Claims:We Claim:

1. A method for dynamically managing trip of a vehicle in real-time, the method comprising:
receiving, by a system associated with a vehicle, trip related data from a user through a device associated with the user or a vehicle;
receiving, by the system, vehicle data from a communication module associated with the vehicle;
receiving, by the system, external data affecting trip of the vehicle from one or more data sources; and
determining, by the system, Distance to Empty (DTE) for the trip based on the trip related data, the vehicle data and the external data using a trained machine learning model associated with the system, wherein the DTE determined in real-time is displayed on display unit of the vehicle or a device associated with the user at pre-defined time intervals for managing the trip of the vehicle.

2. The method as claimed in claim 1, wherein the vehicle is one of an electric vehicle or a hybrid vehicle.

3. The method as claimed in claim 1, wherein the trip related data comprises information associated with source and destination of the trip, number of persons travelling in the vehicle and type of a trip in the vehicle.

4. The method as claimed in claim 3, wherein the type of trip comprises one of one-way trip or a round trip in the vehicle.

5. The method as claimed in claim 1, wherein the vehicle data comprises information associated with current location of the vehicle, current charge level of a battery of the vehicle and current health state of the battery of the vehicle and driving range information from Electronic Control Units (ECUs) configured in the vehicle.

6. The method as claimed in claim 1, wherein the external data comprises weather condition and traffic condition in a driving path from a source to a destination in the trip.

7. The method as claimed in claim 1 comprises receiving information associated with location of one or more stations for charging battery of the vehicle in the driving path from the one or more data sources.

8. The method as claimed in claim 7 further comprises providing a notification to at least one of a display unit of the vehicle or the device associated with the user about location of the one or more stations when current charge level of the battery of the vehicle is less than a predefined threshold.

9. The method as claimed in claim 1 further comprises receiving toll cost information from the one or more data source for the trip planned by the user based on information associated with the type of vehicle, source and destination of the trip and the type of trip.

10. The method as claimed in claim 1 further comprises providing a notification to the user device of travelling essentials required for the trip based on information associated with source and destination of the trip and weather condition in a driving path of the trip.

11. The method as claimed in claim 1, wherein training the machine learning model comprises:
receiving trip related data, vehicle data and external data associated with one or more trips planned by one or more users using one or more vehicles;
pre-processing the received trip related data, the vehicle data, and the external data; and
training the machine learning model for determining the real-time DTE based on the pre-processed data using one or more classification models.

12. The method as claimed in claim 11 further comprises configuring the trained machine learning model in the system for determining the DTE in real time.

13. The method as claimed in claim 11, wherein pre-processing the received trip related data, the vehicle data and the external data comprises:
removing duplicate data;
identifying correlations between the trip related data, vehicle data and the external data;
determining missing data based on the correlations;
converting the received data into a predefined format; and
grouping the received data into one or more factor groups.

14. A system for dynamically managing trip of a vehicle in real-time, the system comprising:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:
receive trip related data from a device associated with a user or the vehicle;
receive vehicle data from a communication module associated with the vehicle;
receive external data affecting trip of the vehicle from one or more data sources; and
determine Distance to Empty (DTE) for the trip based on the trip related data, the vehicle data and the external data using a trained machine learning model associated with the system, wherein the DTE determined in real-time is displayed on display unit of the vehicle or the device associated with the user at pre-defined time intervals for managing the trip of the vehicle.

15. The system as claimed in claim 14, wherein the vehicle is one of an electric vehicle or a hybrid vehicle.

16. The system as claimed in claim 14, wherein the trip related data comprises information associated with source and destination of the trip, number of persons travelling in the vehicle and type of a trip in the vehicle.

17. The system as claimed in claim 16, wherein the type of trip comprises one of one-way trip or a round trip in the vehicle.

18. The system as claimed in claim 14, wherein the processor receives information associated with current location of the vehicle, current charge level of a battery of the vehicle and current health state of the battery and driving range information from Electronic Control Units (ECUs) configured in the vehicle of the vehicle from the one or more data sources.

19. The system as claimed in claim 14, wherein the external data comprises weather condition and traffic condition in a driving path from a source to a destination in the trip.

20. The system as claimed in claim 14, wherein the processor receives information associated with location of one or more stations for charging battery of the vehicle in the driving path from the one or more data sources.

