Description:FIELD OF THE INVENTION

[0001] The present disclosure relates to battery charging reminders. More particularly, the present disclosure relates to a system and a method for generating charging reminders related to charging of an electric vehicle (EV), thereby enabling optimized reminders to be notified to users associated with EVs and facilitating improvement in user behaviour with respect to charging of the EVs.

BACKGROUND

[0002] In recent years, electric vehicles (EVs) have gained widespread popularity due to heightened environmental concerns and increased cost competitiveness with conventional fuel based vehicles. Typically, an EV includes a battery as a power source unit which provides power to an electric motor of the EV. Further, the battery is a rechargeable battery that has to be charged so that the battery may provide optimal power to drive the EV.

[0003] With increased usage of EVs in recent times, a common phenomenon seen among EV riders is range anxiety. Each EV may have a corresponding driving range based on battery capacity of the EVs and driving style of the EV users. EV users tend charge the EVs as per availability of charging points. Further, EV users tend to charge the EVs frequently upon a reduction in the State of Charge (SOC) of the EVs. For instance, EV users may charge the EV more frequently than required or the EV users may charge the EV to higher level of SOCs than required. In some cases, even with sufficient SOC available to complete future trips, EV users still tend to re-charge the EVs. As an example, the EV user may utilize around 20% SOC per day, however, the EV user may still charge the EV each day and maintain the SOC at 100%.

[0004] There exist multiple techniques to notify and remind the EV users when charging of the EV is required. Conventional techniques consider whether a current trip in-progress would be completed based on a remaining SOC and the destination of the trip. A reminder can accordingly be displayed to the EV user to charge the EV at a nearby charging station.

[0005] The conventional techniques are focused on meeting the requirements of upcoming trips to be completed on the EV. That is, the conventional techniques aim to remind the EV user to charge the EV in order to ensure that the EV has sufficient SOC available for the current or immediately upcoming trips. The conventional techniques do not consider the general routine of the EV users. For instance, the conventional techniques do not efficiently consider charging preferences of the EV users. Moreover, there exist no technique that takes into account optimized charging parameters such as battery health and charging costs.

[0006] One such prior art discloses an electric vehicle charging reminding method where a current residual capacity of the vehicle battery is acquired and a predicted power consumption of the electric vehicle for a certain time period is determined. If the difference between the current residual capacity and the predicted power consumption is smaller than or equal to a preset warning threshold value, then a charging prompt is outputted.

[0007] Another prior art discloses an electric vehicle charging reminding method where location of the vehicle and a home location is determined. Further, a vehicle battery charge state and expected electric only range is determined. Based on the determination, it is estimated whether the expected electric only range is less than required for an expected driving trip if the location of the vehicle is within a predetermined threshold of the home location. Based on the estimation, a charging prompt may be outputted.

[0008] Therefore, in view of the above-mentioned problems, it is desirable to provide a system and a method to efficiently generate charging reminders for charging of the EVs taking into account user charging behaviour and consequently improving the user charging behaviour.

SUMMARY

[0009] 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.

[0010] According to an embodiment of the present disclosure, disclosed herein is a method for generating charging reminders related to charging of an electric vehicle (EV). The method comprises obtaining, from a remote server, historical data associated with a plurality of historical charging parameters related to charging of the EV. The plurality of historical charging parameters are indicative of charging preferences of a user associated with the EV. The method further comprises generating one or more optimized charging parameters based on the plurality of historical charging parameters associated with the historical data and a set of charging objectives associated with charging of the EV. The method further comprises determining, using a first trained model amongst a set of pre-defined trained models, a first optimal time instance for generating a charging reminder for charging of the EV based on the one or more optimized charging parameters and a plurality of real-time parameters associated with the EV. Moreover, the method comprises generating, at the first optimal time instance, the charging reminder for charging of the EV.

[0011] According to another embodiment of the present disclosure, disclosed herein is a system for generating charging reminders related to charging of an electric vehicle (EV). The system comprises a memory and at least one processor in communication with the memory. The at least one processor is configured to obtain, from a remote server, historical data associated with a plurality of historical charging parameters related to charging of the EV. The plurality of historical charging parameters are indicative of charging preferences of a user associated with the EV. The at least one processor is further configured to generate one or more optimized charging parameters based on the plurality of historical charging parameters associated with the historical data and a set of charging objectives associated with charging of the EV. The at least one processor is further configured to determine, using a first trained model amongst a set of pre-defined trained models, a first optimal time instance for generating a charging reminder for charging of the EV based on the one or more optimized charging parameters and a plurality of real-time parameters associated with the EV. The at least one processor is further configured to generate, at the first optimal time instance, the charging reminder for charging of the EV.

