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IOT-DRIVEN ENERGY MANAGEMENT SYSTEM FOR HYBRID ELECTRIC VEHICLES WITH MACHINE LEARNING OPTIMIZATION

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IOT-DRIVEN ENERGY MANAGEMENT SYSTEM FOR HYBRID ELECTRIC VEHICLES WITH MACHINE LEARNING OPTIMIZATION

ORDINARY APPLICATION

Published

date

Filed on 26 November 2024

Abstract

IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization is the proposed invention. The proposed invention focuses on understanding the functions of Hybrid Electric Vehicles. The invention focuses on analyzing the parameters of Energy Management System for Hybrid Electric Vehicles using algorithms of IOT Approach.

Patent Information

Application ID202441091941
Invention FieldCOMPUTER SCIENCE
Date of Application26/11/2024
Publication Number48/2024

Inventors

NameAddressCountryNationality
Kannan TAssistant Professor, Dr. NGP Institute of Technology, Coimbatore- 641048IndiaIndia
L. Poornima DeviAssistant Professor, Department of Computer Science & Engineering, SNS College of Technology, Coimbatore- 641107IndiaIndia
Dr A. Hema SekharProfessor, Department of EEE, VEMU Institute of Technology, P.Kothakota, Chittoor- 517112IndiaIndia
Dr K. Balaji Nanda Kumar ReddyAssociate Professor, Department of EEE, Annamacharya Institute of Technology and Sciences, Tirupati- 517520IndiaIndia
Dr.B.PooraniAssistant Professor, Department of Mathematics, St. Joseph's college of engineering, Semmancheri, Chennai 600119IndiaIndia
Dr Ritesh Kumar DewanganProfessor, Mechanical Engineering, Krishna's Vikash Institute of Technology, Raipur- 492099IndiaIndia
Dr Vinita DewanganAssociate Professor, Mathematics, Mats School of Engineering and Infirmation Technology, Mats University, Aarang, Raipur- 492001IndiaIndia
B. Juliet Celine MaryAssistant Professor, Information Technology, K.Ramakrishnan College of Engineering, Samayapuram, Tiruchirappalli- 621112IndiaIndia
R. IndhumathiAssistant Professor, Department of BME, Velalar College of Engineering and Technology, Erode- 638012IndiaIndia
Dr M. KavithaAssistant Professor, Department of IT, Velalar College of Engineering and Technology, Erode- 638012IndiaIndia
Dr Pratibha ShrivastavaProfessor, Mathematics Krishna's Vikash Institute of Technology, Raipur- 492099IndiaIndia
Dr Jyoti Prasad PatraPrincipal Nigam Institute of Engineering and Technology NIET UG PG Diploma Engineering, Odisha India- 754006IndiaIndia

Applicants

NameAddressCountryNationality
Kannan TAssistant Professor, Dr. NGP Institute of Technology, Coimbatore- 641048IndiaIndia
L. Poornima DeviAssistant Professor, Department of Computer Science & Engineering, SNS College of Technology, Coimbatore- 641107IndiaIndia
Dr A. Hema SekharProfessor, Department of EEE, VEMU Institute of Technology, P.Kothakota, Chittoor- 517112IndiaIndia
Dr K. Balaji Nanda Kumar ReddyAssociate Professor, Department of EEE, Annamacharya Institute of Technology and Sciences, Tirupati- 517520IndiaIndia
Dr.B.PooraniAssistant Professor, Department of Mathematics, St. Joseph's college of engineering, Semmancheri, Chennai 600119IndiaIndia
Dr Ritesh Kumar DewanganProfessor, Mechanical Engineering, Krishna's Vikash Institute of Technology, Raipur- 492099IndiaIndia
Dr Vinita DewanganAssociate Professor, Mathematics, Mats School of Engineering and Infirmation Technology, Mats University, Aarang, Raipur- 492001IndiaIndia
B. Juliet Celine MaryAssistant Professor, Information Technology, K.Ramakrishnan College of Engineering, Samayapuram, Tiruchirappalli- 621112IndiaIndia
R. IndhumathiAssistant Professor, Department of BME, Velalar College of Engineering and Technology, Erode- 638012IndiaIndia
Dr M. KavithaAssistant Professor, Department of IT, Velalar College of Engineering and Technology, Erode- 638012IndiaIndia
Dr Pratibha ShrivastavaProfessor, Mathematics Krishna's Vikash Institute of Technology, Raipur- 492099IndiaIndia
Dr Jyoti Prasad PatraPrincipal Nigam Institute of Engineering and Technology NIET UG PG Diploma Engineering, Odisha India- 754006IndiaIndia

