1. A method (300) of maintenance assistance for electric vehicles, the method (300) comprising: rendering (302), by an end user device (102) via a Graphical User Interface (GUI), a set of vehicle maintenance assistance options corresponding to vehicle monitoring, wherein the set of vehicle maintenance assistance options comprises a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance; identifying (304), by the end user device (102), one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options, wherein the user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model, wherein a category associated with the fault is one of a simple fault category or a complex fault category, and wherein identifying comprises, one of: determining (306), using an Artificial Intelligence (AI) model (218), the one or more faulty parts and the fault from the input image; or identifying (308) the one or more faulty parts and the fault based on the user-selected part; for each faulty part of the one or more faulty parts, when the fault is of the simple fault category, retrieving (314), by the end user device (102) via the AI model (218), a repair procedure corresponding to the faulty part from a database based on the fault, wherein the repair procedure comprises a set of actionable steps; and sequentially rendering (316), by the end user device (102) via the GUI, the set of actionable steps of the repair procedure; and when the fault is of the complex fault category, rendering (324), by the end user device (102) via the GUI, the third option to schedule the vehicle maintenance.

2. The method (300) as claimed in claim 1, wherein the set of vehicle maintenance assistance options comprises a fourth option to render a daily vehicle inspection checklist.

3. The method (300) as claimed in claim 1, wherein identifying the one or more faulty parts and the fault based on the user-selected part from the interactive 3D model comprises: upon receiving the user selection corresponding to the second option, rendering (310), via the GUI, the interactive 3D model of the vehicle, wherein the interactive 3D model comprises a plurality of user-selectable parts corresponding to a plurality of parts of the vehicle; and receiving (312), via the GUI, a user selection of the faulty part from the interactive 3D model and the fault associated with the faulty part.

4. The method (300) as claimed in claim 1, wherein when the user selection corresponds to the first option, sequentially rendering the set of actionable steps comprises: rendering (318), via the GUI, a first actionable step overlaid upon a real-time video scan of the faulty part using an Augmented Reality (AR) technique; determining (320), via the AI model (218), whether the first actionable step is successfully completed based on an analysis of the real-time video scan; and upon successful completion of the first actionable step, rendering (322), via the GUI, a second actionable step overlaid upon the real-time video scan of the faulty part using the AR technology.

5. The method (300) as claimed in claim 1, comprising: receiving (402) road-condition information from one or more data sources, wherein the road-condition information comprises at least one of terrain type, road slope, and traffic density; determining (404), by the AI model (218), an optimal driving mode for the vehicle based on the road-condition information and vehicle battery information; and rendering (406), via the GUI, the optimal driving mode for the vehicle.

6. The method (300) as claimed in claim 5, comprising: receiving (502) vehicle data from the one or more data sources, wherein the vehicle data comprises historical vehicle data, real-time sensor data, user driving patterns, and the vehicle battery information; identifying (504) a role associated with a user, wherein the role is one of a vehicle owner role, a hired-driver role, or a fleet-manager role; generating (506), via the AI model (218), a personalized vehicle-performance summary and actionable insights for the user based on the vehicle data; and rendering (508), via the GUI, the personalized vehicle-performance summary and the actionable insights relevant to the role.

7. The method (300) as claimed in claim 5, comprising: determining (602), via the AI model (218), a battery-based geofence for the vehicle based on the vehicle battery information, wherein the battery-based geofence defines an optimal travel radius around a current location of the vehicle based on a remaining battery charge of the vehicle; when the remaining battery charge of the vehicle is below a predefined threshold, identifying (604), via the AI model (218), one or more charging stations located within the battery-based geofence; and rendering (606), via the GUI, the one or more charging stations in a recommended order determined based on at least one of a charging station proximity, charging slot availability, compatibility and operational status, or a charging station historical reliability.

8. A system (100) for providing maintenance assistance for electric vehicles, the system (100) comprising: a processor (104); and a memory (106) communicatively coupled to the processor (104), wherein the memory (106) stores processor executable instructions, which, on execution, causes the processor (104) to: render (302), via a GUI, a set of vehicle maintenance assistance options corresponding to vehicle monitoring, wherein the set of vehicle maintenance assistance options comprises a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance; identify (304) one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options, wherein the user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model, wherein a category associated with the fault is one of a simple fault category or a complex fault category, and wherein identifying comprises, one of: determine (306), using an AI model (218), the one or more faulty parts and the fault from the input image; or identify (308) the one or more faulty parts and the fault based on the user-selected part; for each faulty part of the one or more faulty parts, when the fault is of the simple fault category, retrieve (314), via the AI model (218), a repair procedure corresponding to the faulty part from a database based on the fault, wherein the repair procedure comprises a set of actionable steps; and sequentially render (316), via the GUI, the set of actionable steps of the repair procedure; and when the fault is of the complex fault category, render, via the GUI, the third option to schedule the vehicle maintenance.

9. The system (100) as claimed in claim 8, wherein the set of vehicle maintenance assistance options comprises a fourth option to render a daily vehicle inspection checklist.

10. The system (100) as claimed in claim 8, wherein to identify the one or more faulty parts and the fault based on the user-selected part from the interactive 3D model, the processor executable instructions cause the processor (104) to: upon receiving the user selection corresponding to the second option, render (310), via the GUI, the interactive 3D model of the vehicle, wherein the interactive 3D model comprises a plurality of user-selectable parts corresponding to a plurality of parts of the vehicle; and receive (312), via the GUI, a user selection of the faulty part from the interactive 3D model and the fault associated with the faulty part.

11. The system (100) as claimed in claim 8, wherein when the user selection corresponds to the first option, sequentially render the set of actionable steps, the processor executable instructions cause the processor (104) to: render (318), via the GUI, a first actionable step overlaid upon a real-time video scan of the faulty part using an Augmented Reality (AR) technique; determine (320), via the AI model (218), whether the first actionable step is successfully completed based on an analysis of the real-time video scan; and upon successful completion of the first actionable step, render (322), via the GUI, a second actionable step overlaid upon the real-time video scan of the faulty part using the AR technology.

12. The system (100) as claimed in claim 8, wherein the processor executable instructions cause the processor (104) to: receive (402) road-condition information from one or more data sources, wherein the road-condition information comprises at least one of terrain type, road slope, and traffic density; determine (404), by the AI model (218), an optimal driving mode for the vehicle based on the road-condition information and vehicle battery information; and render (406), via the GUI, the optimal driving mode for the vehicle.

13. The system (100) as claimed in claim 12, wherein the processor executable instructions cause the processor (104) to: receive (502) vehicle data from the one or more data sources, wherein the vehicle data comprises historical vehicle data, real-time sensor data, user driving patterns, and the vehicle battery information; identify (504) a role associated with a user, wherein the role is one of a vehicle owner role, a hired-driver role, or a fleet-manager role; generate (506), via the AI model (218), a personalized vehicle-performance summary and actionable insights for the user based on the vehicle data; and render (508), via the GUI, the personalized vehicle-performance summary and the actionable insights relevant to the role.

