Description:
FIELD OF THE INVENTION

The present disclosure relates to real-time status of charging stations. More particularly, the present disclosure relates to a system and a method that estimate waiting time at a charging station, thereby allowing users associated with electric vehicles (EVs) to save waiting time.

BACKGROUND

With the rise in a number of electric vehicles (EVs) in recent times, there is a high demand for charging stations to meet the charging requirements of the EVs. Each EV has a particular limit range and needs to undergo charging prior to the limit range being exceeded. Charging stations with fast charging capabilities are provided at various geographic locations to meet the requirements of EVs.

Typically, a user associated with an EV may ride the EV to a closest charging station in order to charge the EV. However, many other users are also present at the charging station and a queue is formed to charge their EVs. The user may visit the charging station and find a large queue at the charging station. As a result, a significant time is wasted before the user can charge the EV. Moreover, the user can only wait for other users to complete charging of EVs without any knowledge of the waiting time before the user can start charging of the EV.

There are conventional solutions for queue estimations at the charging stations. However, conventional solutions are mostly prediction based that rely on historical data for queue estimation. Further, most conventional solutions are edge-based solutions which are unable to support high computational power. Further, scaling such solutions across remote places is difficult and the maintenance costs are also high. Moreover, in edge-based solutions, there is no flexibility of modifying and/or updating software and logic.

One such prior art relates to an electric vehicle queuing strategy at charging stations provided over expressways. The charging stations may be provided with two types of parking spaces, a first space that provides both charging and parking facility and a second space that provides only parking facility. The users coming to the charging station may be provided with waiting time and queue length which may be determined based on initialization parameters such as power limit of charging piles at the charging station, number of vehicles, operable times of the charging station, and the like. Historical data of the expressway traffic may also be taken into account. A station inbound list may be generated and vehicles may be assigned to charging piles in sequence. A current charging progress of each charging pile is read, and resultantly, running progress of the charging station is also tracked.

Another prior art relates to a charging queuing method for electric vehicles. The queuing method may be based on working hours of the charging station. When a vehicle enters the charging station, the current working state and charging modes of the charging station may be determined and the vehicles may accordingly be set to charging state or waiting state. The charging modes may include DC fast charging, AC slow charging, and the like. If the selected charging mode is the DC fast charging, then the entry time of the vehicle is obtained and charging waiting sequence is determined. If the selected charging mode is the AC slow charging, the current waiting time and remaining state of charge of the vehicle is considered to update the charging waiting sequence.

Hence, there is a need for a method and a system for estimating total waiting time at charging stations in a reliable and accurate manner, thereby allowing users to be aware of the time that would be spent at charging stations for the purpose of vehicle charging.

SUMMARY

This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.

According to an embodiment of the present disclosure, disclosed herein is a system to estimate a total waiting time for a target vehicle at a designated Electric Vehicle (EV) charging station. The system comprises a determination module configured to determine one or more vehicles within a predefined distance from the designated EV charging station. The system further comprises a prediction module configured to determine a corresponding charging time for each of the one or more vehicles within the pre-defined distance from the designated EV charging station. The system further comprises a summation module configured to determine the total waiting time for the target vehicle based on the corresponding charging time for each of the one or more vehicles within the pre-defined distance from the designated EV charging station. The system further comprises a display module configured to display a total waiting time at one or more user interfaces associated with the target vehicle or the designated EV charging station.

According to another embodiment of the present disclosure, disclosed herein is a method for estimating a total waiting time for a target vehicle at a designated Electric Vehicle (EV) charging station. The method comprises the step of determining, by a determination module associated with a system, one or more vehicles within a pre-defined distance from the designated EV charging station. Further, the method comprises the step of determining, by a prediction module associated with the system, a corresponding charging time for each of the one or more vehicles within the pre-defined distance from the designated EV charging station. Furthermore, the method comprises the step of determining, by a summation module associated with the system, the total waiting time for the target vehicle based on the corresponding charging time for each of the one or more vehicles within the pre-defined distance from the designated EV charging station. Moreover, the method comprises the step of displaying, by a display module associated with the system, a total waiting time at one or more user interfaces associated with the target vehicle or the designated EV charging station.

The method and system of the present disclosure allow a user to view waiting time at a charging station before the user decides to visit the charging station. Further, real time vehicle telemetry data is used in addition to historical data for reliable and accurate estimation of waiting time. Furthermore, cloud-based implementation of the system and method allow flexibility and scalability while reducing edge costs and maintenance. Moreover, for multiple charging stations, a user may view the related waiting times and decide on which charging station to visit so as to save time.

To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure 1A illustrates a block diagram of an embodiment of an Electronic Control Unit (ECU) of a vehicle, in accordance with an embodiment of the present disclosure;

Figure 1B illustrates a block diagram of an environment for estimating total waiting time at a designated EV charging station, in accordance with an embodiment of present disclosure;

Figure 2 illustrates a block diagram of modules associated with a system to estimate total waiting time at a designated EV charging station, in accordance with an embodiment of the present disclosure;

Figure 3A illustrates a process flow depicting a method for estimating a total waiting time for the target vehicle at the designated EV charging station, according to an embodiment of the present disclosure; and

Figure 3B illustrates a process flow depicting a method for determining one or more vehicles within the pre-defined distance from the designated EV charging station, according to an embodiment of the present disclosure.

Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale.

Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION OF FIGURES

For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the present disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the present disclosure relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof.

Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element do not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, “there needs to be one or more…” or “one or more elements is required.”

Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and/or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and/or elements of the proposed disclosure fulfil the requirements of uniqueness, utility, and non-obviousness.

Use of the phrases and/or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and/or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and/or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and/or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and/or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.

Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.

The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

For the sake of clarity, the first digit of a reference numeral of each component of the present disclosure is indicative of the Figure number, in which the corresponding component is shown. For example, reference numerals starting with digit “1” are shown at least in Figure 1. Similarly, reference numerals starting with digit “2” are shown at least in Figure 2.

An Electric Vehicle (EV) or a battery powered vehicle including, and not limited to, two-wheelers such as scooters, mopeds, motorbikes/motorcycles; three-wheelers such as auto-rickshaws, four-wheelers such as cars and other Light Commercial Vehicles (LCVs) and Heavy Commercial Vehicles (HCVs) primarily work on the principle of driving an electric motor using the power from the batteries provided in the EV. Furthermore, the electric vehicle may have at least one wheel which is electrically powered to traverse such a vehicle. The term ‘wheel’ may be referred to any ground-engaging member which allows traversal of the electric vehicle over a path. The types of EVs include Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV) and Range Extended Electric Vehicle. However, the subsequent paragraphs pertain to the different elements of a Battery Electric Vehicle (BEV).

In construction, an EV typically comprises hardware components such as a battery or battery pack enclosed within a battery casing and includes a Battery Management System (BMS), an on-board charger, a Motor Controller Unit (MCU), an electric motor and an electric transmission system. In addition to the hardware components/elements, the EV may be supported with software modules comprising intelligent features including and not limited to navigation assistance, hill assistance, cloud connectivity, Over-The-Air (OTA) updates, adaptive display techniques and so on. The firmware of the EV may also comprise Artificial Intelligence (AI) & Machine Learning (ML) driven modules which enable the prediction of a plurality of parameters such as and not limited to driver/rider behaviour, road condition, charging infrastructures/charging grids in the vicinity and so on. The data pertaining to the intelligent features may be displayed through a display unit present in the dashboard of the vehicle. In one embodiment, the display unit may contain a Liquid Crystal Display (LCD) screen of a predefined dimension. In another embodiment, the display unit may contain a Light-Emitting Diode (LED) screen of a predefined dimension. The display unit may be a water-resistant display supporting one or more User-Interface (UI) designs. The EV may support multiple frequency bands such as 2G, 3G, 4G, 5G and so on. Additionally, the EV may also be equipped with wireless infrastructure such as, and not limited to Bluetooth, Wi-Fi and so on to facilitate wireless communication with other EVs or the cloud.

The ECU of the EV, depicted in Figure 1A, is responsible for managing all the operations of the EV, wherein the key elements of the ECU (10) typically includes (i) a microcontroller core (or processor unit) (12); (ii) a memory unit (14); (iii) a plurality of input (16) and output modules (18) and (iv) communication protocols including, but not limited to CAN protocol, Serial Communication Interface (SCI) protocol and so on. The sequence of programmed instructions and data associated therewith can be stored in a non-transitory computer-readable medium such as memory unit or storage device which may be any suitable memory apparatus such as, but not limited to read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), flash memory, disk drive and the like. In one or more embodiments of the disclosed subject matter, non-transitory computer-readable storage media can be embodied with a sequence of programmed instructions for monitoring and controlling the operation of different components of the EV.

The processor may include any computing system which includes, but is not limited to, Central Processing Unit (CPU), an Application Processor (AP), a Graphics Processing Unit (GPU), a Visual Processing Unit (VPU), and/or an AI-dedicated processor such as a Neural Processing Unit (NPU). In an embodiment, the processor can be a single processing unit or several units, all of which could include multiple computing units. The processor may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor is configured to fetch and execute computer-readable instructions and data stored in the memory. The instructions can be compiled from source code instructions provided in accordance with a programming language such as Java, C++, C#.net or the like. The instructions can also comprise code and data objects provided in accordance with, for example, the Visual Basic™ language, LabVIEW, or another structured or object-oriented programming language. The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning algorithms which include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

Furthermore, the modules, processes, systems, and devices can be implemented as a single processor or as a distributed processor. Also, the processes, modules, and sub-modules described in the various figures of and for embodiments herein may be distributed across multiple computers or systems or may be co-located in a single processor or system. Further, the modules can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit can comprise a computer, a processor, such as the processor, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general-purpose processor which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules may be machine-readable instructions (software) which, when executed by a processor/processing unit, perform any of the described functionalities. In an embodiment, the modules may include a receiving module, a generating module, a comparing module, a pairing module, and a transmitting module. The receiving module, the generating module, the comparing module, the pairing module, and the transmitting module may be in communication with each other. The data serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules. Exemplary structural embodiment alternatives suitable for implementing the modules, sections, systems, means, or processes described herein are provided below.

