KR101526431B1 - Apparatus and method for estimating fuel efficiency of vehicle - Google Patents

Apparatus and method for estimating fuel efficiency of vehicle Download PDF

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KR101526431B1
KR101526431B1 KR1020140057984A KR20140057984A KR101526431B1 KR 101526431 B1 KR101526431 B1 KR 101526431B1 KR 1020140057984 A KR1020140057984 A KR 1020140057984A KR 20140057984 A KR20140057984 A KR 20140057984A KR 101526431 B1 KR101526431 B1 KR 101526431B1
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vehicle
information
group
driving
vehicles
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KR1020140057984A
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Korean (ko)
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김선수
박승창
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현대자동차 주식회사
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K35/00—Instruments specially adapted for vehicles; Arrangement of instruments in or on vehicles
    • B60W2530/145—

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  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)

Abstract

An apparatus for estimating the fuel efficiency of a vehicle and a method thereof are disclosed. An apparatus for estimating the fuel efficiency of a vehicle according to an embodiment of the present invention comprises: a data collecting part collecting driving information, state information, and identification information from a plurality of vehicles including a first vehicle; a driving index calculating part calculating each driving index of the vehicles based on the driving information; a similar group extracting part extracting a similar group of gathering vehicles similar to the first vehicle from the vehicles based on the driving index and the state information; a fuel efficiency estimating part estimating the fuel efficiency of the first vehicle based on driving information and identification information in the similar group; and a vehicle management guide part guiding the vehicle management method or the driving improving method for the first vehicle based on a result of estimating fuel efficiency. According to an embodiment of the present invention, the fuel efficiency of a vehicle may be accurately estimated in consideration of the driving habits of a driver and the current state of the vehicle. Moreover, a vehicle management method and a driving method based on fuel estimating data of the vehicle are provided to the driver so that the driver may increase efficiency in managing and driving the vehicle and may reduce maintenance costs of the vehicle.

Description

[0001] APPARATUS AND METHOD FOR ESTIMATING FUEL EFFICIENCY OF VEHICLE [0002]

An apparatus and method for estimating fuel economy of a vehicle are provided.

Conventionally, the fuel consumption is measured based on the amount of gasoline and the distance traveled by the vehicle. This method uses the actual driving information of the vehicle, so that the fuel consumption can be measured relatively accurately and the fuel consumption can be predicted by using the fuel consumption measurement value calculated based on the actual driving information of the vehicle. However, since the fuel consumption of the vehicle is different depending on the model year of the vehicle, the driving habits of the driver, and the state of components (controller) in the vehicle, the method of calculating the fuel consumption using only the traveling information of the vehicle is less accurate . In addition, since it takes a considerable amount of time to collect the actual running information, it is difficult to estimate the fuel consumption of the vehicle during the actual running information collection period, and accordingly it is difficult to properly manage the vehicle in response to the fuel efficiency drop.

An object to be solved by one embodiment of the present invention is to provide an apparatus and method for estimating the fuel consumption of a vehicle based on driving information and state information collected from a plurality of vehicles.

Embodiments according to the present invention can be used to accomplish other tasks not specifically mentioned other than the above-described tasks.

According to an embodiment of the present invention, there is provided a vehicular drive system including a data collecting unit for collecting driving information, state information, and unique information from a plurality of vehicles including a first vehicle, A similarity group extraction unit for extracting a similar group that is a set of vehicles similar to the first vehicle among the plurality of vehicles based on the operation index and the status information, A fuel consumption predicting unit for predicting the fuel consumption of the first vehicle based on the driving information and the unique information of the group, and a vehicle management guidance unit for guiding the vehicle management method or the driving improvement method of the first vehicle based on the fuel consumption prediction result The present invention proposes an apparatus for estimating the fuel economy of a vehicle.

