Electric fuel efficiency analyzing apparatus and operating method thereof

Abstract

An electric fuel efficiency analyzing apparatus according to an embodiment disclosed in the present document may comprise: a data acquisition unit for acquiring driving information of a vehicle in a designated monitoring section; and a processor that segments the designated monitoring section into a plurality of behavior factors according to a driving state on the basis of the driving information, calculates a standard electric fuel efficiency contribution that is the extent to which each of the plurality of behavior factors affects the electric fuel efficiency of the vehicle, and calculates a score of each of the plurality of behavior factors on the basis of the standard electric fuel efficiency contribution.

Description

Power analysis device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority from Korean Patent Application No. 10-2023-0110809, filed August 23, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] Various embodiments disclosed in this document relate to a pre-sales analysis device and a method of operating the same.

[0005] Due to recent environmental concerns, the popularity of eco-friendly vehicles such as electric and fuel cell vehicles is increasing. These eco-friendly vehicles are equipped with batteries and operate using the battery's electric energy.

[0006] Vehicles provide a function that calculates the vehicle's fuel economy (or driving fuel economy) and outputs it through a cluster or other device. Fuel economy can be calculated as the distance traveled per kWh of electrical energy.

[0007] Typically, fuel economy can be calculated based on the driving distance and electrical energy consumption over a given distance. For example, fuel economy can be calculated as the ratio of electrical energy consumption to driving distance.

[0008] Providing these fuel consumption figures can help drivers plan charging plans before or during a drive. However, fuel consumption can vary even among similar vehicles depending on road conditions, driver habits, and vehicle condition. Furthermore, the provided fuel consumption figures do not provide drivers with insight into the factors affecting fuel consumption.

[0009] One object of various embodiments of the present invention is to provide a fuel efficiency analysis device and an operating method thereof for analyzing factors affecting the fuel efficiency of a vehicle.

[0010] One object of various embodiments of the present invention is to provide a fuel economy analysis device and an operating method thereof for analyzing the cause of an increase or decrease in the current fuel economy of a vehicle compared to the standard fuel economy of the vehicle.

[0011] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0012] A fuel economy analysis device according to an embodiment disclosed in this document may include a data acquisition unit that acquires driving information of a vehicle in a designated monitoring section; and a processor that segments the designated monitoring section into a plurality of behavioral factors according to a driving state based on the driving information, calculates a standard fuel economy contribution index representing the degree to which each of the plurality of behavioral factors influences the fuel economy of the vehicle, and calculates a score for each of the plurality of behavioral factors based on the standard fuel economy contribution index.

[0013] According to an embodiment, the processor may divide the designated monitoring section into a plurality of speed sections satisfying a designated speed condition based on the driving information, divide each of the plurality of speed sections into a plurality of behavior factors satisfying a designated speed change condition, and calculate a standard fuel consumption contribution rate for each of the plurality of behavior factors.

[0014] According to an embodiment, the processor may divide each of the plurality of speed sections into the plurality of action factors based on whether the start speed or the end speed of each of the plurality of speed sections is 0.

[0015] According to an embodiment, the processor may calculate a fuel cost profit / loss value representing a profit / loss of the current fuel cost of the vehicle compared to the standard fuel cost of the vehicle based on the driving information, and may calculate the standard fuel cost contribution based on the fuel cost profit / loss value.

[0016] According to an embodiment, the processor can calculate the standard fuel consumption contribution, which indicates the degree to which the driving of the vehicle in the specified monitoring section has affected the profit and loss of the current fuel consumption compared to the standard fuel consumption, based on the power consumption rate of the vehicle in the specified monitoring section and the fuel consumption profit and loss value.

[0017] According to an embodiment, the processor may calculate a score for each of the plurality of behavior factors based on a probability distribution function regarding a standardized cost contribution of each of the plurality of behavior factors.

[0018] According to an embodiment, the processor may estimate the probability distribution function based on pre-acquired data regarding the standard fuel consumption contribution of each of the plurality of behavioral factors, and calculate a score for each of the plurality of behavioral factors based on a probability corresponding to the standard fuel consumption contribution of the vehicle in the probability distribution function.

[0019] A fuel economy analysis method according to one embodiment disclosed in this document may include a step of segmenting a designated monitoring section into a plurality of behavioral factors according to a driving state based on driving information of a vehicle acquired in the designated monitoring section; a step of calculating a standard fuel economy contribution, which is a degree to which each of the plurality of behavioral factors affects the fuel economy of the vehicle; and a step of calculating a score for each of the plurality of behavioral factors based on the standard fuel economy contribution.

[0020] According to an embodiment, the step of dividing into the plurality of behavior factors may include the step of dividing the designated monitoring section into a plurality of speed sections satisfying a designated speed condition based on the driving information; the step of dividing each of the plurality of speed sections into a plurality of behavior factors satisfying a designated speed change condition; and the step of calculating a standard fuel consumption contribution for each of the plurality of behavior factors.

[0021] According to an embodiment, the step of dividing into the plurality of action factors may include a step of dividing each of the plurality of speed sections into the plurality of action factors based on whether the start speed or the end speed of each of the plurality of speed sections is 0.

[0022] According to an embodiment, the step of calculating the standard fuel consumption contribution may include the step of calculating a fuel consumption profit and loss value representing the profit and loss of the current fuel consumption of the vehicle compared to the standard fuel consumption of the vehicle based on the driving information; and the step of calculating the standard fuel consumption contribution based on the fuel consumption profit and loss value.

[0023] According to an embodiment, after the step of calculating the fuel cost profit and loss, the method may further include a step of calculating the standard fuel cost contribution, which indicates the degree to which the driving of the vehicle in the designated monitoring section has affected the profit and loss of the current fuel cost compared to the standard fuel cost, based on the power consumption rate of the vehicle in the designated monitoring section and the fuel cost profit and loss.

[0024] According to an embodiment, the step of calculating a score for each of the plurality of behavioral factors may include the step of calculating a score for each of the plurality of behavioral factors based on a probability distribution function regarding a standardized cost contribution of each of the plurality of behavioral factors.

[0025] According to an embodiment, the step of calculating a score for each of the plurality of behavior factors may include the step of estimating the probability distribution function based on previously acquired data regarding the standard fuel consumption contribution of each of the plurality of behavior factors; and the step of calculating a score for each of the plurality of behavior factors based on a probability corresponding to the standard fuel consumption contribution of the vehicle in the probability distribution function.

