial regions sbm among the combinations of gasoline consumption and power consumption calculated for each partial region sbm on the map data MP.
  • the display control unit 137 displays information based on the difference between gasoline consumption and power consumption as an indicator for deciding whether to trade-in from the GV vehicle V1 specified by the user U to the EV vehicle V2 specified by the user U. For example, the display control unit 137 displays in a visually determinable manner that the greater the difference between the gasoline consumption and the power consumption, the higher the energy efficiency will be when trading-in from the GV vehicle V1 to the EV vehicle V2.
  • FIG. 3 is a conceptual diagram showing an example of calculating the energy consumption. An overall image of the energy consumption calculation will be described with reference to FIG. 3.
  • FIG. 3 shows an example in which a portion of the target area AR1 in the map data MP is regularly divided into rectangles, so that the target area AR1 includes a plurality of partial areas sbm.
  • the target area AR1 is composed of 16 partial areas sbm, which are partial areas sbm1 to sbm16.
  • the information processing device 100 calculates the energy consumption for each of the partial areas sbm1 to sbm16.
  • the information processing device 100 calculates the gasoline consumption of the GV vehicle V1 designated by the user U and the power consumption of the EV vehicle V2 designated by the user U for each of the partial areas sbm1 to sbm16.
  • the information processing device 100 estimates the previous stage parameters of energy consumption (actual parameters) and stores the estimated parameters.
  • the information processing device 100 moves to the calculation process of energy consumption. Specifically, the information processing device 100 acquires inherent parameters according to the vehicle type of the specified vehicles (V1, V2). Then, the information processing device 100 calculates the energy consumption based on the inherent parameters (vehicle type-dependent parameters) and the previous stage parameters (vehicle type-independent parameters).
  • the estimation unit 132 estimates, through pre-processing, actual performance parameters that statistically indicate the movement conditions when each of the arbitrary vehicles Vx travels through the partial area sbm1. Specifically, the estimation unit 132 acquires driving performance data DA1 obtained when each of the arbitrary vehicles Vx travels through the partial area sbm1 from the driving performance data stored in the driving performance data storage unit 122.
  • the estimation unit 132 then fits the driving history data DA1 to a consumption estimation formula described below to estimate a GV history parameter PA11 as a history parameter corresponding to the GV that will be the vehicle before the transfer.
  • the estimation unit 132 also fits the driving history data DA1 to a consumption estimation formula to estimate an EV history parameter PA12 as a history parameter corresponding to the EV that will be the vehicle after the transfer.
  • the estimation unit 132 estimates performance parameters corresponding to a GV, which is the vehicle before the transfer, and performance parameters corresponding to an EV, which is the vehicle after the transfer, for the partial region sbm1. However, the estimation unit 132 calculates each performance parameter in the same manner for the partial regions sbm2 to sbm16. The estimation unit 132 then stores each performance parameter in the performance parameter storage unit 123.
  • the reception unit 133 receives a designation of the model of the GV before the transfer and a designation of the model of the EV after the transfer. In this case, the information processing device 100 transitions to the calculation process of the energy consumption amount.
  • the first calculation unit 134 acquires the GV vehicle V1 specific parameter PA11 from the specific parameter storage unit 124 as a parameter specific to the vehicle type of the GV vehicle V1 designated as the GV before the transfer. Then, the first calculation unit 134 calculates a first energy consumption amount estimated to be consumed when the GV vehicle V1 travels through the partial area sbm1 based on the GV actual result parameter PA11 and the GV vehicle V1 specific parameter PA11.
  • the first calculation unit 134 similarly calculates the first energy consumption amount that is estimated to be consumed when the GV vehicle V1 travels through each of the partial areas sbm2 to sbm16.
  • the second calculation unit 135 also acquires EV vehicle V2 specific parameters PA12 from the specific parameter storage unit 124 as parameters specific to the vehicle model of the EV vehicle V2 designated as the post-transfer EV. The second calculation unit 135 then calculates a second energy consumption amount estimated to be consumed when the EV vehicle V2 travels through the partial area sbm1 based on the EV actual results parameters PA12 and the EV vehicle V2 specific parameters PA12.