Figure 1B illustrates a block diagram of an environment (101) for estimating waiting time for vehicles at charging stations. The environment (101) comprises a system (100), a designated electric vehicle (EV) charging station (110) (interchangeably referred to as “the charging station” hereinafter), a target vehicle (120), and one or more vehicles (130). The target vehicle (120) and the one or more vehicles (130) are electric vehicles (EVs). The target vehicle (120) is an EV associated with a user desiring to get an estimate of time that would be spent if the user goes to the designated EV charging station (110) for charging the target vehicle (120). The one or more vehicles (130) refers to EVs that are in the vicinity of the charging station (110), including EVs that are being charged/will be charged at the charging station (110).

The system (100), the designated EV charging station (110), the target vehicle (120), and the one or more vehicles are communicably coupled by means of a communication network (140). The communication network (140) may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol (WAP)), the Internet, etc.

In some embodiments, the system (100) may be a standalone entity located at a remote location and connected to the designated EV charging station (110), the target vehicle (120), and the one or more vehicles (130) via the communication network (140). For example, the system (100) may be implemented in a cloud-based architecture or on a physical server (not shown). The system (100) may be configured to determine a total waiting time for the target vehicle (120) at the charging station (110), as will be described in detail further below.

The system (100) comprises a processor (150) and a memory (160). The system (100) further comprises a set of modules (170), as described with reference to Figure 2 further below. The set of modules (170) may be configured to perform their designated functions in conjunction with the memory (160) and the processor (150).

In some embodiments, the memory (160) may be communicatively coupled to the processor (150). In some embodiments, the set of modules may be included within the memory (160). The memory (160) may be configured to store data, and instructions executable by the processor (150). The memory (160) may include a database configured to store data.

In some embodiments, the set of modules (170) may include a set of instructions that may be executed to cause the system (100) to perform any one or more of the methods disclosed herein. The set of modules (170) may be configured to perform the steps of the present disclosure using the data stored in the memory (160), as discussed throughout this disclosure. In an embodiment, each of the set of modules (170) may be hardware units that may be outside the memory (160). Further, the memory (160) may include an operating system for performing one or more tasks of the system (100).

The memory (160) may be operable to store instructions executable by the processor (150). The functions, acts, or tasks illustrated in the figures or described may be performed by the processor (150), in conjunction with the set of modules, for executing the instructions stored in the memory (160). The functions, acts, or tasks are independent of the particular type of instruction set, storage media, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing, and the like.

For the sake of brevity, the architecture and standard operations of the memory (160) and the processor (150) are not discussed in detail. In one embodiment, the memory (160) may be configured to store the information as required by the set of modules and/or the processor (150) to perform one or more functions to estimate the total waiting time for the target vehicle (120) at the charging station (110).

In some embodiments, the memory (160) may communicate via a bus within the system (100). The memory (160) may include, but is not limited to, a non-transitory computer-readable storage media, such as various types of volatile and non-volatile storage media including, but not limited to, random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one example, the memory (160) may include a cache or random-access memory for the processor. In alternative examples, the memory (160) is separate from the processor, such as a cache memory of a processor, the system memory, or other memory.

In one embodiment, the processor (150) 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. In one embodiment, the processor (150) may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or both. The processor (150) may be one or more general processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now-known or later developed devices for analysing and processing data. In some embodiments, the processor (150) may include one or a plurality of processors. The one or the plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an AI-dedicated processor such as a neural processing unit (NPU). The processor (150) may implement a software program, such as code generated manually (i.e., programmed).

Figure 2 illustrates a block diagram depicting the modules (170) of the system (100), in accordance with an embodiment of the present disclosure. The modules (170) comprise a determination module (230), a prediction module (240), a summation module (250), a display module (260), a data acquisition module (270), and a filtering module (280). Referring to Figures 1B and 2 together, the system (100) may be in communication with a user device (210) associated with a user (220) of the target vehicle (120). The user (220) may visit the charging station (110) in order to charge the target vehicle (120). Prior to visiting the charging station (110), the user (220) may check the total waiting time at the charging station (110), i.e., the user (220) may check the waiting time for charging the target vehicle (120) upon visiting the charging station. The user (220) may search for all accessible charging stations on the user device (210), such as, charging stations in proximity of a location of the user. A web-based application or a native application may be provided on the user device (210) and the user (220) may view the accessible charging stations via the web-based application or the native application. The user (220) may view multiple charging stations on the user device (210) and select a relevant charging station (such as charging station (110)) where the user (220) may want to visit to charge the target vehicle (120). A request to view waiting time for the selected charging station may thus be generated and sent to the system (100).

The data acquisition module (270), in conjunction with the processor (150), may be configured to receive location data for the charging station (110). In some embodiments, the location data may be pre-stored in the memory (160) and retrieved from the memory (160). The data acquisition module (270) may be configured to receive location data for the one or more vehicles (130). In an embodiment, the location data for a total number of vehicles that are in the vicinity of the charging station (110) may be received. That is, the data acquisition module (270) may be configured to receive location data for all vehicles that are in the vicinity, such as a pre-defined distance, from the charging station (110). In an embodiment, the location data for the total number of vehicles may be received from the corresponding vehicles. In another embodiment, the location data for the total number of vehicles may be received from a server. The total number of vehicles may include the one or more vehicles (130) in the vicinity of the charging station (110).

