Description:TECHNICAL FIELD
[0001] The present invention relates to the field of electrically powered vehicles and, more particularly to systems, and methods for providing an optimized route for completion of a trip by the electric vehicle.
BACKGROUND
[0002] Electrically powered vehicles such as electric vehicles (EVs) have been steadily gaining popularity. Currently, the electrically powered vehicles have limited driving ranges, for example varying from several tens of miles to a few hundred miles on a single full charge. Since, the traveling range of an electric vehicle is shorter than that of an automobile equipped with an internal combustion engine, the electric vehicle needs to be charged at a special charging station. This means that the electric vehicle may need to make frequent stops at charging stations before it reaches the destination.
[0003] There are many charging stations for the electrically powered vehicles that have been deployed along highways or local roads recently, thereby alleviating some of the concerns of drivers of these vehicles. However, the charging of these electrically powered vehicles may also take a very long time based on their level classification, their charging capacity. If a driver of an electrically powered vehicle selects a random charging station on the driving route to charge his/her vehicle, all the charging equipment positioned at that station may already be in use. Therefore, that driver would have to suffer from the delay caused by waiting for other people to charge their vehicles. This delay may be frustrating to the driver.
[0004] Existing navigation guides or route optimization tools using smart phone apps lack the capability to predict whether a particular charging station will have unoccupied charging equipment by the time the driver reaches that charging station.
[0005] U.S. Patent 2011/0224900 A1 shows a route planning system for planning a navigation route of one electric vehicle by calculating waypoints along the route and searching for charging stations around the route.
[0006] US Patent 2011/0257879 A1, shows a route guidance method for electric vehicles, where route guidance is provided for one electric vehicle by selecting a navigation route which is estimated to consume the least amount of electric power by taking into account the road gradient on the navigation route towards the destination location.
[0007] Although existing methods and devices for navigating routes for electrically powered vehicles are generally adequate for their intended purposes, they have not been entirely satisfactory in every aspect. There is a need in the art for improved navigation guides and route optimization tools for electrically powered vehicles.
[0008] In light of the above-stated discussion, there exists a need for methods and devices for optimizing routes based on trip parameters to complete the trip efficiently.
OBJECT OF THE DISCLOSURE
[0009] A primary objective of the present disclosure is to provide a trip planning system for providing an optimized route for completion of the trip by an electric vehicle based on trip parameters.
[0010] Another objective is to provide a method for providing an optimized route for completion of the trip by an electric vehicle based on trip parameters.
[0011] Yet another objective is to predict whether the trip is feasible at a current SOC (state of charge) in the electric vehicle and recommend the best charging point to make the trip feasible.
SUMMARY OF THE DISCLOSURE
[0012] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0013] An embodiment of the present invention relates to a trip planning system for providing an optimized route for completion of the trip by an electric vehicle. The trip planning system includes a user device, a communication network, a cloud server with a plurality of databases, a memory unit to store a plurality of modules and a processor. The communication network allows communication between the cloud server, the databases, the modules and the user device. The database stores electric vehicle data, trip data, grid data, rank data, user data, location data, route data and the like data.
[0014] Further, the processor is communicably connected to the modules and performs steps to determine whether the electric vehicle needs to charge enroute to complete the trip based on the plurality of trip parameters, selects a list of grids from a plurality of available grids for charging the electric vehicle to complete the trip, rank the list of grids suggested by a grid selection module to charge the electric vehicle for completing the trip a perform a grid feasibility check on the grid recommended to determine feasibility.
[0015] In accordance with an embodiment of the present invention, the plurality of modules includes a trip feasibility module, a grid selection module, a grid ranking module and a grid feasibility module.
[0016] In accordance with an embodiment of the present invention, the trip feasibility module determines whether the electric vehicle needs to charge enroute to complete the trip based on the plurality of trip parameters. In particular, the plurality of trip parameters are starting point of the electric vehicle, destination of the electric vehicle, starting state of charge (SOC) of the electric vehicle, mileage of the electric vehicle. Moreover, the trip feasibility module is configured to retrieve mileage of the electric vehicle calculated by a personalized range prediction analysis, acquire optimized route from starting point of the electric vehicle to destination of the electric vehicle and a total distance, determine shortest route as the optimized route to complete the trip by the electric vehicle, calculate feasibility distance of the trip from starting point to destination of the electric vehicle and determine whether the feasibility distance is greater than the shortest route. In particular, the optimized route is the shortest route retrieved from google maps API. Moreover, the optimized route can be but is not limited to shortest route, fastest route, customized route and combination thereof.
[0017] In accordance with an embodiment of the present invention, the grid selection module selects the list of grids from the plurality of available grids by breaking the optimized route from starting point to destination of the electric vehicle into one or more steps, calculating grid metadata for each of the one or more steps, retrieving from the database the plurality of available grids located in an area, drawing a geometric shape around the one or more steps of the optimized route to select the list of grids falling within the shape. In particular, the geometric shape may be but not limited to a rectangle, circle, square, pentagon or any other shape. Further, the grid metadata includes data such as but not limited to distance of each step, start point and end point of each step.
[0018] In accordance with an alternate embodiment of the present invention, the grid selection module is also configured to provide an input to grid ranking module. Alternatively, the grid selection module shows all the selected grids directly to the user. Further, the user may or may not opt for grid ranks.
[0019] In accordance with an embodiment of the present invention, the grid ranking module ranks the list of grids selected by the grid selection module based on ranking parameters and divides the list of grids based on ranks into a first set, a second set and a third set. The grids are arranged in decreasing order of distance from starting point to grid in each of the three sets. And, the ranking parameters includes metadata of grid, distance of grid from starting point, expected starting state of charge (SOC) at grid, expected starting state of charge (SOC) at destination, grid availability, probability of availability, grid serviceability, past grid usage by the user, user reviews, location preference of the user, deviation from route.
[0020] In accordance with an embodiment of the present invention, the grid feasibility module is configured to validate a real-time feasibility of a grid based on a plurality of grid feasibility check parameters, determine whether starting state of charge (SOC) upon reaching the grid is more than 0 and starting state of charge (SOC) to charge at grid is less than 100 and recommend the grid to the user for charging the electric vehicle only when starting state of charge (SOC) on reaching the grid is more than 0 and starting state of charge (SOC) to charge at Grid is less than 100. In particular, the plurality of grid feasibility check parameters includes distance from starting point to grid, distance of grid to destination, current starting state of charge (SOC) of the electric vehicle, mileage of the electric vehicle. Further, the grid feasibility is performed for top two rank grids in the first set.