21. The system as claimed in claim 20, wherein the processor provides a notification to at least one of a display unit of the vehicle or a user device about location of the one or more stations when current charge level of the battery of the vehicle is less than a predefined threshold.

22. The system as claimed in claim 14, wherein the processor receives toll cost information from the one or more data source for the trip planned by the user based on information associated with the type of vehicle, source and destination of the trip and the type of trip.

23. The system as claimed in claim 14, wherein the processor provides a notification to the user device of travelling essentials required for the trip based on information associated with source and destination of the trip and weather condition in driving path of the trip.

24. The system as claimed in claim 14, wherein the machine learning model is trained by performing steps of:
receiving trip related data, vehicle data and external data associated with one or more trips planned by one or more users using one or more vehicles;
pre-processing the received trip related data, the vehicle data and the external data; and
training the machine learning model to determine the DTE based on the pre-processed data using one or more classification models.

25. The system as claimed in claim 24, wherein the processor configures the trained machine learning model in the system to determine the DTE in real time.

26. The system as claimed in claim 24, wherein pre-processing the received trip related data, the vehicle data and the external data comprises:
removing duplicate data;
identifying correlations between the trip related data, vehicle data and the external data;
determining missing data based on the correlations;
converting the received data into a predefined format; and
grouping the received data into one or more factor groups.
, Description:TECHNICAL FIELD
The present subject matter is related, in general to automobiles s, and more particularly, but not exclusively, to a method and a system for dynamically managing trip of a battery operated vehicle in real-time.

BACKGROUND
Electric vehicles are generally powered using rechargeable batteries. The energy stored in the rechargeable batteries is used as a fuel supply for the electric vehicle. In some instances, the battery drains out of charge and hence the vehicle may halt in the middle of a trip. Thus, there exist an issue when a trip, particularly, long trips planned for electric vehicles. Also, the issue is aggravated by manifolds since there exist no mechanism currently which would determine Distance to Empty (DTE) in real-time i.e., distance to be travelled with available resources in real-time and update DTE in real-time for the trip planned by the user by considering parameters which affect the trip of the vehicle. The existing mechanisms would only consider battery as the only parameter to determine the DTE. Whereas other parameters which would affect the battery usage are not considered and hence the estimated DTE may not be accurate.

The present disclosure is directed to overcome one or more limitations stated above or any other limitation associated with the conventional arts.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY
In an embodiment of the present disclosure, a method for dynamically managing trip of a vehicle in real-time is disclosed. The method comprises receiving, by a system associated with the vehicle, trip related data from a user through a device associated with the user or the vehicle, receiving, by the system, vehicle data from a communication module associated with the vehicle and receiving, by the system, external data affecting trip of the vehicle from one or more data sources. Upon receiving the trip related data, the vehicle data and the external data, the method comprises determining, by the system, Distance To Empty (DTE) for the trip based on the trip related data, the vehicle data and the external data using a trained machine learning model associated with the system. The determined DTE is displayed on a display unit of the vehicle or a device associated with the user at pre-defined time intervals for managing the trip of the vehicle.

In an embodiment, the vehicle is one of an electric vehicle or a hybrid vehicle.

In an embodiment, the trip related data comprises information associated with source and destination of the trip, number of persons travelling in the vehicle and type of a trip in the vehicle.

In an embodiment, the type of trip comprises one of one-way trip or a round trip in the vehicle.

In an embodiment, the vehicle data comprises information associated with current location of the vehicle, current charge level of a battery of the vehicle, current health state of the battery of the vehicle and driving range information available from Electric Control Unit (ECU) configured in the vehicle.

In an embodiment, the external data comprises weather condition, route options and traffic conditions in a driving paths (routes) from a source to a destination in the trip.

In an embodiment, the method comprises receiving information associated with location of one or more stations for charging battery of the vehicle in the driving path from the one or more data sources.

In an embodiment, the method comprises providing a notification to at least one of a display unit of the vehicle or the device associated with the user about location of the one or more stations when current charge level of the battery of the vehicle is less than a predefined threshold.

In an embodiment, the method comprises receiving toll cost information from the one or more data source for the trip planned by the user based on information associated with the type of vehicle, source and destination of the trip and the type of trip.