  • the second calculation unit 135 similarly calculates the second energy consumption amount estimated to be consumed when the EV vehicle V2 travels through each of the partial areas sbm2 to sbm16.
  • the first energy consumption amount is gasoline consumption amount
  • the second energy consumption amount is power consumption amount
  • the conversion unit 136 converts the fuel efficiency calculated for the partial region sbm1 into carbon dioxide emission amount, and similarly converts the power consumption calculated for the partial region sbm1 into carbon dioxide emission amount.
  • the conversion unit 136 also performs a process of converting the gasoline consumption amount and power consumption amount into carbon dioxide emission amount for each of the partial regions sbm2 to sbm16. Note that the conversion unit 136 may convert into the amount of heat generated according to the energy consumption, or the price of the energy consumed, instead of the carbon dioxide emission amount.
  • a pair of carbon dioxide emission amounts corresponding to the GV vehicle V1 and the EV vehicle V2 is obtained for each of the partial areas sbm2 to sbm16.
  • the display control unit 137 calculates the difference in carbon dioxide emission amounts for each partial area sbm based on the combination of carbon dioxide emission amounts obtained for each partial area sbm.
  • the display control unit 137 displays information based on the difference in the partial area sbm on the map data MP as a judgment index for judging how much the energy efficiency will increase or decrease if switching from the GV vehicle V1 to the EV vehicle V2.
  • the display control unit 137 displays, for each partial area sbm, information that serves as a judgment index for the user U to judge whether it is better to switch from the GV vehicle V1 to the EV vehicle V2 or whether it is not better to switch from the GV vehicle V1 to the EV vehicle V2.
  • the display control unit 137 displays the map data MP in which the judgment index is displayed for each partial area sbm to the terminal device 10 accessed in response to the operation of the user U.
  • FIG. 4 shows an example of the display mode of the judgment index in the partial area sbm.
  • the display control unit 137 displays in a visually determinable manner that the greater the difference in carbon dioxide emissions in the positive direction, the higher the energy efficiency when switching from the GV vehicle V1 to the EV vehicle V2.
  • the display control unit 137 also displays in a visually determinable manner that the greater the difference in carbon dioxide emissions in the negative direction, the lower the energy efficiency when switching from the GV vehicle V1 to the EV vehicle V2.
  • the display control unit 137 may visualize whether or not energy efficiency will be high by applying color coding or patterns according to the difference in carbon dioxide emissions to each partial area sbm.
  • the display control unit 137 may further display a numerical value indicating the difference in carbon dioxide emissions for each partial area sbm.
  • the information processing device 100 displays information on the transfer effect for all of partial areas sbm1 to sbm16.
  • the information processing device 100 may allow the user U to select any number of partial areas sbm from partial areas sbm1 to sbm16, and input an action ratio in each selected partial area sbm.
  • the user U may, for example, select partial areas sbm6 and sbm7, and then input an action ratio of "20%" in partial area sbm6, an action ratio of "80%" in partial area sbm6, etc.
  • the display control unit 137 may correct the difference in the amount of carbon dioxide emissions obtained in the partial area sbm6 by the action ratio of "20%”, and may correct the difference in the amount of carbon dioxide emissions obtained in the partial area sbm7 by the action ratio of "80%". For example, when the action ratio in the partial area sbm6 is "20%" and it is determined that the user U's car travel in the partial area sbm6 is less than the reference value, the display control unit 137 may correct the difference in the amount of carbon dioxide emissions to indicate that the GV vehicle V1 is more energy efficient than the EV vehicle V2.
  • the display control unit 137 may correct the difference in the amount of carbon dioxide emissions to indicate that the EV vehicle V2 is more energy efficient than the GV vehicle V1.
  • the information processing device 100 may select multiple partial areas sbm in which a predetermined or greater amount of movement history exists based on the movement history of the user U, and determine the behavior ratio based on the movement history of the user U in each of the selected partial areas sbm.