14. The system (100) as claimed in claim 12, wherein the processor executable instructions cause the processor (104) to: determine (602), via the AI model (218), a battery-based geofence for the vehicle based on the vehicle battery information, wherein the battery-based geofence defines an optimal travel radius around a current location of the vehicle based on a remaining battery charge of the vehicle; when the remaining battery charge of the vehicle is below a predefined threshold, identifying (604), via the AI model (218), one or more charging stations located within the battery-based geofence; and render (606), via the GUI, the one or more charging stations in a recommended order determined based on at least one of a charging station proximity, charging slot availability, compatibility and operational status, or a charging station historical reliability.

Description:DESCRIPTION
Technical Field
[001] This disclosure relates generally to Electric Vehicles (EVs) data management, and more particularly to method and system for maintenance assistance for electric vehicles.
Background
[002] The Electric Vehicle (EV) sector is rapidly evolving, driven by government incentives, technological advancements, and shifting consumer preferences. Despite this progress, 3-wheeler drivers continue to face significant operation challenges, including inefficient route planning, unpredictable traffic conditions, and limited access to timely charging information, which result in range anxiety and lost working hours. Their limited familiarity with EV technology further reduces their efficiency and earning potential.
[003] In the present state of art, many EV assistant applications have been developed to support EV users. However, the existing EV assistant applications may provide limited functional scope. Further, existing EV assistant applications fail to provide personalization and proactive assistance. Further, the existing EV assistant applications fail to provide real-time charging stations. Further, the existing EV assistant applications fail to provide efficient route planning. Further, the existing EV assistant applications fail to provide user-friendly interfaces. Additionally, the existing EV assistant applications fail to provide any direct channel for a user to report issue (or access guided support).
[004] The present invention is directed to overcome one or more limitations stated above or any other limitations associated with the known arts.
SUMMARY
[005] In one embodiment, a method of maintenance assistance for electric vehicles is disclosed. In one example, the method may include rendering, via a Graphical User Interface (GUI), a set of vehicle maintenance assistance options corresponding to vehicle monitoring. The set of vehicle maintenance assistance options may include a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance and or connect with customer care for tele support. The method may further include identifying one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options. The user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3-Dimenstional (3D) model. A category associated with the fault is one of a simple fault category or a complex fault category. The identifying may include determining, using an Artificial Intelligence (AI) model, the one or more faulty parts and the fault from the input image. Alternatively, the identifying may include identifying the one or more faulty parts and the fault based on the user-selected part. For each faulty part of the one or more faulty parts, when the fault is of the simple fault category, the method may further include retrieving, via the AI model, a repair procedure corresponding to the faulty part from a database based on the fault. The repair procedure may include a set of actionable steps. The method may further include sequentially rendering, via the GUI, the set of actionable steps of the repair procedure. Each of the set of actionable steps is overlaid upon a real-time video of the faulty part. When the fault is of the complex fault category, the method may further include rendering, via the GUI, the third option to schedule the vehicle maintenance.
[006] In another embodiment, a system for providing maintenance assistance for electric vehicles is disclosed. In one example, the system may include a processor and a computer-readable medium communicatively coupled to the processor. The computer-readable medium may store processor-executable instructions, which, on execution, may cause the processor to render, via a GUI, a set of vehicle maintenance assistance options corresponding to vehicle monitoring. The set of vehicle maintenance assistance options may include a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance. The processor-executable instructions, on execution, may further cause the processor to identify one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options. The user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model. A category associated with the fault is one of a simple fault category or a complex fault category. The identifying may include determining, using an AI model, the one or more faulty parts and the fault from the input image. Alternatively, the identifying may include identifying the one or more faulty parts and the fault based on the user-selected part. For each faulty part of the one or more faulty parts, when the fault is of the simple fault category, the processor-executable instructions, on execution, may further cause the processor to retrieve, via the AI model, a repair procedure corresponding to the faulty part from a database based on the fault. The repair procedure may include a set of actionable steps. The processor-executable instructions, on execution, may further cause the processor to sequentially render, via the GUI, the set of actionable steps of the repair procedure. Each of the set of actionable steps is overlaid upon a real-time video of the faulty part. When the fault is of the complex fault category, the processor-executable instructions, on execution, may further cause the processor to render, via the GUI, the third option to schedule the vehicle maintenance.
[007] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
[009] FIG. 1 is a block diagram of an exemplary system for providing maintenance assistance for electric vehicles, in accordance with some embodiments of the present disclosure.
[010] FIG. 2 illustrates a functional block diagram of a system for providing maintenance assistance for electric vehicles, in accordance with some embodiments of the present disclosure.
[011] FIGS. 3A and 3B illustrate a flow diagram of an exemplary process for providing maintenance assistance for electric vehicles, in accordance with some embodiments of the present disclosure.
[012] FIG. 4 illustrates a flow diagram of an exemplary process for determining driving mode for electric vehicles, in accordance with some embodiments of the present disclosure.
[013] FIG. 5 illustrates a flow diagram of an exemplary process for generating vehicle-performance summary and actionable insights as per the registered role of the user, in accordance with some embodiments of the present disclosure.
[014] FIG. 6 illustrates a flow diagram of an exemplary process for identifying charging stations for electric vehicles whose charge is low/below a predefined threshold, in accordance with some embodiments of the present disclosure.
[015] FIG. 7 illustrates an exemplary Graphical User Interface (GUI) displaying real-time insights of electric vehicles, in accordance with some embodiments of the present disclosure.
[016] FIG. 8 illustrates an exemplary GUI displaying a daily vehicle inspection checklist, in accordance with some embodiments of the present disclosure.
[017] FIGS. 9A – 9E illustrate exemplary GUIs displaying repair assistance for electric vehicles, in accordance with some embodiments of the present disclosure.
[018] FIG. 10 illustrates an exemplary GUI displaying an interactive 3D model of electric vehicle, in accordance with some embodiments of the present disclosure.
[019] FIGS. 11A-11C illustrate exemplary GUIs displaying smart navigation, in accordance with some embodiments of the present disclosure.
[020] FIG. 12 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
DETAILED DESCRIPTION
[021] Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
[022] Referring now to FIG. 1, an exemplary system 100 for providing maintenance assistance for electric vehicles is illustrated, in accordance with some embodiments of the present disclosure. The system 100 may include an end user device 102. The end user device 102 may be used by a user (e.g., a vehicle driver, a fleet-manger, or a vehicle owner). The end user device 102 may be, for example, but may not be limited to, a smartphone, a tablet, a laptop, or any other computing device, in accordance with some embodiments of the present disclosure. The end user device 102 may identify faulty parts and corresponding faults of a vehicle based on a user input. Further, the end user device 102 may render, via a user interface, a set of actionable steps to address the faults of the vehicle.
[023] As will be described in greater detail in conjunction with FIGS. 2 – 12, the end user device 102 may render, via a Graphical User Interface (GUI), a set of vehicle maintenance assistance options corresponding to vehicle monitoring. The set of vehicle maintenance assistance options may include a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance. The end user device 102 may further identify one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options. The user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model. A category associated with the fault is one of a simple fault category or a complex fault category. To identify the one or more faulty parts and faults, the end user device 102 may determine, using an AI model, the one or more faulty parts and the fault from the input image. Alternatively, the end user device 102 may identify the one or more faulty parts and the fault based on the user-selected part. For each faulty part of the one or more faulty parts, when the fault is of the simple fault category, the end user device 102 may further retrieve, via the AI model, a repair procedure corresponding to the faulty part from a database based on the fault. The repair procedure may include a set of actionable steps. The end user device 102 may further sequentially render, via the GUI, the set of actionable steps of the repair procedure. Each of the set of actionable steps is overlaid upon a real-time video of the faulty part. When the fault is of the complex fault category, the end user device 102 may render, via the GUI, the third option to schedule the vehicle maintenance.