[0026] The fuel consumption analysis device and its operating method according to the embodiment disclosed in this document can analyze factors affecting the fuel consumption of a vehicle.

[0027] The fuel efficiency analysis device and its operation method according to the embodiment disclosed in this document can analyze the cause of an increase or decrease in the current fuel efficiency of a vehicle compared to the standard fuel efficiency of the vehicle.

[0028] In addition, various effects may be provided, either directly or indirectly, through this document.

[0029] FIG. 1a is a schematic diagram illustrating the configuration of a power analysis system according to one embodiment disclosed in this document.

[0030] FIG. 1b is a diagram illustrating the configuration of a power analysis device according to one embodiment disclosed in this document.

[0031] FIG. 2a is a diagram illustrating behavioral factors according to one embodiment disclosed in this document.

[0032] FIG. 2b is a diagram showing a driving speed according to one embodiment disclosed in this document.

[0033] FIGS. 3A and 3B are drawings detailing driving speeds according to one embodiment disclosed in this document.

[0034] FIG. 4a and FIG. 4b are diagrams for explaining the operation of calculating the standard power contribution according to one embodiment disclosed in this document.

[0035] FIG. 5A and FIG. 5B are diagrams illustrating a score calculation operation for a behavioral factor according to one embodiment disclosed in this document.

[0036] FIGS. 6A to 6E are drawings for explaining the output operation of a power analysis device according to one embodiment disclosed in this document.

[0037] FIG. 7 is a flowchart for explaining the operation of a power analysis device according to one embodiment disclosed in this document.

[0038] FIG. 8 is a flowchart for explaining an operation of calculating a power consumption rate and a power cost profit and loss value in a power cost analysis device according to one embodiment disclosed in this document.

[0039] FIG. 9 is a block diagram showing the hardware configuration of a computing system for implementing a power analysis device according to one embodiment disclosed in this document.

[0040] Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that various modifications, equivalents, and / or alternatives of the embodiments of the present invention are included.

[0041] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0042] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0043] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0044] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0045] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0046] The "vehicle" mentioned in the following description may include both a vehicle operated by a driver and an autonomous vehicle capable of autonomous operation without driver intervention. Furthermore, while the following description uses an automobile as an example of a vehicle, the present invention is not limited thereto. For example, the various embodiments described below can also be applied to various means of transportation, such as ships, airplanes, trains, motorcycles, or bicycles.

[0047] FIG. 1a is a schematic diagram illustrating the configuration of a power analysis system according to one embodiment disclosed in this document.

[0048] Referring to FIG. 1A, a fuel consumption analysis system (100) according to one embodiment disclosed in the present document may include a vehicle diagnostic device (20) and a fuel consumption analysis device (30). Here, fuel consumption may be the amount of electric energy consumed by a vehicle (10) per unit driving distance or per unit time.

[0049] According to an embodiment, a vehicle diagnostic device (20) is installed inside a vehicle (10) and can obtain information related to the state of the vehicle (10) or driving of the vehicle (10). Information related to the state of the vehicle (10) may include diagnostic information on components of the vehicle (10) (e.g., battery, sensor, motor, accelerator, deceleration device, etc.). In addition, information related to driving of the vehicle (10) may include speed information, driving distance information, driving time information, driving location information, fuel consumption information, etc. of the vehicle (10).

[0050] According to one embodiment, the vehicle diagnostic device (20) may include an On-Board Diagnostic (OBD) device. Such an OBD device may include not only OBD-I, OBD 1.5, and OBD-II, but also various devices that output information related to the status or driving of the vehicle (10) to an external device (e.g., a power analysis device (30)).

[0051] According to an embodiment, the fuel efficiency analysis device (30) can analyze the fuel efficiency of the vehicle (10) based on various information acquired through the vehicle diagnostic device (20). According to an embodiment, the fuel efficiency analysis device (30) can analyze factors affecting the fuel efficiency of the vehicle (10). In another aspect, the fuel efficiency analysis device (30) can calculate a standard fuel efficiency contribution indicating the degree to which a specific driving section affected the fuel efficiency of the vehicle (10).

[0052] According to an embodiment, the power consumption analysis device (30) can calculate a standard power consumption contribution that indicates the degree to which the driving section of the vehicle (10) (or the section in which the electric edge of the vehicle (10) is consumed) has affected the profit or loss (e.g., increase or decrease) of the current power consumption of the vehicle (10) compared to the standard power consumption (e.g., certified power consumption) of the vehicle (10). Here, the standard power consumption is a power consumption measured in a situation that satisfies a specified measurement condition, and can be measured by the vehicle diagnostic device (20) or the power consumption analysis device (30) or by a specified certification agency. For example, the power consumption analysis device (30) can include a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a wearable device, or a server (e.g., a cloud server), but various embodiments are not limited thereto.

[0053] The content related to the calculation of the standard power contribution of the power analysis device (30) according to the embodiment can be explained in detail through the drawings below.

[0054] FIG. 1b is a block diagram illustrating the configuration of a power analysis device according to one embodiment disclosed in this document.

[0055] Referring to FIG. 1B, the power analysis device (30) may include a data acquisition unit (110), a memory (120), an output device (130), and a processor (140). However, this is merely exemplary, and various embodiments are not limited thereto. For example, at least one of the components of the power analysis device (30) described above may be omitted, or one or more other components (e.g., an input device, at least one sensor, a power management device, etc.) may be added to the configuration of the power analysis device (30). In addition, at least one of the components described above may be integrated with another component.

[0056] According to an embodiment, the data acquisition unit (110) may acquire driving information of the vehicle (10). Here, the driving information may be information collected from the vehicle (10) while the vehicle (10) is driving in a certain section. According to an embodiment, the driving information may be information on the driving distance and the amount of electric energy consumed. According to an embodiment, the driving information may include at least one of the driving time, driving distance, speed, speed, electric energy consumption, and temperature of the vehicle (10) driving in a certain section.

[0057] According to an embodiment, the data acquisition unit (110) can acquire driving information of the vehicle (10) from a vehicle diagnostic device (20) and / or an external server connected via a wired and / or wireless network. According to an embodiment, the data acquisition unit (110) can acquire driving information of other vehicles (not shown) other than the vehicle (10). The other vehicles may be of the same type as the vehicle (10) or may be of a different type.

[0058] According to an embodiment, the data acquisition unit (110) can establish a wired or wireless communication channel between the electric power analysis device (30) and the vehicle diagnostic device (20), and support communication through the established communication channel. In addition, the data acquisition unit (110) can also establish communication with other external devices (e.g., a server) in addition to the vehicle diagnostic device (20).