  • Equation 1 corresponds to a consumption estimation equation for calculating the energy consumption f_1sec for one second from the driving record data for one second.
  • Equation 2 is a consumption estimation equation generated based on Equation 1, and is used to calculate the energy consumption f_1mesh for one mesh from the driving record data for one mesh of the target.
  • the information processing device 100 can therefore use Equation 2 to calculate, for each mesh of the target (i.e., for each partial area sbm of the target), a first energy consumption amount that is estimated to be consumed when the GV vehicle V1 (first moving body) travels through that mesh.
  • the information processing device 100 can also use Equation 2 to calculate, for each mesh of the target (i.e., for each partial area sbm of the target), a second energy consumption amount that is estimated to be consumed when the EV vehicle V2 (second moving body) travels through that mesh.
  • K1, K2, and K3 are inherent parameters that differ for each vehicle model (depend on the vehicle model).
  • K1, K2, and K3 are the vehicle model-dependent parameters described above.
  • T is the total time of the driving history data in the target mesh. In other words, T is the time required for any vehicle Vx to drive the target road link.
  • v is the travel speed of any vehicle Vx when it is traveling on the target road link. Therefore, v(t) indicates the travel speed at a certain time t when any vehicle Vx is traveling on the target road link.
  • a is the acceleration at a certain time t when any vehicle Vx is traveling on the target road link. Therefore, a(t) indicates the acceleration at a certain time t when any vehicle Vx is traveling on the target road link.
  • g is the gravitational acceleration at a certain time t when any vehicle Vx is traveling on the target road link.
  • a1 and a2 are constants that are set according to the conditions of any vehicle Vx.
  • A(t) is a component that varies according to the road gradient of the target road link, but details will be given later.
  • the target road link is a road link included in the target mesh, and may be, for example, a road link that constitutes a search result route between two points arbitrarily determined in the target mesh.
  • the first term on the right side of Equation 2 indicates information about the energy consumed when the vehicle Vx is stopped with the drive source in operation.
  • the engine of the vehicle Vx is running at low speed without placing a load on the engine.
  • the engine is idling.
  • Equation 2 indicates information regarding the changes in energy consumed and recovered and potential energy when accelerating and decelerating vehicle Vx.
  • vehicle Vx accelerates and decelerates it means a driving state in which the speed of vehicle Vx changes over time.
  • vehicle Vx accelerates and decelerates it means a driving state in which the speed of vehicle Vx changes within a specified time.
  • the specified time is a fixed interval of time, for example, per unit time.
  • the third term on the right-hand side of Equation 2 indicates information about the energy consumed by the resistance generated when vehicle Vx is traveling.
  • the traveling state is one in which the speed of the moving body is constant within a specified period of time.
  • the resistance generated when vehicle Vx is traveling is a factor that changes the traveling state of vehicle Vx when vehicle Vx is traveling.
  • the resistance generated when vehicle Vx is traveling is the resistance generated in vehicle Vx due to weather conditions, road conditions, vehicle conditions, etc.
  • S2 indicates the part of the second term on the right side of Equation 2 that is the above-mentioned vehicle-type-independent parameter (performance parameter).
  • S3 indicates the part of the third term on the right side of Equation 2 that is the above-mentioned vehicle-type-independent parameter (performance parameter). Therefore, the estimation unit 132 calculates and stores S2 and S3 in advance in pre-processing.
  • Equation 4 is a calculation formula for calculating the moving distance d_1mesh of the target for one mesh, and is used in the conversion process for converting the units of the first energy consumption and the second energy consumption.
  • the information processing device 100 can calculate the fuel consumption estimated to be consumed when the GV vehicle V1 travels through one target mesh based on Equation 2 and Equation 4.
  • the information processing device 100 can also calculate the power consumption rate estimated to be consumed when the EV vehicle V2 travels through one target mesh based on Equation 2 and Equation 4.
  • the vehicles being compared for the switching effect are GV vehicles or EV vehicles, a comparison can be made between fuel consumption or power consumption rates, and the switching effect can be visualized based on this result.