[024] In some embodiments, the end user device 102 may include one or more processors 104 and a memory 106. Further, the memory 106 may store instructions that, when executed by the one or more processors 104, may cause the one or more processors 104 to provide maintenance assistance for electric vehicles, in accordance with aspects of the present disclosure. The memory 106 may also store various data (for example, an interactive 3D model of a vehicle, a set of actionable steps, a set of vehicle maintenance assistance options, and the like) that may be captured, processed, and/or required by the system 100.
[025] The system 100 may further include a display 108. The system 100 may interact with a user interface 110 accessible via the display 108. The system 100 may also include one or more external devices 112. In some embodiments, the end user device 102 may interact with the one or more external devices 112 over a communication network 114 for sending or receiving various data. The communication network 114 may include, for example, but may not be limited to, a wireless fidelity (Wi-Fi) network, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. The one or more external devices 112 may include, but may not be limited to, a remote server, a laptop, a netbook, a notebook, a smartphone, a mobile phone, a tablet, or any other computing device.
[026] Referring now to FIG. 2, a functional block diagram of a system 200 for providing maintenance assistance for electric vehicles is illustrated, in accordance with some embodiments of the present disclosure. FIG. 2 is explained in conjunction with FIG. 1. The system 200 may be analogous to the system 100. The system 200 may implement the end user device 102. The system 200 may include, within a memory (such as the memory 106), a rendering module 202, a fault identifying module 204, a repair procedure retrieving module 206, a receiving module 208, a determining module 210, an identifying module 212, a generating module 214, and an Artificial Intelligence (AI) module 216. The AI module 216 may include an AI model 218.
[027] Initially, the rendering module 202 may render, via a GUI, a set of vehicle maintenance assistance options corresponding to vehicle monitoring. For example, the vehicle may be, but may not be limited to, an electric vehicle (e.g., Plug-in Hybrid Electric Vehicles (PHEVs), Hybrid Electric Vehicles (HEVs), Fuel Cell Electric Vehicles (FCEVs), Battery Electric Vehicles (BEVs), etc.), a two-wheeler (e.g., motorcycles, scooters, electric-bikes, etc.), a three-wheeler (e.g., auto-rickshaws, E-rickshaws, etc.), or the like. The set of vehicle maintenance assistance options may include a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, a third option to schedule a vehicle maintenance, and a fourth option to render a daily vehicle inspection checklist. For example, the daily vehicle inspection checklist may include several instructions for a user to inspect their vehicle before starting the day.
[028] For example, the image of the part of the vehicle which may be scanned may be, but may not be limited to, an engine, wheels, a battery, windshields, brakes, transmission, gauges and meters (e.g., a fuel gauge, a speedometer, an odometer, or the like), a steering (or handle), or the like. The image of the part of the vehicle may be scanned (or captured) using a camera of a user device.
[029] For example, the 3D model of the vehicle may include, but may not be limited to, an external part (e.g., hood, bumpers, windshields, or the like), an internal part (e.g., an engine, transmission, radiator, fuel tank, exhaust system, battery, alternator, or the like), chassis, brakes, gauges and meters, steering system, or the like.
[030] Further, the fault identifying module 204 may identify one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options. For example, the one or more faulty parts and their corresponding faults may be, but may not be limited to, an engine (e.g., overheating, lack of lubrication, timing belt failure, etc.), a transmission (e.g., delayed shifting, gear slipping, fluid leakage, etc.), a braking system (e.g., malfunctioning anti-lock braking system, (ABS), worn-out brake pads and discs, leaking brake lines, etc.), wheels (e.g., tread separation, excessive wear, cracked (or broken) wheels, etc.), a steering system (e.g., loss of power steering, etc.), an electrical system (e.g., bad wiring, alternator (or battery) failure, or the like), or the like.
[031] The user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model. A category associated with the fault is one of a simple fault category or a complex fault category. For example, the simple fault category may include, but may not be limited to, wiper blade wear, low tire pressure, loose (or missing) bolts, blown fuse, or the like. For example, the complex fault category may include, but may not be limited to, ABS (Anti-lock Braking System) malfunction, regenerative braking malfunction, overheating of an engine due to radiator blockage (or water pump failure), or the like.
[032] In some embodiments, the fault identifying moule 204 may determine, via the AI model 218, the one or more faulty parts and the fault from the input image.
[033] In some other embodiments, the fault identifying module 204 may identify the one or more faulty parts and the fault based on the user-selected part. To identify the one or more faulty parts and faults based on the user-selected part, upon receiving the user selection corresponding to the second option, the rendering module 202 may render, via the GUI, the interactive 3D model of the vehicle. The interactive 3D model may include a plurality of user-selectable parts corresponding to a plurality of parts of the vehicle. Further, the receiving module 208 may receive, via the GUI, a user selection of the faulty part from the interactive 3D model and the fault associated with the faulty part.
[034] For each faulty part of the one or more faulty parts, when the fault is of the simple fault category, the repair procedure retrieving module 206 may retrieve, via the AI model 218, a repair procedure corresponding to the faulty part from a database 220 based on the fault. The repair procedure may include a set of actionable steps. Upon retrieving the set of actionable steps, the rendering module 202 may sequentially render, via the GUI, the set of actionable steps of the repair procedure.
[035] When the user selection corresponds to the first option, the rendering module 202 may render, via the GUI, a first actionable step overlaid upon a real-time video scan of the faulty part using an Augmented Reality (AR) technology. Upon rendering the first actionable step, the determining module 210 may determine, via the AI model 218, whether the first actionable step is successfully completed based on an analysis of the real-time video scan. Upon successful completion of the first actionable step, the rendering module 202 may render, via the GUI, a second actionable step overlaid upon the real-time video scan of the faulty part using the AR technology. When the fault is the complex fault category, the rendering module 202 may render, via the GUI, the third option to schedule the vehicle maintenance.
[036] Additionally, the receiving module 208 may receive road-condition information from one or more data sources. For example, the one or more data sources may be, but may not be limited to, Global Positioning System (GPS), vehicle sensors, or the like. The road-condition information may include at least one terrain type (e.g., smooth, rough, off-road, wet (or slippery), sandy, or the like), road slope (e.g., uphill, downhill, linear, inclined, or the like), and traffic density (e.g., high, moderate, low, or the like). Further, the determining module 210 may determine, via the AI model 218, an optimal driving mode for the vehicle based on the road-condition information and vehicle battery information. For example, the driving mode may be, but may not be limited to, normal, off-road, sand, sport, custom, or the like. Further, the rendering module 202 may render, via the GUI, the optimal driving mode for the vehicle.
[037] Additionally, the receiving module 208 may receive vehicle data from the one or more data sources. The vehicle data may include historical vehicle data, real-time sensor data, user driving patterns (e.g., an aggressive driving pattern (such as frequent rapid acceleration, harsh breaking, etc.), an eco-friendly driving pattern (such as smooth acceleration, steady speed, etc.), or the like), and the vehicle battery information (e.g., battery voltage, battery temperature, battery health, or the like).
[038] During initial user registration, a user may select a role from a set of user roles. The role may be one of a vehicle owner role, a hired-driver role, or a fleet-manager role. In an embodiment, the identifying module 212 may identify a role associated with a user from the user registration details. Further, the generating module 214 may generate, via the AI model 218, a personalized vehicle-performance summary and actionable insights for the user based on the vehicle data. Further, the rendering module 202 may render, via the GUI, the personalized vehicle-performance summary and the actionable insights relevant to the role.
[039] Additionally, the determining module 210 may determine, via the AI model, a battery-based geofence for the vehicle based on the vehicle battery information. The battery-based geofence defines an optimal travel radius around a current location of the vehicle based on a remaining battery charge of the vehicle. When the remaining battery charge of the vehicle is below a predefined threshold, the identifying module 212 may identify, via the AI model, one or more charging stations located within the battery-based geofence. Further, the rendering module 202 may render, via the GUI, the one or more charging stations in a recommended order determined based on at least one of a charging station proximity, charging slot availability, compatibility and operational status, or a charging station historical reliability.
[040] It should be noted that all such aforementioned modules 202 – 220 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 202 – 220 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 202 – 220 may be implemented as dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 202 – 220 may also be implemented in a programmable hardware device such as a field programmable gate array (FPGA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 202 – 220 may be implemented in software for execution by various types of processors (e.g., processor 104). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.