In an embodiment, the method comprises providing a notification to the user device of travelling essentials required for the trip based on information associated with source and destination of the trip and weather condition in a driving path of the trip.

In an embodiment, the method comprises training the machine learning model. In order to train the machine learning model, the following steps are performed. At first, the method comprises receiving trip related data, vehicle data and external data associated with one or more trips planned by one or more users using one or more vehicles. Further, the step of pre-processing the received trip related data, the vehicle data, and the external data is performed. Thereafter, the model is trained for determining the real-time DTE based on the pre-processed data using one or more classification models.

In an embodiment, the method comprises configuring the trained machine learning model in the system for determining the DTE in real time.

In an embodiment, the pre-processing method comprises following steps. At first, duplicate data among the received trip related data, the vehicle data and the external data is removed, thereafter correlations between the trip related data, vehicle data and the external data is identified. Further the method comprises determining missing data if any and the received data is converted to a predefined format. The received data is grouped into one or more factor groups.

In an embodiment of the present disclosure, a system for dynamically managing trip of a vehicle in real-time is disclosed. The system comprises a processor and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to receive data trip related data from a user through an associated user device, vehicle data from a communication module associated with the vehicle and external data affecting trip of the vehicle from one or more data sources. The processor determines DTE for the trip based on the trip related data, the vehicle data and the external data using a trained machine learning model associated with the system. The DTE determined in real-time is displayed on display unit of the vehicle at pre-defined time intervals for managing the trip of the vehicle.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which:

Fig.1a shows an exemplary environment for dynamically managing trip of a vehicle in real-time in accordance with some embodiments of the present disclosure.

Fig.1b shows a block diagram of a system for dynamically managing trip of a vehicle in real-time in accordance with some embodiments of the present disclosure.

Fig.2 shows a flowchart illustrating a method for dynamically managing trip of a vehicle in real-time in accordance with some embodiments of the present disclosure.

Fig.3 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown.

DETAILED DESCRIPTION
In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
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 specific forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
The terms “comprises”, “comprising”, “includes”, “including” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method 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 device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.
The present disclosure relates to a method and a system for dynamically managing the trip of a vehicle in real-time. In one embodiment, the system is associated with a vehicle. In another embodiment, the system may be configured in the vehicle. As an example, the vehicle may be an electric vehicle or a hybrid vehicle. The system at first receives trip related data from a user through a device associated with the user or a vehicle. As an example, the trip related data comprises information associated with the source and destination of the trip, number of persons travelling in the vehicle, and type of trip in the vehicle. As an example, the device associated with the vehicle may be a mobile phone or any electronic device associated with the user which is capable of receiving and transmitting information. The type of trip may be one of one-way trip or a round trip in the vehicle.

The system also receives vehicle data from a communication module associated with the vehicle and external data affecting trip of the vehicle from one or more data sources. The vehicle data may include information associated with the current location of the vehicle, current charge level of a battery of the vehicle, current health state of the battery of the vehicle and driving range information available from one or more Electronic Control Units (ECUs) configured in the vehicle. The external data comprises weather condition and traffic condition in a driving path from the source to the destination in the trip. The system may also receive information associated with location of one or more stations for charging battery of the vehicle in the driving path from the one or more data sources and toll cost information from the one or more data sources for the trip planned by the user. The toll cost information may be identified based on information associated with the type of vehicle, source and destination of the trip and the type of trip. In an embodiment, when the current charge level of the battery of the vehicle is less than a predefined threshold, the system may provide a notification to at least one of a display unit of the vehicle or the device associated with the user about location of the one or more stations so that the battery may be recharged.

Based on the trip related data, vehicle data and external data, the system determines Distance To Empty (DTE) for the trip using a trained machine learning model associated with the system. The DTE indicates distance to be travelled with the available charge in the battery of the vehicle. In real-time, the determined DTE is displayed on a display unit or a display interface of the vehicle at pre-defined time intervals for managing the trip of the vehicle.

In an embodiment, the system provides a notification to the user device of travelling essentials required for the trip based on information associated with source and destination of the trip and weather conditions in a driving path of the trip.

In this manner, the present disclosure discloses a method and system for dynamically managing trip of a vehicle in real-time by determining DTE to the user about how much distance user can travel with the existing resources.

Fig.1a shows an exemplary environment for dynamically managing trip of a vehicle in real-time in accordance with some embodiments of the present disclosure.