  • a comparison can be made between fuel consumption or power consumption rates, and the switching effect can be visualized based on this result.
  • a conversion process into carbon dioxide emissions, heat, or the price of consumed energy is required to match the units.
  • Formula 5 is used to calculate the carbon dioxide emission amount CO2_1mesh_before when the GV vehicle V1 designated by the user U as the GV before the transfer (before) travels through one target mesh. Specifically, formula 5 is generated based on formula 2 (and formula 3) and CO2 count_before.
  • the estimation unit 132 has already estimated S2 and S3 for each mesh by pre-processing using the actual results parameters (actual results parameters for GV) corresponding to the GV that will be the vehicle before the transfer.
  • the first calculation unit 134 acquires K1_before, K2_before, and K3_before as vehicle-type specific parameters (GV vehicle V1 specific parameters) of the GV vehicle V1 specified by the user U.
  • the first calculation unit 134 calculates the first energy consumption estimated to be consumed when the GV vehicle V1 travels through the target mesh by applying S2 and S3 estimated for the target mesh and the vehicle-type specific parameters (K1_before, K2_before, and K3_before) to Equation 2.
  • the conversion unit 136 calculates the carbon dioxide emission amount CO2_1mesh_before by performing a conversion process in which the calculation result by the first calculation unit 134 is multiplied by, for example, the CO2 emission coefficient of gasoline as the CO2 coefficient_before.
  • Formula 6 is used to calculate the carbon dioxide emissions CO2_1mesh_after when EV vehicle V2 designated by user U as the EV after switching (after) travels through one target mesh. Specifically, formula 6 is generated based on formula 2 (and formula 3) and CO2 count_after.
  • the estimation unit 132 has already estimated S2 and S3 for each mesh by pre-processing using the actual results parameters (EV actual results parameters) corresponding to the EV that will be the vehicle after the switch.
  • the second calculation unit 135 acquires K1_after, K2_after, and K3_after as vehicle-type specific parameters (EV vehicle V2 specific parameters) of the EV vehicle V2 specified by the user U.
  • the second calculation unit 135 calculates the second energy consumption estimated to be consumed when the EV vehicle V2 travels through the target mesh by applying S2 and S3 estimated for the target mesh and the vehicle-type specific parameters (K1_after, K2_after, and K3_after) to Equation 2.
  • the conversion unit 136 calculates the carbon dioxide emission amount CO2_1mesh_after by performing a conversion process in which the calculation result by the second calculation unit 135 is multiplied by, for example, a basic emission coefficient as the CO2 coefficient_after.
  • the display control unit 127 can also calculate the transfer effect by applying the travel distance d_1mesh calculated using formula 4, the carbon dioxide emission CO2_1mesh_before calculated using formula 5, and the carbon dioxide emission CO2_1mesh_after calculated using formula 6 to formula 7.
  • Formula 7 is a formula for calculating the transfer effect_1mesh per distance in one target mesh, and uses the difference between the carbon dioxide emission CO2_1mesh_after and the carbon dioxide emission CO2_1mesh_before.
  • the information processing device 100 calculates the energy consumption and the switching effect based on formulas 1 to 7 has been described, but the information processing device 100 can actually use different calculation formulas between the GV before the switch and the EV after the switch.
  • the above formulas 1 and 2 may take into account the situation when the GV before the switch travels on a road link with a slope and the situation when the EV after the switch travels on a road link with a slope. This point will be described below.
  • Equation 1 If the gradient component a+g*sin( ⁇ ) included in Equation 1 satisfies the condition of Equation 8 (i.e., if any vehicle Vx is traveling up a road link with a road gradient ⁇ ), Equation 1 can be rewritten as Equation 9.
  • A(t) in Equation 2 can be expressed as Equation 10.
  • Equation 10 indicates the slope parallel component of the gravitational acceleration g at time t when any vehicle Vx was traveling up a road link with a road gradient ⁇ . Therefore, under the condition of Equation 8, the information processing device 100 calculates the first energy consumption amount using Equation 2 to which A(t) expressed by Equation 10 is applied.
  • Equation 1 when the gradient component a+g*sin( ⁇ ) included in Equation 1 satisfies the condition of Equation 11 (i.e. when any vehicle Vx is traveling downhill on a road link with a road gradient of ⁇ ), Equation 1 can be rewritten as Equation 12. This is because gasoline consumption can be considered to be essentially zero when traveling downhill due to the use of engine braking.