[041] As will be appreciated by one skilled in the art, a variety of processes may be employed for providing maintenance assistance for electric vehicles. For example, the exemplary system 100 and the associated end user device 102, may provide maintenance assistance for electric vehicles, by the processes discussed herein. In particular, as will be appreciated by those of ordinary skill in the art, control logic and/or automated routines for performing the techniques and steps described herein may be implemented by the system 100 and the associated end user device 102 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the system 100 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some or all of the processes described herein may be included in the one or more processors on the system 100.
[042] Referring now to FIGS. 3A and 3B, an exemplary process 300 for providing maintenance assistance for electric vehicles is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. FIGS. 3A and 3B are explained in conjunction with FIGS. 1 and 2. The process 300 may be implemented by the end user device 102 of the system 100. In some embodiments, the process 300 may include rendering, by a rendering module (such as the rendering module 202) via a GUI, a set of vehicle maintenance assistance options corresponding to vehicle monitoring, at step 302. The set of vehicle maintenance assistance options may include a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, a third option to schedule a vehicle maintenance, and a fourth option to render a daily vehicle inspection checklist.
[043] Further, the process 300 may include identifying, by a fault identifying module (such as the fault identifying module 204), one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options, at step 304. The user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model. A category associated with the fault is one of a simple fault category or a complex fault category. The step 304 may include steps 306 and 308.
[044] In some embodiments, the process 300 may include determining, by the fault identifying module using an AI model (such as the AI model 218), the one or more faulty parts and the fault from the input image, at step 306.
[045] In some other embodiments, the process 300 may include identifying, by the fault identifying module, the one or more faulty parts and the fault based on the user-selected part, at step 308. The step 308 may include steps 310 and 312. To identify the one or more faulty parts and faults based on the user-selected part, upon receiving the user selection corresponding to the second option, the process 300 may include rendering, by the rendering module via the GUI, the interactive 3D model of the vehicle, at step 310. The interactive 3D model may include a plurality of user-selectable parts corresponding to a plurality of parts of the vehicle. Further, the process 300 may include receiving, by a receiving module (such as the receiving module 208) via the GUI, a user selection of the faulty part from the interactive 3D model and the fault associated with the faulty part, at step 312.
[046] Further, for each faulty part of the one or more faulty parts, when the fault is of the simple fault category, the process 300 may include retrieving, by a repair procedure retrieving module (such as the repair procedure retrieving module 206) via the AI model, a repair procedure corresponding to the faulty part from a database (such as the database 220) based on the fault, at step 314. The repair procedure may include a set of actionable steps. Upon retrieving the repair procedure, the process 300 may include sequentially rendering, by the rendering module via the GUI, the set of actionable steps of the repair procedure, at step 316. The step 316 may include steps 318, 320, and 322.
[047] When the user selection corresponds to the first option, the process 300 may include rendering, by the rendering module via the GUI, a first actionable step overlaid upon a real-time video scan of the faulty part using an AR technology, at step 318. Upon rendering the first actionable step, the process 300 may include determining, by a determining module (such as the determining module 210) via the AI model, whether the first actionable step is successfully completed based on an analysis of the real-time video scan, at step 320. Upon successful completion of the first actionable step, the process 300 may include rendering, by the rendering module via the GUI, a second actionable step overlaid upon the real-time video scan of the faulty part using the AR technology, at step 322.
[048] When the fault is of the complex fault category, the process 300 may include rendering, by the rendering module via the GUI, the third option to schedule the vehicle maintenance, at step 324.
[049] Referring now to FIG. 4, an exemplary process 400 for determining driving mode for electric vehicles is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. FIG. 4 is explained in conjunction with FIGS. 1, 2, and 3. The process 400 may be implemented by the end user device 102 of the system 100. In some embodiments, the process 400 may include receiving, by a receiving module (such as the receiving module 208), road-condition information from one or more data sources, at step 402. The road-condition information may include at least one of terrain type, road slope, and traffic density.
[050] Upon receiving the road-condition information, the process 400 may include determining, by a determining module (such as the determining module 210) via an AI model (such as the AI model 218), an optimal driving mode for the vehicle based on the road-condition information and vehicle battery information, at step 404. Further, the process 400 may include rendering, by a rendering module (such as the rendering module 202) via a GUI, the optimal driving mode for the vehicle, at step 406.
[051] Referring now to FIG. 5, an exemplary process 500 for generating vehicle-performance summary and actionable insights is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. FIG. 5 is explained in conjunction with FIGS. 1, 2, 3, and 4. The process 500 may be implemented by the end user device 102 of the system 100. In some embodiments, the process 500 may include receiving, by a receiving module (such as receiving module 208), vehicle data from one or more data sources, at step 502. The vehicle data may include historical vehicle data, real-time sensor data, user driving patterns, and vehicle battery information.
[052] Upon receiving the vehicle data, the process 500 may include identifying, by an identifying module (such as the identifying module 212), a role associated with a user, at step 504. The role is one of a vehicle owner role, a hired-driver role, or a fleet-manager role. Once the role is identified, the process 500 may include generating, by a generating module (such as the generating module 214) via an AI model (such as the AI model 218), a personalized vehicle-performance summary and actionable insights for the user based on the vehicle data, at step 506. Further, the process 500 may include rendering, by a rendering module (such as the rendering module 202) via a GUI, the personalized vehicle-performance summary and the actionable insights relevant to the role, at step 508.
[053] Referring now to FIG. 6, an exemplary process 600 for identifying charging stations for electric vehicles is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. FIG. 6 is explained in conjunction with FIGS. 1, 2, 3, 4, and 5. The process 600 may be implemented by the end user device 102 of the system 100. In some embodiments, the process 600 may include determining, by a determining module (such as the determining module 210) via an AI model (such as the AI model 218), a battery-based geofence for the vehicle based on vehicle battery information, at step 602. The battery-based geofence defines an optimal travel radius around a current location of the vehicle based on a remaining battery charge of the vehicle.
[054] When the remaining battery charge of the vehicle is below a predefined threshold, the process 600 may include identifying, by an identifying module (such as the identifying module 212) via the AI model, one or more charging stations located within the battery-based geofence, at step 604. Further, the process 600 may include rendering, by a rendering module (such as the rendering module 202) via a GUI, the one or more charging stations in a recommended order determined based on at least one of a charging station proximity, charging slot availability, compatibility and operational status, or a charging station historical reliability, at step 606.
[055] Referring now to FIG. 7, an exemplary GUI 700 displaying real-time insights of electric vehicles is illustrated, in accordance with some embodiments of the present disclosure. FIG. 7 is explained in conjunction with FIGS. 1 – 6. The GUI 700 may include a plurality of sections providing real-time insights. The real-time insights may include a weather forecast (e.g., temperature (e.g., 23°C) and weather condition (e.g., sunny), a current location (e.g., Bangaluru), daily cost saving of a user (e.g., Today-215 rupees), a battery level (e.g., 80% battery remaining), an estimated driving range (e.g., 125Km available based on remaining battery), a driving performance (e.g., distance travelled (e.g., 1530 Km)), a vehicle-performance summary (e.g., battery health (e.g., 80%), battery heat (e.g., 30°C), and motor heat (e.g., 20°C ), a smart map view, a last sync status (e.g., last synched 2 mins ago). For example, when a user may connect an end user device (such as the end user device 102) to a vehicle (e.g., electric auto rikshaw), the GUI 700 may display only essential real-time insights, such as the navigation, the battery level, and an emergency option for easy access.
[056] The GUI 700 may further include a set of options corresponding to a vehicle. The set of options may include a my vehicle option, an emergency situation option, and a community option. The my vehicle option may allow a user (e.g., a driver) to interact with an interactive 3D model of the vehicle. The emergency option may provide a step-by-step guidance corresponding to fault in the vehicle. Alternatively, the emergency option may provide a recommendation to initiate a service booking request corresponding to the fault. The community option may provide a digital space where the user may interact, learn, and support each other. For example, the user may ask questions (or queries), share experiences, post doubts, and comments on queries corresponding to vehicle to help other users through the community option. Additionally, a vehicle manufacturer may share updates corresponding to vehicle with the users through the community option. The GUI 700 may further include a vehicle care option. The GUI 700 may further include a menu icon. The menu icon may be used for accessing additional options and settings.