The determination module (230) may be configured to, in conjunction with the filtering module (280) and the processor (150), determine the one or more vehicles (130) that are within the pre-defined distance from the charging station (110). The one or more vehicles (130) may be determined from among the total number of vehicles within the vicinity of the charging station (110). The total number of vehicles may refer to all the vehicles in the vicinity of the charging station (110) while the one or more vehicles (130) may refer to the vehicles that may be undergo charging at the charging station (110). Based on the location data of the charging station (110) and each of the total number of vehicles, the determination module (230) may be configured to determine the total number of vehicles, including the one or more vehicles (130), within the pre-defined distance from the charging station (110).

In some embodiments, determination of the total number of vehicles by the determination module (230) may be based on a GeoHash technique. For instance, unique identifiers may be assigned to various geographic locations, and then, a live location of the vehicles may be mapped to a particular GeoHash ID. In some embodiments, determination of the total number of vehicles by the determination module (230) may be based on a Euclidean distance, i.e., a direct distance between any two geopoints. In some embodiments, determination of the total number of vehicles by the determination module (230) may be based on a triangulation technique. It is appreciated that the total number of vehicles may be determined based on various other determination techniques, as would be apparent to a skilled person in the art in view of the present disclosure.

The filtering module (280) may be configured to filter the total number of vehicles in order to determine the one of more vehicles (130) amongst the total number of vehicles. The filtering module (280) may be configured to filter the total number of vehicles based on corresponding vehicle state data, indicative of state of the vehicles as described below, associated with each vehicle of the total number of vehicles. The data acquisition module (270) may be configured to receive vehicle state data for each of the total number of vehicles, and the filtering module (280) may utilize the vehicle state data to filter the one or more vehicles (130) from the total number of vehicles.

For a vehicle of the total number of vehicles, the vehicle state data may be indicative of a state of the vehicle. The state of the vehicle may include one of:

A charging state indicating that the vehicle is currently being charged at the charging station.
A riding state indicating that the vehicle is currently being ridden/driven and is not stationary. For instance, the vehicle may be passing by the charging station.
A short stationary state indicating that the vehicle has been stationary for a less than a predetermined period of time, such as, one hour. For instance, the vehicle may be waiting in queue at the charging station, and thus, be in a stationary state for a short period of time.
A long stationary state indicating that the vehicle has been stationary for more than a predetermined period of time, such as, one hour. For instance, the vehicle may be parked in the vicinity of the charging station and may not be in a queue at the charging station.

The filtering module (280) may be configured to filter and select vehicles, from the total number of vehicles, having the corresponding state as the charging state and the short stationary state. The selected vehicles may be determined as the one or more vehicles (130) within the pre-defined distance from the charging station (110), i.e., the vehicles that may be in line to be charged at the charging station (110). The filtering module (280) may be configured to reject vehicles, from the total number of vehicles, having the corresponding state as the riding state and the long stationary state. Accordingly, only the filtered vehicles are considered to determine the waiting time at the charging station, thereby leading to accurate estimations.

For the one or more vehicles (130) within the pre-defined distance from the charging station (110), the prediction module (240) may be configured to determine a corresponding charging time for each of the one or more vehicles (130). The charging time may be indicative of a predicted time that the corresponding vehicle may spent at the charging station and the predicted time may be determined by the prediction module (240) based on a corresponding end-State Of Charge (SOC) for the corresponding vehicle. The end-SOC may be indicative of a predicted SOC value up to which the corresponding vehicle may be charged at the charging station (110).

The prediction module (240) may be configured to determine the corresponding end-SOC for each vehicle of the one or more vehicles (130), thereby predicting a SOC value up to which the corresponding vehicle of the one or more vehicles (130) may be charged. The prediction module (240) may be configured to determine the corresponding end-SOC for each vehicle of the one or more vehicles (130) based on one or more parameters associated with the corresponding vehicle, as detailed further below.

In order to determine the corresponding end-SOC for each vehicle of the one or more vehicles (130), the data acquisition module (270) may be configured to receive a current SOC associated with each vehicle. The current SOC of a vehicle may be indicative of a SOC value at which the vehicle is currently charged. In some embodiments, the data acquisition module (270) may be configured to receive the current SOC for each vehicle, from the corresponding vehicle of the one or more vehicles (130).

Further, the data acquisition module (270) may be configured to receive one or more corresponding parameters for each vehicle. In accordance with non-limiting embodiments, the one or more parameters for a corresponding vehicle may include one or more of:
An estimated distance travelled associated with a riding pattern, which may be determined based on an average distance travelled by a user associated with the corresponding vehicle. Based on the riding pattern, the estimated distance that the user may travel may be determined. In some embodiments, the riding pattern may be stored in the memory (160).
An estimated distance to travel, which may be determined based on information associated with most visited places of the user associated with the corresponding vehicle. Based on the information, the estimated distance that the user may travel may be determined.
A distance to a fixed destination, which may be determined based on a navigation application associated with the corresponding vehicle. For instance, the user may enter a destination to travel to on the navigation application, and the estimated distance to the entered destination may be determined. In some embodiments, the navigation application may include Google MapsTM, MapmyIndiaTM, and the like.