The environment 100 comprises an electronic device 101 associated with a user or with the vehicle 103, a vehicle 103, one or more data sources 105 and a system 107. The system 107 comprises a processer 113 interfacing the memory 115 (shown in Fig.1b). The system 107 may also include an Input/Output (I/O) interface 111 (shown in Fig.1b). In one embodiment, the system 107 is associated with the vehicle 103. As an example, the system may be a cloud platform. In another embodiment, the system 107 is configured in the vehicle 103. The vehicle 103 may be an electric vehicle or a hybrid vehicle.

As an example, a user may plan a long trip using the vehicle 103, where vehicle 103 may be electric vehicle or a hybrid vehicle. For planning a long trip in the electric vehicle or a hybrid vehicle there are constraints due to the limited battery charge in the electric vehicle. To overcome this constraint, present disclosure discloses a system 107 which may be configured in or associated with the vehicle 103 for dynamically managing trip of the vehicle 103. So, to dynamically manage the trip of the vehicle 103, the processor 113 may first receive trip related data 117, from a user through the device [electronic device 101] associated with the user or a vehicle 103. The device associated with the user may be an electronic device 101 capable of receiving and transmitting information. As an example, the device may be a mobile phone associated with the user. The user may also provide the trip related data 117 through a display interface of the vehicle 103. The trip related data 117 may comprise information associated with source and destination of the trip, number of persons travelling in the vehicle 103 and type of trip in the vehicle. Based on the number of persons in the vehicle, load of the vehicle 103 may be determined. The type of trip is one of one way trip or a round trip in the vehicle 103. Simultaneously, the processor 113 may receive, vehicle data 118 from a communication module associated with the vehicle 103 and external data 119 affecting trip of the vehicle 103 from one or more data sources 105. The vehicle data 118 may comprise information associated with current location of the vehicle, current charge level of a battery of the vehicle, current health state of the battery of the vehicle 103 and driving range information based on the current state of charge and battery state of health available from one or more Electronic Control Units (ECUs) configured in the vehicle.

The system 107 also receives external data 119 from one or more data sources 105. The external data 119 may comprise weather condition and traffic condition in a driving path from a source to a destination in the trip. As an example, when the user is traveling from Satara to Mumbai which is the source and destination respectively, heavy traffic may be anticipated, and the temperature predicted may be 26 degrees.

In an embodiment, the trip related data 117, the vehicle data 118 and external data 119 are received sequentially or simultaneously. Similarly, the one or more actions described above may be performed sequentially or simultaneously. Upon receiving the trip related data 117, vehicle data 118 and the external data 119, the system 107 determines the DTE 109 in real-time for the trip using a trained machine learning model associated with the system.

In an embodiment, the system 107 provides a notification to at least one of a display interface of the vehicle 103 or the device associated with the user about location of the one or more stations when current charge level of the battery of the vehicle 103 is estimated to be less than a predefined threshold. As an example, the system 107 may indicate two charging stations at location 1 and location 2 in the driving path from source and destination such as Satara to Mumbai. In an embodiment, the system 107 may also provide information about charger type, real-time charging point availability (in use or vacant) and charge per unit cost at each charging station.

In an embodiment, the system 107 receives toll cost information from the one or more data sources 105 for the trip planned by the user based on information associated with the type of vehicle 103, source and destination of the trip and the type of trip. As an example, the user while traveling in electric vehicle 103 from Satara to Mumbai may come across 3 toll stations in one-way trip. So, the toll information based on 3 toll stations may be indicated to the user of the vehicle 103.

In an embodiment, the system 107 provides a notification to the device associated with the user of travelling essentials required for the trip based on information associated with source and destination of the trip and weather condition in a driving path of the trip. As an example, the user travelling from Satara to Mumbai will be provided with information relating to travelling essentials like charger and umbrella.

Fig.1b shows a block diagram of a system for dynamically managing trip of a vehicle in real-time in accordance with some embodiments of the present disclosure.

In one implementation, the system 107 receives data from an electronic device 101 associated with the user or the device associated with the vehicle 103 and from one or more data sources 105. As an example, the data is stored within the memory 115. In an embodiment, the data includes trip related data 117, vehicle data 118, external data 119. The system 107 also includes DTE data 120 and other data 121. In the illustrated Fig. 1b, one or more modules stored in the memory 115 are described herein in detail.