[057] Referring now to FIG. 8, an exemplary GUI 800 displaying a daily vehicle inspection checklist is illustrated, in accordance with some embodiments of the present disclosure. FIG. 8 is explained in conjunction with FIGS. 1 - 7. The GUI 800 may include a vehicle check option (analogous to the fourth option of the set of vehicle maintenance assistance options). For example, a user may access the vehicle check option anytime from the vehicle care section (or option). The vehicle check option may provide a daily vehicle inspection checklist to the user. The daily vehicle inspection checklist may offer a step-by-step guidance to the user to inspect their vehicle before starting the day. The daily vehicle inspection checklist may be displayed using visual cues (e.g., pictures, videos, symbols, arrows, charts, or the like) and easy-to-follow instructions (e.g., maintain your vehicle daily fitness in 10 steps). The instructions may be displayed in a language selected by the user. For example, the language may be, but may not be limited to, ‘English’, ‘Hindi’, ‘Telugu’, ‘Marathi’, or the like.
[058] In some embodiments, the GUI 800 may include a language selection option. The language selection option may allow the user to select their preferred language. For example, when the user may click on the vehicle check option through the GUI (such as the 800) of a user device (e.g., a mobile phone), then the vehicle check option may display the daily vehicle inspection checklist on the GUI. For example, the daily vehicle inspection checklist may include 10 easy steps for the user to inspect their vehicle. A first step may be “Check Battery Condition”, a second step may be “Check Tyre Pressure”, a third step may be “Check Charging Port”, or the like.
[059] Referring now to FIGS. 9A-9E, exemplary GUIs displaying repair assistance for electric vehicles are illustrated, in accordance with some embodiments of the present disclosure. FIGS. 9A – 9E are explained in conjunction with FIGS. 1 - 8. As depicted in FIG. 9A, a GUI 900A may include a vehicle care option. The vehicle care option may further include a virtual vehicle companion option, a daily checklist option, a book new service option. The virtual vehicle companion option may provide a repair guidance to a user for addressing (or repairing) a faulty part (or component) of a vehicle. For example, the user may click (or slide) the option to select the virtual vehicle option. The book new service option may allow the user to book a service directly with a manufacturer (or repairer) to address the faulty part of the vehicle.
[060] As depicted in FIG. 9B, a GUI 900B may include a virtual vehicle companion option. The virtual vehicle companion option may further include a scan option. The scan option may be used for scanning the faulty part (or component) of the vehicle. For example, the user may scan the faulty part (e.g., a front wheel) of the vehicle using the scan option through a camera of the user device. Additionally, during the scanning, the GUI 900B may display suggestions (e.g., “Point your device to desired part and tap to capture”) for the user. In some embodiments, the GUI 900B may include a camera option. For example, the user may capture an image of the faulty part of the vehicle using the camera option through the camera of the user device.
[061] As depicted in FIG. 9C, a GUI 900C may include a command selection option. The command selection option may include one or more repair actions corresponding to the received scanned image of the faulty part of the vehicle. For example, the command selection option may include ‘Replace Vehicle Wheel’ and ‘Repair Puncture’. By way of an example, the user may select any one of the commands displayed on the GUI 900C as per their requirements using the command selection option. In an embodiment, the user may select the ‘Replace Vehicle Wheel’ option as the repair action.
[062] As depicted in FIG. 9D, a GUI 900D may display a list of tools required to address the user selected repair action (i.e., ‘Replace Vehicle Wheel’). The list of tools may be displayed using a symbol (or name) of the tools in a user preferred language. For example, the list of tools may include a wrench, a screwdriver, an allen key, a trim removal tool.
[063] As depicted in FIG. 9E, a GUI 900E may display step-by-step repair instructions to repair the faulty part of the vehicle. For example, the GUI 900E may display a set of repair steps to replace the front wheel of the vehicle (e.g., a first step may be “Take 19mm L-shaped wrench”, a second step may be “start loosening a collar Nut”, a third step may be “Remove the Collar Nut”). The GUI 900E may also display an AR-based visual guidance to repair the faulty part of the vehicle. For example, animated tools and tips appear to visually demonstrate how to perform the steps (e.g., where to place the L-shaped wrench and in which direction the L-shaped wrench should be rotated).
[064] Referring now to FIGS. 10, an exemplary GUI 1000 displaying an interactive 3D model of an electric vehicle is illustrated, in accordance with some embodiments of the present disclosure. FIG. 10 is explained in conjunction with FIGS. 1, 2, 3, 4, 5, 6, 7, 8, and 9A-9E.
[065] In FIG. 10, the GUI 1000 may display a ‘my vehicle’ option. The GUI 1000 may further display an interactive 3D model of a vehicle (e.g., an electric auto rikshaw). For example, a user may rotate, zoom in, and zoom out one or more parts of the vehicle through tough gestures (such as tapping, stretching, pinching, clicking, or the like). Additionally, the user may access detailed information corresponding to the one or more parts of the vehicle by tapping (or clicking) on the corresponding part. Also, the user may check whether a component of the vehicle is self-serviceable or require expert help by tapping on the corresponding component of the vehicle. By way of an example, the user may tap on a front wheel of the vehicle using the GUI of the user device to know about their functionality (e.g., size and dimension (e.g., 120/70-12 inches), tread pattern (e.g., enhanced traction), speed capacity (e.g., up to 93 mph (150 km/h)), load capacity (e.g., 300Kg), and material used (e.g., high-quality rubber) and serviceability (e.g., self-serviceable or expert help).
[066] The GUI 1000 may further include vehicle layer option. The GUI 1000 may further include a search bar option. The search bar option may be used to search (or find) specific part of the vehicle. The GUI 1000 may further include a vehicle performance option. The vehicle performance option may provide information corresponding to the performance of the vehicle. For example, a performance detail of the electric auto rikshaw may be, a battery life (e.g., 80%), a battery health (e.g., Good), a battery heat (e.g., 59 °C at 12 PM and 44 °C at 8 PM).
[067] Referring now to FIGS. 11A-11C, exemplary GUIs 1100A-1100C displaying a smart navigation is illustrated, in accordance with some embodiments of the present disclosure. FIGS. 11A-11C are explained in conjunction with FIGS. 1, 2, 3, 4, 5, 6, 7, 8, 9A-9E, and 10.
[068] In FIG. 11A, the GUI 1100A may display a current location of a vehicle (e.g., Frazer Town). The GUI 1100A may further display a user selected route from the current location to a destination (e.g., Frazer Town to Austin Town). The user selected route may be highlighted with a different colour (e.g., green). The GUI 1100A may further display a route summary (e.g., destination (i.e., Austin Town), pick up time (e.g., 12:00 PM), drop off (e.g., 01hr 05min (25km)), range left (e.g., 83Km), destination arrival date and time (e.g., 29 March and 12:50 PM). The GUI 1100A may further display a current speed of the vehicle (e.g., 60Km/h). The GUI 1100A may further display upcoming road conditions corresponding to the user selected route (e.g., low terrain). The upcoming road conditions may be represented in a 3D model on the navigation. In some embodiments, the GUI 1100A may display (or recommend) a driving mode based on the upcoming road conditions (e.g., use power saver mode in low terrain).
[069] The GUI 1100A may further display a real-time traffic update corresponding to the user selected route (e.g., heavy traffic ahead). In some embodiments, the GUI 1100A may display the driving mode based on the real-time traffic update (e.g., heavy traffic ahead, switch to ECO mode (i.e., conserve battery and extend range)). In some other embodiments, the GUI 1100A may display the driving mode based on the upcoming road conditions and the real-time traffic conditions. The GUI 1100A may further include a start option. The start option may be used to start (or initiate) a navigation. The GUI 1100A may further include a search bar option. The search bar option may allow the user to search for destinations based on their requirements.
[070] In FIG. 11B, the GUI 1100B may display a smart map activation. The smart map may be automatically activated based on a predefined battery level (e.g., 20%). The GUI 1100B may further display a range limit of the vehicle. The range limit may be an estimated maximum travel distance achievable by the vehicle based on the remaining battery level. For example, the range limit of the vehicle may be highlighted with yellow coloured circle (geofence). The GUI 1100B may further display one or more nearest charging stations based on the remaining battery level (e.g., 20% battery level). For example, the nearest charging station from the current location of the vehicle may be e.g. OneA station (i.e., 2.7Km away). The GUI 1100B may further display a current speed of the vehicle (e.g., 30Km/hr).
[071] In FIG. 11C, the GUI 1100C may display charging stations details corresponding to the user selected charging stations. The charging station details may include a charging station name (e.g., OneA charging station), a charging station location (e.g., Richmond Park), a charging station status (e.g., Active), charging station rating (e.g., 5.0), available slots in the charging station (e.g., 3 slots), a connector type (e.g., Type 2), Estimated Time to Arrival (ETA) (e.g., 10 mins), distance (e.g., 5.2 Km). The GUI 1100C may include a start journey option. The start journey option may be used to initiate the booking of the slot.
[072] The GUI 1100C may further display a feedback-input form corresponding to the user selected charging station. The feedback input form may include a rating option. For example, the user may rate their experience using the rating option. By way of an example, the user may provide ‘2-star’ rating to the charging station. The feedback input form may further include an issue section option. The issue selection option may allow the user to report issues corresponding to the charging station. For example, the issue selection option may include a plurality of issues (such as payment issue, charging station was closed, charging station was not operational, or the like) corresponding to the charging station. For example, the user may select the issue (e.g., charging station was closed). The feedback input form may include a submit feedback option. The submit feedback option may be used to submit the feedback input form. This feedback will be visible to the charging station maintenance team as well as other electric vehicle drivers who are part of this ecosystem.