[0059] According to an embodiment, the data acquisition unit (110) may include a communication circuit. For example, the data acquisition unit (110) may include a wireless communication circuit (e.g., a cellular communication circuit or a short-range wireless communication circuit) or a wired communication circuit (e.g., a local area network (LAN) communication circuit or a power line communication circuit).

[0060] According to an embodiment, the memory (120) may store data used by at least one component (e.g., processor (140)) of the power analysis device (30). For example, the data may include software (or instructions related thereto), input data, or output data. In one embodiment, the instructions, when executed by the processor (140), may cause the power analysis device (30) to perform operations defined by the instructions.

[0061] According to an embodiment, the memory (120) may include one or more software.

[0062] According to an embodiment, the memory (120) can store data acquired from the data acquisition unit (110). For example, the memory (120) can store at least one of driving time, driving distance, speed, electric energy consumption, and temperature, which are information related to driving of the vehicle (10).

[0063] According to an embodiment, the memory (120) may include a memory of a flash memory type, a hard disk type, a micro type, and a card type (e.g., a Secure Digital Card (SD card) or an eXtream Digital Card (XD card)), and a storage medium of at least one type of memory of a Random Access Memory (RAM), a Static RAM (SRAM), a Read-Only Memory (ROM), a Programmable ROM (PROM), an Electrically Erasable PROM (EEPROM), a Magnetic RAM (MRAM), a magnetic disk, and an optical disk, and the various embodiments are not limited thereto.

[0064] According to an embodiment, the output device (130) can output information related to the operation of the power analysis device (30) to the outside of the power analysis device (30). According to one embodiment, the output device (130) can include a display configured to output visual information. For example, the display can include one or more of a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, a 3D display, and a transparent display. In addition, the display may also include a touch sensor configured to detect a touch.

[0065] However, this is merely an example, and various embodiments are not limited thereto. For example, information related to the operation of the power analysis device (30) may be provided as auditory information. In this case, the output device (130) may further include an audio output device (e.g., a speaker) configured to output an acoustic signal to the outside of the power analysis device (30).

[0066] According to an embodiment, the processor (140) can control at least one other component of the power analysis device (30) (e.g., data acquisition unit (110), memory (120), and output device (130)) and perform various data processing or operations.

[0067] According to an embodiment, the processor (140) may execute software stored in the memory (120) to control at least one other component (e.g., hardware or software component) of the power analysis device (30) connected to the processor (140) and perform various data processing or operations.

[0068] According to an embodiment, the processor (140) can analyze the fuel efficiency (or driving fuel efficiency) of the vehicle (10) based on various information obtained through the vehicle diagnostic device (20). The fuel efficiency can be calculated as the distance that can be driven per 1 kWh of electric energy. A description related to the processor (140) will be described with reference to FIGS. 2a to 2b, 3a to 3b, 4a to 4b, 5a to 5b, and 6a to 6e, which will be described later.

[0069] Below, a method for analyzing the fuel efficiency of a vehicle (10) by a fuel efficiency analysis device (30) through a processor (140) is described.

[0070] According to an embodiment, the processor (140) can calculate the fuel efficiency (or driving fuel efficiency) for the vehicle (10) based on various information obtained through the vehicle diagnostic device (20). According to an embodiment, the fuel efficiency can be calculated as the distance that can be driven per 1 kWh of electric energy.

[0071] For example, the processor (140) may calculate fuel economy (or driving fuel economy) based on the driving information of the vehicle (10) acquired from the data acquisition unit (110). Here, the driving information may include information on the driving distance of a certain section and the amount of electric energy consumed. In this regard, the processor (140) may calculate fuel economy using the ratio of the amount of electric energy consumed to the driving distance.

[0072] Providing these fuel consumption data can help drivers plan charging while driving or before. However, fuel consumption can vary even among similar vehicles depending on road conditions, driver habits, and vehicle condition. Fuel consumption data alone cannot provide drivers with insight into the factors that influence fuel consumption.

[0073] In this regard, the processor (140) can analyze factors affecting the fuel economy of the vehicle (10) based on various pieces of information acquired through the data acquisition unit (110), as described below. For example, the processor (140) can calculate a standard fuel economy contribution rate indicating the degree of influence on the profit or loss (e.g., increase or decrease) of the current fuel economy of the vehicle (10) compared to the standard fuel economy (e.g., certified fuel economy) of the vehicle (10).

[0074] According to one embodiment, the processor (140) may divide (or segment) the driving section of the vehicle (10) in which electric energy is consumed into a plurality of sections, and may calculate a standard fuel efficiency contribution rate for each of the divided sections. According to one embodiment, the processor (140) may divide the driving section of the vehicle (10) into a plurality of behavioral factors based on the driving state of the vehicle (10).

[0075] According to an embodiment, the driving state of the vehicle (10) may be determined based on the speed of the vehicle (10). For example, the processor (140) may determine the driving state of the vehicle (10) based on at least one of the speed, the amount of change in speed (i.e., acceleration), the starting speed, and the ending speed of the vehicle (10). According to an embodiment, the driving state of the vehicle (10) may be determined based on the amount of electric energy consumed. For example, the processor (140) may determine the driving state of the vehicle (10) based on the fuel efficiency of the vehicle (10).

[0076] The process by which the processor (140) divides the driving section into multiple action factors based on the driving state of the vehicle (10) is explained with reference to FIGS. 2a and 2b.

[0077] Figure 2a is a diagram illustrating behavioral factors according to one embodiment disclosed in this document. Figure 2b is a diagram illustrating driving speed according to one embodiment disclosed in this document.

[0078] Referring to FIG. 2A, multiple behavioral factors can be categorized based on the driving state of the vehicle (10). The processor (140) can categorize a driving section into multiple behavioral factors based on a scale that can represent the driving state of the vehicle (10). For example, the processor (140) can categorize a driving section into multiple behavioral factors based on the degree to which the driving state affects fuel economy.

[0079] According to an embodiment, the processor (140) may classify a deceleration or acceleration section into multiple action factors based on the magnitude of the change in deceleration or acceleration (i.e., acceleration). According to an embodiment, the processor (140) may classify a section in which the magnitude of acceleration is greater than or equal to a predetermined standard as rapid acceleration or rapid deceleration, and a section in which the magnitude of acceleration is less than or equal to the predetermined standard as acceleration or deceleration. For example, the processor (140) may classify acceleration and deceleration of 10 km / h or more per second as rapid acceleration and rapid deceleration, respectively.