[0021] Another embodiment of the present invention relates to a method to provide an optimized route for completion of a trip by an electric vehicle. The method includes steps of obtaining a plurality of trip parameters for completion of the trip, determining, by a trip feasibility module, a trip feasibility check to determine whether the electric vehicle needs to charge enroute to complete the trip based on the plurality of trip parameters, suggesting a list of grids from a plurality of available grids for charging the electric vehicle to complete the trip, ranking the selected list of grids, performing a real time grid feasibility check on the grid recommended.
[0022] In accordance with an embodiment of the present invention, the method includes a step to specify any attraction to be visited by the user between starting point and destination.
[0023] In accordance with an embodiment of the present invention, the electric vehicle is any of a battery electric vehicle (BEV), a hybrid electric vehicle (HEV), a Plug-in Hybrid electric vehicle (PHEV) Fuel Cell electric vehicle (FCEV), a two-wheeler electric bike, a three wheeler electric vehicle.
[0024] In accordance with an embodiment of the present invention, the user device includes a desktop computer, a laptop computer, a user computer, a tablet computer, a personal digital assistant (PDA), a cellular telephone, a communication network appliance, a camera, a smartphone, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, an email device, a game console, or a combination of any these data processing devices or other data processing devices.
[0025] These and other aspects herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawing. The foregoing objectives are attained by employing a trip planning system for providing optimized route for completion of a trip and a method thereof.

BRIEF DESCRIPTION OF DRAWINGS
[0026] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0027] Fig. 1A is a block diagram illustrating a trip planning system for providing an optimized route to complete a trip by an electric vehicle in accordance with an embodiment of the invention;
[0028] Fig. 1 B is a block diagram illustrating a plurality of modules in accordance with an embodiment of the present invention;
[0029] Fig. 2A is an exemplary pictorial snapshot illustrating selection of the grids in accordance with one embodiment of the present invention;
[0030] Fig. 2B is an exemplary pictorial snapshot illustrating selection of the grids in accordance with another embodiment of the present invention;
[0031] Fig. 2C is an exemplary pictorial snapshot illustrating ranking of the grids in accordance with an embodiment of the present invention;
[0032] Fig. 3 is a flowchart illustrating a method for providing an optimized route for completion of a trip by an electric vehicle in accordance with an embodiment of the invention;
[0033] Fig. 4 is a flow chart illustrating a method for determining a trip feasibility in accordance with an embodiment of the invention;
[0034] Fig. 5 is a flow chart illustrating a method for selecting a list of grids from the plurality of available grids by the grid selection module in accordance with an embodiment of the invention;
[0035] Fig. 6 is a flow chart illustrating a method for ranking a list of grids by the grid ranking module in accordance with an embodiment of the invention;
[0036] Fig. 7 is a flow chart illustrating a method for performing grid feasibility check for the top rank grids by the grid feasibility module in accordance with an embodiment of the invention; and
[0037] Fig. 8 is a pictorial snapshot of an interface displayed on user device in accordance with an embodiment of the invention.
[0038] It should be noted that the accompanying figure is intended to present illustrations of a few examples of the present disclosure. The figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION
[0039] Those skilled in the art will be aware that the present disclosure is subject to variations and modifications other than those specifically described. It is to be understood that the present disclosure includes all such variations and modifications. The disclosure also includes all such steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any or more of such steps or features.
[0040] For convenience, before further description of the present disclosure, certain terms employed in the specification, and examples are collected here. These definitions should be read in the light of the remainder of the disclosure and understood as by a person of skill in the art. The terms used herein have the meanings recognized and known to those of skill in the art, however, for convenience and completeness, particular terms and their meanings are set forth below.
[0041] The articles "a", "an" and "the" are used to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article.
[0042] The terms "comprise" and "comprising" are used in the inclusive, open sense, meaning that additional elements may be included. It is not intended to be construed as "consists of only". Throughout this specification, unless the context requires otherwise the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated element or step or group of element or steps but not the exclusion of any other element or step or group of element or steps.
[0043] The term "including" is used to mean "including but not limited to". "Including" and "including but not limited to" are used interchangeably. The accompanying drawing is used to help easily understand various technical features and it should be understood that the alternatives presented herein are not limited by the accompanying drawing. As such, the present disclosure should be construed to extend to any alterations, equivalents and substitutes in addition to those which are particularly set out in the accompanying drawing. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.
[0044] Conditional language used herein, such as, among others, "can," "may," "might," "may," “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain alternatives include, while other alternatives do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more alternatives or that one or more alternatives necessarily include logic for deciding, with or without other input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular alternative. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.
[0045] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain alternatives require at least one of X, at least one of Y, or at least one of Z to each be present.
[0046] Terms starting point or origin can be used interchangeably for convenience throughout the draft.
[0047] Terms mileage or range can be used interchangeably for convenience throughout the draft.
[0048] Terms SOC or state of charge can be used interchangeably for convenience throughout the draft.
[0049] Terms API is used for application programming interface for convenience throughout the draft.
[0050] Fig. 1 is a block diagram illustrating a trip planning system 100 for providing an optimized route to complete a trip by the electric vehicle in accordance with an embodiment of the invention. The trip planning system 100 operates in a vehicle environment. In particular, the system 100 includes a cloud server 104 with a plurality of databases 102A-102I (hereinafter cumulatively referred to as database 102), a memory 106 with a plurality of modules 107, a communication network 108 and a processor 110 and a user device 112.
[0051] In accordance with an embodiment of the present invention, the database 102 stores data such as but not limited to electric vehicle data, trip data, grid data, rank data, user data, location data, route data.
[0052] In accordance with an embodiment of the present invention, the cloud server 104 may be configured to communicate with the user device 112, the trip planning system 100 and the processor 110 via the communication network 108. In particular, the cloud server 104 may be, but not limited to a cloud server, a web server, an application server, a proxy server, a network server, or a server farm, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the remote server 104, including known, related art, and/or later developed technologies.
[0053] In some implementations, the cloud server 104 can communicate with the system 100 via a virtual private network (VPN), Secure Shell (SSH) tunnel, or other secure network connection.