In one embodiment, the data may be stored in the memory 115 in the form of various data structures. Additionally, the aforementioned data can be organized using data models, such as relational or hierarchical data models. The other data 121 may store data, including temporary data and temporary files, generated by modules for performing the various functions of the system 107.

In an embodiment, the trip related data 117 is received from a user through a device associated with the user or a device associated with vehicle 103. The trip related data 117 corresponds to a trip planned by the user using the vehicle. The trip related data 117 comprises information associated with source and destination of the trip, number of persons travelling in the vehicle 103 and type of a trip in the vehicle. The data corresponding to type of trip comprises one-way trip or a round trip in the vehicle.

In an embodiment, the vehicle data 118 is received from a communication module associated with the vehicle 103. As an example, the vehicle data 118 comprises information associated with current geo-location of the vehicle, current charge level of a battery of the vehicle, current health state of the battery of the vehicle 103 and driving range information. The driving range information indicates the driving range or distance travelled by the vehicle 103 based on the current state of charge of the battery and battery state of health.

In an embodiment, the external data 119 affecting trip of the vehicle 103 is received from one or more data sources 105. As an example, one or more third party application servers may provide external data 119 to the one or more data sources 105. The one or more data sources 105 may be a central server associated with one or more application servers, wherein one or more application servers update data in the central repository. The external data 119 may be weather data and traffic data.

In an embodiment, the DTE data 120 is the data associated with the DTE 109 which is determined based on the trip related data 117, the vehicle data 118 and the external data 119 using a trained machine learning model.

In an embodiment, the data stored in the memory 115 are processed by the modules of the system 107. The modules may be stored within the memory 115 as shown in Fig.1b. In an example, the modules, communicatively coupled to the processor 113, may also be present outside the memory 115.

In one implementation, the modules may include, for example, a receiving module 123, a determination module 124, a display / output module 125, and other modules 126. The other modules 126 may be used to perform various miscellaneous functionalities of the system 107. It will be appreciated that such aforementioned modules may be represented as a single module or a combination of different modules.

In an embodiment, the receiving module 123 may be configured to receive data from electronic device 101 associated with the user or the vehicle 103 and one or more data sources 105 through the I/O Interface 111. The receiving module 123 receives trip related data 117, wherein the trip related data 117 may include, but not limited to, source and destination of the trip, number of persons travelling in the vehicle 103 and type of a trip in the vehicle 103 received from the device associated with the user or the vehicle. The receiving module 123 may receive vehicle data 118 from the communication module of the vehicle 103. The vehicle data 118 may be current location of the vehicle 103, current charge level of a battery of the vehicle 103, current health state of the battery of the vehicle 103 and driving range information. The receiving module 123 may also receive external data 119 affecting trip of the vehicle 103 such as weather data and traffic data in the driving path of the vehicle 103 from source to destination. In an embodiment, the receiving module 123 may also receive information associated with current location of one or more charging stations in the driving path, the charging type of each charging station and cost per unit charge from the one or more data sources 105.

In an embodiment, the determination module 124 is configured to determine DTE 109 for the trip based on the trip related data 117, vehicle data 118 and the external data 119. The DTE 109 is determined using a trained machine learning model. The determined DTE 109 is displayed on a display interface of the vehicle 103 through the display / output module 125. The machine learning model is trained by first providing trip related data 117, vehicle data 118 and external data 119 associated with one or more trips planned by one or more users using one or more vehicles. The machine learning model performs pre-processing the received trip related data 117, the vehicle data 118, and the external data 119 and thereafter determines the real-time DTE 109 based on the pre-processed data using one or more classification models. As an example, the one or more classification models may include, but not limited to, Decision Tree, Random Forest and K Nearest Neighbors [KNN]. Under pre-processing the method comprises removing duplicate data, identifying correlations between the trip related data 117, vehicle data 118 and the external data 119, determining missing data based on the correlations and converting the received data into a predefined format. The method also comprises grouping the received data into one or more predefined factor groups. The usage of predefined factors is exampled in detail in the exemplary scenario.