  • A(t) in Equation 2 can be expressed as Equation 13. Therefore, under the conditions of Equation 11, the information processing device 100 calculates the first energy consumption using Equation 2 to which A(t) expressed by Equation 13 is applied.
  • Equation 1 If the gradient component a+g*sin( ⁇ ) included in Equation 1 satisfies the condition of Equation 14 (i.e., if any vehicle Vx is traveling up a road link with a road gradient ⁇ ), Equation 1 can be rewritten as Equation 15. In this case, A(t) in Equation 2 can be expressed as Equation 16. Equation 16 indicates the slope parallel component of the gravitational acceleration g at time t when any vehicle Vx was traveling up a road link with a road gradient ⁇ . Therefore, under the condition of Equation 14, the information processing device 100 calculates the second energy consumption using Equation 2 to which A(t) expressed by Equation 16 is applied.
  • Equation 1 when the gradient component a+g*sin( ⁇ ) included in Equation 1 satisfies the condition of Equation 17 (i.e. when any vehicle Vx is traveling downhill on a road link with a road gradient of ⁇ ), Equation 1 can be rewritten as Equation 18.
  • Equation 18 ⁇ is the energy frequency rate.
  • EVs have the ability to recover energy through regenerative braking (power generation) when traveling downhill. This characteristic is taken into account in Equation 18, and as a result, A(t) in Equation 2 can be expressed as Equation 20. Therefore, under the conditions of Equation 17, the information processing device 100 calculates the second energy consumption amount using Equation 2 to which A(t) expressed by Equation 19 is applied.
  • Pre-treatment procedure] 5 is a flowchart showing a procedure of a pre-processing according to the embodiment.
  • the pre-processing is executed in advance as a process preceding the calculation process of the energy consumption amount.
  • the estimation unit 132 acquires driving performance data for each partial area sbm that constitutes the map data MP (step S501).
  • the estimation unit 132 estimates actual parameters (actual parameters for GV) corresponding to the GV, which is the vehicle before the transfer, for each partial area sbm based on the driving history data, and also estimates actual parameters (actual parameters for EV) corresponding to the EV, which is the vehicle after the transfer, for each partial area sbm (step S502).
  • the estimation unit 132 stores the performance parameters estimated in step S502 in the performance parameter storage unit 123 (step S503).
  • FIG. 6 is a flowchart showing the procedure of the energy consumption calculation process according to the embodiment.
  • the reception unit 133 determines whether or not input of information specifying the model of the GV before the transfer and information specifying the model of the EV after the transfer has been received from the user (step S601). While the reception unit 133 has not received input of information (step S601; No), it waits until it receives input of information.
  • the reception unit 133 receives input of information (step S601; Yes), it outputs the input information in association with the actual parameters estimated in step S502 to a processing unit that calculates the energy consumption (step S602). Specifically, the reception unit 133 outputs a set of information specifying the type of GV before the change and the actual parameters for GV estimated for each partial area sbm to the first calculation unit 134. The reception unit 133 also outputs a set of information specifying the type of EV after the change and the actual parameters for EV estimated for each partial area sbm to the second calculation unit 135.
  • a calculation process of the energy consumption amount based on the output data output in step S602 is executed (step S603).
  • the first calculation unit 134 acquires the vehicle type-specific parameters (parameters specific to the GV vehicle V1) of the GV vehicle V1 designated as the GV before the transfer, and calculates the gasoline consumption amount when the GV vehicle V1 travels in each partial area sbm based on the GV actual results parameters and the GV vehicle V1 specific parameters.
  • the second calculation unit 135 acquires the vehicle type-specific parameters (parameters specific to the EV vehicle V2) of the EV vehicle V2 designated as the EV after the transfer, and calculates the gasoline consumption amount when the EV vehicle V2 travels in each partial area sbm based on the EV actual results parameters and the EV vehicle V2 specific parameters.
  • the conversion unit 136 performs a conversion process for each combination of gasoline consumption and power consumption calculated for each partial region sbm to match the units between the gasoline consumption and power consumption included in that combination (step S604).