Based on the current SOC of the corresponding vehicle and the one or more parameters associated with the corresponding vehicle, the end-SOC for the corresponding vehicle may be determined by the prediction module (240). In some embodiments, a riding efficiency associated with a user of the corresponding vehicle may further be taken into account by the prediction module (240) to determine the end-SOC for the corresponding vehicle. The riding efficiency for the user may be determined based on riding pattern associated with the corresponding vehicle, such as, based on a distance travelled per SOC drop of the corresponding vehicle. In some embodiments, the riding efficiency may be stored in the memory (160).

As an example, when considering the estimated distance travelled as a parameter, the end-SOC may be determined based on equation (1):
end-SOC=current SOC+(estimated distance travelled)/(riding efficiency) …(1)

As another example, when considering the estimated distance to travel as a parameter, the end-SOC may be determined based on equation (2):
end-SOC=current SOC+(estimated distance to travel )/(riding efficiency) …(2)

As yet another example, when considering the distance to a fixed destination as a parameter, the end-SOC may be determined based on equation (3):
end-SOC=current SOC+(distance to a fixed destination)/(riding efficiency) …(3)

In some non-limiting embodiments, the one or more parameters for a corresponding vehicle may further include one or more of:
proximity of the charging station (110) to other accessible EV charging stations.
availability timings of all accessible EV charging stations.
location of the charging station (110), in that, the duration for which a user may charge the corresponding vehicle at the charging station (110) may depend on whether the charging station (110) is located in a remote area or in a busy area.
a charging pattern, in that, past charging history of the user associated with the corresponding vehicle may be pre-stored in the memory (160).

Accordingly, the corresponding end-SOC for each vehicle of the one or more vehicles (130) may be determined by the prediction module (240) based on the current SOC, the one or more corresponding parameters, and the rising efficiency. Further, the prediction module (240) may be configured to determine the corresponding charging time for each vehicle of the one or more vehicles (130) based on the determined corresponding end-SOC and a charging rate. In some embodiments, the charging rate may be prestored in the memory (160) and may be retrieved from the memory (160) in order to determine the charging time for each vehicle of the one or more vehicles (130).

Once the corresponding charging time for each vehicle of the one or more vehicles (130) is determined, the total waiting time for the target vehicle (120) may be determined by the summation module (250). The summation module (250) may be configured to sum the corresponding charging time for each vehicle of the one or more vehicles (130) in order to determine the total waiting time for the target vehicle (120). The total waiting time may thus indicate to the user associated with the target vehicle (120), the live status of the charging station (110) and how much wait the user may have to do if the user goes to the charging station (110).

In some embodiments, the display module (260) may be configured to display the total waiting time at one or more user interfaces associated with the target vehicle (120) or the charging station (110). In some embodiments, the display module (260) may be configured to display the total waiting time at a first user interface associated with the user of the target vehicle (120). In an embodiment, the first user interface may be a display unit provided at the target vehicle (120). In another embodiment, the first user interface may be a display unit associated with a user device of the user, such as, a display unit under control of a web-based or native application within the user device of the user. Accordingly, the user may view the total waiting time associated with the charging station (110) and decide whether or not to visit the charging station (110) to charge the target vehicle (120).

In some embodiments, the display module (260) may be configured to display the total waiting time at a second user interface associated with the charging station (110). In some embodiments, the second user interface may be a display unit provided at the charging station (110). In some embodiments, the display module (260) may further be configured to display a number of the one or more vehicles (130) within the pre-defined distance from the charging station (110), thus allowing the user to view how many vehicles may be in queue at the charging station (110).

In some embodiments, the summation module (250) may be configured to determine the total waiting time in response to receiving a user input from a user of the target vehicle (120), the user input being indicative of a request to view the total waiting time. For instance, the user of the target vehicle (120) may send a request to view the total waiting time by means of a web-based or a native application associated with the user device (210) of the user. Upon receiving the user input, the summation module (250) may determine the total waiting time. Further, the display module may be configured to display the total waiting time in response to receiving a user input from a user of the target vehicle (120), such as, on a user interface of the user device associated with the web-based or native application.

It is appreciated that although a single charging station (110) is shown in Figure 1, there may be multiple charging stations similar to the charging station (110). The system (100) may be configured to determine the total waiting time corresponding to each of the multiple charging stations in the manner as described above with reference to Figures 1-2. The total waiting time corresponding to each of the multiple charging stations may be displayed to the user, and thus, the user can choose a charging station from all accessible charging stations that has the least waiting time.

Figure 3A illustrates a process flow depicting a method (300) for estimating a total waiting time for the target vehicle (120) at the charging station (110), according to an embodiment of the present disclosure. In one embodiment, the steps of the method (300) may be performed by the system (100), as discussed above.

At step (310), the method (300) comprises determining, by the determination module (230), the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110). In some embodiments, to determine the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110), the method (300) may further comprise steps (310A) - (310D), as shown in Figure 3B.