[0080] In an embodiment, the processor (140) may distinguish an action factor based on whether the vehicle (10) involves a start / stop. In an embodiment, the processor (140) may distinguish a deceleration or acceleration section into a plurality of action factors based on whether the deceleration or acceleration section includes a point where the speed is 0. In another aspect, the processor (140) may distinguish a deceleration or acceleration section into a plurality of action factors based on whether the starting speed or the ending speed is 0 in the deceleration or acceleration section. For example, the processor (140) may distinguish a section where the vehicle (10) accelerates from a stationary state and a section where the vehicle decelerates from a stationary state as starting acceleration (101) and stationary deceleration (103), respectively, and may distinguish an acceleration or deceleration section that does not include a stationary state of the vehicle (10) as shift acceleration (102) or shift deceleration (104), respectively.

[0081] According to an embodiment, the plurality of behavior factors may include at least one of a starting acceleration (101) section (e.g., a section where the starting speed is 0 and includes acceleration of 20 km / h or more per second), a variable acceleration (102) section (e.g., a section where the starting speed is not 0 and includes acceleration of 20 km / h or more per second), a stop-deceleration (103) section (e.g., a section where the ending speed is 0 and includes deceleration of less than 20 km / h per second), a variable deceleration (104) section (e.g., a section where the ending speed is not 0 and includes deceleration of less than 20 km / h per second), and an average speed (105) section (e.g., a section where the average acceleration of the section is less than 0.2).

[0082] Referring to FIG. 2B, the processor (140) can divide the driving section of the vehicle (10) into multiple behavioral factors. The graph illustrated in FIG. 2B may be a graph showing the speed (unit: km / h) of the vehicle (10) against time (unit: sec). According to the embodiment of FIG. 2A, the section t1 to t2 may be an average speed (105) section, the section t2 to t3 may be a starting acceleration (101) section, and the section t3 to t4 may be a stop-deceleration (103) section. In addition, the section t5 to t6 may be a shift acceleration (102) section, and the section t6 to t7 may be a shift deceleration (104) section.

[0083] The processor (140) can effectively analyze the relationship between the vehicle's (10) behavior factors and fuel efficiency by distinguishing the behavior factors of the vehicle (10) based on whether or not they include a point where the speed is 0, rather than the magnitude of the acceleration. In other words, distinguishing the behavior factors based on the magnitude of the acceleration may not be ideal as a criterion for classifying the behavior factors of the vehicle (10). This is because there is no standard for the magnitude of acceleration that is applicable even across different vehicle types, and rapid acceleration and / or rapid deceleration inevitably have a negative impact on fuel efficiency. In addition, because the rate at which rapid acceleration / deceleration occurs accounts for a small percentage of the entire driving, distinguishing the behavior factors based on the magnitude of the acceleration and calculating the fuel efficiency may be problematic.

[0084] In contrast, distinguishing behavioral factors by whether they include a point where the speed is 0, i.e., whether they involve starting / stopping, can be a standard applicable across different vehicle types. In addition, the rate at which gear changes involving starting / stopping occur throughout the entire drive is higher than the rate at which rapid acceleration / deceleration occur. Therefore, distinguishing behavioral factors by whether they involve starting / stopping can be a classification method that better represents the driving of the vehicle (10). In addition, the fuel efficiency analysis device (30) can provide the user with information on which driving habits have a positive or negative effect on fuel efficiency by analyzing the degree to which each of the above behavioral factors affects the fuel efficiency of the vehicle (10). Through this, the user can analyze the effect of each behavioral factor on the fuel efficiency of the vehicle (10) and improve his / her driving habits and the fuel efficiency of the vehicle (10).

[0085] According to one embodiment, the processor (140) may divide the monitoring section, in which the consumption of electric energy is monitored by the driving of the vehicle (10), into a plurality of sections, and may calculate a standard fuel efficiency contribution rate for each of the divided sections. At this time, the processor (140) may divide the monitoring section into a plurality of speed sections. The calculation operation of the standard fuel efficiency contribution rate in the monitoring section may be described with reference to FIGS. 3A and 3B.

[0086] FIGS. 3A and 3B are drawings detailing driving speeds according to one embodiment disclosed in this document.

[0087] Referring to FIG. 3A, the processor (140) can divide the monitoring section (200) based on the speed of the vehicle (10). For example, the processor (140) can divide the monitoring section (200) into a first section (e.g., high-speed section) (210) corresponding to a first speed range (e.g., 81 km / h or more), a second section (e.g., medium-speed section) (220) corresponding to a second speed range (e.g., 31 km / h to 80 km / h), and a third section (e.g., low-speed section) (230) corresponding to a third speed range (e.g., 1 km / h to 30 km / h).

[0088] Thereafter, the processor (140) can calculate a first standard power contribution, a second standard power contribution, and a third standard power contribution for each of the first section (210), the second section (220), and the third section (230).

[0089] According to one embodiment, the processor (140) may use the following <Mathematical Formula 1> to calculate the standard power contribution.

[0090] <Mathematical Formula 1>

[0091] Standard power contribution = power consumption rate * power cost profit and loss value

[0092] Power consumption = E i / E T , profit and loss = M i / E i -M S / E T

[0093] In the above <Mathematical Formula 1>, the power consumption rate (E i / E T ) is the total power consumption (E T ) for the power consumed in the corresponding section (E i ) means the amount occupied by the company is expressed in numbers, and the profit and loss value (M) i / E i -M S / E T ) is the vehicle's standard fuel consumption (M S / E T) and the driving distance in that section (M i ) compared to the power consumed in the corresponding section (E i ) is calculated by the vehicle's standard fuel consumption (M S / E T ) compared to the current electricity bill (M) i / E i ) can mean profit and loss. Here, M S can refer to the total drivable distance according to the standard fuel consumption. For example, the fuel consumption profit and loss value can be expressed as a positive or negative value. In addition, a section with a positive value means a section with a higher fuel consumption than the standard fuel consumption, and a section with a negative value means a section with a lower fuel consumption than the standard fuel consumption.

[0094] The above <Mathematical Formula 1> can be explained through FIG. 4a, FIG. 4b, and <Mathematical Formula 2> and <Mathematical Formula 3> below.

[0095] FIG. 4a and FIG. 4b are diagrams for explaining the operation of calculating the standard power contribution according to one embodiment disclosed in this document.