  • step S604 the conversion unit 136 performs a conversion process for each combination of gasoline consumption and power consumption calculated for each partial region sbm to match the units between the gasoline consumption and power consumption included in that combination.
  • the display control unit 137 calculates the difference between the converted data into which the gasoline consumption amount has been converted and the converted data into which the electricity consumption amount has been converted (step S605).
  • the display control unit 137 causes the map data MP in which the difference calculated in step S605 is visualized as the transfer effect for each partial area sbm to be displayed on the terminal device 10 of the user U (step S606).
  • the information processing device 100 calculates a first energy consumption amount when the GV vehicle V1 travels through the partial area sbm and a second energy consumption amount when the EV vehicle V2 travels through the partial area sbm, and displays information based on a comparison between the first energy consumption amount and the second energy consumption amount in the partial area sbm on the map data MP.
  • the first calculation unit 134 may calculate a first energy consumption amount when the GV vehicle V1 travels along a road link
  • the second calculation unit 135 may calculate a second energy consumption amount when the EV vehicle V2 travels along the road link.
  • the display control unit 137 may display information based on a comparison between the first energy consumption amount and the second energy consumption amount in the road link on the map data MP.
  • the information processing device 100 can calculate the first energy consumption and the second energy consumption using equation 2, etc., as in the above embodiment, but in this case, it is sufficient to calculate the first energy consumption and the second energy consumption for one link of the target, rather than for one mesh of the target.
  • the information processing device 100 calculates the difference between the carbon dioxide emission amount calculated from the first energy consumption amount and the carbon dioxide emission amount calculated from the second energy consumption amount as the switching effect.
  • the information processing device 100 may convert the first energy consumption amount and the second energy consumption amount into heat amounts, compare the heat amounts, and calculate the difference as the switching effect.
  • the information processing device 100 may also convert the first energy consumption amount and the second energy consumption amount into energy consumption prices, compare the energy consumption prices, and calculate the difference as the switching effect.
  • FIG. 7 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device 100 according to the embodiment.
  • the computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, a HDD 1400, a communication interface (I/F) 1500, an input/output interface (I/F) 1600, and a media interface (I/F) 1700.
  • the CPU 1100 operates based on the programs stored in the ROM 1300 or the HDD 1400, and controls each component.
  • the ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, and programs that depend on the hardware of the computer 1000, etc.
  • HDD 1400 stores programs executed by CPU 1100 and data used by such programs.
  • Communication interface 1500 receives data from other devices via a specified communication network and sends it to CPU 1100, and transmits data generated by CPU 1100 to other devices via the specified communication network.
  • the CPU 1100 controls an output device such as a display and an input device such as a keyboard via the input/output interface 1600.
  • the CPU 1100 acquires data from the input device via the input/output interface 1600.
  • the CPU 1100 also outputs generated data to the output device via the input/output interface 1600.
  • the media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200.
  • the CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program.
  • the recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.
  • the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130.
  • the CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a specified communication network.
  • each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure.
  • the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Educational Technology (AREA)
  • Navigation (AREA)
PCT/JP2023/029274 2023-08-10 2023-08-10 情報処理装置、情報処理方法、および、情報処理プログラム Pending WO2025032809A1 (ja)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/JP2023/029274 WO2025032809A1 (ja) 2023-08-10 2023-08-10 情報処理装置、情報処理方法、および、情報処理プログラム
JP2025539071A JPWO2025032809A1 (https=) 2023-08-10 2023-08-10