[0054] In accordance with an embodiment of the present invention, the data utilized by the processor may be sent as notifications to the cloud server 104.
[0055] In accordance with an embodiment of the present invention, the communication network 108 is configured for providing communication links for communicating with the cloud server 104, the user device 112, memory 106, modules 107 and processor 110.
[0056] In particular, the communication network 108 may any communication network, such as, but not limited to, the Internet, wireless networks, local area networks, wide area networks, private networks, a cellular communication network, corporate network having one or more wireless access points or a combination thereof connecting any number of mobile clients, fixed clients, and servers and so forth. Examples of communication network 108 may include the Internet, a WIFI connection, a Bluetooth connection, a Zigbee connection, a communication network, a wireless communication network, a 3G communication, network, a 4G communication network, a 5G communication network, a USB connection, or any combination thereof. For example, the communication may be based through a radio-frequency transceiver (not shown). In addition, short-range communication may occur, such as using Bluetooth, Wi-Fi, or other such transceivers.
[0057] It will be appreciated that the network connections shown are illustrative and other means of establishing a communications link between the computers may be used. The existence of any of various network protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, and of various wireless communication technologies such as GSM, CDMA, WiFi, and WiMAX, is presumed, and the various computing devices and system components described herein may be configured to communicate using any of these network protocols or technologies.
[0058] In some implementations, the trip planning system 100 may be a distributed client/server system that spans one or more communication networks (not shown).
[0059] In accordance with an embodiment of the present invention, the memory 106 is configured to store multiple modules 107. The multiple modules 107 include a trip feasibility module 150, a grid selection module 160, a grid ranking module 170 and a grid feasibility module 180.
[0060] For example, memory 106 may store software used by the user device 112, such as an operating system (not shown), application programs (not shown), and an associated internal database (not shown).
[0061] In accordance with an embodiment of the present invention, the processor 110 is communicably connected to the modules 107 to perform a series of computer-readable instructions to determine whether the electric vehicle needs to charge enroute to complete the trip based on the plurality of trip parameters, select a list of grids from a plurality of available grids for charging the electric vehicle to complete the trip, rank the list of grids selected by a grid selection module and recommend a grid to charge the electric vehicle to complete the trip after performing a real time grid feasibility check on the selected grid.
[0062] In accordance with one embodiment of the present invention, the processor 110 may be any well-known processor, but not limited to processors from Intel Corporation. Alternatively, in another embodiment, the processor 110 may be a dedicated controller such as an ASIC. Further, the processor 110 may be any of an ARM, MIPS, SPARC, or INTEL® IA-32 microcontroller or the like.
[0063] Similarly, in yet another embodiment of the present invention, the processor 110 comprises a collection of processors which may or may not operate in parallel.
[0064] In accordance with yet another embodiment of the present invention, the processor 110, which may be any processor-driven device, such as may include one or more microprocessors and memories or other computer-readable media operable for storing and executing computer-executable instructions.
[0065] As used herein, the term "computer-readable media" may describe any form of computer memory or memory device, such as, but not limited to, a random-access memory ("RAM") or a non-volatile memory, such as a hard disk, memory card, ROM, RAM, DVD, CD-ROM, USB Flash drive, write-capable, and read-only memories an EPROM, or an EEPROM.
[0066] Examples of processor-driven devices may include, but are not limited to, a server computer, a mainframe computer, one or more networked computers, a desktop computer, a personal computer, an application-specific circuit, a microcontroller, a minicomputer, or any other processor-based device.
[0067] In accordance with an embodiment of the present invention, the processor 110 may execute any set of instructions directly as computer executable codes or indirectly (such as scripts). In that regard, the terms “instructions,” and “steps” may be used interchangeably herein. The instructions may be stored in object code form for direct processing by the processor, or in any other computer language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.
[0068] In accordance with an embodiment of the present invention, the processor may be remotely placed or locally placed on the server.
[0069] In accordance with an embodiment of the present invention, the trip planning system 100 may also include one or more input/output ("I/O") ports (e.g., serial ports, (e.g., RS233 port, USB, etc.) (not shown) and one or more network interfaces. The I/O port or ports may be operable to communicate with input/output devices, such as an internal and/or external display, keypad, mouse, pointing device, control panel, touch screen display, another computer-based device, printer, remote control, microphone, speaker, etc., which facilitates user interaction with the trip planning system 100.
[0070] In accordance with an embodiment of the present invention, the displaying unit displays the optimized route on a user device 112. The display unit can be implemented, for example, using one or more computing systems. By way of a non-limiting example, the optimized route for the electric vehicle is displayed on the application launch icon in a launch area of a display of the user device 112. Displaying of the optimized route is performed by the display unit with a preloading application in memory of the user device 112 based on data stored in a cloud server 104.
[0071] In accordance with an embodiment of the present invention, the user device 112 may include a desktop computer, a laptop computer, a user computer, a tablet computer, a personal digital assistant (PDA), a cellular telephone, a communication network appliance, a camera, a smartphone, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, an email device, a game console, or a combination of any these data processing devices or other data processing devices. Furthermore, the user device 114 can be provided access to and/or receive application software executed and/or stored on any of the remote server 104.
[0072] In some examples, user device 112 performs functions of a social communication network (not shown) to the cloud server 104. In some implementations, the user device 112 can communicate wirelessly through a communication interface, which may include digital signal processing circuitry where necessary.
[0073] In accordance with an embodiment of the present invention, the electric vehicle is anyone but not limited of a battery electric vehicle (BEV), a hybrid electric vehicle (HEV), a Plug-in Hybrid electric vehicle (PHEV) Fuel Cell electric vehicle (FCEV), a two wheeler electric bike, a three wheeler electric vehicle.
[0074] In accordance with an embodiment, the trip planning system 100 may notify recommendations based on daily user pattern. For example, the trip planning system 100 might predict that the user is going to office in morning and inform the user whether that the trip to the office is feasible with the current SOC or the user needs to charge the electric vehicle at a particular grid point.
[0075] Fig. 1B is a block diagram illustrating a plurality of modules in accordance with an embodiment of the present invention. The plurality of modules 107 include a trip feasibility module 150, a grid selection module 160, a grid ranking module 170 and a grid feasibility module 180.