Exemplary scenario

As an example, consider a user may be traveling from Satara to Mumbai which is about 260 Kms. Consider 3 passengers are travelling in the planned long trip and it is a one-way or a single trip. So, the trip related data 117 comprises:

Source and destination information- Satara and Mumbai
Number of users/Passengers –3
Type of trip- One-way

The user provides the trip related data 117 through the electronic device 101 associated with the user which is a mobile phone or an Input/Output Interface of the vehicle 103. Thereafter, the system 107 receives vehicle data 118 from the communication module associated with the vehicle 103. The vehicle data 118 comprises:
Current charge level in the battery- 90%
Current Heath state of battery-95%
Driving range of vehicle 103–190Kms
Current location of vehicle-Satara

The system 107 may also receive information associated with battery age. As an example, the battery age may be –1 Year Old.

Further, the system 107 receives external data 119 from one or more data sources 105. As an example, the external data 119 may include:
weather data-27 degree
traffic data- Dense Traffic

The system 107 provides these information to the machine learning model. The machine learning model is trained based on trip related data 117, vehicle data 118 and external data 119 associated with one or more trips planned by one or more users using one or more vehicles. The below Table 1 indicates current DTE and target DTE 109 for two vehicles, vehicle 1 and vehicle 2 by considering various factors such as battery age, traffic condition, weather condition, current SOC, current DTE [vehicle DTE], Physical load and one or more predefined factors associated with battery age, physical load, weather condition and traffic condition.

VIN [Vehicle Identification Number] 123456-Vehicle 1 654321- Vehicle 2
Current SOC 90 50
Vehicle DTE 225 125
Battery Age 1 2
Physical Load 3 3
Traffic Density 3 3
Ambient Temp 3 2
Factor_Battery Age 0 0
Factor_ Physical Load 5 5
Factor_ Traffic Density 5 5
Factor_ Ambient Temp 5 0
Table 1

Further, the training of the machine learning model is explained below. As an example, consider the capacity of electric vehicle 103 [vehicle 1] main battery to be 300 Ah, and when the battery is fully charged, total driving range of the vehicle 103 is 250 Kms and the current State of Charge (SoC) of the battery is 90%. Hence, the battery capacity based on SOC will be 270Ah. [300*(90/100)]. As an example, consider the vehicle 103 battery age is less than 2 Years. As an example, the predefined factor to be used for calculation for battery age is 0. Hence, Total Battery Capacity according to Battery age will be 270 [270-(270*0/100)]. So, the battery age may also affect the DTE 109. The DTE 109 after considering the battery age may be 225 [250*270/300]. Similarly, the other parameters such as traffic and physical load may also affect DTE 109. For example, the physical load in vehicle 103 is mentioned as 3 passengers, the predefined factor to be used for calculations of physical load is 5. Hence effect on DTE 109 will be 213.75 [225-225*5/100].
For example, it is anticipated for dense traffic in the driving path from Satara to Mumbai. The predefined factor to be used for calculations of traffic is 15. Hence, effect of DTE 109 will be 181.69 [213.75- (213.75*15)/100].

Also, the trip is planned from Satara, whose temperature is 27°C therefore the predefined factor to be used for calculations of weather is 5. Hence, the effect of weather on the DTE 109 will be 172.61 [181.69- (181.69*5)/100].

In this manner, the machine learning model determines the DTE 109 based on the received trip related data 117, vehicle data 118 and the external data 119 and the predefined factors used for each data. Once the DTE 109 is determined the DTE 109 [Target DTE] is also indicated in the below Table 2.

VIN [Vehicle Identification Number] 123456-Vehicle 1 654321-Vehicle 2
Current SOC 90 50
Vehicle DTE 225 125
Battery Age 1 2
Physical Load 3 3
Traffic Density 3 3
Ambient Temp 3 2
Factor_Battery Age 0 0
Factor_ Physical Load 5 5
Factor_ Traffic Density 5 5
Factor_ Ambient Temp 5 0
Target DTE 172.96 95.89
Table 2

Based on the received trip related data 117, vehicle data 118 and the external data 119, the machine learning model determines the DTE 109 as 172.96. So, the vehicle 103 may travel only for about 172.96 Km. But the vehicle 103 has to further travel 90 Kms to reach the destination.

As per the calculation, the determined DTE 109 is 172.96. So, the system 107 may provide notification of one or more charging stations for charging the battery when the vehicle 103 is about 160 Km away from the source so that the vehicle battery is charged well before the battery drains out.