At step (310A), the data acquisition module (270) receives location data for the designated EV charging station (110).

At step (310B), the data acquisition module (270) receives location data for a total number of vehicles within the pre-defined distance from the designated EV charging station (110).

At step (310C), the determination module (230) determines the total number of vehicles within the pre-defined distance from the designated EV charging station (110) based on the location data for the designated EV charging station (110) and the location data for each of the total number of vehicles.

At step (310D), the one or more vehicles within the pre-defined distance from the designated EV charging station (110) are determined by the filtering module (280) and resultantly, the total number of vehicles are determined based on a corresponding vehicle state data associated with each vehicle of the total number of vehicles.

Referring again to FIG. 3A, at step (320), the method (300) comprises determining, by the prediction module (240), a corresponding charging time for each of the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110).

In some embodiments, determining the corresponding charging time comprises determining, by the prediction module (240), a corresponding end-state of charge (SOC) for each vehicle of the one or more vehicles (130) based on one or more corresponding parameters associated with each vehicle. As described above, the corresponding end-SOC is indicative of a predicted SOC up to which each vehicle will be charged. Further, determining the corresponding charging time comprises determining, by the prediction module (240), the corresponding charging time for each vehicle of the one or more vehicles (130) based on a charging rate and the corresponding end-SOC.

In some embodiments, determining the corresponding end-SOC for each vehicle of the one or more vehicles (130) comprises receiving, by the data acquisition module (270), a current SOC associated with each vehicle, and one or more corresponding parameters for each vehicle. In some embodiments, the one or more corresponding parameters include one or more of an estimated distance travelled associated with a riding pattern, an estimated distance to travel associated with a user visit history, and a distance to a fixed destination. Further, determining the corresponding end-SOC comprises determining, by the prediction module (240), the corresponding end-SOC for each vehicle based on the received one or more corresponding parameters, the current SOC, and a corresponding riding efficiency.

At step (330), the method (300) comprises determining, by the summation module (250), the total waiting time for the target vehicle (120) based on the corresponding charging time for each of the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110). In some embodiments, determining the total waiting time for the target vehicle (120) comprising summing, by the summation module (250), the corresponding charging time for each vehicle of the one or more vehicles (130).

At step (340), the method (300) comprises displaying, by the display module (260), the total waiting time at one or more user interfaces associated with the target vehicle (120) or the designated EV charging station (110). In some embodiments, displaying the total waiting time comprises displaying, by the display module (260), the total waiting time at a first user interface associated with a user of the target vehicle (120), and a second user interface associated with the designated EV charging station (110).

In some embodiments, filtering the total number of vehicles comprises receiving, by the data acquisition module (270), the corresponding vehicle state data indicative of a state of the vehicle, for each vehicle of the total number of vehicles. Further, filtering the total number of vehicles comprises filtering and selecting, by the filtering module (280), vehicles having state as one of a charging state or a short stationary state to be the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110). The vehicles having status as one of a riding state or a long stationary state are rejected.

While the above-discussed steps in Figures 3A-3B are shown and described in a particular sequence, the steps may occur in variations to the sequence in accordance with various embodiments. Further, a detailed description related to the various steps of Figures 3A-3B is already covered in the description related to Figures 1-2 and is omitted herein for the sake of brevity.

In an exemplary use case, a user may be riding an EV (two-wheeler) and may want to charge the EV at a nearby EV charging station. The user may use a web-based application/native application on a user device to view the nearby charging stations. The user may further want to see the waiting time at various nearby charging stations so that the user can visit the charging station with the least waiting time. The user may use the web-based application to select one charging station and upon selection of the charging station, the waiting time at the charging station may be displayed to the user. To determine the waiting time, a cloud-based system may obtain location data of other vehicles that are in the vicinity of the selected charging station. The system may filter the other vehicles to select only those vehicles which are in a charging state or are expected to undergo charging at the selected charging station. The vehicles which are parked or being driven may be rejected. For the filtered vehicles, the corresponding end-SOCs, and resultantly, the corresponding charging times are determined by the system taking into account parameters such as current SOC, charging rate, estimated distance, etc. With the charging times determined for the filtered vehicles, the total waiting time is determined by the system and displayed to the user on the user device. As a non-limiting example, the system may determine the waiting time at the selected charging station to be 10 minutes and a notification mentioning ‘waiting time is 10 minutes’ may be displayed on the user device. The user may thus be aware of the waiting time and proceed to the selected charging station to charge the vehicle.

In another exemplary use case, a user may want to visit a nearby EV charging station in order to charge an EV. The user may use a native application/web-based application on a user device to view the nearby charging stations. The user may further want to see the waiting times at various nearby charging stations as well as a number of vehicles in queue at the charging stations. The user may use the native application to select one charging station and the cloud-based system may determine the waiting times as well as the number of vehicles in queue at the selected charging station. As described above, the waiting time may be determined by the system based on the location of other vehicles in the vicinity of the selected charging station, filtering of the vehicles to reject parked or driven vehicles, and corresponding charging times of the vehicles. For the selected vehicle, the system may determine the waiting time to be 30 minutes and 7 vehicles in queue. Accordingly, a notification may be displayed on the user device. As a non-limiting example, the notification may mention ‘waiting time is 30 minutes and 7 vehicles in queue’. However, the user may be reluctant to wait for 30 minutes at the charging station, and thus, the user may select a different charging station. The system may determine the waiting time and the queue of vehicles for the different charging station. As a non-limiting example, the system may determine the waiting time to be 10 minutes and 2 vehicles in queue. The user may thus decide to visit the other charging station as the waiting time is lesser compared to the previously selected charging station, thereby saving time.

