TECHNICAL FIELD
[001] The present subject matter described herein, in general, relates to a system and a method for analysing fuel efficiency of a plurality of vehicles, and more particularly a system and a method for analysing fuel efficiency of a plurality of connected vehicles.
BACKGROUND
[002] In the current world, vehicles efficiency has become one of the foremost research areas in the automobile industry. The focus on fuel efficiency has intensified because of limited fuel supply, spiralling fuel costs, and the adverse impact on environment. Generally, fuel consumption depends on multiple independent variables like engine condition, tire pressure, road conditions, and driving behaviour. Typically, most of the data on vehicle fuel consumption is available under standard operation conditions and there is no way to relate a particular vehicle, standard operation conditions and actual driving conditions. In other words, it is very difficult to predict the optimum fuel consumption for the vehicle since there is a plethora of complex factors which decides the actual fuel consumption. Further, conventional system and methodologies fail in informing a user on how his vehicle fares against different vehicles.
[003] Generally, in the current environment agencies and vehicle manufactures lack effective mechanism of obtaining actual fuel efficiency and emission data and comparing the actual emission data with the emission norms and ideal fuel efficiency. Typically, environment agencies work in a reactive mode when a problem is brought to the notice of the agencies. Thus, conventional methodologies lack in enabling proactive actions. Further, the manufactures lack actual operating data to enable optimization of vehicle designs.
SUMMARY
[004] Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations or versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system and a method for analysing fuel efficiency of a plurality of vehicles. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[005] In one implementation, a system for analysing fuel efficiency of a plurality of vehicles is disclosed. In one aspect, the system may obtain data from a plurality of vehicles. The data may comprise vehicle data and driving data. Further, the system may compute a driving pattern score and a driving condition score for one or more of the plurality of vehicles based on the data. Furthermore, the system may categorize each of the plurality of vehicles in to one or more categories based on one or more of the data, a vehicle manufacturer, a vehicle model, the driving pattern score, and the driving condition score. Subsequently, the system may generate an average fuel efficiency and an emission level for one or more of the categories. Finally, the system may provide an alert to a user of a vehicle based on a comparison of a fuel efficiency of the user’s vehicle, the average fuel efficiency user vehicle’s category and predefined threshold.
[006] In one implementation, a method for analysing fuel efficiency of a plurality of vehicles is disclosed. In one aspect, the method may comprise obtaining data from a plurality of vehicles. The data may comprise vehicle data and driving data. Further, the method may comprise computing a driving pattern score and a driving condition score for one or more of the plurality of vehicles based on the data. Furthermore, the method may comprise categorizing each of the plurality of vehicles in to one or more categories based on one or more of the data, a vehicle manufacturer, a vehicle model, the driving pattern score, and the driving condition score. Subsequently, the method may comprise generating an average fuel efficiency and an emission level for one or more of the categories. Finally, the method may comprise providing an alert to a user of a vehicle based on a comparison of a fuel efficiency of the user’s, the average fuel efficiency user vehicle’s category and predefined threshold.
[007] In yet another implementation, non-transitory computer readable medium embodying a program executable in a computing device for analysing fuel efficiency of a plurality of vehicles is disclosed. In one aspect, the program may comprise a program code for obtaining data from a plurality of vehicles. The data may comprise vehicle data and driving data. Further, the program may comprise a program code for computing a driving pattern score and a driving condition score for one or more of the plurality of vehicles based on the data. Furthermore, the program may comprise a program code for categorizing each of the plurality of vehicles in to one or more categories based on one or more of the data, a vehicle manufacturer, a vehicle model, the driving pattern score, and the driving condition score. Subsequently, the program may comprise a program code for generating an average fuel efficiency and an emission level for one or more of the categories. Finally, the program may comprise a program code for providing an alert to a user of a vehicle based on a comparison of a fuel efficiency of the user’s vehicle, the average fuel efficiency user vehicle’s category and predefined threshold.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating of the present subject matter, an example of construction of the present subject matter is provided as figures; however, the invention is not limited to the specific method and system disclosed in the document and the figures.
[009] The present subject matter is described detail with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer various features of the present subject matter.
[010] Figure 1 illustrates a network implementation of a system for analysing fuel efficiency of a plurality of vehicles, in accordance with an embodiment of the present subject matter.
[011] Figure 2 illustrates the system analysing fuel efficiency of a plurality of vehicles, in accordance with an embodiment of the present subject matter.