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 type of artificial intelligence (AI) that allows computers to learn and improve from experience without being explicitly programmed. ML uses algorithms to analyze large amounts of data, identify patterns, and make predictions. The more data a machine learning system is exposed to, the more accurate it becomes. A financial organization can train a machine learning system to identify patterns in known transactions and predict whether new transactions are fraudulent.
[0003] A number of different types of 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] US8078330B2: Automatic energy management is provided, in even the most complex multi-building system. The necessity of a human operator for managing energy in a complex, multi-building system is reduced and even eliminated. Computer-based monitoring and computer-based recognition of adverse energy events (such as the approach of a new energy peak) is highly advantageous in energy management. Immediate automatic querying of energy users within a system of buildings for energy curtailment possibilities is provided. Such immediate, automatic querying may be answered by the energy users through artificial intelligence and/or neural network technology provided to or programmed into the energy users, and the queried energy users may respond in real-time. Those real-time computerized responses with energy curtailment possibilities may be received automatically by a data processing facility, and processed in real-time. Advantageously, the responses from queried energy users with energy curtailment possibilities may be automatically processed into a round-robin curtailment rotation which may be implemented by a computer-based control system. Thus, impact on occupants is minimized, and energy use and energy cost may be beneficially reduced in an intelligent, real-time manner. The invention also provides for early-recognition of impending adverse energy events, optimal response to a particular energy situation, real-time analysis of energy-related data, etc.
[0005] The Internet of Things (IOT) is a network of physical objects that are connected to the internet and can communicate with each other and with the cloud. IOT devices can include everyday objects like toothbrushes, vacuums, and cars, as well as more sophisticated industrial tools. The goal of IOT is to create devices that can communicate with each other and with users in real time. The proposed invention focuses on analyzing the Energy Management System for Hybrid Electric Vehicles through algorithms of IOT 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 food packing boxes and freezer packaging 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 smart food packaging box that is user-friendly, cost-effective freshness indicator and an IOT based system that transparently discloses the detail of the packaged shrimps 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 food packaging systems now present in the prior art, the present invention provides an improved and cost-effective freshness indicator especially while exporting sea foods. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved packaging system for frozen shrimps 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 IOT techniques for analyzing the parameters of Energy Management System for Hybrid Electric Vehicles. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization 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 Hybrid Electric Vehicles. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization 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 IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization, 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] An IOT-driven energy management system utilizes the Internet of Things (IoT) technology to collect real-time data on energy consumption from various sensors and devices across a facility, allowing for automated monitoring, analysis, and optimization of energy usage to achieve significant cost savings and improve efficiency through intelligent control mechanisms.
[0018] A hybrid electric vehicle (HEV) is a vehicle that uses both an internal combustion engine and an electric motor to power the vehicle. HEVs use the internal combustion engine to drive the vehicle and charge the battery. The electric motor can also provide extra power, especially during acceleration and starting. The battery is charged through regenerative braking, which captures energy that would normally be lost during braking. The proposed invention focuses on implementing the algorithms of Machine Learning Approach for studying the functions of Hybrid Electric Vehicles.
[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 IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization 100. The proposed invention 100 considers electric vehicle 101 which is analysed using analysis unit 102. The monitoring device 103 will monitor the energy of electric vehicle 101. The analysis unit 102 will analyse the battery of electric vehicle 101. The machine learning unit 104 will run the predictive algorithm and display the results on display 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. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization, comprises of:
Monitoring device;
Analysis unit;
Predictive unit;
Display unit and
Machine learning unit.
2. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization, according to claim 1, includes a monitoring device, wherein the monitoring device will monitor the energy of electric vehicle.
3. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization, according to claim 1, includes an analysis unit, wherein the analysis unit will analyse the battery of electric vehicle.
4. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization, according to claim 1, includes a display unit, wherein the display unit will display the results of predictive unit.
5. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization, according to claim 1, includes a machine learning unit, wherein the machine learning unit will run predictive unit.
6. IOT-Driven Energy Management System for Hybrid Electric Vehicles with Machine Learning Optimization, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the energy management system for hybrid electric vehicles.

Documents

NameDate
202441091941-COMPLETE SPECIFICATION [26-11-2024(online)].pdf26/11/2024
202441091941-DRAWINGS [26-11-2024(online)].pdf26/11/2024
202441091941-FORM 1 [26-11-2024(online)].pdf26/11/2024
202441091941-FORM-9 [26-11-2024(online)].pdf26/11/2024

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