[0096] Referring to FIGS. 4a and 4b, in the entire monitoring section (300) including the first section (310), the second section (320), and the third section (330), the difference (④) between the standard fuel consumption (340) of the vehicle and the current fuel consumption (350) of the vehicle can be expressed as the sum of the first standard fuel consumption contribution (①) for the first section (310), the second standard fuel consumption contribution (②) for the second section (320), and the third standard fuel consumption contribution (③) for the third section (330).

[0097] This difference in power consumption (④) can be derived through the following <Mathematical Formula 2>.

[0098] <Mathematical Formula 2>

[0099] (M S -M T ) / E T = ④ / (E T * cos(θ S)) = (①+②+③) / ( E T * cos(θ S )) = 1 / ( E T * cos(θ S )) * (L1* sin(θ i - θ S ) + L2* sin(θ i - θ S ) + L3* sin(θ i - θ S ))

[0100] Based on the above <Mathematical Formula 2>, the standard electric power contribution can be derived through the below <Mathematical Formula 3>.

[0101] <Mathematical Formula 3>

[0102] i section standard contribution fuel consumption (km / kWh) = (L i * sin(θ i - θ S )) / (E T * cos(θ S )) = (E i * sin(θ i - θ S )) / (E T * cos(θ S ) * cos(θ i )) = (E i * (sin(θ i )cos(θ S ) - cos(θ i )sin(θ S ))) / (E T * cos(θ S ) * cos(θ i )) = E i / E T * (M i / E i - M S / E T )

[0103] According to one embodiment, an example of a standard electric power contribution calculated based on the above <Mathematical Formula 1> can be summarized in below.

[0104] Section 1 Section 2 Section 3 Driving distance 6 km 12 km 8 km Section energy consumption 3 kWh 3 kWh 4 kWh Standard electricity consumption 3 km / kWh Electricity consumption (section electricity consumption) 2 km / kWh 4 km / kWh 2 km / kWh Total electricity consumption 2.6 km / kWh Electricity consumption rate 0.3 0.3 0.4 Electricity profit and loss value - 1 + 1 - 1 Standard electricity consumption contribution - 0.3 0.3 - 0.4

[0105] Referring to above, the sum of the standard fuel consumption and the standard fuel consumption contribution of each section (e.g., 3-(0.3)+(0.3)-(0.4)=2.6) is the same as the fuel consumption for the entire section (e.g., section 1 to section 3) (e.g., 26 km / 10 kWh=2.6). In addition, although the fuel consumption of section 1 (e.g., 2 km / kWh) and section 3 (e.g., 2 km / kWh) are the same, the standard fuel consumption contribution of section 1 (e.g., -0.3) and the standard fuel consumption contribution of section 3 (e.g., -0.4) are different. This means that the standard fuel consumption contribution in the same fuel consumption section may be different depending on the driver's driving behavior. In this regard, the processor (140) can accurately analyze the factors affecting the fuel consumption through the standard fuel consumption contribution and provide the result to the driver.

[0106] As described above, the power consumption analysis device (30) can calculate the power consumption rate and the power cost profit and loss value based on the driving information of the vehicle (10) obtained through the vehicle diagnostic device (20). However, this is merely exemplary, and various embodiments are not limited thereto. For example, at least a portion of the power consumption rate and the power cost profit and loss value may be calculated by the vehicle diagnostic device (20) or another external device and provided to the power consumption analysis device (30).

[0107] In one embodiment, the processor (140) may divide the monitoring interval into multiple speed intervals, and each speed interval may further be divided into multiple intervals, when calculating the standard fuel efficiency contribution. In another embodiment, the processor (140) may divide each speed interval into multiple behavioral factors. Embodiments related to behavioral factors have been described with reference to FIGS. 2A and 2B , and thus may not be described again herein.

[0108] A description of the calculation of the standardized contribution to the multiple behavioral factors is provided with reference to Fig. 3b.

[0109] Referring to FIG. 3b, the processor (140) can divide the first section (210) of the monitoring section (200) into speed change sections such as a shift acceleration (102) section (211) and a shift deceleration (104) section (212). In addition, the processor (140) can divide the second section (220) of the monitoring section (200) into speed change sections such as a starting acceleration (101) section (221), a stop-deceleration (103) section (222), a shift acceleration (102) section (223), and a shift deceleration (104) section (224). In addition, the processor (140) can divide the third section (230) of the monitoring section (200) into speed change sections such as a starting acceleration (101) section (231), a stop deceleration (103) section (232), a gear deceleration (104) section (233), and a gear acceleration (102) section (234).

[0110] According to an embodiment, the processor (140) may distinguish only some sections that satisfy specified conditions among the starting acceleration (101) section (221), the stop-deceleration (103) section (222), the shift acceleration (102) section (223), and the shift deceleration (104) section (224) in the second section (220) of the monitoring section (200) as speed change sections. Of course, the processor (140) may also distinguish only some sections that satisfy specified conditions among the multiple speed sections of the monitoring section (200) as speed change sections.

[0111] Thereafter, the processor (140) can calculate the standard power contribution for each speed section.

[0112] As described above, the standard fuel efficiency contribution may be calculated based on a section (e.g., a monitoring section) where electric energy is consumed by driving the vehicle (10). However, this is merely exemplary, and various embodiments are not limited thereto. For example, the processor (140) may additionally calculate the standard fuel efficiency contribution based on a section where energy is consumed in a state where the vehicle is not actually driving (e.g., a stationary state).

[0113] In one embodiment, the processor (140) may calculate a power consumption rate based on the power consumed in a stationary state. For example, the processor (140) may calculate a standard power consumption contribution for a stationary state based on a power consumption rate calculated based on the power consumed by an air conditioning device (e.g., an air conditioner, a heating device, etc.) and a power cost profit and loss value calculated in a stationary state (e.g., a driving distance of 0 km).

[0114] According to an embodiment, the processor (140) may calculate a score of a standardized cost contribution for each of a plurality of behavioral factors based on the calculated standardized cost contribution. The operation of the processor (140) to calculate the score may be described with reference to FIGS. 5A and 5B.

[0115] FIG. 5A and FIG. 5B are diagrams illustrating a score calculation operation for a behavioral factor according to one embodiment disclosed in this document.

[0116] Referring to FIG. 5A, the processor (140) may calculate a score of the standardized cost contribution for each of the plurality of behavioral factors based on a probability distribution function relating to the standardized cost contribution of each of the plurality of behavioral factors. The probability distribution function (Cumulative Density Function) may be a function indicating the probability that an arbitrary random variable is less than or equal to a specific value.