[012] Figure 3 illustrates a method for analysing fuel efficiency of a plurality of vehicles, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[013] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods for analysing fuel efficiency of a plurality of vehicles, similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods for analysing fuel efficiency of a plurality of vehicles are now described. The disclosed embodiments for analysing fuel efficiency of a plurality of vehicles are merely examples of the disclosure, which may be embodied in various forms.
[014] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments for analysing fuel efficiency of a plurality of vehicles. However, one of ordinary skill in the art will readily recognize that the present disclosure for analysing fuel efficiency of a plurality of vehicles is not intended to be limited to the embodiments described, but is to be accorded the widest scope consistent with the principles and features described herein.
[015] In an implementation, a system and method for analysing fuel efficiency of a plurality of vehicles, is described. In the implementation data may be obtained from a plurality of vehicles. In one other example, the data may be obtained from local database configured to collect data from one or more of the vehicles. In another example, the data may comprise vehicle data and driving data. The vehicle data may comprise emission data, speed data, braking frequency data, RPM data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle and route data. The driving data may comprise frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns.
[016] Further to obtaining data, a driving pattern score and a driving condition score may be computed. The driving pattern score and the driving condition score may be computed for one or more of the plurality of vehicles based on the data. The driving pattern score may be indicative a good driving, a bad driving, or an average driving. In one example, driving pattern score may be a rating from 1 to 10 where 1 indicates good driving and 10 indicates bad driving. The driving condition score may comprise a road condition score and a traffic condition score. The road condition score may be indicative a good road condition, a bad road condition, and an average road condition. In one other example, the road condition score may be a rating from 1 to 10 where 1 indicates good road condition score and 10 indicates bad road condition score. The traffic condition score may be indicative a good traffic condition, a bad traffic condition, and an average traffic condition. In one other example, the traffic condition score may be a rating from 1 to 10, where 1 indicates good traffic condition score and 10 indicates bad traffic condition score.
[017] Upon computation of a driving pattern score and a driving condition score, each of the plurality of vehicles may be categorized. The plurality of vehicles may be categorized in to one or more categories based on one or more of the data, a vehicle manufacturer, a vehicle model, the driving pattern score, and the driving condition score. In one example, the plurality of vehicles may be categorized in to one or more categories based on the make, the model and the manufacture of the vehicle, vehicle age, the driving pattern score, and the driving condition score.
[018] Further to computing the driving pattern score, and the driving condition score, an average fuel efficiency and an emission level for one or more of the categories may be generated. The average fuel efficiency and the emission level may be generated based on the data. Subsequent to generating the average fuel efficiency and the emission level, an alert may be provided. In one example, the alert may be provided to a user of a vehicle based on a comparison of a fuel efficiency of the user’s vehicle and the average fuel efficiency of the user vehicle’s category. The alert may be an indication to the user to perform an maintenance of the vehicle.
[019] Referring now to Figure 1, a network implementation of a system 102 for analysing fuel efficiency of a plurality of vehicles, in accordance with an embodiment of the present subject matter may be described. In one embodiment, the present subject matter is explained considering that the system 102 may be implemented as a standalone system connects to a network. It may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment and the like.
[020] In one implementation, the system 102 may comprise the cloud-based computing environment in which the user may operate individual computing systems configured to execute remotely located applications. In another embodiment, the system 102 may also be implemented on a client device hereinafter referred to as a user device 104. It may be understood that the system implemented on the client device supports a plurality of browsers and all viewports. Examples of the plurality of browsers may include, but not limited to, Chrome™, Mozilla™, Internet Explorer™, Safari™, and Opera™. It will also be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2 … and 104-N, collectively referred to as user devices 104 hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[021] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[022] In the implementation, vehicle 108-1, 108-2…108-N may be Wi-Fi or 3G enabled. Further, the vehicles 108 may be communicatively coupled to local systems 112-1, 112-2….. 112-N via transmission towers 110-1, 110-2…. 110-N. Further, the local systems 112 are communicatively coupled with system 102 via the network 106. Further, the local systems are configured to receive data from the vehicles 108 and transmit the data to system 102 for analysis.
[023] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
[024] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[025] The memory 206 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[026] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include a computing module 212, a categorizing module 214, a generation module 216 and an other module 218. The other modules 218 may include programs or coded instructions that supplement applications and functions of the system 102. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102.