[0076] In accordance with an embodiment of the present invention, the trip feasibility module 150 is configured to determine whether the electric vehicle can render the trip distance with the current SOC of the electric vehicle or requires charging enroute to complete the trip. In particular, the trip feasibility module 150 makes this determination based on the plurality of trip parameters such as but not limited to starting point of the electric vehicle, destination of the electric vehicle, starting state of charge (SOC) of the electric vehicle, mileage of the electric vehicle.
[0077] The trip feasibility module 150 is configured to retrieve mileage or range of the electric vehicle by a personalized range or a standard range, acquire an optimized route from the starting point of the electric vehicle to the destination of the electric vehicle and a total distance, by determining the shortest route as the optimized route to complete the trip by the electric vehicle. The distance from origin to destination is acquired by Google’s direction API or any other direction API known or to be developed in future. In particular, the optimized route can be but is not limited to shortest route, fastest route, customized route and combination thereof.
[0078] Further, trip feasibility module 150 calculates the feasibility distance of the trip from starting point to destination of the electric vehicle and determines whether the feasibility distance is greater than the shortest route/trip distance. The feasibility distance is calculated as follows:
Feasible Distance = ((Current_soc-10) * Mileage of bike)
wherein; 10 = Buffer SOC
[0079] In accordance with one embodiment of the present invention, if the shortest route/ trip distance is equal to the feasible distance then the trip is possible with current SOC without any charging enroute.
[0080] In accordance with another embodiment of the present invention, if the shortest route/ trip distance is greater than the feasible distance then the trip is possible with current SOC without any charging enroute.
[0081] In accordance with another embodiment of the present invention, if the shortest route/ trip distance is less than the feasible distance then the trip cannot be completed with the current SOC in the electric vehicle and the electric vehicle requires charging enroute to the destination.
[0082] In an exemplary example, for an electric vehicle with a bike_id “s_100”, current_soc is 50 SOC, distance from origin to destination is 50 kms and range is 0.8km_per_soc. Then the feasible distance = (0.8 x (50-10)) = 32 kms. Since, actual trip distance is greater feasible distance, so the trip is not possible with current SOC and the electric vehicle requires charging enroute to complete the trip.
[0083] In accordance with an embodiment of the present invention, the personalized range predicts the distance the electric vehicle can cover with an SOC left in the bike. The personalized range prediction analysis calculates a personalized range of the electric vehicle and updates at a predefined duration in the database 102. Further, the personalized range predicted depends on distance travelled by the electric vehicle in each mode, model of the electric vehicle, driver/riding behavior (aggressive vs efficient rider), tyre pressure, terrain, traffic conditions, road conditions and the like factors.
[0084] In accordance with an embodiment of the present invention, the mode of the electric vehicle may be an eco-mode, sport mode, ride mode and warp mode.
[0085] In an embodiment, the personalized range analysis is calculated based on the current battery efficiency of the bike.
[0086] In an alternative embodiment, the personalized range analysis is an average range based on their historic mode usage.
[0087] In an example bike s_100 in the past 3 months has ridden 50% distance in Eco mode, 25% in sport mode and 25% in warp mode. And, True Range of Eco mode is 0.75 km per soc, True Range of Sport mode is 0. 65 km per soc and True Range of Warp mode is 0.55 km per soc. Then, the personalized range for the bike is (0.75*50 + 0.65*25 + 0.55*25)/100 = 0.675 km per soc.
[0088] In an embodiment, for each variant, the bike declares a True Range for each mode. In an example, for a bike “X”, the mileage can vary from 0.55 km_per_soc (sport mode) to 0.75 km_per_soc (eco mode). And, for bike “Y”, the mileage varies from 0.65 km_per_soc (warp mode) to 1.05 km_per_soc (eco mode). It may be noted by the person skilled in the art that the range is equivalent to the mileage of a petrol vehicle.
[0089] Once the trip feasibility module 150 confirms that the trip is not possible with current SOC, then the grid selection module 160 selects a list of grids from all the available grids.
[0090] The grid selection module 160 is configured to break the optimized route from starting point to final destination or to intermediate destination of the electric vehicle into one or more steps, calculating grid metadata for each of the one or more steps, retrieve from the database the plurality of available grids located in an area and drawing a geometric shape around the one or more steps of the optimized route to select the list of grids falling within the geometric shape. The grid metadata may include but is not limited to distance of each step, start point and end point of each step expected starting state of charge (SOC) at grid step, expected starting state of charge (SOC) at destination, grid availability, probability of availability, grid serviceability, past grid usage by the user, other user reviews, location preference, deviation from route.
[0091] In accordance with an embodiment of the present invention, the geometric shape may be but not limited to rectangle, circle, square, pentagon or any other shape.
[0092] In accordance with an alternate embodiment of the present invention, the grid selection module is also configured to provide an input to grid ranking module. Alternatively, the grid selection module show all the selected grids directly to the user. Further, the user may or may not opt for grid ranks. The grid ranking module 170 is configured to rank the list of grids selected by the grid selection module 160 based on a plurality of grid ranking factors to recommend the best grid to charge the electric vehicle in order to complete the trip. In particular, the plurality of grid ranking factors includes metadata of grid, distance of grid from starting point/origin, expected starting state of charge (SOC) at grid, expected starting state of charge (SOC) at destination, grid familiarity, grid past experience, grid comfortability, grid availability, probability of availability, grid serviceability, past grid usage by the user, other user reviews, location preference, deviation from route and the like.
[0093] In an exemplary implementation, a grid with better serviceability is given a higher rank or a grid traveled by the user frequently is ranked high. In particular, the serviceability is based on but not limited to historic factors of the grid, previous fare charging, probability of downtime and the like.
[0094] In an exemplary example, if the trip is during night, then the grids available during nights are given high rank. It may be noted that different grids may have different working patterns, some grids are open only during working hours, on the weekdays.
[0095] Further, based on the locations of the electric vehicle trip the grid ranking module 170 may predict preference of grids.
[0096] In an exemplary implementation, if a user wishes to visit any intermediate location such as Cafes, Malls, Restaurants, Commercial Buildings, Petrol pumps, Guarded buildings/apartments, Paid/Free parking areas then the grid ranking module recommends grids closer to that intermediate location.
[0097] In some embodiments, low rank grids deviating from the route are also recommended if the user deviates the optimized route.
[0098] In another implementation, the grid far from the origin is ranked high as the electric vehicle can travel the farthest distance by charging at the last possible grid. To find the farthest grid from origin, any known or to be developed directions API may be used.