[073] As will be also appreciated, the above-described techniques may take the form of computer or controller implemented processes and apparatuses for practicing those processes. The disclosure can also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, solid state drives, CD-ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer or controller, the computer becomes an apparatus for practicing the invention. The disclosure may also be embodied in the form of computer program code or signal, for example, whether stored in a storage medium, loaded into and/or executed by a computer or controller, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.
[074] The disclosed methods and systems may be implemented on a conventional or a general-purpose computer system, such as a personal computer (PC) or server computer. Referring now to FIG. 12, an exemplary computing system 1200 that may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, or the like) is illustrated. Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 1200 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, personal entertainment device, DVR, and so on, or any other type of special or general-purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 1200 may include one or more processors, such as a processor 1202 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller or other control logic. In this example, the processor 1202 is connected to a bus 1204 or other communication medium. In some embodiments, the processor 1202 may be an Artificial Intelligence (AI) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).
[075] The computing system 1200 may also include a memory 1206 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 1202. The memory 1206 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 1202. The computing system 1200 may likewise include a read only memory (“ROM”) or other static storage device coupled to bus 1204 for storing static information and instructions for the processor 1202.
[076] The computing system 1200 may also include a storage devices 1208, which may include, for example, a media drive 1210 and a removable storage interface. The media drive 1210 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 1212 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 1210. As these examples illustrate, the storage media 1212 may include a computer-readable storage medium having stored therein particular computer software or data.
[077] In alternative embodiments, the storage devices 1208 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into the computing system 1200. Such instrumentalities may include, for example, a removable storage unit 1214 and a storage unit interface 1216, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 1214 to the computing system 1200.
[078] The computing system 1200 may also include a communications interface 1218. The communications interface 1218 may be used to allow software and data to be transferred between the computing system 1200 and external devices. Examples of the communications interface 1218 may include a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a micro USB port), Near field Communication (NFC), etc. Software and data transferred via the communications interface 1218 are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 1218. These signals are provided to the communications interface 1218 via a channel 1220. The channel 1220 may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 1220 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.
[079] The computing system 1200 may further include Input/Output (I/O) devices 1222. Examples may include, but are not limited to a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I/O devices 1222 may receive input from a user and also display an output of the computation performed by the processor 1202. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 1206, the storage devices 1208, the removable storage unit 1214, or signal(s) on the channel 1220. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 1202 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 1200 to perform features or functions of embodiments of the present invention.
[080] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 1200 using, for example, the removable storage unit 1214, the media drive 1210 or the communications interface 1218. The control logic (in this example, software instructions or computer program code), when executed by the processor 1202, causes the processor 1202 to perform the functions of the invention as described herein.
[081] Various embodiments provide method and system for maintenance assistance for electric vehicles. The disclosed method and system may render, via a GUI, a set of vehicle maintenance assistance options corresponding to vehicle monitoring. The set of vehicle maintenance assistance options may include a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance. Further, the disclosed method and system may identify one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options. The user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model. A category associated with the fault is one of a simple fault category or a complex fault category. To identify the one or more faulty parts, the disclosed method and system may determine, using an AI model, the one or more faulty parts and the fault from the input image. Alternatively, the disclosed method and system may identify the one or more faulty parts and the fault based on the user-selected part. Further, for each faulty part of the one or more faulty parts, when the fault is of the simple fault category, the disclosed method and system may retrieve, via the AI model, a repair procedure corresponding to the faulty part from a database based on the fault. The repair procedure may include a set of actionable steps. Moreover, the disclosed method and system may sequentially render, via the GUI, the set of actionable steps of the repair procedure. Each of the set of actionable steps is overlaid upon a real-time video of the faulty part. Thereafter, when the fault is of the complex fault category, the disclosed method and system may render, via the GUI, the third option to schedule the vehicle maintenance.
[082] Thus, the disclosed method and system try to overcome the technical problem of maintenance assistance for electric vehicles. The disclosed method and system may provide a real-time tracking of battery health and vehicle components along with self-service repair guidance using an image recognition and 3D models. This may lead to reduced service dependency and lower maintenance costs. Further, the disclosed method and system may provide terrain-aware, battery-sensitive route planning and live updates on charging station availability. This may help drivers avoid unexpected breakdowns and reduce range anxiety. Further, the disclosed method and system may provide an automatic booking service at nearby stations. Additionally, the disclosed method and system may allow users to report faulty chargers. This may improve infrastructure reliability and ensure uninterrupted travel. Further, the disclosed method and system may assist fleet operators with centralized vehicle management and help service personnel with guided diagnostics and repair workflows for faster and more accurate servicing.
[083] Further, the disclosed method and system may provide an intuitive interface with localized language support. This may help the drivers to use an application with limited digital literacy, especially in underserved regions. Further, the disclosed method and system may reduce anxiety of the drivers by offering real-time insights and intelligent route planning. Additionally, the disclosed method and system may reduce uncertainty around battery life and charging access. This may lead to boost confidence in EV ownership through approachable technology and proactive support, especially for new or less tech-savvy users. Further, the disclosed method and system may promote independence by enabling users to manage vehicle health and travel decisions with ease and clarity. Further, the disclosed method and system may create a unified EV ecosystem where manufacturers, service consumers, charging operators, and service personnel work together seamlessly. This may enhance reliability, efficiency, and user experience across the entire infrastructure. Further, the disclosed method and system may encourage wider EV adoption by building user confidence, lowering operational costs, and supporting cleaner transportation through smarter and more reliable mobility solutions.
[084] In light of the above mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
[085] The specification has described method and system for maintenance assistance for electric vehicles. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[086] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[087] It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims. , Claims:CLAIMS
I/We Claim:
1. A method (300) of maintenance assistance for electric vehicles, the method (300) comprising:
rendering (302), by an end user device (102) via a Graphical User Interface (GUI), a set of vehicle maintenance assistance options corresponding to vehicle monitoring, wherein the set of vehicle maintenance assistance options comprises a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance;
identifying (304), by the end user device (102), one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options, wherein the user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model, wherein a category associated with the fault is one of a simple fault category or a complex fault category, and wherein identifying comprises, one of:
determining (306), using an Artificial Intelligence (AI) model (218), the one or more faulty parts and the fault from the input image; or
identifying (308) the one or more faulty parts and the fault based on the user-selected part;
for each faulty part of the one or more faulty parts,
when the fault is of the simple fault category,
retrieving (314), by the end user device (102) via the AI model (218), a repair procedure corresponding to the faulty part from a database based on the fault, wherein the repair procedure comprises a set of actionable steps; and
sequentially rendering (316), by the end user device (102) via the GUI, the set of actionable steps of the repair procedure; and
when the fault is of the complex fault category, rendering (324), by the end user device (102) via the GUI, the third option to schedule the vehicle maintenance.

2. The method (300) as claimed in claim 1, wherein the set of vehicle maintenance assistance options comprises a fourth option to render a daily vehicle inspection checklist.

3. The method (300) as claimed in claim 1, wherein identifying the one or more faulty parts and the fault based on the user-selected part from the interactive 3D model comprises:
upon receiving the user selection corresponding to the second option,
rendering (310), via the GUI, the interactive 3D model of the vehicle, wherein the interactive 3D model comprises a plurality of user-selectable parts corresponding to a plurality of parts of the vehicle; and
receiving (312), via the GUI, a user selection of the faulty part from the interactive 3D model and the fault associated with the faulty part.

4. The method (300) as claimed in claim 1, wherein when the user selection corresponds to the first option, sequentially rendering the set of actionable steps comprises:
rendering (318), via the GUI, a first actionable step overlaid upon a real-time video scan of the faulty part using an Augmented Reality (AR) technique;
determining (320), via the AI model (218), whether the first actionable step is successfully completed based on an analysis of the real-time video scan; and
upon successful completion of the first actionable step, rendering (322), via the GUI, a second actionable step overlaid upon the real-time video scan of the faulty part using the AR technology.

5. The method (300) as claimed in claim 1, comprising:
receiving (402) road-condition information from one or more data sources, wherein the road-condition information comprises at least one of terrain type, road slope, and traffic density;
determining (404), by the AI model (218), an optimal driving mode for the vehicle based on the road-condition information and vehicle battery information; and
rendering (406), via the GUI, the optimal driving mode for the vehicle.