[027] The memory 206, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The memory 206 may include data generated as a result of the execution of one or more modules in the other module 220. In one implementation, the memory may include data 210. Further, the data 210 may include a system data 222 for storing data processed, computed received and generated by one or more of the modules 208. Furthermore, the data 210 may include other data 224 for storing data generated as a result of the execution of one or more modules in the other module 220.
[028] In one implementation, at first, a user may use the client device 104 to access the system 102 via the I/O interface 204. The user may register them using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102 for obtaining information.
COMPUTING MODULE 212
[029] Referring to figure 2, in an embodiment the computation module 212 may obtain data from connected vehicles. In one other example, the data may be obtained from local database configured to collect data from one or more of the connected vehicles. In another example, the data may comprise vehicle data and driving data. The vehicle data may comprise speed data, braking frequency data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle, vehicle age, transmission gear, engine rpm accelerator pedal position, GPS location, emission data and route data. The driving data may comprise frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns, and traffic data. In the implementation, the computation module 212 may store the data in system data 220.
[030] In the embodiment, further to obtaining data, the computation module 212 may compute a driving pattern score and a driving condition score. The driving pattern score and the driving condition score may be computed for one or more of the plurality of vehicles based on the data. The driving pattern score may be indicative a good driving, a bad driving, or an average driving. In one example, driving pattern score may be a rating from 1 to 10 where 1 indicates good driving and 10 indicates bad driving. In one example, the driving pattern score may be 1, when the speed of the vehicle and the gear correct and the driving pattern score may be 10 if the gear and the speed of the vehicle are not correct. In one example, high frequency of braking or high frequency of urgent braking may result in driving pattern score of 10. A low frequency of braking or low frequency of urgent braking may result in driving pattern score of 1. In one more example, high frequency high acceleration may result in driving pattern score of 10. In one other example, gear-speed ratio, idle time, harsh acceleration, harsh braking may be utilized to compute driving pattern score.
[031] The driving condition score may comprise a road condition score and a traffic condition score. In one example, the driving condition score may be computed based on the road condition score and the traffic condition score. In the implementation, the computation module 212 may store the driving pattern score and the driving condition score in system data 220.
[032] In one example, the road condition score may be indicative a good road condition, a bad road condition, and an average road condition. In one other example, the road condition score may be a rating from 1 to 10 where 1 indicates good road condition score and 10 indicates bad road condition score. In one example, high number of pot holes may result in road condition score of 10. A less number of pot holes may result in driving score of 1. In one more example, high traffic conditions, indicated from low average speed of a vehicle may be given a road condition score of 10 indication bad road condition. In the implementation, the computation module 212 may store the road condition score in system data 220.
[033] In one example, the traffic condition score may be indicative a good traffic condition, a bad traffic condition, and an average traffic condition. In one other example, the traffic condition score may be a rating from 1 to 10 where 1 indicates good traffic condition score and 10 indicates bad traffic condition score. In one example, high traffic may result in traffic condition score of 10. A less traffic may result in driving score of 1. In one more example, high traffic conditions, indicated from low average speed of a vehicle and frequent braking may be given a traffic condition score of 10 indication bad traffic conditions. In the implementation, the computation module 212 may store the driving score and the traffic condition score in system data 220. In the implementation, the computation module 212 may store the traffic condition score in system data 220.
CATEGORIZING MODULE 214
[034] In the implementation, upon computation of a driving pattern score and a driving condition score, the categorizing module 214 may categorize the vehicle. In one example, the plurality of vehicles may be categorized in to one or more categories based on one or more of the data, a vehicle manufacturer, a vehicle model, the driving pattern score, and the driving condition score. In one example, the plurality of vehicles may be categorized in to one or more categories based on the make, the model and the manufacture of the vehicle, vehicle age, the driving pattern score, and the driving condition score.
[035] In one example, the model may be City™ and Verna™ and the manufacturer of the vehicle may be Hyundai™, and Honda™, vehicle age may be 0-3 years, and 3-6 years, the driving pattern score may be 1, 10, and the driving condition score may be l, 10.
[036] In one other example, the vehicle may be categorized in to following categories:
Table 1: Categories of Vehicle
Category Vehicle make Model Driving pattern score Driving condition score Vehicle age (Years)
I. Honda™ City-2014™ Good Good 0 – 3
II. Honda™ City-2014™ Good Average 0 – 3
III. Honda™ City-2014™ Average Average 0 – 3
IV. Honda™ City-2014™ Poor Poor 0 – 3
V. Hyundai™ Verna™ Good Good 0 – 3
VI. Hyundai™ Verna™ Good Average 4 – 6