[0099] Further, the grid ranking module 170 divides the list of grids based on ranks into a first set, a second set and a third set and arranges the grids in decreasing order of distance from starting point to grid in each of the three sets. The first set includes the grids having expected SOC at grid >20 and expected SOC at destination >20. The second set includes grids having SOC at grid >10 and expected SOC at destination >10. And, the third set includes grids having SOC at grid <=10 and expected SOC at destination <=10.
[00100] In accordance with an embodiment of the present invention, to calculate expected SOC the distance from origin to grid projection is calculated and then by using the personalized range the SOC equivalent for the electric vehicle to travel the distance is calculated.
[00101] The expected SOC at grid is calculated by:
Expected SOC to reach grid = Current SOC of bike - SOC to reach grid
[00102] The SOC to reach grid is calculated by:
SOC to reach the grid =Personalized Range*Distance from origin to grid _projection
[00103] The SOC to reach grid is calculated by:
SOC to reach destination = Personalized Range * Distance from origin to grid_projection
[00104] In accordance with an embodiment of the present invention, the grid ranking module 170 is configured to recommend a forward grid ranking. In particular, the forward grid ranking is the multi-grid recommendation for single point trip or a multi-point trip. Moreover, in forward grid ranking multiple grids are used for charging the electric vehicle and the grid projection in the previous iteration acts as the starting point for the next grid. In simple words, if the charge from the first ranked grid is not sufficient to complete the trip then the next grid is iterated to charge the electric vehicle. Each grid acts as the starting point for the next grid and, this continues until the last grid charges the electric vehicle to complete the trip.
[00105] In an example If an electric vehicle takes a trip from point A to point D and charges at grid G1. After charging at G1 the electric vehicle still cannot complete the trip to D. Then by the forward ranking the grid G1 will be the new origin and a new grid point G2 is recommend for charging the electric vehicle to reach D. Thus, the total trip becomes Origin to G1 to G2 to destination.
[00106] The grid feasibility module 180 is configured to calculate the distance from the origin to grid and grid to destination, convert the distance to SOC requirement using mileage of the electric vehicle, validate the real-time feasibility of a grid based the grid metadata, determine whether starting state of charge (SOC) upon reaching the grid is more than 0 and starting state of charge (SOC) to charge at grid is less than 100 and recommend the real-time feasible grid to the user for charging the electric vehicle. If that grid is not feasible then the real-time feasibility for the next ranked grid is checked.
[00107] In one embodiment, top two rank grids are validated by the grid feasibility module 180 for the real-time feasibility of a grid. However, it may be understood by a person skilled in the art that the top n* recommended grids can be validated by the grid feasibility module 180.
[00108] In an example, the grid feasibility module 180 checks the real-time feasibility of a grid rank 1 and rank 2 for a trip from origin A to destination B. If the total distance between the origin A and grid rank 1 is 25 km, SOC on reaching the grid is 15 and SOC to charge at the grid is 85, then the grid is feasible in real time and will be recommended to the user for charging the electric vehicle. If the total distance from origin A to grid rank 2 is 30 kms, SOC on reaching the grid rank 2 is 2 and SOC to charge at the grid is 80, then the grid is not feasible in real time and will not be recommended.
[00109] In accordance with an embodiment of the present invention, the trip planning system 100 may provide a single grid recommendation or a multi grid recommendation. In particular, in a single grid recommendation only one single grid is recommended to charge the electric vehicle to complete the trip. And, in multi grid recommendation multiple grids are recommended to charge the electric vehicle to complete the trip.
[00110] In accordance with one embodiment of the present invention, for a round trip the trip planning system 100 may provide a single grid recommendation or a multi grid recommendation.
[00111] In an exemplary example, for a round-trip from point A to B and a return journey covered from B-A. A single grid recommendation for a round trip may be provided after conducting the feasibility check at waypoint B.
[00112] In accordance with another embodiment of the present invention, for a single point trip the trip planning system 100 may provide a single grid recommendation or a multi grid recommendation.
[00113] In accordance with yet another embodiment of the present invention, for a multi-point trip the trip planning system 100 may provide a single grid recommendation or a multi grid recommendation.
[00114] In another exemplary example, for a multi-point trip from A-B-C-D, multiple grids are recommended after conducting the feasibility check at waypoint B, C and D.
[00115] Fig. 2A is an exemplary pictorial snapshot illustrating selection of the grids in accordance with one embodiment of the present invention. Rectangles with a constant width of 2 kms is drawn around the one or more steps of the optimized route to select the list of grids falling within the rectangle. However, it may be obvious to a person skilled in the art that the width of a rectangle is dynamic and can be adjusted depending on the size of step. For small steps, only grids closer to the optimized route are selected. And, for long stretches of road, select grids even 2km away from the step can also be selected.
[00116] Fig. 2B is an exemplary pictorial snapshot illustrating selection of the grids in accordance with another embodiment of the present invention. In particular, a circle may be drawn at the vertex (i.e.) start point and end point of the step to select the grids falling within the circle.
[00117] Fig. 2C is an exemplary pictorial snapshot illustrating ranking of the grids in accordance with an embodiment of the present invention. In particular, the grids falling within the rectangle are ranked based on the ranking parameters explained above for charging the electric vehicle.
[00118] Fig. 3 is a flowchart illustrating a method for providing an optimized route for completion of a trip by an electric vehicle in accordance with an embodiment of the invention. The steps may be rearranged and may not follow the process in only the manner as depicted in the flow chart.
[00119] The method 300 starts at step 302 and proceeds to step 304. At step 302, obtaining at least one trip parameter for completion of the trip. In particular, the at least one trip parameter may include anyone or a combination of a starting point of the electric vehicle, a final destination of the electric vehicle, an intermediate destination of the electric vehicle, a starting state of charge (SOC) of the electric vehicle, and a range of the electric vehicle.
[00120] In an embodiment, the least one trip parameter may be obtained as user inputs from the users or drivers of electric vehicles.
[00121] At step 304, a trip feasibility check is executed by a trip feasibility module 150 to determine whether the electric vehicle needs to charge enroute to complete the trip based on the trip parameter.
[00122] In one embodiment, when the determination is “YES” and the trip is feasible based on the trip parameters. Then the method proceeds to step 305. At step 305, the electric vehicle continues the trip without any grid recommendations.