[0012] 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

[0013] 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:

[0014] 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;

[0015] Figure 1B illustrates a block diagram depicting an environment for generating charging reminders related to charging of an electric vehicle, in accordance with an embodiment of the present disclosure;

[0016] Figure 2 illustrates a block diagram of the system for generating charging reminders related to charging of the electric vehicle, in accordance with an embodiment of the present disclosure;

[0017] Figure 3A illustrates a process flow depicting a method for generating charging reminders related to charging of the electric vehicle, in accordance with an embodiment of the present disclosure;

[0018] Figure 3B illustrates a process flow depicting a method for generating one or more optimized charging parameters, in accordance with an embodiment of the present disclosure; and

[0019] Figure 3C illustrates a process flow depicting a method for determining a first optimal time instance for generating the charging reminder, in accordance with an embodiment of the present disclosure.

[0020] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale.

[0021] 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 disclosure 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

[0022] 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.

[0023] 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.

[0024] 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.”

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

[0030] 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.

[0031] 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 Vehicles (BEVs), Hybrid Electric Vehicles (HEVs) and Range Extended Electric Vehicles. However, the subsequent paragraphs pertain to the different elements of a Battery Electric Vehicle (BEV).

[0032] 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) and Machine Learning (ML) driven modules which enable the prediction of a plurality of parameters such as, but 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.

[0033] 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 the 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.

[0034] The processor may include any computing system which includes, but is not limited to, a 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.

[0035] 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 perform 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.

[0036] Figure 1B illustrates a block diagram depicting an environment (100) for generating charging reminders related to charging of an electric vehicle (EV). The environment (100) comprises a system (110) configured to generate charging reminders for an EV (120). The system (110) is communicably coupled to the EV (120). In an embodiment, the EV (120) may comprise the ECU (10) as described above with reference to Figure 1A. In an embodiment, the system (110) is integrated with the EV (120), in that, the system (110) is an on-device system. In another embodiment, the system (110) is implemented in a cloud-based architecture or on a physical server (not shown) in communication with the EV (120) via a communication network. It is appreciated that although a single EV is depicted in Figure 1B, the details as described further below are equally applicable for a plurality of EVs. The details described further below with respect to the EV (120) are also applicable for each of the plurality of EVs.

[0037] The environment (100) may further include a remote server (130) in communication with the system (110) and the EV (120). The remote server (130) may be configured to store information associated with the EV (120), such as, historical data of charging patterns and behaviour of a user (140) associated with the EV (120). In particular, the historical data may be associated with a plurality of historical charging parameters related to charging of the EV (120). The historical data may be collected frequently at pre-defined time intervals and stored at the remote server (130) and may be accessed to generate charging reminders, as will be described in detail further below. The system (110) may be in communication with an electronic device (160) (alternatively referred to as a user device) associated with the user (140) of the EV (120). In some embodiments, the charging reminders generated by the system (110) may be displayed to the user (140) via the user device (160). In some embodiments, the charging reminders generated by the system (110) may be displayed to the user (140) via a display interface of the EV (120).

[0038] As shown in Figure 1B, the system (110), the EV (120), and remote server (130) are communicably coupled by means of a communication network (150). The communication network (150) 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.

[0039] Referring to Figure 2, a block diagram of the system (110) is illustrated, in accordance with an embodiment of the present disclosure. The system (110) includes a processor (210) and a memory (220). The system (110) further comprises a set of modules (230). The processor (210) may be configured to perform designated functions in conjunction with the memory (220) and the set of modules (230).

[0040] In some embodiments, the memory (220) may be communicatively coupled to the processor (210). In some embodiments, the set of modules may be included within the memory (220). The memory (220) may be configured to store data, and instructions executable by the processor (210). The memory (220) may include a database configured to store data.

[0041] In some embodiments, the set of modules (230) may include a set of instructions that may be executed to cause the system (110), in particular, the processor (210), to perform any one or more of the methods disclosed herein. The set of modules (230) in conjunction with the processor (210) may be configured to perform the steps of the present disclosure using the data stored in the memory (220), as discussed throughout this disclosure. In an embodiment, each of the set of modules (230) may be hardware units that may be outside the memory (220). Further, the memory (220) may include an operating system for performing one or more tasks of the system (110).

[0042] The memory (220) may be operable to store instructions executable by the processor (210). The functions, acts, or tasks illustrated in the figures or described may be performed by the processor (210), in conjunction with the set of modules (230), for executing the instructions stored in the memory (220). 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.

[0043] For the sake of brevity, the architecture and standard operations of the memory (220) and the processor (210) are not discussed in detail. In one embodiment, the memory (220) may be configured to store the information as required by the set of modules and/or the processor (210) to perform one or more functions to generate charging reminders related to charging of the EV (120).

