A MACHINE LEARNING DRIVEN APPROACH TO SECURE AND EFFICIENT ELECTRIC VEHICLE CHARGING DEMAND PREDICTION USING CRYPTOGRAPHY

Abstract

A Machine Learning Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography is the proposed invention. The proposed invention focuses on understanding the functions of Cryptography. The invention focuses on analyzing the parameters of Secure and Efficient Electric Vehicle Charging using algorithms of Machine Learning Approach.

Specification

Description:[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] Machine learning (ML) is a subset of artificial intelligence (AI) that allows machines to learn from data and improve their performance without being explicitly programmed. Machine learning (ML) algorithms use data to make predictions or classifications. They adjust weights to reduce the difference between the model estimate and a known example. Machine learning (ML) is used in many applications, including banking, online shopping, and social media.
[0003] A number of different types of electronic vehicle analysis systems that are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] US20130179061A1: An expert system manages a power grid wherein charging stations are connected to the power grid, with electric vehicles connected to the charging stations, whereby the expert system selectively backfills power from connected electric vehicles to the power grid through a grid tie inverter (if present) within the charging stations. In more traditional usage, the expert system allows for electric vehicle charging, coupled with user preferences as to charge time, charge cost, and charging station capabilities, without exceeding the power grid capacity at any point. A robust yet accurate state of charge (SOC) calculation method is also presented, whereby initially an open circuit voltage (OCV) based on sampled battery voltages and currents is calculated, and then the SOC is obtained based on a mapping between a previously measured reference OCV (ROCV) and SOC. The OCV-SOC calculation method accommodates likely any battery type with any current profile.
[0005] An electric vehicle (EV) is a vehicle that uses an electric motor and battery to run, instead of a gasoline engine. All-electric vehicles (BEVs) also known as battery electric vehicles, these vehicles can only be powered by an electric motor and battery. Plug-in hybrid electric vehicles can be powered by both an electric motor and battery, and an internal combustion engine. The proposed invention focuses on analyzing the Secure and Efficient Electric Vehicle Charging through algorithms of Machine Learning Approach.
[0006] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0007] In the view of the foregoing disadvantages inherent in the known types of electronic vehicle analysis systems now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved Machine Learning-Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0008] In the view of the foregoing disadvantages inherent in the known types of electronic vehicle analysis systems now present in the prior art, the present invention provides an improved one. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved Machine Learning-Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography which has all the advantages of the prior art and none of the disadvantages.
[0009] The Main objective of the proposed invention is to design & implement a framework of Machine Learning techniques for analyzing the parameters of Secure and Efficient Electric Vehicle Charging. A Machine Learning-Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction is analyzed.
[0010] Yet another important aspect of the proposed invention is to design & implement a framework of Machine Learning techniques that will consider on understanding the functions of Cryptography. A Machine Learning-Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction is analyzed by predictive unit. The results of prediction are displayed on the display unit.
[0011] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF DRAWINGS
[0013] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the schematic view of A Machine Learning Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography, according to the embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0014] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0015] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one" and the word "plurality" means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like are included in the specification solely for the purpose of providing a context for the present invention.
[0016] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of", "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0017] Electric vehicle (EV) charging demand prediction is the process of accurately estimating the demand for charging EVs within a short period of time. This is important for managing and scheduling charging facilities and vehicles in real-time. EV charging demand prediction can be used for infrastructure development, policy making, and operating EV fleets and charging facilities.
[0018] Cryptography is the practice of using algorithms, hashes, and signatures to protect information. It's a key cybersecurity tool that helps to keep sensitive information safe from hackers and cybercriminals. The word "cryptography" comes from the Greek word kryptos, which means "hidden". The origin of cryptography is usually dated from about 2000 B.C., with the Egyptian practice of hieroglyphics. The proposed invention focuses on implementing the algorithms of Machine Learning Approach for studying the functions of Cryptography.
[0019] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0020] Figure 1 illustrates the schematic view of A Machine Learning Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography 100. The proposed invention 100 includes an EV vehicle 101 which is analysed using analysis unit 102. The battery monitoring unit 103 which is connected to the cloud server 104. The battery optimization is achieved using machine learning unit 105 in which predictive unit 106 will display the results of prediction is also fed into the battery monitoring unit 103 is fed with inputs from predictive unit 106.
[0021] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
, Claims:1. A Machine Learning Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography, comprises of:
Battery monitoring unit;
Predictive unit and
Display unit.
2. A Machine Learning Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography, according to claim 1, includes a battery monitoring unit, wherein the battery monitoring unit which is connected to the cloud server.
3. A Machine Learning Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the electric vehicle charging demand prediction using cryptography.
4. A Machine Learning Driven Approach to Secure and Efficient Electric Vehicle Charging Demand Prediction Using Cryptography, according to claim 1, includes a display unit, wherein the display unit will display the results of predictive unit.

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