[00123] In another embodiment, when the determination is “NO” and the trip is not feasible based on the trip parameters and the electric vehicle needs to charge enroute to complete the trip. Then the method proceeds to 306.
[00124] At step 306, a list of grids from a plurality of available grids is recommended by a grid selection module 160 in real-time for charging the electric vehicle to complete the trip.
[00125] At step 308, the list of grids is recommended to charge the electric vehicle is ranked by a grid ranking module 170.
[00126] At step 310, a real time grid feasibility check is performed on at least one top ranked grid by the grid feasibility module 180.
[00127] In one embodiment, when the grid feasibility determination is “YES” and the grid is feasible in real time. Then the method proceeds to step 312.
[00128] At step 312, the grid feasible in real time is recommended to charge the electric vehicle in order to complete the trip.
[00129] In another embodiment, when the determination is “NO” and is not feasible in real time. Then the method proceeds to step 314. At step 314, no grid recommendation is made.
[00130] Fig. 4 is a flow chart illustrating a method for determining a trip feasibility in accordance with an embodiment of the invention. The method 400 starts at step 405 and proceeds to step 410- 435.
[00131] At step 405, mileage of the electric vehicle is retrieved by personalized range or a standard range. In particular, the personalized range is calculated by a personalized range prediction analysis. The mileage of the electric vehicle is updated at a predefined duration and stored in the database 102.
[00132] At step 410, the optimized route from the starting point of the electric vehicle to the destination of the electric vehicle along with a total distance is acquired. The distance from origin to destination is acquired by Google’s direction API or any other direction API known or to be developed in future. In particular, the shortest route between the origin and destination at the given time is selected as the optimized route to complete the trip by the electric vehicle.
[00133] At step 415, the feasibility distance of the trip is calculated from starting point to destination of the electric vehicle.
[00134] At step 420, a determination is made whether the feasibility distance is greater than the shortest route/ trip distance. Step 420 proceeds to step 430 or 435.
[00135] In one embodiment, when the determination is “YES” and the feasibility distance is greater than the shortest route/ trip distance, then the method proceeds to step 435.
[00136] At step 435, the electric vehicle can complete the trip with the current SOC without any need of charging enroute to destination.
[00137] In another embodiment, when the determination is “NO” and the feasibility distance for the trip is not greater than the shortest route/ trip distance and the trip cannot be completed with the current SOC in the electric vehicle then the method proceeds to step 430.
[00138] At step 430, a list of grids is recommended to charge the electric vehicle enroute to complete the trip. The trip planning system 100 selects the list grids as explained below in flow chart 500.
[00139] Fig. 5 is a flow chart illustrating a method for selecting a list of grids from the plurality of available grids by the grid selection module 160 in accordance with an embodiment of the invention. The method 500 starts at step 505 and proceeds to step 510, 515, 520,525.
[00140] At step 505, the optimized route from starting point to destination of the electric vehicle is broken into one or more steps. In particular, the steps are straight line roads.
[00141] At step 510, grid metadata for each of the one or more steps is calculated. In particular, the grid metadata includes but is not limited to distance of each step, start point and end point of each step expected starting state of charge (SOC) at grid step, expected starting state of charge (SOC) at destination, grid availability, probability of availability, grid serviceability, past grid usage by the user, other user reviews, location preference, deviation from route.
[00142] At step 515, the plurality of available grids located in an area is retrieved from the database in real time by a grid API known to be developed in future.
[00143] At step 520, a geometric shape is drawn around the one or more steps of the optimized route to select the list of grids falling within the shape. In particular, the geometric shape may be but not limited to a rectangle, circle, square, pentagon or any other shape.
[00144] Fig. 6 is a flow chart illustrating a method for ranking a list of grids by the grid ranking module 170 in accordance with an embodiment of the invention. The method starts at step 605 and proceeds to step 610.
[00145] At step 605, the list of grids selected are ranked based on a plurality of grid ranking factors. In particular, the grids falling within the drawn geometric shape are selected for ranking. The plurality of grid ranking factors includes metadata of grid, distance of grid from starting point, expected starting state of charge (SOC) at grid, expected starting state of charge (SOC) at destination, grid familiarity, grid past experience, grid comfortability, grid availability, probability of availability, grid serviceability, past grid usage by the user, other user reviews, location preference, deviation from route.
[00146] At step 610, the list of grids are divided into a first set, a second set and a third set based on ranks. And, in each of three sets grids are arranged in decreasing order of distance from starting point to grid.
[00147] Fig. 7 is a flow chart illustrating a method for performing grid feasibility check for the top rank grids by the grid feasibility module 180 in accordance with an embodiment of the invention. The method 700 starts at step 705 and proceeds to step 710-720.
[00148] At step 705, the feasibility of the top rank grids is validated based on the grid metadata. The grid metadata includes a distance from starting point to grid, a distance of grid to destination, a current starting state of charge (SOC) of the electric vehicle, a range of the electric vehicle, an expected starting state of charge (SOC) at grid, an expected starting state of charge (SOC) at destination, a grid availability, a probability of availability, a grid serviceability, a grid preference, a past grid usage by the user, other user reviews, a location of a grid, and deviation from route grid selection near any attraction to be visited.
[00149] At step 710, a determination is made whether the starting state of charge (SOC) upon reaching the grid is more than 0 and starting state of charge (SOC) to charge at Grid is less than 100.
[00150] In one embodiment, when the determination is “YES” and the starting state of charge (SOC) upon reaching the grid is more than 0 and starting state of charge (SOC) to charge at Grid is less than 100. Then the method proceeds to step 715.
[00151] At step 715, after the feasibility check the grid is recommended for charging the electric vehicle.
[00152] In another embodiment, when the determination is “NO” and starting state of charge (SOC) on reaching the grid is not more than 0 and starting state of charge (SOC) to charge at grid is not less than 100. Then the method proceeds to step 720.
[00153] At step 720, the next rank grid is selected for grid feasibility check. The method steps 705-715 is repeated to recommend the grid for charging the electric vehicle.
[00154] The method 300, 400, 500, 600, 700 however, is exemplary only and not limiting. The method may be altered, e.g., by having stages added, removed, or rearranged.
[00155] Fig. 8 is an exemplary screenshot illustrating an interface displayed on the user device in accordance with an embodiment of the present invention. The user device 112 displays the optimized route with charging grids to charge the electric vehicle enroute to complete the trip.
[00156] In accordance with an embodiment, to view the optimized route the user enters the user credentials including an email & a password which he used at the time of user registration. On successful validation, the user logs into the trip planning application. After logging into the trip planning application, the user views the dashboard interface with one or more dashboard items on the user device 112. The one or more dash items include a profile menu, a user menu, a payment menu and alike menu. The profile menu comprises the user information, the plurality of data fields including a Profile Image, Name of the User, Location of the user, Phone number, Email id, and alike information. The user menu displays optimized route, list of grids, ranking of grids and real time feasible grids. The payment menu displays a plurality of payment methods for users to make payment for the charging of the electric vehicle. The payment is done through any payment methods selected from internet banking, a debit card payment, a credit card payment, a digital wallet or a unified payment interface (UPI) and similar payment methods.
[00157] In accordance with another embodiment of the present invention, the user saves one or more bank accounts or one or more card details for making payments of the estimated bill. Alternatively, the payment menu saves card details including Card Name, Card Number, Expiry Date and CVV and alike details for fast payment. Moreover, the user selects any one of the debit cards and credit cards as a primary card for payments. Subsequently, the user deletes card details of any one of the saved debit cards and saved credit cards at any time. Further, the payment menu saves bank account details including Name of the Account Holder, Bank Name, Sort Code, IFSC Code, Bank address, CIF number, a five-digit branch code, country code, registered mobile number and alike details.
[00158] While the detailed description has shown, described, and pointed out novel features as applied to various alternatives, it can be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the scope of the disclosure. As can be recognized, certain alternatives described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others.
[00159] The disclosures and the description herein are intended to be illustrative and are not in any sense limiting the invention, defined in scope by the following claims.
, C , Claims:We claim,
1. A method to provide an optimized route for completion of a trip by an electric vehicle, wherein the method comprising steps of:
obtaining at least one trip parameter for completion of the trip;
determining, by a trip feasibility module (150), a trip feasibility check to determine whether the electric vehicle needs to charge enroute to complete the trip based on the at least one trip parameter;
recommending, by a grid selection module (160), a list of grids from a plurality of available grids for charging the electric vehicle to complete the trip;
ranking, by a grid ranking module (170), the list of grids recommended by a grid selection module (160) to charge the electric vehicle;
performing, by a grid feasibility module (180), a real time grid feasibility check on at least one top ranked grid recommended by the grid ranking module (170); and
recommending, after a real time grid feasibility check, at least one grid to charge the electric vehicle in order to complete the trip.
2. The method as claimed in claim 1, wherein the at least one trip parameter includes anyone or a combination of a starting point of the electric vehicle, a final destination of the electric vehicle, an intermediate destination of the electric vehicle, a starting state of charge (SOC) of the electric vehicle, and a range of the electric vehicle.
3. The method as claimed in claim 1, wherein the method determines feasibility of the trip by:
retrieving a range for the electric vehicle by one of a personalized range or a standard range, wherein the personalized range is calculated by a personalized range prediction analysis;
acquiring the optimized route from starting point of the electric vehicle to a final destination or an intermediate destination of the electric vehicle and a total distance thereof;
determining a shortest route as the optimized route to complete the trip by the electric vehicle;
calculating feasibility distance of the trip from starting point to destination of the electric vehicle;
determining whether the feasibility distance is greater than the shortest route; and
recommending the at least one grid to charge the electric vehicle when the feasibility distance is not greater than the shortest route.
4. The method as claimed in claim 1, wherein the list of grids from the plurality of available grids is selected by:
breaking the optimized route from starting point to final destination or intermediate destination of the electric vehicle into one or more steps;
calculating a grid metadata for each of the one or more steps of the optimized route;
retrieving from a database (102) the plurality of available grids located in an area; and
drawing a geometric shape around the one or more steps of the optimized route to select the list of grids falling within the geometric shape,
wherein the grid metadata includes distance of each step, start point and end point of each step expected starting state of charge (SOC) at grid step, expected starting state of charge (SOC) at destination, grid availability, probability of availability, grid serviceability, past grid usage by the user, other user reviews, location preference, deviation from route and alike.
5. The method as claimed in claim 4, wherein the geometric shape may be a rectangle, circle, square, pentagon or any other shape.
6. The method as claimed in claim 1, wherein the method comprises steps of:
ranking the list of grids selected by the grid selection module (160) based on a plurality of grid ranking factors; and
dividing the list of grids based on ranks into a first set, a second set and a third set;
wherein in each of three sets grids are arranged in decreasing order of distance from starting point to grid.
7. The method as claimed in claim 6, wherein the plurality of grid ranking factors includes metadata of grid, distance of grid from starting point, expected starting state of charge (SOC) at grid, expected starting state of charge (SOC) at destination, grid familiarity, grid past experience, grid comfortability, grid availability, probability of availability, grid serviceability, past grid usage by the user, other user reviews, location preference, deviation from route and alike.
8. The method as claimed in claim 1, wherein the grid feasibility check is performed for at least top two rank grids in the first set.
9. The method as claimed in claim 8, wherein the grid feasibility check for the at least top two rank grids is performed by:
validating the feasibility of the at least top two rank grids based on the grid metadata;
determining whether starting state of charge (SOC) upon reaching the grid is more than 0 and starting state of charge (SOC) to charge at Grid is less than 100; and
recommending the grid to the user for charging the electric vehicle when starting state of charge (SOC) on reaching the grid is more than 0 and starting state of charge (SOC) to charge at grid is less than 100;
` wherein the grid metadata includes a distance from starting point to grid, a distance of grid to destination, a current starting state of charge (SOC) of the electric vehicle, a range of the electric vehicle, an expected starting state of charge (SOC) at grid, an expected starting state of charge (SOC) at destination, a grid availability, a probability of availability, a grid serviceability, a grid preference, a past grid usage by the user, other user reviews, a location of a grid, and deviation from route grid selection near any attraction to be visited.
10. The method as claimed in claim 1, wherein the electric vehicle is any of a battery electric vehicle (BEV), a hybrid electric vehicle (HEV), a Plug-in Hybrid electric vehicle (PHEV) Fuel Cell electric vehicle (FCEV), a two wheeler electric bike, a three wheeler electric vehicle.
11. The method as claimed in claim 1, wherein the trip is a round trip, a multi-point trip or a single point trip.
12. The method as claimed in claim 1, wherein the recommendation is a single grid recommendation or a multi grid recommendation.