[0117] According to an embodiment, the processor (140) may estimate a probability distribution function based on pre-acquired data regarding the standard fuel consumption contribution of each of a plurality of behavioral factors. The processor (140) may pre-acquire data regarding the standard fuel consumption contribution of each of a plurality of behavioral factors through the data acquisition unit (110). According to an embodiment, the processor (140) may acquire data regarding past driving of the vehicle (10). In addition, according to an embodiment, the processor (140) may acquire data regarding current or past driving of other vehicles (not shown) in addition to the vehicle (10). The pre-acquired data may be big data stored in a server.

[0118] According to an embodiment, the processor (140) may calculate a probability distribution function based on pre-acquired data for each of a plurality of behavioral factors. As described above, the probability distribution function (Cumulative Density Function) may be a function that represents the probability that an arbitrary random variable is less than or equal to a specific value. According to an embodiment, the probability distribution function (Cumulative Density Function) may include a discrete probability mass function (401) and / or a continuous probability density function (403).

[0119] According to an embodiment, the processor (140) may calculate the probability for the standard contribution ratio as a probability distribution function. For example, the processor (140) may calculate the standard contribution ratio as a probability variable (i.e., x-axis or independent variable) and the probability that each standard contribution ratio is less than or equal to a specific value as a function value of the probability variable function (i.e., y-axis or dependent variable).

[0120] According to an embodiment, the processor (140) may calculate a discrete probability mass function (401) corresponding to the standard contribution ratio, and estimate the discrete probability mass function (401) as a continuous probability density function (403). The discrete probability mass function (401) may be a probability distribution function having discrete values, and the continuous probability density function (403) may be a probability distribution function having continuous values. According to an embodiment, since the pre-acquired data on the standard contribution ratio of each of the plurality of behavioral factors are discrete values, the graph of the discrete probability mass function (401) may have discrete function values. In this case, there is a problem that the deviation of the probability increases at the boundary where the function value of the discrete probability mass function (401) increases. Therefore, the processor (140) may eliminate this deviation by estimating the discrete probability mass function (401) as a continuous probability density function (403). Through this, the processor (140) may calculate a reliable probability value and score.

[0121] Referring to FIG. 5B, the processor (140) may calculate a score of a standard fuel efficiency contribution for each of a plurality of behavioral factors through a probability distribution function of each of the plurality of behavioral factors. In an embodiment, the processor (140) may calculate a score based on a probability corresponding to a standard fuel efficiency contribution for a specific behavioral factor of the vehicle (10) in a continuous probability density function (403) for the specific behavioral factor.

[0122] For example, FIG. 5b may be a continuous probability density function (403) of a standard fuel efficiency contribution for a specific behavioral factor (e.g., starting acceleration (101)). If the standard fuel efficiency contribution for the starting acceleration (101) of a vehicle (10) is 0.2, the function value corresponding to the probability variable 0.2 in the continuous probability density function (403) may be 0.97. Accordingly, the processor (140) may calculate the score of the starting acceleration (101) as 97 points.

[0123] In one example, a higher score on the standardized cost contribution for a specific behavioral factor indicates that the specific behavioral factor has a positive impact on cost. Conversely, a lower score on the standardized cost contribution for a specific behavioral factor indicates that the specific behavioral factor has a negative impact on cost.

[0124] Accordingly, the fuel economy analysis device (30) can analyze the degree to which each behavioral factor positively or negatively influences fuel economy based on the standard fuel economy contribution score for each behavioral factor. Furthermore, the fuel economy analysis device (30) can analyze the fuel economy of the current vehicle (10) compared to that of other vehicles. Accordingly, users can objectively determine which behavioral factor positively or negatively influences fuel economy based on the calculated scores. Through this, users can improve their driving habits and enhance the fuel economy of the vehicle (10).

[0125] According to various embodiments, the processor (140) may output information related to the calculated standard power contribution and score (i.e., power contribution information). Contents related to the power contribution information are described with reference to FIGS. 6A to 6E.

[0126] FIGS. 6A, 6B, 6C, 6D, and 6E are diagrams for explaining the output operation of a power analysis device according to one embodiment disclosed in this document.

[0127] Referring to FIG. 6A, the processor (140) may output (400) fuel consumption contribution information through the output device (130). According to an embodiment, the processor (140) may output (400) fuel consumption contribution information including first fuel consumption contribution information (410) related to recent fuel consumption (or recent driving) and second fuel consumption contribution information (420) related to fuel consumption history (or past driving history). According to an embodiment, the processor (140) may receive a user's input and selectively output the first fuel consumption contribution information (410) or the second fuel consumption contribution information (420). Alternatively, the processor (140) may output the first fuel consumption contribution information (410) and the second fuel consumption contribution information (420) together, either by receiving a user's input or regardless of a user's input.

[0128] According to one embodiment, the first fuel efficiency contribution information (410) may include an average fuel efficiency (e.g., recent average fuel efficiency: 7.4 km / kWh) (411) based on recent driving and a standard fuel efficiency contribution (412) for each speed range monitored in the recent driving. Additionally, the first fuel efficiency contribution information (410) may include a graph (414) of the standard fuel efficiency contribution for each monitored speed range. Through this, the driver can recognize a speed range that improves or reduces fuel efficiency, and as a result, the driver's driving habits for improving fuel efficiency may be induced.

[0129] Additionally or optionally, the processor (140) may provide detailed standard fuel efficiency contribution for at least one speed range based on user input while the first fuel efficiency contribution information (410) is being output.

[0130] Referring to FIG. 6B, the processor (140) may output a standard fuel efficiency contribution (416) for each of a plurality of behavior factors. For example, when a speed range (e.g., a low-speed range) is selected through the standard fuel efficiency contribution (412) for each speed range or the graph (414) for the standard contribution fuel efficiency, the processor (140) may output a standard fuel efficiency contribution (416) for each of the behavior factors (e.g., starting acceleration (101), shift acceleration (102), stop-deceleration (103), shift deceleration (104), average speed (105), rapid acceleration, and rapid deceleration) for the selected speed range. Through this, the driver may recognize an action that relatively lowers fuel efficiency and an action that relatively improves fuel efficiency in the selected speed range.