The present invention provides systems and methods that allow a user to view the total waiting times for accessible charging stations and decide to visit the charging station with the least waiting time. As a result, the user may save time in the process of charging the target vehicle. The estimation of the total waiting time is accurate and reliable as the real-time data of the vehicles are considered. Moreover, as the system may be cloud-based, flexibility and scalability may be provided.

While specific language has been used to describe the present disclosure, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.

It will be appreciated that the modules, processes, systems, and devices described above can be implemented in hardware, hardware programmed by software, software instruction stored on a non-transitory computer readable medium or a combination of the above. Embodiments of the methods, processes, modules, devices, and systems (or their sub-components or modules), may be implemented on a general-purpose computer, a special-purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmed logic circuit such as a programmable logic device (PLD), programmable logic array (PLA), field-programmable gate array (FPGA), programmable array logic (PAL) device, or the like. In general, any process capable of implementing the functions or steps described herein can be used to implement embodiments of the methods, systems, or computer program products (software program stored on a non-transitory computer readable medium).

Furthermore, embodiments of the disclosed methods, processes, modules, devices, systems, and computer program product may be readily implemented, fully or partially, in software using, for example, object or object-oriented software development environments that provide portable source code that can be used on a variety of computer platforms. Alternatively, embodiments of the disclosed methods, processes, modules, devices, systems, and computer program product can be implemented partially or fully in hardware using, for example, standard logic circuits or a very-large-scale integration (VLSI) design. Other hardware or software can be used to implement embodiments depending on the speed and/or efficiency requirements of the systems, the particular function, and/or particular software or hardware system, microprocessor, or microcomputer being utilized.

In this application, unless specifically stated otherwise, the use of the singular includes the plural and the use of “or” means “and/or.” Furthermore, use of the terms “including” or “having” is not limiting. Any range described herein will be understood to include the endpoints and all values between the endpoints. Features of the disclosed embodiments may be combined, rearranged, omitted, etc., within the scope of the invention to produce additional embodiments. Furthermore, certain features may sometimes be used to advantage without a corresponding use of other features. , Claims:We Claim:

1. A system (100) to estimate a total waiting time for a target vehicle (120) at a designated Electric Vehicle (EV) charging station (110), the system (100) comprising:
a determination module (230) configured to determine one or more vehicles (130) within a predefined distance from the designated EV charging station (110);
a prediction module (240) configured to determine a corresponding charging time for each of the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110);
a summation module (250) configured to determine the total waiting time for the target vehicle (120) based on the corresponding charging time for each of the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110); and
a display module (260) configured to display a total waiting time at one or more user interfaces associated with the target vehicle (120) or the designated EV charging station (110).

2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a data acquisition module (270) and a filtering module (280), and wherein to determine the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110):
the data acquisition module (270) is configured to:
receive location data for the designated EV charging station (110), and
receive location data for a total number of vehicles within the pre-defined distance from the designated EV charging station (110);
the determination module (230) is configured to determine the total number of vehicles within the pre-defined distance from the designated EV charging station (110) based on the location data for the designated EV charging station (110) and the location data for each of the total number of vehicles; and
the filtering module (280) is configured to determine the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110) by filtering the total number of vehicles on the basis of a corresponding vehicle state data associated with each vehicle of the total number of vehicles.

3. The system (100) as claimed in claim 2, wherein to filter the total number of vehicles:
the data acquisition module (270) is configured to receive, for each vehicle of the total number of vehicles, the corresponding vehicle state data indicative of a state of the vehicle; and
the filtering module (280) is configured to:
filter and select vehicles having state as one of a charging state or a short stationary state to be the one or more vehicles (130) within the pre-defined distance from the designated EV charging station, wherein vehicles having status as one of a riding state or a long stationary state are rejected.

4. The system (100) as claimed in claim 1, wherein to determine the corresponding charging time for each of the one or more vehicles (130), the prediction module (280) is configured to:
determine a corresponding end-state of charge (SOC) for each vehicle of the one or more vehicles (130) based on one or more corresponding parameters associated with each vehicle, wherein the corresponding end-SOC is indicative of a predicted SOC up to which each vehicle will be charged; and
determine the corresponding charging time for each vehicle of the one or more vehicles (130) based on a charging rate and the corresponding end-SOC.

5. The system (100) as claimed in claim 4, wherein to determine the corresponding end-SOC for each vehicle of the one or more vehicles (130):
the data acquisition module (270) is configured to:
receive a current SOC associated with each vehicle, and
receive the one or more corresponding parameters for each vehicle, wherein the one or more corresponding parameters include one or more of an estimated distance travelled associated with a riding pattern, an estimated distance to travel associated with a user visit history, and a distance to a fixed destination; and
the prediction module (240) is configured to determine the corresponding end-SOC for each vehicle based on the received one or more corresponding parameters, the current SOC, and a corresponding riding efficiency.