[0044] In some embodiments, the memory (220) may communicate via a bus within the system (110). The memory (220) 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 embodiment, the memory (220) may include a cache or random-access memory for the processor. In alternative embodiments, the memory (220) is separate from the processor, such as a cache memory of a processor, the system memory, or other memory.

[0045] In one embodiment, the processor (210) 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 (210) may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or both. The processor (210) 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 (210) 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 (210) may implement a software program, such as code generated manually (i.e., programmed).

[0046] The set of modules (230) comprise an acquisition module (232), an optimization module (234), a display module (235), a determination module (236), a generation module (238), and a feedback module (239). The system (110) may comprise a set of pre-defined trained models (240) to facilitate the generation of the charging reminders. In the illustrated embodiment, the set of pre-defined models (240) may be stored in the system (110), for instance, in the memory (220). In another embodiment, the set of pre-defined models (240) may be stored in the remote server (130). In an embodiment, the set of pre-defined models (240) may include a first trained model (242) and a second trained model (244). It is appreciated that the term “trained model” and “model” may be used interchangeably in the present disclosure.

[0047] Referring to Figures 1B and 2 together, the processor (210) in conjunction with the acquisition module (232) may be configured to obtain historical data associated with the EV (120) from the remote server (130). The historical data may include a plurality of historical charging patterns related to the charging of the EV (120). The historical data may be regularly collected at pre-defined intervals and stored in the remote server (130). The historical data, i.e., the plurality of historical charging parameters indicate charging preferences of the user (140) associated with the EV (120). In other words, the plurality of historical charging parameters indicates historical patterns of how the user (140) charges the EV (120).

[0048] In non-limiting embodiments, the plurality of historical charging parameters may include one or more of:
- Proportion of energy charged from commercial charging points: a number between 0 to 1.
- Proportion of charging sessions at private / domestic charging points: a number between 0 to 1.
- At a particular time-period in the historic data, for a particular time duration,, how often the user has been charging : a number between 0 and 1.
- At a particular time-period in the historic data, various levels of historic start State of Charge (SOC): a set of number between 0 and 1.
- At all time-periods in the historic data, various levels of historic start SOC : a set of number between -1 and 1.
- At a particular time-period in the historic data, various levels of energy used in the 24 hours subsequent to the particular time-period: a set of number greater than or equal to 0.
- At a particular time-period in the historic data, various levels of energy used in the 12 hours subsequent to the particular time-period: a set of number greater than or equal to 0.
- At a particular time-period in the historic data, various levels of energy used in the 6 hours subsequent to the particular time-period: a set of number greater than or equal to 0.
- At a particular time-period in the historic data, various levels of energy in the 1 hour subsequent to the particular time-period: a set of number greater than or equal to 0.

[0049] Further, the processor (210) in conjunction with the optimization module (234) may be configured to generate one or more optimized charging parameters based on the plurality of historical charging parameters associated with the historical data and a set of charging objectives associated with charging of the EV. In an embodiment, the set of charging objectives may be stored in the remote server (130) or the memory (220).

[0050] The optimization module (234) may be configured to access the set of charging objectives. In non-limiting embodiments, the set of charging objectives may be related to one or more of a battery health associated with the EV, a number of charging sessions, cost of charging, and a number of unique charging locations related to charging of the EV.

[0051] In order to meet one or more of the set of charging objectives, the optimization module (234) may be configured to modify the plurality of historical parameters of the historical data. Based on the modified plurality of historical charging parameters, the one or more optimized charging parameters may be generated by the optimization module (234).

[0052] In an embodiment, the one or more optimized charging parameters may be stored in the memory (220) and/or the remote server (130). In an embodiment, the one or more optimized charging parameters may be used to train the first model (242) to determine whether a charging reminder is required to be generated, and further, determining a first optimal time instance for generating the charging reminder. The first optimal time instance may refer to a suitable time when the charging reminder may be required, based on the one or more optimized charging parameters and a plurality of real-time parameters associated with the EV (120). In an embodiment, the processor (210) may be configured to continuously (in real-time) determine whether generation of the charging reminder is required, and in case the charging reminder is required, the processor (210) may determine the specific time instance as the first optimal time instance. The processor (210) in conjunction with the determination module (236) may be configured to determine the first optimal time instance for generating the charging reminder using the first trained model (242). The first optimal time instance may be generated based on the one or more optimized charging parameters and a plurality of real-time parameters associated with the EV (120).

[0053] In an embodiment, the plurality of real-time parameters associated with the EV (120) may be collected by the system (110). In an embodiment, the plurality of real-time parameters associated with the EV (120) may be provided to the system (110) by other components of the EV (120), such as, the ECU of the EV or sensors installed in the EV (120).