[037] In the implementation, the categorizing module 214 may store categorized vehicles in system data 220.
GENERATION MODULE 216
[038] In the embodiment, subsequent to computing the driving pattern score, and the driving condition score, the generation module 216 may generate an average fuel efficiency and an emission level for one or more of the categories. The average fuel efficiency and the emission level may be generated based on the data. In the above example, the average fuel efficiency and the emission level for one or more of the categories may be computed based on an average of the fuel efficiency of the entire number of vehicle in a particular category.
[039] In one other example, the average fuel efficiency for one or more vehicle category may be generated as below:

Table 2: Average fuel efficiency for categories of Vehicle
Category Vehicle make Model Driving pattern score Driving condition score Vehicle age (Years) Avg. Fuel efficiency km/litre
I. Honda™ City-2014™ Good Good 0 – 3 18.2
II. Honda™ City-2014™ Good Average 0 – 3 16.5
III. Honda™ City-2014™ Average Average 0 – 3 15.4
IV. Honda™ City-2014™ Poor Poor 0 – 3 11.3
V. Hyundai™ Verna™ Good Good 0 – 3 17.5
VI. Hyundai™ Verna™ Good Average 4 – 6 14.2

[040] In the implementation, the generation module 216 may store the average fuel efficiency and the emission level in the system data 220.
[041] Upon generation of the average fuel efficiency and the emission level, the generation module 216 may provide an alert. In one example, the alert may be provided to a user of a vehicle based on a comparison of a fuel efficiency of the user’s vehicle and the average fuel efficiency of the user vehicle’s category. Further, the alert may be provided if the variation between the fuel efficiency of the user’s vehicle and the average fuel efficiency of the user vehicle’s category is above a predefined threshold. In one example, the alert may be an indication to the user to perform maintenance of the vehicle. In one other example, the alert may be an indication of failure of a vehicle part. In one more example, the alert may be in the form of a SMS, voice call, or a Watsapp™ message.
[042] In one other embodiment, the generation module 216 may further compute fuel consumption of the vehicles based on the vehicle data. In one more example, the fuel consumption may be computed for plurality of vehicles based on the vehicle data, vehicle age, driving pattern score, driving condition score, model and manufacturer of the vehicle. The fuel consumption may be further computed for various routes. The generation module 216 may store the fuel consumption in the system data 220.
[043] Upon computation of the fuel consumption, the generation module 216 may determine a change in fuel consumption based on a change in the data, vehicle age, driving pattern score, driving condition score, route model and manufacturer of the vehicle. Further, the revised fuel consumption may be provided to the user to compare with the current fuel consumption. Further, the generation module 216 may store the revised fuel consumptions in the system data 220.
[044] In one other embodiment, the generation module 216 may further identify variance based on comparison of the average fuel efficiency of the vehicle category and an ideal fuel efficiency of a vehicle published by the manufacturer of the vehicle. The variance may further be provided to various car manufactures to enable optimization of the car designs. Further, the generation module 216 may also store the variance in the system data 220.
[045] Exemplary embodiments for analysing fuel efficiency of a plurality of vehicles discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[046] Some embodiments enable the system and the method to increase fuel efficiency.
[047] Some embodiments enable the system and the method to reduce overall emission
[048] Some embodiments enable the system and the method to compare vehicles.
[049] Some embodiments enable the system and the method to alert the user of a problem.
[050] Some embodiments enable the system and the method to alert a user to perform maintain.
[051] Referring now to Figure 3, a method 300 for analysing fuel efficiency of a plurality of vehicles is shown, in accordance with an embodiment of the present subject matter. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types.
[052] The order in which the method 300 for analysing fuel efficiency of a plurality of vehicles is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300 or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 may be considered to be implemented in the above described system 102.
[053] At block 302, data from a plurality of vehicles may be obtained. In one example, the data may comprise vehicle data and driving data. In an implementation, computing module 212 may obtain data and store the data in system data 220.
[054] At block 304, a driving pattern score and a driving condition score for one or more of the plurality of vehicles is computed based on the data. In the implementation, the computing module 212 may compute a driving pattern score and a driving condition score and store the driving pattern score and the driving condition score in system data 220.
[055] At block 306, each of the plurality of vehicles may be categorized in to one or more categories based on based on one or more of the data, a vehicle manufacturer, a vehicle model, the driving pattern score, and the driving condition score. In the implementation, the categorizing module 214 may categorize each of the plurality of vehicles in to one or more categories and store the categorization data in system data 220.
[056] At block 308, an average fuel efficiency and an emission level for one or more of the categories may be generated. In the implementation, the generation module 216 may generate an average fuel efficiency and an emission level for one or more of the categories and store the average fuel efficiency and the emission level in system data 220.
[057] At block 310, an alert may be provided to a user of a vehicle based on a comparison of a fuel efficiency of the user’s vehicle, the average fuel efficiency user vehicle’s category and a predefined threshold. In the implementation, the generation module 216 may provide an alert to a user of a vehicle based on a comparison of a fuel efficiency of the user’s vehicle and the average fuel efficiency user vehicle’s category and store the alert in system data 220.
[058] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include a method for analysing fuel efficiency of a plurality of vehicles.
[059] Although implementations for methods and systems for analysing fuel efficiency of a plurality of vehicles have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for analysing of a plurality of vehicles.