Here, the driving information includes actual fuel consumption information, and the similar group extracting unit extracts a driving group similarity group for extracting a first group, which is a group of vehicles similar to the first vehicle, among the plurality of vehicles based on the driving index And a vehicle state similar group extracting unit for extracting a second group that is a set of vehicles similar to the first vehicle in the first group based on the state information, The fuel consumption of the first vehicle can be predicted based on the fuel consumption information and the unique information.

Also, the driving information includes actual fuel consumption information, and the similar group extracting unit extracts a driving group similarity group for extracting a first group, which is a group of vehicles similar to the first vehicle, among the plurality of vehicles based on the driving index And a vehicle state similarity group extracting unit that extracts a second group that is a set of vehicles similar to the first vehicle among the plurality of vehicles based on the status information, The fuel consumption of the first vehicle can be predicted based on the actual fuel mileage information and the unique information of the group.

In addition, the driving information is driving related information of the vehicle, the state information is component related information that affects the fuel efficiency of the vehicle, and the unique information may be basic information of the vehicle.

The vehicle management guidance unit may guide the inspection or replacement of parts that affect the fuel economy of the first vehicle, or guide the driving method of the first vehicle.

According to an embodiment of the present invention, there is provided an information processing method including the steps of collecting driving information, state information, and unique information from a plurality of vehicles including a first vehicle, Extracting a first group which is a set of vehicles similar to the operating index of the first vehicle based on the operating indexes of the plurality of vehicles, Extracting a second group which is a set of vehicles similar to the state information of the first vehicle, predicting the fuel economy of the first vehicle based on the second group of travel information and the unique information, And analyzing the fuel economy improvement method of the first vehicle based on the prediction result.

Here, the driving information includes actual fuel consumption information, and the fuel consumption prediction step may predict the fuel consumption of the first vehicle based on the actual fuel mileage information and the unique information of the second group.

According to an embodiment of the present invention, there is provided an information processing method including the steps of collecting driving information, state information, and unique information from a plurality of vehicles including a first vehicle, Extracting a first group which is a set of vehicles similar to the operation index of the first vehicle based on the operation indexes of the plurality of vehicles, Extracting a second group which is a set of vehicles similar to the state information of the first vehicle, predicting the fuel consumption of the first vehicle based on the driving information and the unique information of the first group or the second group, And analyzing the fuel economy improvement method of the first vehicle based on the fuel economy prediction result of the first vehicle.

Here, the driving information includes actual fuel consumption information, and the fuel consumption prediction step may predict the fuel consumption of the first vehicle based on the actual fuel consumption information and the unique information of the first group or the second group.

Further, the analysis of the fuel consumption improvement method may guide the inspection or replacement of the parts affecting the fuel consumption of the first vehicle, or may guide the driving method of the first vehicle.

According to one embodiment of the present invention, the fuel consumption of the vehicle can be predicted accurately in consideration of the driving habit of the vehicle driver and the current state of the vehicle. Further, by providing the driver with the vehicle management method and the driving method on the basis of the fuel consumption prediction data of the vehicle, the driver can improve the efficiency of vehicle management and operation, and reduce the cost required for vehicle management and maintenance.

1 is an apparatus for estimating the fuel consumption of a vehicle according to a first embodiment of the present invention.
2 is a method for estimating the fuel economy of a vehicle according to the first embodiment of the present invention.
FIG. 3 is a fuel consumption comparative graph according to the first embodiment of the present invention. FIG.
4 is a fuel consumption comparison graph according to a second embodiment of the present invention.
5 is an apparatus for estimating the fuel consumption of a vehicle according to a second embodiment of the present invention.
6 is a method for estimating the fuel economy of a vehicle according to the second embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Whenever a component is referred to as "including" an element throughout the specification, it is to be understood that the element may include other elements, not the exclusion of any other element, unless the context clearly dictates otherwise. Also, the term "part" in the description means a unit for processing at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software.

Throughout the specification, "driving information " refers to driving related information of the vehicle, and includes information such as the number of times of operation, the driving distance, the driving time, the driving speed, the number of times of using the driving mode, acceleration / deceleration, idling or actual fuel consumption.

The term "state information" throughout the specification refers to the in-vehicle controller, that is, the component-related information that affects the fuel efficiency of the vehicle, and includes information such as the type, state, wear degree, or deterioration degree of the component.

In the specification, "unique information" means basic information of the vehicle and includes information such as model year, model, date of registration, fuel type, or amount of displacement.

1 is an apparatus for estimating the fuel consumption of a vehicle according to a first embodiment of the present invention.

1, a vehicle fuel consumption estimating apparatus 10 includes a data collecting unit 11, a driving index calculating unit 12, a similar group extracting unit 13, a fuel consumption predicting unit 14, a vehicle management informing unit 15 ), And a database unit 16, and is connected to the first vehicle 20 and a plurality of vehicles through a wired / wireless communication network.

The data collecting unit 11 collects driving information, state information, and unique information from the first vehicle 20 and a plurality of vehicles.

The driving index calculating unit 12 calculates a probability distribution graph based on the driving information of a plurality of vehicles collected from the data collecting unit 11 and calculates a probability distribution graph based on the running of the first vehicle 20 collected from the data collecting unit 11 The operation index is calculated by indexing the operation pattern based on the information. Here, the operation index of the first vehicle 20 means the position of the first vehicle 20 within the probability distribution graphs of the plurality of vehicles, and is calculated based on the probability distribution graph of the plurality of vehicles, Value.

The similarity group extracting unit 13 includes a traveling index similarity group extracting unit 13-1 and a vehicle state similarity group extracting unit 13-2.

The driving index similarity group extracting unit 13-1 extracts a vehicle including a driving index similar to the driving index of the first vehicle 20 among a plurality of vehicles (hereinafter, referred to as a "first group"). For example, when the operation index of the first vehicle 20 is 40, the vehicle including the operation index of 40 + 5 is included in the first group.

The vehicle state similarity group extracting unit 13-2 extracts a vehicle including state information that is the same as or similar to the state information of the first vehicle 20 in the first group (hereinafter referred to as a "second group"). At this time, the second group can be extracted based on the previously set in-vehicle parts, and the second group can be extracted based on the overall durability based on the plurality of parts in the vehicle.

The fuel consumption predicting unit 14 predicts the fuel consumption of the first vehicle 20 based on the driving information and the unique information of the second group. For example, based on the fuel economy of the second group, the fuel economy is predicted considering the model year of the first vehicle 20 and the driving distance.

The vehicle management guidance unit (15) transmits the vehicle management method to the first vehicle (20) based on the fuel consumption prediction result of the fuel consumption prediction unit (14). For example, when the fuel efficiency of the first vehicle 20 is lower than the average fuel efficiency of the second group, the average state information of the second group is compared with the state information of the first vehicle 20. [ As a result of the comparison, in-vehicle part information that affects the fuel efficiency of the first vehicle 20 is extracted, and the information related to replacement or repair of the relevant part is displayed.

The vehicle management guide unit (15) transmits the vehicle driving method to the first vehicle (20) based on the fuel consumption prediction result of the fuel consumption prediction unit (14). For example, when the fuel efficiency of the first vehicle 20 is lower than the average fuel efficiency of the second group, based on the result of comparison between the average driving information of the second group and the driving information of the first vehicle 20, A driving mode control method or an operating habit improvement method for improving fuel economy of the vehicle 20. Also, the operation mode control method or the driving habit improvement method for improving the fuel economy of the first vehicle 20 is referred to by referring to the driving information of some vehicles located in the upper portion of the fuel economy in the second group.

The database unit 16 stores vehicle management and operation methods according to driving information, state information, unique information, operating index, first group information, a second group, a fuel consumption prediction result, and a fuel consumption prediction result.

2 is a method for estimating the fuel economy of a vehicle according to the first embodiment of the present invention.

First, the driving information and the status information are collected from the first vehicle 20 and a plurality of vehicles through the data collecting unit 11 (S10).

Then, the operating index is calculated based on the driving information of the first vehicle 20 and the plurality of vehicles (S20). Specifically, a probability distribution graph is calculated based on the running information of a plurality of vehicles, and a running index of the first vehicle 20, which means the position of the first vehicle 20 in the probability distribution graph, is calculated. In this case, the operating index is an index of the operating pattern of the first vehicle 20 calculated through the analysis of the operating information of the first vehicle 20, and is represented by a value between about 0 and 100 based on the probability distribution graph .

Table 1 below is an example of calculating the operation index average value and the standard deviation of the plurality of vehicles and the operation index of the first vehicle 20. [


 A plurality of vehicles The first vehicle medium Standard Deviation medium Operating index


Driving Information Frequency of operation 2.3 times 0.2 3.2 times 62 Operating time 40 minutes 5 25 minutes 32 Operating distance 150km 7.2 40 km 21 Average speed 61 km / h 1.1 70 km / h 78 Acceleration / Deceleration Index 5kph / sec 0.4 3kph / sec 40 Idling time 15 minutes 1.2 22 minutes 30 ADAS 1 operation 5.1 times 0.1 2.3 times 23 ADAS 2 operation 2.1 times 0.1 2.1 times 50 ... ... ... ... ...

Then, a similar group of the first vehicle 20 among the plurality of vehicles is extracted (S30).

Step S30 includes a step S31 of generating a first group including a running index similar to the running index of the first vehicle 20 among a plurality of vehicles, And a second group including the vehicle status (S32).

The step S30 may perform the step S32 or the steps S31 and S32 based on the result of the step S31.

For example, if it is determined in step S31 that the operation index similarity degree determination range is set to the " operation index +/- 5 ", the first vehicle 20, If the operation index of the operating frequency of the vehicle is 62, the vehicle having the operating frequency of 57 to 67 among the plurality of vehicles is extracted to generate the first group. In step S31, the first group can be generated based on Table 1 on the basis of the travel time, the travel distance, the average speed, the acceleration / deceleration index, the idle time, the ADAS 1 operation, and the ADAS 2 operation. Thereafter, in step S32, the state values of a plurality of parts in the vehicle are extracted at a specific time point based on the state information of the first group of vehicles and the first vehicle 20, And calculates the overall durability of the first vehicle 20 and the vehicles of the group.

Table 2 below is an example showing the state values of the second vehicle included in the first group and the state values of the first vehicle at a specific time point.

part weight The first vehicle state value The second vehicle state value A 30 51 41 B 25 46 43 C 15 35 34 D 10 33 39 E 20 29 38

Specifically, in step S32, the total durability of the five components A, B, C, D, and E that affect the fuel consumption can be calculated by applying weight values for each component set in advance for the first vehicle and the second vehicle . Further, a second group is generated by extracting a vehicle whose state information is similar to that of the first vehicle in the first group based on the similarity determination criterion (range) of the preset overall durability.

A method of performing steps S31 and S32 will be described. In step S31, a vehicle having a similar operation index to the first vehicle 20 is extracted from a plurality of vehicles based on the range determination range, In step S32, based on the state information similarity degree determination range, a vehicle in which state information is similar to that of the first vehicle 20 among a plurality of vehicles is extracted to generate the fourth group.

At this time, the similarity determination criterion or range of steps S31 and S32 may be preset by the driver through the terminal or mobile in the vehicle, or may be provided from the vehicle management center at the remote place.

Then, the fuel consumption of the first vehicle 20 is predicted (S40). Specifically, the fuel consumption of the first vehicle 20 is predicted based on the driving information or the unique information of the second group.

FIG. 3 is a fuel consumption comparative graph according to the first embodiment of the present invention. FIG.

3 is a graph comparing fuel consumption of the second group and the first vehicle based on the result of performing step S32 based on the result of step S31 in step S30.

In FIG. 3, the graph A is an average fuel efficiency graph of the second group based on the travel distance, and the graph B is a fuel consumption forecast graph of the first vehicle 20 based on the travel distance. 3, it can be seen that the fuel efficiency of the first vehicle 20 is lower than the average fuel efficiency of the second group on the basis of the current time (current) when the driving distance is 7000 km. When the driving distance is 14000 km The fuel consumption of the first vehicle 20 can be predicted.

4 is a fuel consumption comparison graph according to a second embodiment of the present invention.

4 is a graph comparing the fuel consumption of the third group, the fourth group, and the first vehicle based on the result of performing the steps S31 and S32 in step S30.

In FIG. 4, the graph C is the upper 10% average fuel efficiency graph in the third driving range reference, Graph D is the upper 10% average fuel consumption graph in the fourth driving range reference graph, ). In this case, the graph E is calculated by using the average fuel efficiency graphs of the graphs C and D, but the present invention is not limited thereto. It is also possible to calculate the graph E based on the graph of either the graph C or the graph D have.

The fuel consumption comparison graphs of FIGS. 3 and 4 are based on the driving distance, but the present invention is not limited thereto. The fuel consumption comparison graph may be calculated based on the running time or the running time based on the registration date.

Returning to the description of FIG. 2, the fuel efficiency performance of the first vehicle 20 is determined (S50).

As a result of determining the fuel efficiency of the first vehicle 20, when the fuel efficiency of the first vehicle 20 is lower than that of the plurality of vehicles, the fuel efficiency improvement method is analyzed (S60) and the analyzed result is transmitted to the first vehicle 20 (S70). At this time, the fuel consumption improving method can be analyzed on the basis of the state information of the first vehicle 20 or on the basis of the running information of the first vehicle 20.

The method based on the state information of the first vehicle 20 in step S60 compares the component state value of the first vehicle 20 with the component state values of the plurality of vehicles, Can be calculated. For example, as a result of comparing the state values of the first vehicle and the second vehicle in Table 2, it is necessary to recognize the necessity of maintenance or replacement of the components A and E of the first vehicle 20, , It is possible to guide the driver to the vehicle or to make reservations at the vehicle repair shop. Also, as shown in Table 3 below, it is possible to recommend the optimal driving mode for improving the fuel efficiency to the driver by referring to the driving mode setting method corresponding to the parts that affect the fuel consumption decrease.

Table 3 below is an example of how to set the operating mode corresponding to the problem parts.

part How to set the driving mode A Two-wheel drive mode B Active Eco mode C Wheel mode change & Active Eco mode ... ... A, B Two-wheel drive mode & Wheel mode change B, C Wheel mode change & Active Eco mode & ISG Always ON A, E Active Eco mode & two-wheel drive mode ... ...

For example, to improve the fuel economy of the first vehicle 20 based on Table 3 for parts A and E that require maintenance or replacement in Table 2, it is recommended to the driver to operate the vehicle in the Active Eco mode and the two-wheel drive mode .

The method based on the driving information of the first vehicle 20 at step S60 is a method of comparing the driving information of the first vehicle 20 with the driving information of a plurality of vehicles having higher fuel economy than the first vehicle 20, It is possible to provide a driver of the first vehicle 20 with a driving habit improvement method for improving fuel economy based on some information among the driving information.

Table 4 below is an example of comparison of driving information between the first vehicle and the second vehicle. At this time, it is assumed that the second vehicle has higher fuel efficiency than the first vehicle, and the second vehicle may be a plurality of vehicles having higher fuel economy than the first vehicle.


 The first vehicle The second vehicle medium Operating index medium Operating index


Driving Information Frequency of operation 3.2 times 62 2.3 times 50 Operating time 50 minutes 32 45 minutes 55 Operating distance 40 km 35 40 km 35 Average speed 70 km / h 78 72 km / h 60 Acceleration / Deceleration Index 9kph / sec 40 3kph / sec 60 Idling time 20 minutes 30 6 minutes 80 ADAS 1 operation 2.3 times 23 2.1 times 25 ADAS 2 operation 2.1 times 50 2.2 times 52 ... ... ... ... ...

For example, in Table 4, it can be seen that the acceleration / deceleration index of the first vehicle is higher than that of the second vehicle and the idling time is longer. If the acceleration / deceleration index and the idling time are equal to or greater than a preset threshold value, To the driver of the first vehicle 20. [0050]

Table 5 below shows an example of how to improve driving habits according to the results of the comparison of driving information.

Driving Information How to improve driving habits Acceleration / Deceleration Index Active Eco mode Always ON, above the threshold value Acceleration / Idling time Voice guidance when idling is detected, ISG always on, Active Eco mode always on ... ...

The operating mode setting method of Table 3 and the operating habit improvement method of Table 5 are provided from the remote vehicle management center and can be updated periodically.

5 is an apparatus for estimating the fuel consumption of a vehicle according to a second embodiment of the present invention.

5, a vehicle fuel consumption estimating apparatus 40 is connected to a data collecting apparatus 30 that collects driving information, state information, and unique information from a plurality of vehicles and calculates a driving index, and is connected to a wired / A second operation index calculating unit 42, a similar group extracting unit 43, a fuel consumption predicting unit 44, and a vehicle management information unit 45. [

The function of each component of the vehicle fuel consumption estimating device 40 and the data collecting device 30 is the same as that of the vehicle fuel consumption estimating device according to the first embodiment, and thus a duplicated description will be omitted.

6 is a method for estimating the fuel economy of a vehicle according to the second embodiment of the present invention.

The method of estimating the vehicle fuel consumption of FIG. 6 is divided into the fuel consumption estimating device 40 and the data collecting device 30, and the actual steps are the same as the fuel consumption estimating method of the vehicle according to the first embodiment , Redundant explanations are omitted.

While the present invention has been particularly shown and described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It belongs to the scope.

10: fuel efficiency estimating device 11: data collecting part
12: operation index calculating unit 13: similar group extracting unit
13-1: Operation index similarity group extracting unit 13-2: Vehicle state similarity group extracting unit
14: Fuel economy prediction unit 15: Vehicle management information unit
16:

Claims (10)

A data collecting unit for collecting driving information, state information, and unique information from a plurality of vehicles including a first vehicle,
An operating index calculating unit for calculating an operating index of each of the plurality of vehicles based on the operating information,
A similar group extracting unit for extracting a similar group that is a set of vehicles similar to the first vehicle among the plurality of vehicles based on the operation index and the status information,
A fuel consumption predicting unit for predicting fuel consumption of the first vehicle based on the driving information and the unique information of the similar group,
And a vehicle management information unit that guides the vehicle management method or the operation improvement method of the first vehicle based on the fuel economy prediction result. The method of claim 1,
The driving information includes actual fuel consumption information,
Wherein the similarity group extracting unit includes: a travel exponent group extracting unit for extracting a first group, which is a set of vehicles similar to the first vehicle, among the plurality of vehicles based on the travel index; A vehicle state similar group extracting unit that extracts a second group that is a set of vehicles similar to the first vehicle,
Wherein the fuel consumption predicting unit predicts the fuel consumption of the first vehicle based on the actual fuel efficiency information of the second group and the unique information. The method of claim 1,
The driving information includes actual fuel consumption information,
Wherein the similarity group extracting unit comprises: an operation index similar group extracting unit for extracting a first group, which is a group of vehicles similar to the first vehicle, among the plurality of vehicles based on the operation index; A vehicle state similar group extracting unit that extracts a second group that is a set of vehicles similar to the first vehicle among the plurality of vehicles,
Wherein the fuel consumption predicting unit predicts the fuel consumption of the first vehicle based on the actual fuel mileage information and the unique information of the first group or the second group. 4. The method according to any one of claims 1 to 3,
Wherein the driving information is driving related information of the vehicle, the state information is information related to parts that affects the fuel efficiency of the vehicle, and the unique information is basic information of the vehicle. 4. The method according to any one of claims 1 to 3,
Wherein the vehicle management guidance unit guides the inspection or replacement of parts that affect fuel economy of the first vehicle or guides the driving method of the first vehicle. Collecting driving information, state information, and unique information from a plurality of vehicles including a first vehicle,
Calculating a driving index of each of the plurality of vehicles based on the driving information,
Extracting a first group that is a set of vehicles similar to the running index of the first vehicle based on the running index of the plurality of vehicles,
Extracting a second group which is a set of vehicles similar to the state information of the first vehicle based on the state information of the first group,
Predicting the fuel economy of the first vehicle based on the driving information and the unique information of the second group, and
And analyzing the fuel economy improvement method of the first vehicle based on the fuel economy prediction result of the first vehicle. The method of claim 6,
The driving information includes actual fuel consumption information,
Wherein the fuel consumption prediction step predicts the fuel consumption of the first vehicle based on the actual fuel mileage information and the unique information of the second group. Collecting driving information, state information, and unique information from a plurality of vehicles including a first vehicle,
Calculating a driving index of each of the plurality of vehicles based on the driving information,
Extracting a first group that is a set of vehicles similar to the running index of the first vehicle based on the running index of the plurality of vehicles,
Extracting a second group which is a set of vehicles similar to the state information of the first vehicle based on the state information of the plurality of vehicles,
Predicting the fuel economy of the first vehicle based on the driving information and the unique information of the first group or the second group, and
And analyzing the fuel economy improvement method of the first vehicle based on the fuel economy prediction result of the first vehicle. 9. The method of claim 8,
The driving information includes actual fuel consumption information,
Wherein the fuel consumption predicting step predicts the fuel economy of the first vehicle based on the actual fuel mileage information and the unique information of the first group or the second group. 10. The method according to any one of claims 6 to 9,
Wherein the fuel consumption improvement method analysis step guides the inspection or replacement of parts affecting the fuel consumption of the first vehicle or guides the driving method of the first vehicle.
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WO2023009039A1 (en) * 2021-07-27 2023-02-02 "Omnicomm Online" Limited Liability Company Method for generating an energy-efficient track for a vehicle
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RU2777851C1 (en) * 2021-12-06 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Vehicle with the function of forming a recovery energy efficient track of a vehicle in operation equipped with an electricity energy recovery system when braking, when the operating vehicle is driving across the point of road, including the road section
WO2023106970A1 (en) * 2021-12-07 2023-06-15 "Omnicomm Online" Limited Liability Company Method for generating an energy-efficient track for a vehicle
RU2777853C1 (en) * 2021-12-07 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Vehicle with the function of forming an energy-efficient track of the operating vehicle when the operating vehicle is moving on the section of the way including the obligatory stop point
RU2777852C1 (en) * 2021-12-07 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Method for forming an energy-efficient track of operating vehicle when operating vehicle is moving on a section of path including obligatory stop point
RU2777855C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Vehicle with the function of forming an energy-efficient navigation route of the operating vehicle
RU2777854C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Device for formation of energy-efficient navigation route of operated vehicle
RU2777857C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Device for formation of modified energy-efficient track of operated vehicle
RU2777858C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Vehicle with the function of forming a modified energy efficient track of the operating vehicle
RU2777856C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" System for formation of modified energy-efficient track for operated vehicle
RU2777860C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Method for forming modified energy efficient track of operating vehicle and machine readable data carrier
RU2777862C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" System for formation of energy-efficient navigation route of operated vehicle
RU2777861C1 (en) * 2021-12-22 2022-08-11 Общество С Ограниченной Ответственностью "Омникомм Онлайн" Method for forming energy efficient navigation route of operating vehicle
WO2023121516A1 (en) * 2021-12-22 2023-06-29 "Omnicomm Online" Limited Liability Company Method for generating a modified energy-efficient track for a vehicle
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