Fig.2 shows a flowchart illustrating a method for dynamically managing trip of a vehicle in real-time in accordance with some embodiments of the present disclosure.

As illustrated in Fig.2, the method comprises one or more blocks for dynamically managing trip of a vehicle 103 in real-time using a system 107. The method may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

The order in which the method is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block 201, the method comprises receiving trip related data 117 from a user through a device associated with the user or a vehicle 103. The trip related data 117 is associated with source and destination of the trip, number of persons travelling in the vehicle 103 and type of a trip in the vehicle 103 i.e., one-way trip or a round trip in the vehicle 103.

At block 202, the method comprises receiving the vehicle data 118 from a communication module associated with the. The vehicle data 118 comprises information associated with current location of the vehicle, current charge level of a battery of the vehicle, current health state of the battery of the vehicle 103 and driving range information based on the current state of charge and battery state of health available from one or more Electronic Control Units configured in the vehicle 103.

At block 203, the method comprises receiving external data 119 affecting trip of the vehicle 103 from one or more data sources 105. External data 119 may comprise weather condition and traffic condition in a driving path from a source to a destination in the trip. The external data 119 may also include toll cost information and information associated with location of one or more stations for charging battery of the vehicle 103 in the driving path from the one or more data sources 105.

At block 204, the method comprises determining the Distance to Empty (DTE) 109 for the trip based on the trip related data 117, the vehicle data 118 and the external data 119 using a trained machine learning model in real-time.

Computer System

Fig.3 illustrates a block diagram of an exemplary computer system 300 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 300 is used for dynamically managing trip of a vehicle in real-time. The computer system 300 may include a central processing unit (“CPU” or “processor”) 302. The processor 302 may comprise at least one data processor for executing program components for executing user or system-generated business processes. The processor 302 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.
The processor 302 may be disposed in communication with one or more input/output (I/O) devices (311 and 312) via I/O interface 301. The I/O interface 301 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I/O interface 301, the computer system 300 may communicate with one or more I/O devices 311 and 312. The computer system 300 may receive data from electronic device 101 and one or more data sources 105.
In some embodiments, the processor 302 may be disposed in communication with a communication network 309 via a network interface 303. The network interface 303 may communicate with the communication network 309. The network interface 303 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.
The communication network 309 can be implemented as one of the several types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 309 may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 309 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.
In some embodiments, the processor 302 may be disposed in communication with a memory 305 (e.g., RAM 313, ROM 314, etc. as shown in Fig. 3) via a storage interface 304. The storage interface 304 may connect to memory 305 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
The memory 305 may store a collection of program or database components, including, without limitation, user /application 306, an operating system 307, a web browser 308, mail client 315, mail server 316, web server 317 and the like. In some embodiments, computer system 300 may store user /application data 306, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as OracleR or SybaseR.
The operating system 307 may facilitate resource management and operation of the computer system 300. Examples of operating systems include, without limitation, APPLE MACINTOSHR OS X, UNIXR, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLER IOSTM, GOOGLER ANDROIDTM, BLACKBERRYR OS, or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system 400, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, APPLE MACINTOSHR operating systems, IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), UnixR X-Windows, web interface libraries (e.g., AJAXTM, DHTMLTM, ADOBE® FLASHTM, JAVASCRIPTTM, JAVATM, etc.), or the like.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. 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., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
In an embodiment, the present disclosure provides a method and system for dynamically managing trip of a vehicle in real-time.

In an embodiment, the present disclosure determines the DTE in real time and updates the DTE in real-time for the trip by considering various parameters affecting the trip and hence the determined DTE is accurate.

In an embodiment, the present disclosure accurately determines the DTE. The aspect of when the vehicle battery is draining is identified well before and one or more charging stations are recommended for recharging the battery. Hence, the user can plan long trips with ease.

The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise. The enumerated listing of items does not imply that any or all the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Referral Numerals:
Reference Number Description
100 Environment
101 Electronic device
103 Vehicle
105 One or more data sources
107 System
109 DTE
111 I/O Interface
113 Processor
115 Memory
117 Trip related data
118 Vehicle data
119 External data
120 DTE data
121 Other data
123 Receiving module
124 Determination module
125 Display/Output module
126 Other modules