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2023/029274 WO2025032809A1 (ja) 2023-08-10 2023-08-10 情報処理装置、情報処理方法、および、情報処理プログラム

Publications (1)

Publication Number Publication Date
WO2025032809A1 true WO2025032809A1 (ja) 2025-02-13

Family

ID=94533742

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2023/029274 Pending WO2025032809A1 (ja) 2023-08-10 2023-08-10 情報処理装置、情報処理方法、および、情報処理プログラム

Country Status (2)

Country Link
JP (1) JPWO2025032809A1 (https=)
WO (1) WO2025032809A1 (https=)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006078326A (ja) * 2004-09-09 2006-03-23 Toyota Motor Corp 燃費情報提供システム
WO2010122666A1 (ja) * 2009-04-24 2010-10-28 トヨタ自動車株式会社 車載機及び情報処理センター
JP2011027507A (ja) * 2009-07-23 2011-02-10 Navitime Japan Co Ltd ナビゲーションシステム、ナビゲーション装置、ナビゲーションサーバ、および、燃費比較方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006078326A (ja) * 2004-09-09 2006-03-23 Toyota Motor Corp 燃費情報提供システム
WO2010122666A1 (ja) * 2009-04-24 2010-10-28 トヨタ自動車株式会社 車載機及び情報処理センター
JP2011027507A (ja) * 2009-07-23 2011-02-10 Navitime Japan Co Ltd ナビゲーションシステム、ナビゲーション装置、ナビゲーションサーバ、および、燃費比較方法

Also Published As

Publication number Publication date
JPWO2025032809A1 (https=) 2025-02-13

Similar Documents

Publication Publication Date Title
CN110126841B (zh) 基于道路信息和驾驶风格的纯电动汽车能耗模型预测方法
US20240094734A1 (en) Power management, dynamic routing and memory management for autonomous driving vehicles
Yavasoglu et al. Implementation of machine learning based real time range estimation method without destination knowledge for BEVs
CN114802189B (zh) 车辆的能耗控制方法、装置、车辆及存储介质
CN105459842B (zh) 电动汽车续航里程的估算方法
CN111670340B (zh) 一种车辆剩余行驶里程的获取方法、电子设备及车辆
CN111497679A (zh) 一种纯电动汽车能耗监测优化方法及系统
WO2022007689A1 (zh) 车辆的充电提醒方法、设备、程序、存储介质及车辆
CN112224089A (zh) 基于能耗的行程规划方法、装置、电子设备及存储介质
JP5980170B2 (ja) シミュレーション装置、シミュレーション方法及びプログラム
CN109733248A (zh) 基于路径信息的纯电动汽车剩余里程模型预测方法
CN103959014B (zh) 能量消耗量估计装置、能量消耗量估计方法
CN108489500A (zh) 一种基于能耗经济性的全局路径规划方法及系统
CN107844613A (zh) 基于数据分析的电动车续航管理方法
CN104379387B (zh) 能量推定装置、车辆用信息系统、服务器装置
JP2024529003A (ja) 規定のルートに沿った車両の移動に関する車両のエネルギー消費量を予測するための、およびルート設定のための方法およびシステム
CN108073076B (zh) 车辆控制方法和装置
CN106323331A (zh) 预测车辆行驶里程的方法及装置
CN115936281A (zh) 电动汽车的充电规划方法、装置、电动汽车和存储介质
CN108846571A (zh) 一种网联化电动汽车宏观能量消耗估计方法
JP2005291081A (ja) 燃料消費量またはｃｏ２排出量の予測モデル作成方法、装置、およびプログラム
CN116075446A (zh) 耗电量估计装置、耗电量估计方法及耗电量估计程序
JP2013002850A (ja) ナビゲーション装置
WO2025032809A1 (ja) 情報処理装置、情報処理方法、および、情報処理プログラム
JP2009068945A (ja) 道路勾配推定装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23948522

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2025539071

Country of ref document: JP

Kind code of ref document: A

WWE Wipo information: entry into national phase

Ref document number: 2025539071

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE