DESC:METHOD AND SYSTEM FOR DETERMINING ACTUAL ENERGY CONSUMPTION PER UNIT DISTANCE TRAVELLED FOR ELECTRIC VEHICLES
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims priority from Indian Provisional Patent Application No. 202221062146 filed on 01/11/2022, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
The present disclosure generally relates to electric vehicles. Particularly, the present disclosure relates to a system and a method for actual energy consumption per unit distance travelled for electric vehicles.
BACKGROUND
Recently, there has been a rapid development in electric vehicles because of their ability to resolve pollution-related problems and serve as a clean mode of transportation. Generally, electric vehicles include a battery pack, powerpack, and/or combination of electric cells for storing electricity required for the propulsion of the vehicles. The electrical power stored in the battery pack of the electric vehicle is supplied to the traction motor for moving the electric vehicle forming the powertrain of the electric vehicle.
The biggest constraint with the electric vehicle is the range associated with the state of charge of the powerpack electric vehicle. The powerpack of the electric vehicle requires a substantial amount of time to recharge. Thus, there is a great hesitance in the acceptance of EVs due to anxiety in the depletion of the State of charge (SoC) of the powerpack. In real application, the rate of depletion of charge is affected by various internal factors and external factors. The internal factors or events are directly associated with the driving pattern of the vehicle. It may include average acceleration and deceleration, rate of throttle changes, etc. On the other side, external factors may include traffic volume, road conditions, etc.
Conventionally, the determination of the range of the electric vehicle is calculated using the remaining state of charge of the powerpack of the electric vehicle. In recent developments, the range determination methodologies have improved as most of the internal factors are accounted for while determining the power consumption per unit of distance travelled, thus, determining the remaining range of the vehicle.
However, external factors such as traffic volume and road conditions majorly affect the energy consumption of the electric vehicle. For example, the rough terrain would require more power consumption by the motor as the friction between the tire and the road surface would be higher. However, the existing range determination methodologies do not account for the external factors leading to inaccurate and unreliable determination of the remaining range of the electric vehicle.
Therefore, there exists a need for a mechanism to accurately determine the actual energy consumption of the electric vehicle that overcomes one or more problems associated as set forth above.
SUMMARY
An object of the present disclosure is to provide a system for determining actual energy consumption for an electric vehicle.
Another object of the present disclosure is to provide a method for determining actual energy consumption for an electric vehicle.
In accordance with the first aspect of the present disclosure, there is provided a system for determining actual energy consumption for an electric vehicle. The system comprises at least one vision sensor to capture a visual data in vicinity of the electric vehicle, at least one inertial sensor to capture a plurality of parameters associated with electric vehicle travel conditions, a GPS sensor to identify a location of the electric vehicle, and a data processing arrangement. The data processing arrangement is configured to receive the visual data, the plurality of parameters, and the location, receive a state of charge of a powerpack of the electric vehicle, and determine the actual energy consumption by correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack.
The present disclosure provides a system for determining actual energy consumption for an electric vehicle. The system, as disclosed in the present disclosure is advantageous in terms of accurately determining the actual energy consumed per unit distance travelled by the electric vehicle. Furthermore, the system, as disclosed in the present disclosure is advantageous in terms of accounting for real-life road conditions in calculating the actual energy consumed per unit distance travelled by the electric vehicle. Furthermore, the system, as disclosed by the present disclosure is advantageous in terms of accurately and reliably determining the remaining range of the electric vehicle before it is required to be recharged.
In accordance with the second aspect of the present disclosure, there is provided a method of determining actual energy consumption for an electric vehicle. The method comprises receiving a visual data in vicinity of the electric vehicle, receiving a plurality of parameters associated with electric vehicle travel conditions, receiving a location of the electric vehicle, receiving a state of charge of a powerpack of the electric vehicle, and correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack, to determine the actual energy consumption.
Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 illustrates a block diagram of a system for determining actual energy consumption for an electric vehicle, in accordance with an aspect of the present disclosure.
FIG. 2 illustrates a flow chart of a method for determining actual energy consumption for an electric vehicle, in accordance with another aspect of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of a system for determining actual energy consumption for an electric vehicle and is not intended to represent the only forms that may be developed or utilized. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
The terms “comprise”, “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
As used herein, the terms ‘electric vehicle’, ‘EV’, and ‘EVs’ are used interchangeably and refer to any vehicle having stored electrical energy, including the vehicle capable of being charged from an external electrical power source. This may include vehicles having batteries that are exclusively charged from an external power source, as well as hybrid vehicles which may include batteries capable of being at least partially recharged via an external power source. Additionally, it is to be understood that the ‘electric vehicle’ as used herein includes electric two-wheelers, electric three-wheelers, electric four-wheelers, electric pickup trucks, electric trucks, and so forth.
As used herein, the terms “power source” “battery pack”, “battery”, and “power pack” are used interchangeably and refer to multiple individual battery cells connected to provide a higher combined voltage or capacity than what a single battery can offer. The power pack is designed to store electrical energy and supply it as needed to various devices or systems. Power packs, as referred herein may be used for various purposes such as power electric vehicles and other energy storage applications. Furthermore, the power pack may include additional circuitry, such as a battery management system (BMS), to ensure the safe and efficient charging and discharging of the battery cells. The power pack comprises a plurality of cell arrays which in turn comprises a plurality of battery cells.
As used herein, the terms “instrument cluster” “display interface”, and “display unit” are used interchangeably and refer to a digital display, analog display, or a combination thereof capable of displaying various information related to the vehicle. The display interface also allows the driver to interact with the vehicle's information and entertainment system. The display interface may display information about at least one of: vehicle speed, RPM of the powertrain, fuel level, odometer, navigation maps, audio, and climate control settings, warning messages, and so forth. The display interface may comprise an input mechanism such as a touchscreen. The display interface may be capable of presenting information including text, two-dimensional visual images, and/or three-dimensional visual images. Additionally, the display interface may present information in the form of audio and haptics. The display interface may include but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. Alternatively, the display interface may utilize other display technologies.
As used herein, the terms “vision sensor” “imaging sensor” and “vision unit” are used interchangeably and refer to a device that is capable of capturing still or moving images. The image capturing device comprises a lens, an image sensor, and an image processor. The lens focuses light onto the image sensor, which converts the light into an electrical signal. The image processor then converts the electrical signal into a digital image.
As used herein, the terms “processing unit”, ‘data processing arrangement’ and ‘processor’ are used interchangeably and refer to a computational element that is operable to respond to and process image signals and generate responsive commands to control other sub-systems in a system. Optionally, the processing unit includes but is not limited to, a microprocessor, a microcontroller, an image signal processor, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a combination thereof. Furthermore, the term “processor” may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Furthermore, the processing unit may comprise ARM Cortex-M series processors, such as the Cortex-M4 or Cortex-M7, or any similar processor designed to handle real-time tasks with high performance and low power consumption. Furthermore, the processing unit may comprise custom and/or proprietary processors.
As used herein, the terms “communication unit”, and “communication module” are used interchangeably and refer to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between the system and the display interface of the vehicle. The communication unit may utilize Wi-Fi, Bluetooth, Zigbee, or a combination thereof to communicate between the system and the display interface of the vehicle. Additionally, the communication module utilizes wired or wireless communication that can be carried out via any number of known protocols, including, but not limited to, Internet Protocol (IP), Wireless Access Protocol (WAP), Frame Relay, or Asynchronous Transfer Mode (ATM). Moreover, although the communication module described herein is being implemented with TCP/IP communications protocols, the communication module may also be implemented using IPX, Appletalk, IP-6, NetBIOS, OSI, any tunneling protocol (e.g., IPsec, SSH), or any number of existing or future protocols.
As used herein, the terms “Inertial Measurement Unit sensor”, “IMU sensor”, “inertial sensor”, and “sensor” are used interchangeably and refer to an inertial measurement unit sensor, that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of accelerometers, gyroscopes, and sometimes magnetometers. It is to be understood that accelerometers measure the acceleration of the body in three dimensions, gyroscopes measure the angular rate of the body in three dimensions, and magnetometers measure the direction of the magnetic field.
As used herein, the term “user” refers to a person operating an electric vehicle.
As used herein, the term “parameters associated with the electric vehicle travel conditions” refers to various parameters that are generated in response to the travel of electric vehicles on the road. The parameters include but are not limited to pitch, yaw, roll, vibration, and bumping of the electric vehicle.
As used herein, the term “road conditions” refers to various physical conditions of the road on which the electric vehicle is travelling. The road conditions may include but are not limited to potholes, uneven surfaces, ditches, speed breakers, and so forth.
As used herein, the term “GPS sensor” refers to a sensor that receives a signal from the global positioning system to determine the location of an object or person.
As used herein, the terms “server arrangement, and “server” are used interchangeably and refer to a remote computing unit with organization of one or more CPUs, memory, databases, network interfaces, etc. to provide required information via network-based communication.
As used herein, the term “object detection module” refers to a computer vision software module capable of determining distinct objects in an image or a plurality of images.
As used herein, the term “vicinity of the vehicle” refers to the angular extent of the scene that can be captured by the vision sensor. The vicinity of the vehicle is an area around the electric vehicle. The vicinity of the vehicle can be measured horizontally, vertically, or diagonally.
As used herein, the term “communicably coupled” refers to a bi-directional connection between the various components of the system and entities outside the system. The bi-directional connection between the various components of the system enables the exchange of data between two or more components of the system. Similarly, the bi-directional connection between the system and other elements/modules enables the exchange of data between the system and the other elements/modules.
Figure 1, in accordance with an embodiment, describes a system 100 for determining actual energy consumption for an electric vehicle. The system 100 comprises at least one vision sensor 102 to capture a visual data in vicinity of the electric vehicle, at least one inertial sensor 104 to capture a plurality of parameters associated with electric vehicle travel conditions, a GPS sensor 106 to identify a location of the electric vehicle, and a data processing arrangement 108. The data processing arrangement 108 is configured to receive the visual data, the plurality of parameters, and the location, receive a state of charge of a powerpack of the electric vehicle, and determine the actual energy consumption by correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack.
The present disclosure provides a system 100 for determining actual energy consumption for an electric vehicle. The system 100 is advantageous in terms of accurately determining the actual energy consumed per unit distance travelled by the electric vehicle. Furthermore, the system 100 is advantageous in terms of accounting for real-life road conditions in calculating the actual energy consumed per unit distance travelled by the electric vehicle. Furthermore, the system 100 is advantageous in terms of accurately and reliably determining the remaining range of the electric vehicle before it is required to be recharged.
In an embodiment, the data processing arrangement 108 employs an object detection module for processing the visual data to identify actual traffic conditions and road conditions in the vicinity of the electric vehicle. It is to be understood that the object detection module processes the visual data to identify distinct objects such as vehicles and traffic signals to identify actual traffic conditions. Similarly, the object detection module processes the visual data to identify distinct objects such as potholes, ditches, speed breakers, etc. to determine the actual road conditions.
In an embodiment, the data processing arrangement 108 is configured to generate a vibration profile of the electric vehicle based on the captured plurality of parameters associated with electric vehicle travel conditions. Beneficially, the vibration profile of the electric vehicle is indicative of the actual road conditions.
In an embodiment, the data processing arrangement 108 is configured to employ a trained machine learning model to categorize the vibration profile to identify the road conditions. It is to be understood that the machine learning model may be trained using supervised or unsupervised learning. Beneficially, the trained machine learning model accurately categorizes the vibration profile to identify the road conditions. Furthermore, the road conditions identified using the visual data and the vibration profile are combined for further processing by the data processing arrangement 108.
In an embodiment, the data processing arrangement 108 is configured to tag the actual traffic conditions and the road conditions in a map. Beneficially, the tagging of the map with the actual traffic conditions and the road conditions would update the map for future instances.
In an embodiment, the data processing arrangement 108 is configured to communicate the map to a server arrangement. It is to be understood that the system 100 comprises a communication module to communicate with the server arrangement. Beneficially, the communication of the tagged map to the server arrangement would enable the use of the updated map by other vehicles for determining the estimated time of arrival on a destination and range determination of the vehicle.
In an embodiment, the data processing arrangement 108 is configured to revise an estimated time of arrival of the electric vehicle to a destination based on the identified actual traffic conditions and the road conditions. It is to be understood that an estimated time of arrival is calculated at the start of the journey of the electric vehicle. Beneficially, the calculated estimated time of arrival is revised to show a more accurate estimated time of arrival based on the identified actual traffic conditions and the road conditions.
In an embodiment, the data processing arrangement 108 is configured to calculate the actual energy consumption of the electric vehicle by correlating: an actual distance travelled by the electric vehicle, the state of charge of the powerpack, the actual traffic conditions, and the road conditions identified using the visual data and the vibration profile. Beneficially, external factors such as the actual traffic conditions and the road conditions are accounted for to accurately determine the actual energy consumption of the electric vehicle.
In an embodiment, the system 100 comprises an output device 110 configured to display the actual energy consumption for the electric vehicle and the estimated time of arrival to a user of the electric vehicle. Beneficially, the user of the electric vehicle is provided with the accurate and reliable range of the electric vehicle and the revised estimated time of arrival, as determined by the system 100.
In a specific embodiment, the output device 110 is an instrument cluster of the electric vehicle. Beneficially, the instrument cluster of the electric vehicle readily displays the actual energy consumption for the electric vehicle and the estimated time of arrival to the user.
In an embodiment, the system 100 comprises at least one vision sensor 102 to capture a visual data in vicinity of the electric vehicle, at least one inertial sensor 104 to capture a plurality of parameters associated with electric vehicle travel conditions, a GPS sensor 106 to identify a location of the electric vehicle, and a data processing arrangement 108. The data processing arrangement 108 is configured to receive the visual data, the plurality of parameters, and the location, receive a state of charge of a powerpack of the electric vehicle, and determine the actual energy consumption by correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack. Furthermore, the data processing arrangement 108 employs an object detection module for processing the visual data to identify actual traffic conditions and road conditions in the vicinity of the electric vehicle. Furthermore, the data processing arrangement 108 is configured to generate a vibration profile of the electric vehicle based on the captured plurality of parameters associated with electric vehicle travel conditions. Furthermore, the data processing arrangement 108 is configured to employ a trained machine learning model to categorize the vibration profile to identify the road conditions. Furthermore, the data processing arrangement 108 is configured to tag the actual traffic conditions and the road conditions in a map. Furthermore, the data processing arrangement 108 is configured to communicate the map to a server arrangement. Furthermore, the data processing arrangement 108 is configured to revise an estimated time of arrival of the electric vehicle to a destination based on the identified actual traffic conditions and the road conditions. Furthermore, the data processing arrangement 108 is configured to calculate the actual energy consumption of the electric vehicle by correlating: an actual distance travelled by the electric vehicle, the state of charge of the powerpack, the actual traffic conditions, and the road conditions identified using the visual data and the vibration profile. Furthermore, the system 100 comprises an output device 110 configured to display the actual energy consumption for the electric vehicle and the estimated time of arrival to a user of the electric vehicle. Furthermore, the output device 110 is an instrument cluster of the electric vehicle.
Figure 2, describes method 200 for determining actual energy consumption for an electric vehicle. The method 200 starts at step 202 and finishes at step 210. At step 202, the method 200 comprises receiving a visual data in vicinity of the electric vehicle, via at least one vision sensor 102. At step 204, the method 200 comprises receiving a plurality of parameters associated with electric vehicle travel conditions, via at least one inertial sensor 104. At step 206, the method 200 comprises receiving a location of the electric vehicle, via a GPS sensor 106. At step 208, the method 200 comprises receiving a state of charge of a powerpack of the electric vehicle. At step 210, the method 200 comprises correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack, via a data processing arrangement 108, to determine the actual energy consumption.
In an embodiment, the method 200 comprises employing an object detection module for processing the visual data to identify actual traffic conditions and road conditions in the vicinity of the electric vehicle.
In an embodiment, the method 200 comprises generating a vibration profile of the electric vehicle based on the captured plurality of parameters associated with electric vehicle travel conditions.
In an embodiment, the method 200 comprises employing a trained machine learning model to categorize the vibration profile to identify the road conditions.
In an embodiment, the method 200 comprises tagging the actual traffic conditions and the road conditions in a map.
In an embodiment, the method 200 comprises revising an estimated time of arrival of the electric vehicle to a destination based on the identified actual traffic conditions and the road conditions.
In an embodiment, the method 200 comprises calculating the actual energy consumption of the electric vehicle by correlating: an actual distance travelled by the electric vehicle, the state of charge of the powerpack, the actual traffic conditions, and the road conditions identified using the visual data and the vibration profile.
In an embodiment, the method 200 comprises displaying the actual energy consumption for the electric vehicle and the estimated time of arrival to a user of the electric vehicle, via an output device 110.
In an embodiment, the method 200 comprises receiving a visual data in vicinity of the electric vehicle, via at least one vision sensor 102. The method 200 comprises receiving a plurality of parameters associated with electric vehicle travel conditions, via at least one inertial sensor 104. The method 200 comprises receiving a location of the electric vehicle, via a GPS sensor 106. The method 200 comprises receiving a state of charge of a powerpack of the electric vehicle. The method 200 comprises correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack, via a data processing arrangement 108, to determine the actual energy consumption. Furthermore, the method 200 comprises employing an object detection module for processing the visual data to identify actual traffic conditions and road conditions in the vicinity of the electric vehicle. Furthermore, the method 200 comprises generating a vibration profile of the electric vehicle based on the captured plurality of parameters associated with electric vehicle travel conditions. Furthermore, the method 200 comprises employing a trained machine learning model to categorize the vibration profile to identify the road conditions. Furthermore, the method 200 comprises tagging the actual traffic conditions and the road conditions in a map. Furthermore, the method 200 comprises revising an estimated time of arrival of the electric vehicle to a destination based on the identified actual traffic conditions and the road conditions. Furthermore, the method 200 comprises calculating the actual energy consumption of the electric vehicle by correlating: an actual distance travelled by the electric vehicle, the state of charge of the powerpack, the actual traffic conditions, and the road conditions identified using the visual data and the vibration profile. Furthermore, the method 200 comprises displaying the actual energy consumption for the electric vehicle and the estimated time of arrival to a user of the electric vehicle, via an output device 110.
It would be appreciated that all the explanations and embodiments of the system 100 also apply mutatis-mutandis to the method 200.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed”, “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combinations of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, and “is” used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings, and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

,CLAIMS:WE CLAIM:
1. A system (100) for determining actual energy consumption for an electric vehicle, the system (100) comprising:
- at least one vision sensor (102) to capture a visual data in vicinity of the electric vehicle;
- at least one inertial sensor (104) to capture a plurality of parameters associated with electric vehicle travel conditions;
- a GPS sensor (106) to identify a location of the electric vehicle; and
- a data processing arrangement (108) configured to:
- receive the visual data, the plurality of parameters, and the location;
- receive a state of charge of a powerpack of the electric vehicle; and
- determine the actual energy consumption by correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack.
2. The system (100) as claimed in claim 1, wherein the data processing arrangement (108) employs an object detection module for processing the visual data to identify actual traffic conditions and road conditions in the vicinity of the electric vehicle.
3. The system (100) as claimed in claim 1, wherein the data processing arrangement (108) is configured to generate a vibration profile of the electric vehicle based on the captured plurality of parameters associated with electric vehicle travel conditions.
4. The system (100) as claimed in claim 3, wherein the data processing arrangement (108) is configured to employ a trained machine learning model to categorize the vibration profile to identify the road conditions.
5. The system (100) as claimed in claim 1, wherein the data processing arrangement (108) is configured to tag the actual traffic conditions and the road conditions in a map.
6. The system (100) as claimed in claim 5, wherein the data processing arrangement (108) is configured to communicate the map to a server arrangement.
7. The system (100) as claimed in claim 1, wherein the data processing arrangement (108) is configured to revise an estimated time of arrival of the electric vehicle to a destination based on the identified actual traffic conditions and the road conditions.
8. The system (100) as claimed in claim 1, wherein the data processing arrangement (108) is configured to calculate the actual energy consumption of the electric vehicle by correlating: an actual distance travelled by the electric vehicle, the state of charge of the powerpack, the actual traffic conditions and the road conditions identified using the visual data and the vibration profile.
9. The system (100) as claimed in claim 1, wherein the system (100) comprises an output device (110) configured to display the actual energy consumption for the electric vehicle and the estimated time of arrival to a user of the electric vehicle.
10. The system (100) as claimed in claim 9, wherein the output device (110) is an instrument cluster of the electric vehicle.
11. A method (200) of determining actual energy consumption for an electric vehicle, the method (200) comprises:
- receiving a visual data in vicinity of the electric vehicle, via at least one vision sensor (102);
- receiving a plurality of parameters associated with electric vehicle travel conditions, via at least one inertial sensor (104);
- receiving a location of the electric vehicle, via a GPS sensor (106);
- receiving a state of charge of a powerpack of the electric vehicle; and
- correlating the received visual data, the plurality of parameters, and the location with the state of charge of the powerpack, via a data processing arrangement (108), to determine the actual energy consumption.
12. The method (200) as claimed in claim 11, wherein the method (200) comprises employing an object detection module for processing the visual data to identify actual traffic conditions and road conditions in the vicinity of the electric vehicle.
13. The method (200) as claimed in claim 11, wherein the method (200) comprises generating a vibration profile of the electric vehicle based on the captured plurality of parameters associated with electric vehicle travel conditions.
14. The method (200) as claimed in claim 11, wherein the method (200) comprises employing a trained machine learning model to categorize the vibration profile to identify the road conditions.
15. The method (200) as claimed in claim 11, wherein the method (200) comprises tagging the actual traffic conditions and the road conditions in a map.
16. The method (200) as claimed in claim 11, wherein the method (200) comprises revising an estimated time of arrival of the electric vehicle to a destination based on the identified actual traffic conditions and the road conditions.
17. The method (200) as claimed in claim 11, wherein the method (200) comprises calculating the actual energy consumption of the electric vehicle by correlating: an actual distance travelled by the electric vehicle, the state of charge of the powerpack, the actual traffic conditions, and the road conditions identified using the visual data and the vibration profile.
18. The method (200) as claimed in claim 11, wherein the method (200) comprises displaying the actual energy consumption for the electric vehicle and the estimated time of arrival to a user of the electric vehicle, via an output device (110).

Dated 31 October 2023 Kumar Tushar Srivastava
IN/PA- 3973
Agent for the Applicant