6. The method (300) as claimed in claim 5, comprising:
receiving (502) vehicle data from the one or more data sources, wherein the vehicle data comprises historical vehicle data, real-time sensor data, user driving patterns, and the vehicle battery information;
identifying (504) a role associated with a user, wherein the role is one of a vehicle owner role, a hired-driver role, or a fleet-manager role;
generating (506), via the AI model (218), a personalized vehicle-performance summary and actionable insights for the user based on the vehicle data; and
rendering (508), via the GUI, the personalized vehicle-performance summary and the actionable insights relevant to the role.

7. The method (300) as claimed in claim 5, comprising:
determining (602), via the AI model (218), a battery-based geofence for the vehicle based on the vehicle battery information, wherein the battery-based geofence defines an optimal travel radius around a current location of the vehicle based on a remaining battery charge of the vehicle;
when the remaining battery charge of the vehicle is below a predefined threshold, identifying (604), via the AI model (218), one or more charging stations located within the battery-based geofence; and
rendering (606), via the GUI, the one or more charging stations in a recommended order determined based on at least one of a charging station proximity, charging slot availability, compatibility and operational status, or a charging station historical reliability.

8. A system (100) for providing maintenance assistance for electric vehicles, the system (100) comprising:
a processor (104); and
a memory (106) communicatively coupled to the processor (104), wherein the memory (106) stores processor executable instructions, which, on execution, causes the processor (104) to:
render (302), via a GUI, a set of vehicle maintenance assistance options corresponding to vehicle monitoring, wherein the set of vehicle maintenance assistance options comprises a first option to scan an image of a part of the vehicle, a second option to interact with an interactive 3D model of the vehicle, and a third option to schedule a vehicle maintenance;
identify (304) one or more faulty parts of the vehicle and a fault in each of the one or more faulty parts based on a user input and a user selection of the set of vehicle maintenance assistance options, wherein the user input is one of an input image of a region of the vehicle or a user-selected part from a plurality of user-selectable vehicle parts of the interactive 3D model, wherein a category associated with the fault is one of a simple fault category or a complex fault category, and wherein identifying comprises, one of:
determine (306), using an AI model (218), the one or more faulty parts and the fault from the input image; or
identify (308) the one or more faulty parts and the fault based on the user-selected part;
for each faulty part of the one or more faulty parts,
when the fault is of the simple fault category,
retrieve (314), via the AI model (218), a repair procedure corresponding to the faulty part from a database based on the fault, wherein the repair procedure comprises a set of actionable steps; and
sequentially render (316), via the GUI, the set of actionable steps of the repair procedure; and
when the fault is of the complex fault category, render, via the GUI, the third option to schedule the vehicle maintenance.

9. The system (100) as claimed in claim 8, wherein the set of vehicle maintenance assistance options comprises a fourth option to render a daily vehicle inspection checklist.

10. The system (100) as claimed in claim 8, wherein to identify the one or more faulty parts and the fault based on the user-selected part from the interactive 3D model, the processor executable instructions cause the processor (104) to:
upon receiving the user selection corresponding to the second option,
render (310), via the GUI, the interactive 3D model of the vehicle, wherein the interactive 3D model comprises a plurality of user-selectable parts corresponding to a plurality of parts of the vehicle; and
receive (312), via the GUI, a user selection of the faulty part from the interactive 3D model and the fault associated with the faulty part.

11. The system (100) as claimed in claim 8, wherein when the user selection corresponds to the first option, sequentially render the set of actionable steps, the processor executable instructions cause the processor (104) to:
render (318), via the GUI, a first actionable step overlaid upon a real-time video scan of the faulty part using an Augmented Reality (AR) technique;
determine (320), via the AI model (218), whether the first actionable step is successfully completed based on an analysis of the real-time video scan; and
upon successful completion of the first actionable step, render (322), via the GUI, a second actionable step overlaid upon the real-time video scan of the faulty part using the AR technology.
12. The system (100) as claimed in claim 8, wherein the processor executable instructions cause the processor (104) to:
receive (402) road-condition information from one or more data sources, wherein the road-condition information comprises at least one of terrain type, road slope, and traffic density;
determine (404), by the AI model (218), an optimal driving mode for the vehicle based on the road-condition information and vehicle battery information; and
render (406), via the GUI, the optimal driving mode for the vehicle.

13. The system (100) as claimed in claim 12, wherein the processor executable instructions cause the processor (104) to:
receive (502) vehicle data from the one or more data sources, wherein the vehicle data comprises historical vehicle data, real-time sensor data, user driving patterns, and the vehicle battery information;
identify (504) a role associated with a user, wherein the role is one of a vehicle owner role, a hired-driver role, or a fleet-manager role;
generate (506), via the AI model (218), a personalized vehicle-performance summary and actionable insights for the user based on the vehicle data; and
render (508), via the GUI, the personalized vehicle-performance summary and the actionable insights relevant to the role.

14. The system (100) as claimed in claim 12, wherein the processor executable instructions cause the processor (104) to:
determine (602), via the AI model (218), a battery-based geofence for the vehicle based on the vehicle battery information, wherein the battery-based geofence defines an optimal travel radius around a current location of the vehicle based on a remaining battery charge of the vehicle;
when the remaining battery charge of the vehicle is below a predefined threshold, identifying (604), via the AI model (218), one or more charging stations located within the battery-based geofence; and
render (606), via the GUI, the one or more charging stations in a recommended order determined based on at least one of a charging station proximity, charging slot availability, compatibility and operational status, or a charging station historical reliability.