[0054] In non-limiting embodiments, the plurality of real-time parameters associated with the EV (120) may include one or more of:
- Parking state of the EV (is the EV currently parked: True or False).
- Plugged in status of the EV (is the EV currently plugged into a charger: True or False).
- Charging state of the EV (is the EV currently being charged: True or False).
- Availability of a portable charger: True or False.
- Distance to a nearest commercial charging point based on the current location of the EV: a number greater than or equal to 0.
- Number of commercial charging points reachable with a current SOC of the EV: a number greater than or equal to 0.
- Difference between various levels of historic start SOC for charges done at commercial charging points and a current SOC of the EV: a number between -1 and 1
- Distance to a nearest point user has previously charged at: a number greater than or equal to 0.
- Number of previously charged at points reachable with the current SOC: a number greater than or equal to 0.
- Difference between various levels of historic start SOC for charges done at the nearest charging point and the current SOC: a set of number between -1 and 1.
- Difference between various levels of historic start SOC for charges done at the nearest charging point and the current SOC: a set of number between -1 and 1.
- Proportion of energy charged at a nearest point (based on current location of the EV (120)): a number between 0 and 1.
- Proportion of charging sessions at the nearest point: a number between 0 and 1.
- Difference between SOC needed to reach a destination and the current SOC: a number greater than or equal to 0
- Number of charging points enroute and reachable with the current SOC: a number greater than or equal to 0.
- Whether the destination is a commercial charging point: True or False.
- Whether the destination is a previous charged at point: True or False.
- Number of charging points where the EV has previously been charged enroute and reachable with the current SOC: a number greater than or equal to 0.

[0055] It is appreciated that in the non-limiting examples of the historical charging parameters and real-time parameters mentioned above, a positive number (+1) may represent a state where the EV is at a lower SOC than a historical start SOC when charging from a commercial charger while a negative number (-1) may represent a state where the EV is at a higher SOC than a historical start SOC when charging from a commercial charger. Further, the values ‘0’ and ‘1’ may represent percentages where 1 is highest (say, 100%) and 0 is lowest (say, 0%).

[0056] As described above, the processor (210) in conjunction with the determination module (236) may be configured to determine the first optimal time instance of generating the charging reminder. The determination module (236) may be configured to generate a set of probability values using the first trained model (242). The set of probability values may indicate a probability of charging the EV to a corresponding set of SOC values based on the one or more optimized charging parameters. For instance, in a non-limiting example, the set of probability values may indicate that at a next charging session, the probability of charging to 40% is 10%, the probability of charging to 50% is 20%, the probability of charging to 80% is 20%, and so on.

[0057] The determination module (236) may further be configured to compare the set of probability values with a set of pre-defined threshold values. In an embodiment, the set of pre-defined threshold values may be stored in the memory (220) and/or the remote server (130). The set of pre-defined threshold values may correspond to specific SOC values or a range of SOC values. For instance, in a non-limiting example, the threshold values may be 90% for a specific SOC value such that in case the probability of charging to the specific SOC value is greater than 90%, then a determination may be made that a charging reminder is required to be generated for reminding the user to charge to the specific SOC value. In another example, the threshold values may be 90% for a range of SOC values such that in case a sum of probabilities of charging above a specific SOC value is greater than 90%, then a determination may be made that a charging reminder is required to be generated to remind the user to charge by at least the specific value. Accordingly, the determination module (236) may be configured to determine whether the charging reminder is required to be generated for charging the EV (120) based on the comparison of the set of probability values with the set of pre-defined threshold values.

[0058] Once it is determined that the charging reminder is required to be generated, the determination module (236) may be configured to determine the first optimal time instance for generating the charging reminder based on the plurality of real-time parameters associated with the EV (120). The first optimal time may indicate a suitable time to remind the user (140) to charge the EV in accordance with the set of charging objectives. For instance, the first optimal time may be a time when charging of the EV (120) may be initiated to maximize battery health. In an embodiment, the first optimal time may be the time when charging of the EV may be initiated. In another embodiment, the first optimal time may be a time to generate the charging reminder and notify the user to charge the EV (120) at a suitable time in the future.

[0059] Further, the processor (210) in conjunction with the generation module (238) may be configured to generate at the first optimal time instance the charging reminder for charging the EV. The charging reminder may indicate one or more of a target SOC value to charge the EV, a target time period to charge the EV, and a target charging location to charge the EV. The charging reminder at the first time instance may thus facilitate charging of the EV (120) in an optimized manner. The charging reminder at the first time instance may also enhance the charging behavior of the user (140) in accordance with the set of charging objectives.

[0060] In an embodiment, the processor (210) in conjunction with the display module (235) may be configured to display the charging reminder at a user interface of the user device (160). In an embodiment, the processor (210) in conjunction with the display module (235) may be configured to display the charging reminder at a user interface of the EV (120). The generated charging reminder may be displayed to the user (140) in the form of a notification or an alert.

[0061] In an embodiment, the historical data stored in the remote server (130) may be utilized to train the second model (244). The second model (244) may be used by the processor (210) to determine whether charging reminders are required to be generated for charging the EV (120) and a second optimal time for generating the charging reminder. As the second model (244) is trained on the historical data, the second trained model (244) may be utilized to generate charging reminders as per the historical user patterns for charging of the EV (120), i.e., as per the user preferences to charge the EV (120).

[0062] The processor (210) may be configured to determine using the second trained model (244) whether the charging reminder is required to be generated for charging of the EV (120). Further, upon a determination that the charging reminder is required to be generated, the processor (210) may be configured to determine the second optimal time instance for generating the charging reminder for charging of the EV. At the second optimal time instance, the charging reminder may be generated for charging of the EV (120). In an embodiment, the charging reminder may be displayed at one or more of the user interface associated with the EV (120) or the user interface associated with the user device (160) of the user (140) of the EV (120).

[0063] In an embodiment, the second optimal time instance may be different from the first optimal time instance, since the optimal time instance is based on optimized charging parameters while the first optimal time instance is based on the historical charging parameters. In alternate embodiments, the second optimal time instance may be same as the first optimal time instances, for instance, in cases where the historical charging patterns of the user match the charging objectives.

[0064] Further, the processor (210) in conjunction with the feedback module (239) may be configured to receive feedback associated with the generated charging reminder. In an embodiment, the feedback may be received from the user (140) associated with the EV (120), for instance, by providing user inputs with respect to the charging reminder being displayed on the user device (160) at the first optimal time instance and/or the second optimal time instance. In an embodiment, the feedback may be automatically generated based on user responses to the charging reminder being displayed on the user device (160), for instance, whether the user (140) proceeds to charge the EV (120) in response to receiving the charging reminder at the first optimal time instance and/or the second optimal time instance. The receiving feedback may be utilized to further train the first model (242) and/or the second model (244), thereby enabling optimization of the first model (242) and/or the second model (244).

[0065] Figure 3A illustrates a process flow depicting a method (300) for generating charging reminders related to charging of the EV, according to an embodiment of the present disclosure. In one embodiment, the steps of the method (300) may be performed by the system (110), as discussed above.

[0066] At step (310), the historical data associated with the plurality of historical charging parameters related to charging of the EV may be obtained from the remote server (130) by the acquisition module (232). The plurality of historical charging parameters may be indicative of charging preferences of a user associated with the EV.

[0067] At step (320), one or more optimized charging parameters may be generated by the optimization module (234) based on the plurality of historical charging parameters associated with the historical data and the set of charging objectives associated with charging of the EV.

[0068] In some embodiments, to generate the one or more optimized charging parameters, the method (300) may further comprise steps (320A) – (320C), as shown in Figure 3B.

[0069] At step (320A), the set of charging objectives associated with charging of the EV (120) may be accessed by the optimization module (234). In an embodiment, the set of charging objectives may be related to one or more of the battery health associated with the EV (120), the number of charging sessions, cost of charging, and the number of unique charging locations related to charging of the EV (120).

[0070] At step (320B), the plurality of historical charging parameters associated with the historical data may be modified by the optimization module (234) to meet one or more of the charging objectives.

[0071] At step (320C), the one or more optimized charging parameters may be generated by the optimization module (234) based on the modified plurality of historical charging parameters.

[0072] Referring again to Figure 3A, at step (330), the first optimal time instance for generating the charging reminder for charging of the EV may be determined by the determination module (236) using the first trained model (242) amongst the plurality of pre-defined trained models (240). The first optimal time instance may be generated based on the one or more optimized charging parameters and a plurality of real-time parameters associated with the EV (120).

[0073] In some embodiments, the first trained model (242) amongst the set of pre-defined trained models (240) may be trained on the one or more optimized charging parameters.

[0074] In some embodiments, to determine the first optimal time instance for generating the charging reminder, the method (300) may further comprise steps (330A) – (330D), as shown in Figure 3C.

[0075] At step (330A), the set of probability values indicative of the probability of charging the EV (120) to a corresponding set of SOC values may be calculated by the determination module (236) based on the one or more optimized charging parameters.

[0076] At step (330B), the set of probability values may be compared by the determination module (236) with a set of pre-defined threshold values.

[0077] At step (330C), based on the comparison, a determination may be made as by the determination module (236) to whether the charging reminder is required to be generated for charging of the EV (120).

[0078] At step (330D), upon a determination that the charging reminder is required to be generated, the first optimal time instance for generating the charging reminder may be determined by the determination module (236) based on the plurality of real-time parameters associated with the EV (120).

[0079] Referring again to Figure 3A, at step (340), the charging reminder for charging of the EV (120) may be generated by the generation module (238) at the first optimal time instance. In an embodiment, the charging reminder may be indicative of one or more of the target State of Charge (SOC) value to charge the EV (120), the target time period to charge the EV (120), and the target charging location to charge the EV (120).

[0080] In some embodiments, the set of pre-defined trained models (240) comprises the second trained model (244), and further, a determination may be made using the second trained model whether the charging reminder is required to be generated for charging of the EV (120). Further, upon a determination that the charging reminder is required to be generated, the second optimal time instance for generating the charging reminder may be determined. Moreover, at the second optimal time instance, the charging reminder may be generated for charging of the EV. In an embodiment, the second optimal time instance may be different from the first optimal time instance. In an embodiment, the second trained model (244) may be trained on the historical data associated with the plurality of historical charging parameters related to charging of the EV (120).

[0081] In some embodiments, feedback associated with the generated charging reminder may be received from the user associated with the EV (120). Based on the received feedback, the first trained model may be further trained to better optimize the first model.

[0082] In some embodiments, the charging reminder may be displayed at one or more of a user interface associated with the EV (120) or a user interface associated with an electronic device (160) of the user (140) of the EV (120).

[0083] While the above-discussed steps in Figures 3A-3C 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-3C is already covered in the description related to Figures 1B-2 and is omitted herein for the sake of brevity.

[0084] In an exemplary use case, the user (140) drives the EV (120) on weekdays and does not drive the EV (120) on weekends. The user (140) has a similar usage on each weekday, in which, the user (140) uses about 30% SOC when travelling between a first location ( for example, home) to another location (for example , office). Further, each weekday after riding the EV (120) and completing the trip between the first location and the second location, the user (140) charges the EV (120) to 100% SOC. The data associated with the charging behaviour of the user (140) is collected and stored at the remote server (130) as historical data. Based on the historical data, the second trained model (244) may facilitate generation of a charging reminder each weekday after the user (140) has completed the trip between the first location and the second location and returned to the first location. The charging reminder is based on the charging preferences of the user (140), and accordingly, indicates that the user (140) should charge the EV (120) to 100% SOC. The first trained model (242) may generate the charging reminder at a different time as compared to the second trained model (244). The first trained model (242) considers optimized parameters for charging the EV (120) as well as the set of charging objectives (for instance, battery health). Based on the optimized parameters, the first trained model (242) may deduce that generation of charging reminders on each weekday is not required since, starting from 100% SOC and considering usage of 30% SOC each weekday, the EV (120) need not be charged after completing each trip, rather, the EV (120) may be charged after completing a minimum of two trips and a maximum of three trips. Accordingly, the second trained model (244) may facilitate the generation of the charging reminder every alternate weekday or after every two weekdays. Thus, charging reminders based on both user preferences as well as optimized parameters is generated and displayed to the user (140).

[0085] In another exemplary use case, the user (140) rides the EV (120) between multiple locations. Based on the riding style of the user (140), an average driving range for the EV (120) is approximately 70 km. On weekdays (Monday-Friday), the user (140) drives the EV (120) between a first location (say, home) and a second location (say, office) which is at a distance of 8 km from the first location. On each weekday, around 23% SOC would be consumed based on the driving style of the user (140) and the travelled distance. On weekends (Saturday-Sunday), the user (140) may ride the EV (120) to multiple locations at varied distances. For instance, the user (140) may ride the EV (120) anywhere between 0 to 150 kms on each day of the weekend. Regarding the charging parameters associated with the user (140), the user (140) generally charges the EV at the first location (home) and does not own a portable charger. Further, the user (140) generally maintains the SOC of the EV greater than 50% and generally charges to 100% SOC.

[0086] Assuming a scenario on Tuesday night, the EV (120) has 65% SOC available. Considering the typical usage of the EV (120) on weekdays and weekends, the charging reminders will be generated using the first trained model (242) and the second trained model (244). The second trained model (244) considers the historical user parameters (usage of 23% SOC each day, the user maintains at least 50% SOC, etc.). Starting from 65% SOC on Tuesday night, the user (140) can utilize 23% SOC on Wednesday and the remaining SOC of the EV (120) would be 42%. The second trained model (244) will facilitate generation of charging reminder on Wednesday night so that the EV (120) has 100% SOC on Thursday morning, 77% SOC on Friday morning, and 54% SOC after completing the riding trip on Friday.

[0087] The first trained model (242) considers the optimized charging parameters and the set of charging objectives. Starting from 65% SOC on Tuesday night, the user (140) can utilize 23% SOC on both Wednesday and Friday such that the EV (120) is at 42% SOC on Wednesday night and 29% SOC on Thursday night. The first trained model (242) will facilitate generation of charging reminder on Thursday night and suggest the user (140) to charge the EV (120) to 33% SOC so as to cover the riding trip for Friday. In some cases, the user may accept the suggestion and the charging will be scheduled accordingly. On Friday night, both the first trained model (242) and the second trained model (244) may facilitate generation of charging reminders to charge the EV (120) to 100% SOC considering possible extended usage of the EV (120) on weekends.

[0088] On Saturday, the user (140) may ride the EV (120) for extended distances. When the SOC of the EV (120) drops below 50%, the second trained model (244) may identify public charging points accessible along the riding path of the EV (120) and generate charging reminder to charge the EV (120) at a suitable charging point. On the other hand, the first trained model (242) may estimate that the SOC of the EV (120) may drop below a minimum SOC required to reach a nearest public charging point or a charging point where the EV (120) has historically been charged multiple times. The first trained model (242) may accordingly generate the charging reminder.

[0089] On Saturday night, both the first trained model (242) and the second trained model (244) may facilitate generation of charging reminders to charge the EV (120) to 100% SOC considering inconsistent riding patterns on Sunday. On Sunday, the user (140) may ride the EV (120) for extended periods. On Sunday night, the EV (120) may be at 42% SOC. Based on historical data of the user (140), the second trained model (244) may facilitate generation of charging reminder to charge the EV (120) to 100% SOC (since user maintains at least 50% SOC and typically charges to 100% SOC). Based on the optimized parameters, the first trained model (242) may not generate charging reminder on Sunday night since the EV (120) has sufficient SOC to complete the riding trip for Monday (weekday).

[0090] The present disclosure provides system and method for generation of charging reminders based on historical user behaviour with respect to charging of the EVs as well as based on optimized charging parameters. The disclosed system and method not only facilitate generation of charging reminders for charging of EVs but also enable the user’s charging behaviour to be optimized for better battery health, minimum charging sessions, minimum incurred charging costs, and other objectives. The disclosed system and method aim to understand user preference and routine and generate the charging reminders to fit into and improve the general user charging behaviour. This would further reduce range anxiety for users that regularly ride EVs in their daily lives.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

Reference numbers:

Components Reference Numbers
ECU (10)
Processor unit (12)
Memory unit (14)
Input module (16)
Output module (18)
Environment (100)
System (110)
EV (120)
Remote server (130)
User (140)
Communication network (150)
User device (160)
Processor (210)
Memory (220)
Set of modules (230)
Acquisition module (232)
Optimization module (234)
Display module (235)
Determination module (236)
Generation module (238)
Feedback module (239)
Set of pre-defined models (240)
First trained model (242)
Second trained model (244) , Claims:1. A method (300) for generating charging reminders related to charging of an electric vehicle (EV) (120), the method (300) comprising:
obtaining (310), from a remote server (130), historical data associated with a plurality of historical charging parameters related to charging of the EV (120), wherein the plurality of historical charging parameters are indicative of charging preferences of a user (140) associated with the EV (120);
generating (320) one or more optimized charging parameters based on the plurality of historical charging parameters associated with the historical data and a set of charging objectives associated with charging of the EV (120);
determining (330), using a first trained model (242) amongst a set of pre-defined trained models (240), a first optimal time instance for generating a charging reminder for charging of the EV (120) based on the one or more optimized charging parameters and a plurality of real-time parameters associated with the EV (120); and
generating (340), at the first optimal time instance, the charging reminder for charging of the EV (120).

2. The method (300) as claimed in claim 1, wherein determining the first optimal time instance for generating the charging reminder comprises:
calculating (330A), using the first trained model, a set of probability values indicative of a probability of charging the EV (120) to a corresponding set of State of Charge (SOC) values based on the one or more optimized charging parameters;
comparing (330B) the set of probability values with a set of pre-defined threshold values;
determining (330C), based on the comparison, whether the charging reminder is required to be generated for charging of the EV (120); and
upon a determination that the charging reminder is required to be generated, determining (330D) the first optimal time instance for generating the charging reminder based on the plurality of real-time parameters associated with the EV (120).

3. The method (300) as claimed in claim 1, wherein the charging reminder is indicative of one or more of a target State of Charge (SOC) value to charge the EV (120), a target time period to charge the EV (120), and a target charging location to charge the EV (120).

4. The method (300) as claimed in claim 1, wherein the first trained model (242) amongst the set of pre-defined trained models (240) is trained on the one or more optimized charging parameters.

5. The method (300) as claimed in claim 1, wherein the set of pre-defined trained models (240) comprises a second trained model (244), and wherein the method (300) comprises:
determining, using the second trained model (244), whether the charging reminder is required to be generated for charging of the EV (120); and
upon a determination that the charging reminder is required to be generated, determining a second optimal time instance for generating the charging reminder for charging of the EV (120);
generating, at the second optimal time instance, the charging reminder for charging of the EV (120), wherein the second optimal time instance is different from the first optimal time instance.

6. The method (300) as claimed in claim 5, wherein the second trained model (244) amongst the set of pre-defined trained models (240) is trained on the historical data associated with the plurality of historical charging parameters related to charging of the EV (120).

7. The method (300) as claimed in claim 1, wherein generating the one or more optimized charging parameters comprises:
accessing (320A) the set of charging objectives associated with charging of the EV (120), the set of charging objectives being related to one or more of a battery health associated with the EV, a number of charging sessions, cost of charging, and a number of unique charging locations related to charging of the EV (120);
modifying (320B) the plurality of historical charging parameters associated with the historical data to meet one or more of the charging objectives; and
generating (320C) the one or more optimized charging parameters based on the modified plurality of historical charging parameters.

8. The method (300) as claimed in claim 1, comprising:
receiving, from the user (140) associated with the EV (120), feedback associated with the generated charging reminder; and
training the first trained model (242) based on the received feedback, thereby optimizing the first trained model (242).

9. The method (300) as claimed in claim 1, comprising:
displaying the charging reminder at one or more of a user interface associated with the EV (120) or a user interface associated with an electronic device (160) of the user (140) of the EV (120).

10. A system (110) for generating charging reminders related to charging of an electric vehicle (EV) (120), the system (110) comprising:
a memory (220);
at least one processor (210) in communication with the memory (220), the at least one processor (210) being configured to:
obtain, from a remote server (130), historical data associated with a plurality of historical charging parameters related to charging of the EV (120), wherein the plurality of historical charging parameters are indicative of charging preferences of a user (140) associated with the EV (120);
generate one or more optimized charging parameters based on the plurality of historical charging parameters associated with the historical data and a set of charging objectives associated with charging of the EV (120);
determine, using a first trained model (242) amongst a set of pre-defined trained models (240), a first optimal time instance for generating a charging reminder for charging of the EV (120) based on the one or more optimized charging parameters and a plurality of real-time parameters associated with the EV (120); and
generate, at the first optimal time instance, the charging reminder for charging of the EV (120).

11. The system (110) as claimed in claim 10, wherein to determine the first optimal time instance for generating the charging reminder, the at least one processor (210) is configured to:
calculate, using the first trained model (242), a set of probability values indicative of a probability of charging the EV (120) to a corresponding set of State of Charge (SOC) values based on the one or more optimized charging parameters;
compare the set of probability values with a set of pre-defined threshold values;
determine, based on the comparison, whether the charging reminder is required to be generated for charging of the EV (120); and
upon a determination that the charging reminder is required to be generated, determine the first optimal time instance for generating the charging reminder based on the plurality of real-time parameters associated with the EV (120).

12. The system (110) as claimed in claim 10, wherein the charging reminder is indicative of one or more of a target State of Charge (SOC) value to charge the EV (120), a target time period to charge the EV (120), and a target charging location to charge the EV (120).

13. The system (110) as claimed in claim 10, wherein the first trained model (242) amongst the set of pre-defined trained models (240) is trained on the one or more optimized charging parameters.

14. The system (110) as claimed in claim 10, wherein the set of pre-defined trained models (240) comprises a second trained model (244), and wherein the at least one processor (210) is configured to:
determine, using the second trained model (244), whether the charging reminder is required to be generated for charging of the EV (120); and
upon a determination that the charging reminder is required to be generated, determine a second optimal time instance for generating the charging reminder for charging of the EV (120);
generate, at the second optimal time instance, the charging reminder for charging of the EV (120), wherein the second optimal time instance is different from the first optimal time instance.

15. The system (110) as claimed in claim 14, wherein the second trained model (244) amongst the set of pre-defined trained models (240) is trained on the historical data associated with the plurality of historical charging parameters related to charging of the EV (120).

16. The system (110) as claimed in claim 10, wherein to generate the one or more optimized charging parameters, the at least one processor (210) is configured to:
access the set of charging objectives associated with charging of the EV (120), the set of charging objectives being related to one or more of a battery health associated with the EV (120), a number of charging sessions, cost of charging, and a number of unique charging locations related to charging of the EV (120);
modify the plurality of historical charging parameters associated with the historical data to meet one or more of the charging objectives; and
generate the one or more optimized charging parameters based on the modified plurality of historical charging parameters.

17. The system (110) as claimed in claim 10, wherein the at least one processor (210) is configured to:
receive, from the user (140) associated with the EV (120), feedback associated with the generated charging reminder; and
train the first trained model (242) based on the received feedback, thereby optimizing the first trained model (242).

18. The system (110) as claimed in claim 10, wherein the at least one processor (210) is configured to:
display the charging reminder at one or more of a user interface associated with the EV (120) or a user interface associated with an electronic device (160) of the user (140) of the EV (120).