Claims:1. A method for analysing fuel efficiency of a plurality of vehicles, the method comprising:
obtaining, by a processor, data from a plurality of vehicles, wherein the data comprises vehicle data and driving data;
computing, by the processor, a driving pattern score and a driving condition score for one or more of the plurality of vehicles based on the data;
categorizing, by the processor, each of the plurality of vehicles in to one or more categories based on one or more of the data, a vehicle manufacturer and a vehicle model, the driving pattern score, and the driving condition score;
generating, by the processor, an average fuel efficiency and an emission level for one or more of the categories; and
providing, by the processor, an alert to a user of a vehicle based on a comparison of a fuel efficiency of the vehicle, the average fuel efficiency the vehicle’s category and a predefined threshold.

2. The method of claim 1, further comprises
computing, by the processor, a fuel consumption for one or more of the plurality of vehicles based on the vehicle data, vehicle age, driving pattern score, driving condition score, model and manufacturer of the vehicle; and
determining, by the processor, a change in the fuel consumption based on a change in the data, vehicle age, driving pattern score, driving condition score, model and manufacturer of the vehicle.

3. The method of claim 1 further comprises identifying, by the processor, a variance based on comparison of the average fuel efficiency of a vehicle’s category and an ideal fuel efficiency of the vehicle published by the manufacturer of the vehicle.