6. The system (100) as claimed in claim 1, wherein to determine the total waiting time for the target vehicle (120):
the summation module (250) is configured to sum the corresponding charging time for each vehicle of the one or more vehicles (130).

7. The system (100) as claimed in claim 1, wherein to display the total waiting time at the one or more user interfaces associated with the target vehicle (120) or the designated EV charging station (110):
the display module (260) is configured to:
display the total waiting time at a first user interface associated with a user of the target vehicle (120), and a second user interface associated with the designated EV charging station (110).

8. The system (100) as claimed in claim 1, wherein the summation module (250) is configured to determine the total waiting time for the target vehicle (120) in response to receiving, from a user device of a user associated with the target vehicle (120), a user input indicative of a request to view the total waiting time, and
wherein the display module (260) is configured to display the total waiting time in response to receiving the user input.

9. A method (300) for estimating a total waiting time for a target vehicle (120) at a designated Electric Vehicle (EV) charging station (110), the method comprising the steps of:
determining (310), by a determination module (230) associated with a system (100), one or more vehicles (130) within a pre-defined distance from the designated EV charging station (110);
determining (320), by a prediction module (240) associated with the system (100), a corresponding charging time for each of the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110);
determining (330), by a summation module (250) associated with the system (100), the total waiting time for the target vehicle (120) based on the corresponding charging time for each of the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110); and
displaying (340), by a display module (260) associated with the system (100), a total waiting time at one or more user interfaces associated with the target vehicle (120) or the designated EV charging station (110).

10. The method (300) as claimed in claim 9, wherein determining the one or more vehicles (130) within the pre-defined distance from the designated EV charging station (110) comprises the steps of:
receiving (310A), by a data acquisition module (270) associated with the system (100), location data for the designated EV charging station (110);
receiving (310B), by the data acquisition module (270), location data for a total number of vehicles within the pre-defined distance from the designated EV charging station (110);
determining (310C), by the determination module (230), the total number of vehicles within the pre-defined distance from the designated EV charging station (110) based on the location data for the designated EV charging station (110) and the location data for each of the total number of vehicles; and
determining (310D) the one or more vehicles within the pre-defined distance from the designated EV charging station (110) based on filtering, by a filtering module (280) associated with the system (100), the total number of vehicles on the basis of a corresponding vehicle state data associated with each vehicle of the total number of vehicles.

11. The method (300) as claimed in claim 10, wherein filtering the total number of vehicles comprises the steps of:
for each vehicle of the total number of vehicles:
receiving, by the data acquisition module (270), the corresponding vehicle state data indicative of a state of the vehicle;
filtering and selecting, by the filtering module (280), vehicles having state as one of a charging state or a short stationary state to be the one or more vehicles (130) within the pre-defined distance from the designated EV charging station, wherein vehicles having status as one of a riding state or a long stationary state are rejected.

12. The method (300) as claimed in claim 9, wherein determining the corresponding charging time for each of the one or more vehicles (130) comprises the steps of:
determining, by the prediction module (280), a corresponding end-state of charge (SOC) for each vehicle of the one or more vehicles (130) based on one or more corresponding parameters associated with each vehicle, wherein the corresponding end-SOC is indicative of a predicted SOC up to which each vehicle will be charged; and
determining, by the prediction module (240), the corresponding charging time for each vehicle of the one or more vehicles (130) based on a charging rate and the corresponding end-SOC.

13. The method (300) as claimed in claim 12, wherein determining the corresponding end-SOC for each vehicle of the one or more vehicles (130) comprises the steps of:
receiving, by the data acquisition module (270), a current SOC associated with each vehicle;
receiving, by the data acquisition module (270), the one or more corresponding parameters for each vehicle, wherein the one or more corresponding parameters include one or more of an estimated distance travelled associated with a riding pattern, an estimated distance to travel associated with a user visit history, and a distance to a fixed destination; and
determining, by the prediction module (240), the corresponding end-SOC for each vehicle based on the received one or more corresponding parameters, the current SOC, and a corresponding riding efficiency.

14. The method (300) as claimed in claim 9, wherein determining the total waiting time for the target vehicle (120) comprising summing, by the summation module (250), the corresponding charging time for each vehicle of the one or more vehicles (130).

15. The method (300) as claimed in claim 9, wherein displaying the total waiting time at the one or more user interfaces associated with the target vehicle (120) or the designated EV charging station (110) comprises:
displaying, by the display module (260), the total waiting time at a first user interface associated with a user of the target vehicle (120), and a second user interface associated with the designated EV charging station (110).

16. The method (300) as claimed in claim 9, wherein:
the summation module (250) is configured to determine the total waiting time for the target vehicle (120) in response to receiving, from a user device of a user associated with the target vehicle (120), a user input indicative of a request to view the total waiting time, and
the display module (260) is configured to display the total waiting time in response to receiving the user input.