[0131] According to one embodiment, when outputting the fuel cost contribution information, the processor (140) may also provide information related to the profit and loss cost incurred according to the standard fuel cost contribution. In this regard, the processor (140) may calculate the profit and loss cost by multiplying the standard fuel cost contribution by the unit energy cost (e.g., cost per kWh). In addition, when outputting the fuel cost contribution information, the processor (140) may also provide information on the proportion of the entire driving section occupied by sections with at least a high standard fuel cost contribution or sections with a low standard fuel cost.

[0132] According to one embodiment, the processor (140) may provide a comparison result between the standard fuel economy contribution according to past driving and the standard fuel economy contribution according to current driving through the second fuel economy contribution information (420) related to the fuel economy history. At this time, the processor (140) may select any one of the standard fuel economy contributions according to a plurality of stored past driving and compare it with the standard fuel economy contribution according to current driving. For example, at least one of the past driving in which optimal fuel economy driving was achieved, the driving performed immediately before the current driving, the past driving performed on a route different from the current driving, and the plurality of past driving performed over a certain period of time may be determined as a comparison target to be compared with the current driving.

[0133] Referring to FIG. 6c, the processor (140) can output second fuel consumption contribution information (420) including a graph (422) comparing the fuel consumption decreased or increased in the current driving compared to the past driving and text information (424) about the comparison result.

[0134] Additionally or optionally, the second fuel consumption contribution information (420) may include an object (e.g., a detailed information confirmation icon) (426) that causes the output of detailed information on how the fuel consumption decreased or increased in the current driving compared to the past driving. Accordingly, when a user input for the object is detected, the processor (140) may output the detailed information.

[0135] Referring to FIG. 6d, the processor (140) may provide detailed information (428) regarding sections in which fuel economy decreased or increased in the current driving compared to past driving, or detailed information regarding behavioral factors that decreased or increased fuel economy for those sections. In FIG. 6d, the comparison result between fuel economy of past driving and fuel economy of current driving is provided as detailed information (428) through a radial graph, but various embodiments are not limited thereto.

[0136] Referring to FIG. 6E , the processor (140) may output detailed information (430) related to the score for the standard fuel consumption contribution. According to an embodiment, the processor (140) may output the standard fuel consumption contribution for each behavioral factor and / or the score of the standard fuel consumption contribution for each behavioral factor. It may be difficult for a user to determine whether each behavioral factor has a positive or negative impact on driving based on the numerical value of the standard fuel consumption contribution for each behavioral factor. In contrast, the score of the standard fuel consumption contribution for each behavioral factor allows the user to intuitively analyze whether each behavioral factor has a positive or negative impact on fuel consumption. Therefore, the user can establish an appropriate driving plan by considering fuel consumption and behavioral factors.

[0137] Hereinafter, the operation method of the electric power analysis device (30) according to the embodiment will be specifically described with reference to FIGS. 7 and 8.

[0138] FIG. 7 is a flowchart for explaining the operation of a power analysis device according to one embodiment disclosed in this document.

[0139] The operations illustrated in FIG. 7 may be performed via the power analysis device (30) of FIG. 1B. While the operations in the following embodiments may be performed sequentially, they are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel. Furthermore, at least one of the operations below may be omitted depending on the embodiment.

[0140] Referring to FIG. 7, in operation S101, the processor (140) may divide (or segment) the monitoring section into multiple sections. According to one embodiment, the processor (140) may divide the monitoring section into multiple speed sections. According to one embodiment, the processor (140) may divide the monitoring section into multiple behavioral factors.

[0141] In operation S102, the processor (140) can calculate the power consumption rate and the power cost profit and loss value for each action factor. The power consumption rate means a numerical representation of the amount of power consumed in each section relative to the total power consumption in the monitoring section, and the power cost profit and loss value can mean a numerical representation of the current power cost profit and loss compared to the vehicle's standard power cost (e.g., official power cost).

[0142] In operation S103, the processor (140) may calculate a standard fuel consumption contribution based on the power consumption rate and the fuel cost profit and loss value. The standard fuel consumption contribution may be information indicating the degree of influence (e.g., increase or decrease) of the current fuel consumption of the vehicle (10) compared to the standard fuel consumption (e.g., official fuel consumption). According to one embodiment, the processor (140) may utilize the aforementioned <Mathematical Formula 1> in calculating the standard fuel consumption contribution.

[0143] In operation S104, the processor (140) may calculate a score of the standard fuel contribution for each behavior factor based on the calculated standard fuel contribution. According to an embodiment, the processor (140) may calculate and / or estimate a probability distribution function regarding the standard fuel contribution for each of a plurality of behavior factors. In addition, the processor (140) may calculate a score of the standard fuel contribution for each of the plurality of behavior factors based on the probability distribution function.

[0144] FIG. 8 is a flowchart for explaining an operation of calculating a power consumption rate and a power cost profit and loss value in a power cost analysis device according to one embodiment disclosed in this document.

[0145] The operations illustrated in FIG. 8 may be performed via the power analysis device (30) of FIG. 1B. While the operations in the following embodiments may be performed sequentially, they are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel. Furthermore, at least one of the operations below may be omitted depending on the embodiment.

[0146] Referring to FIG. 8, in operation S201, the processor (140) may divide the monitoring section into multiple speed sections. According to one embodiment, the processor (140) may divide the monitoring section into a low-speed section, a medium-speed section, and a high-speed section.

[0147] In operation S202, the processor (140) can divide each speed section into multiple action factors. The multiple action factors can include at least one of a starting acceleration (101), a shift acceleration (102), a stop-deceleration (103), a shift deceleration (104) section, and an average speed (105). In addition, the power analysis device (30) can also divide only some action factors that satisfy a specified condition among the multiple speed change sections included in each speed section.

[0148] In operation S203, the processor (140) can calculate the power consumption rate and power cost profit and loss value for each of the plurality of behavior factors. Thereafter, the power cost analysis device (30) can calculate the standard power cost contribution for each of the plurality of behavior factors.

[0149] FIG. 9 is a block diagram showing the hardware configuration of a computing system for implementing a power analysis device according to one embodiment disclosed in this document.

[0150] Referring to FIG. 9, a computing system (1000) according to one embodiment disclosed in the present document may include a memory (1010), an MCU (1020), an input / output I / F (1030), and a communication I / F (1040).

[0151] The memory (1010) can store various programs, such as a vehicle (10) driving characteristic data collection program, a latent variable extraction program, a driving speed profile generation program, a probability distribution analysis program, and a fuel consumption analysis program. In addition, the memory (1010) can store various information, including vehicle (10) driving characteristic data, latent variables, and the like.