13. The method as claimed in claim 1, wherein the grid ranking module (170) is configured to recommend a forward grid ranking.
14. The method as claimed in claim 13, wherein the forward grid ranking is the multi-grid recommendation for the multi point trip.
15. The method as claimed in claim 3, wherein the personalized range of the electric vehicle depends on distance travelled by the electric vehicle in each mode, model of the electric vehicle, driver behavior, tyre pressure, terrain area, traffic conditions, road conditions.
16. The method as claimed in claim 15, wherein the personalized range is an average range of eco mode, wrap mode, ride mode and sport mode.
17. A trip planning system (100) for providing an optimized route for completion of a trip by an electric vehicle comprising:
a user device (112) to display the optimized route to charge the electric vehicle enroute the trip based on data retrieved from a cloud server;
a communication network (108) to allow communication between the cloud server (104), a plurality of databases (102), a plurality of modules (107) and the user device (112);
wherein the cloud server (104) including:
the plurality of databases (102) to store data including electric vehicle data, trip data, grid data, rank data, user data, location data, route data and alike data;
a memory unit (106) to store the plurality of modules (107) including a trip feasibility module (150), a grid selection module (160), a grid ranking module (170) and a grid feasibility module (180); and
a processor (110) to perform steps to:
determine whether the electric vehicle needs to charge enroute to complete the trip based on at least one trip parameter;
select a list of grids from a plurality of available grids for charging the electric vehicle to complete the trip;
rank the list of grids to charge the electric vehicle for completing the trip;
perform a real time grid feasibility check on at least one top ranked grid recommended; and
recommend at least one grid to charge the electric vehicle in order to complete the trip.
18. The trip planning system (100) as claimed in claim 17, wherein the at least one trip parameter includes starting point of the electric vehicle, destination of the electric vehicle, starting state of charge (SOC) of the electric vehicle, range of the electric vehicle and alike.
19. The trip planning system (100) as claimed in claim 17, wherein the electric vehicle is a Battery electric vehicle (BEV), a hybrid electric vehicle (HEV), a Plug-in Hybrid electric vehicle (PHEV) Fuel Cell electric vehicle (FCEV), a two wheeler electric bike, a three wheeler electric vehicle.
20. The trip planning system (100) as claimed in claim 17, wherein the trip feasibility module (150) determines feasibility of the trip by:
retrieving a range for the electric vehicle by one of a personalized range or a standard range, wherein the personalized range is calculated by a personalized range prediction analysis;
acquiring the optimized route from starting point of the electric vehicle to final destination or intermediate destination of the electric vehicle and a total distance thereof;
determining a shortest route as the optimized route to complete the trip by the electric vehicle;
calculating feasibility distance of the trip from starting point to destination of the electric vehicle;
determining whether the feasibility distance is greater than the shortest route; and
recommending the at least one grid to charge the electric vehicle when the feasibility distance is not greater than the shortest route.
21. The trip planning system (100) as claimed in claim 17, wherein the grid selection module (160) select the list of grids from the plurality of available grids by:
breaking the optimized route from starting point to final destination or intermediate destination of the electric vehicle into one or more steps;
calculating a grid metadata for each of the one or more steps;
retrieving from the database (102) the plurality of available grids located in an area; and
drawing a geometric shape around the one or more steps of the optimized route to select the list of grids falling within the geometric shape, and
wherein the grid metadata includes distance of each step, start point and end point of each step, expected starting state of charge (SOC) at grid step, expected starting state of charge (SOC) at destination, probability of availability, grid availability, grid serviceability, past grid usage by the user, other user reviews, location preference, deviation from route and alike.
22. The trip planning system (100) as claimed in claim 21, wherein the geometric shape may be a rectangle, circle, square, pentagon or any other shape.
23. The trip planning system (100) as claimed in claim 17, wherein the grid ranking module (170) is configured to:
rank the one or more grids selected by the grid selection module (160) based on a plurality of grid ranking factors; and
divide the one or more grids based on ranks into a first set, a second set and a third set;
wherein the one or more grids are arranged in decreasing order of distance from starting point to grid in each of three sets.
24. The trip planning system (100) as claimed in claim 23, wherein the plurality of grid ranking factors includes metadata of grid, distance of grid from starting point, expected starting state of charge (SOC) at grid, expected starting state of charge (SOC) at destination, grid availability, probability of availability, grid serviceability, past grid usage by the user, other user reviews, location preference of the user, deviation from route, grid familiarity, grid past experience, grid comfortability.
25. The trip planning system (100) as claimed in claim 23, wherein the grid feasibility check is performed for at least top two rank grids in the first set.
26. The trip planning system (100) as claimed in claim 25, wherein the grid feasibility module (180) performs grid feasibility by:
validating the feasibility of a grid based the grid metadata;
determining whether starting state of charge (SOC) upon reaching the grid is more than 0 and starting state of charge (SOC) to charge at grid is less than 100; and
recommending the grid to the user for charging the electric vehicle when starting state of charge (SOC) on reaching the grid is more than 0 and starting state of charge (SOC) to charge at Grid is less than 100;
wherein the grid metadata includes distance from starting point to grid, distance of grid to destination, current starting state of charge (SOC) of the electric vehicle, range of the electric vehicle expected starting state of charge (SOC) at grid, expected starting state of charge (SOC) at destination, grid availability, probability of availability, grid serviceability, grid preference, past grid usage by the user, other user reviews, location of grid, deviation from route grid selection near any attraction to be visited.
27. The trip planning system (100) as claimed in claim 17, wherein the trip is a round trip, a multi-point trip or a single point trip.
28. The trip planning system (100) as claimed in claim 17, wherein a recommendation is a single grid recommendation or a multi grid recommendation.
29. The trip planning system (100) as claimed in claim 17, wherein the grid ranking module (170) is configured to recommend a forward grid ranking.
30. The trip planning system (100) as claimed in claim 29, wherein the forward grid ranking is the multi-grid recommendation for the multi point trip.
31. The trip planning system as claimed in claim 20, wherein the personalized range of the electric vehicle depends on distance travelled by the electric vehicle in each mode, model of the electric vehicle, driver behavior, tyre pressure, terrain area, traffic conditions, road conditions.
32. The trip planning system (100) as claimed in claim 31, wherein the personalized range is an average range of eco mode, wrap mode, ride mode and sport mode.