4. The method of claim 1, wherein the vehicle data comprises one or more of speed data, braking frequency data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle, vehicle age, transmission gear, engine rpm accelerator pedal position, GPS location, emission data and route data.
5. The method of claim 1, wherein the driving data comprises frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns, and traffic data.

6. The method of claim 1, wherein the driving pattern score is indicative of one of a good driving, a bad driving, and an average driving.

7. The method of claim 1, wherein the driving condition score comprises a road condition score and a traffic condition score.

8. The method of claim 1, wherein the traffic condition score is indicative of one of a good traffic condition, a bad traffic condition, and an average traffic condition.

9. The method of claim 1, wherein the road condition score is indicative of one of a good road condition, a bad road condition, and an average road condition.

10. A system for analysing fuel efficiency of a plurality of vehicles, the system comprising:
a memory; and
a processor coupled to the memory, wherein the processor is capable of executing instructions to perform steps of:
obtaining data from a plurality of vehicles, wherein the data comprises vehicle data and driving data;
computing a driving pattern score and a driving condition score for one or more of the plurality of vehicles based on the data;
categorizing each of the plurality of vehicles in to one or more categories based on one or more of the data, a vehicle manufacturer and a vehicle model, the driving pattern score, and the driving condition score;
generating an average fuel efficiency and an emission level for one or more of the categories; and
providing an alert to a user of a vehicle based on a comparison of a fuel efficiency of the user’s vehicle, the average fuel efficiency user vehicle’s category and a predefined threshold.

11. The system of claim 8, further comprises
computing a fuel consumption for one or more of plurality of vehicles based on the vehicle data, vehicle age, driving pattern score, driving condition score, model and manufacturer of the vehicle; and
determining a change in the fuel consumption based on a change in the data, vehicle age, driving pattern score, driving condition score, model and manufacturer of the vehicle.

12. The system of claim 8 further comprises identifying a variance based on comparison of the average fuel efficiency of a vehicle’s category and an ideal fuel efficiency of the vehicle published by the manufacturer of the vehicle.

13. The system of claim 8, wherein the vehicle data comprises one or more speed data, braking frequency data, gear data, engine data, distance travelled, make and model of vehicle, manufacture of the vehicle, vehicle age, transmission gear, engine rpm accelerator pedal position, GPS location and route data.

14. The system of claim 8, wherein the driving data comprises frequency of braking, force of braking, time of driving, duration of driving, number of turns, number of sudden turns, traffic data.

15. The system of claim 8, wherein the driving pattern score is indicative of one of a good driving, a bad driving, and an average driving.

16. The method of claim 1, wherein the driving condition score comprises a road condition score and a traffic condition score.

17. The method of claim 1, wherein the traffic condition score is indicative of one of a good traffic condition, a bad traffic condition, and an average traffic condition.

18. The system of claim 8, wherein the road condition score is indicative of one of a good road condition, a bad road condition, and an average road condition.
19. A non-transitory computer program product having embodied thereon a computer program for analysing fuel efficiency of a plurality of vehicles, the computer program product storing instructions, the instructions comprising instructions for:
obtaining data from a plurality of vehicles, wherein the data comprises vehicle data and driving data;
computing a driving pattern score and a driving condition score for one or more of the plurality of vehicles based on the data, wherein the driving condition score comprises a road condition score and a traffic condition score;
categorizing each of the plurality of vehicles in to one or more categories based on one or more of the data, a vehicle manufacturer, a vehicle model, the driving pattern score, and the driving condition score;
generating an average fuel efficiency and an emission level for one or more of the categories;
providing an alert to a user of a vehicle based on a comparison of an fuel efficiency of the user’s vehicle and the average fuel efficiency user vehicle’s category
computing a fuel consumption for one or more of the plurality of vehicles based on the vehicle data, vehicle age, driving pattern score, driving condition score, model and manufacturer of the vehicle;
determining a change in the fuel consumption based on a change in the data; and
identifying a variance based on comparison of the average fuel efficiency of a vehicle’s category and an ideal fuel efficiency of the vehicle published by the manufacturer of the vehicle.