[0152] Such memories (1010) may be provided in multiple numbers as needed. The memories (1010) may be volatile memories or non-volatile memories. As volatile memories (1010), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (1010), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (1010) listed above are merely examples and are not limited to these examples.

[0153] The MCU (1020) may be a processor that executes various programs (e.g., a driving characteristic data collection program, a latent variable extraction program, a driving speed profile generation program, a probability distribution analysis program, a power analysis program, etc.) stored in the memory (1010), processes various information including driving characteristic data, latent variables, etc. through these programs, and performs the functions of the processor (140) included in the power analysis device (30) shown in the aforementioned FIGS. 1A to 8.

[0154] The input / output I / F (1030) can provide an interface that enables data transmission and reception between an input device (not shown) such as a keyboard, mouse, or touch panel, and an output device (not shown) such as a display and the MCU (1020).

[0155] The communication I / F (1040) is a component capable of transmitting and receiving various data with the server, and may be any device capable of supporting wired or wireless communication. For example, the power analysis device (30) can transmit and receive various information, including the vehicle's (10) driving speed and power consumption, from a separately provided external server via the communication I / F (1040).

[0156] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a module that is recorded in a memory (1010) and processed by an MCU (1020) to perform each function illustrated in FIG. 1b, for example.

[0157] Although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0158] In addition, terms such as "include," "comprise," or "have" described above, unless specifically stated otherwise, mean that the corresponding component can be included, and therefore should be interpreted to include other components rather than excluding other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0159] The foregoing disclosure outlines features of several embodiments to enable those skilled in the art to better understand the aspects of the present disclosure. Those skilled in the art will readily appreciate that the present disclosure can be readily used as a basis for designing or modifying other structures to achieve the same purposes or advantages of the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent structures do not depart from the scope of the present disclosure, and that various changes, substitutions, and modifications can be made herein without departing from the scope of the present disclosure.

Claims

1. A data acquisition unit that acquires driving information of a vehicle in a designated monitoring section; and Based on the above driving information, the designated monitoring section is segmented into multiple behavioral factors according to the driving status, Calculate the standard fuel consumption contribution, which is the degree to which each of the above multiple behavioral factors affects the fuel consumption of the vehicle, A power analysis device including a processor that calculates a score for each of the plurality of behavioral factors based on the above standard power contribution.

2. In claim 1, The above processor, Based on the above driving information, the designated monitoring section is divided into multiple speed sections that satisfy the designated speed conditions, Each of the above multiple speed sections is divided into multiple action factors that satisfy the specified speed change conditions, A fuel consumption analysis device that calculates a standard fuel consumption contribution for each of the above multiple behavioral factors.

3. In claim 2, The above processor, A power analysis device that divides each of the plurality of speed sections into the plurality of action factors based on whether the start speed or the end speed of each of the plurality of speed sections is 0.

4. In claim 1, The above processor, Based on the driving information, a fuel cost profit / loss value is calculated that represents the profit / loss of the current fuel cost of the vehicle compared to the standard fuel cost of the vehicle, A fuel cost analysis device that calculates the standard fuel cost contribution based on the above fuel cost profit and loss value.

5. In claim 4, The above processor, A power consumption analysis device that calculates the standard power consumption contribution, which indicates the degree to which the driving of the vehicle in the designated monitoring section has affected the current power consumption profit and loss compared to the standard power consumption, based on the power consumption rate and the power consumption profit and loss value of the vehicle in the designated monitoring section.

6. In claim 1, The above processor, A power analysis device that calculates a score for each of the plurality of behavior factors based on a probability distribution function regarding the standard power contribution of each of the plurality of behavior factors.

7. In claim 6, The above processor, Estimating the probability distribution function based on pre-acquired data on the standard contribution of each of the above multiple behavioral factors, A fuel consumption analysis device that calculates a score for each of the plurality of behavioral factors based on a probability corresponding to the standard fuel consumption contribution of the vehicle in the above probability distribution function.

8. A step of segmenting the designated monitoring section into multiple behavioral factors according to the driving status based on the vehicle driving information obtained in the designated monitoring section; A step of calculating a standard fuel consumption contribution, which is the degree to which each of the above plurality of behavioral factors affects the fuel consumption of the vehicle; and A method for analyzing fuel efficiency, comprising the step of calculating a score for each of the plurality of behavioral factors based on the standard fuel efficiency contribution.

9. In claim 8, The step of dividing into the above multiple behavioral factors is: A step of dividing the designated monitoring section into a plurality of speed sections that satisfy designated speed conditions based on the driving information; A step of dividing each of the plurality of speed sections into a plurality of action factors that satisfy a specified speed change condition; and A method for analyzing fuel consumption, comprising the step of calculating a standard fuel consumption contribution for each of the above-mentioned multiple behavioral factors.

10. In claim 9, The step of dividing into the above multiple behavioral factors is: A method for analyzing power consumption, comprising a step of dividing each of the plurality of speed sections into the plurality of behavior factors based on whether the start speed or the end speed of each of the plurality of speed sections is 0.

11. In claim 8, The steps for calculating the above standard fuel cost contribution are: A step of calculating a fuel cost profit / loss value representing the profit / loss of the current fuel cost of the vehicle compared to the standard fuel cost of the vehicle based on the driving information; and A fuel cost analysis method comprising a step of calculating the standard fuel cost contribution based on the fuel cost profit and loss value.

12. In claim 11, After the step of calculating the above profit and loss value, A fuel economy analysis method further comprising a step of calculating the standard fuel economy contribution, which indicates the extent to which the driving of the vehicle in the designated monitoring section has affected the current fuel economy profit and loss compared to the standard fuel economy, based on the power consumption rate of the vehicle and the fuel economy profit and loss value in the designated monitoring section.

13. In claim 8, The step of calculating the score for each of the above multiple behavioral factors is: A method for analyzing fuel efficiency, comprising the step of calculating a score for each of the plurality of behavioral factors based on a probability distribution function regarding the standard fuel efficiency contribution of each of the plurality of behavioral factors.

14. In claim 13, The step of calculating the score for each of the above multiple behavioral factors is: A step of estimating the probability distribution function based on pre-acquired data regarding the standard electric cost contribution of each of the plurality of behavioral factors; and A fuel economy analysis method comprising a step of calculating a score for each of the plurality of behavioral factors based on a probability corresponding to the standard fuel economy contribution of the vehicle in the probability distribution function.

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