Recent Advances in Energy Harvesting Technologies / edited by Shailendra Rajput [and three others].

Format:

Book

Contributor:

Rajput, Shailendra, editor.

Sharma, Abhishek (Robotics engineer), editor.

Jately, Vibhu, editor.

Ram, Mangey, editor.

Series:

River Publishers series in energy sustainability and efficiency

Language:

English

Subjects (All):

Artificial intelligence--Engineering applications.

Artificial intelligence.

Physical Description:

1 online resource (253 pages) : illustrations.

Edition:

First edition.

Place of Publication:

Milton : River Publishers, 2023.

Summary:

Energy demand is continuously rising, mainly due to population growth and rapid economic development. There are substantial worries about the environmental effects of fossil fuels in addition to the uncertainties surrounding the long-term sustainability of non-renewable energy sources. Environmental safety concerns are driving an increase in the demand for renewable energy production. Numerous efforts have been paid to harvest energy from ambient sources, e.g. solar, wind, thermal, hydro, mechanical, etc. This book discusses the application of artificial intelligence (AI) for energy harvesting. The implementation of metaheuristics and AL algorithms in the field of energy harvesting system will provide a quick start for the researchers and engineers who are new to this area. Energy harvesting technologies are growing very speedily, hence it is necessary to summarize recent advances in energy harvesting methodology. Over the recent years, a considerable amount of effort has been devoted, both in industry and academia, towards the performance modelling and evaluation of energy harvesting technologies. This book is the result of a collaborative effort among different researchers in the fields of energy harvesting and artificial intelligence. Technical topics discussed in the book include: - Hybrid algorithms - Mechanical to electrical energy conversion - Swarm intelligence - MPPT technologies - Polymer nanocomposites.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Table of Contents

Preface

Acknowledgement

List of Figures

List of Tables

List of Contributors

List of Abbreviations

Chapter 1: AI in Energy Harvesting

1.1: Introduction

1.2: Energy from Mechanical Vibrations

1.3: Fundamentals of Vibrational Energy Harvesting

1.4: Piezoelectric and Triboelectric Nanogenerators

1.5: Electro-mechanical Energy Harvesting

1.6: Piezoelectric Energy Harvesters

1.7: Triboelectric Energy Harvester (TENG)

1.8: Artificial Intelligent in Energy Harvesting 1.9: Artificial Intelligent in Energy Harvesters

1.10: Philosophy of AI in Energy Harvesting

1.11: Limitation and Future Scope of AI in Energy Harvesting

1.12: Conclusion

Chapter 2: Application of the ANN Method in Water Energy Harvesting

2.1: Introduction

2.2: Soft Computing

2.2.1: Soft-computing properties

2.3: Artificial Neural Networks (ANN)

2.4: Application of ANN in Hydrology

2.4.1: In stream-flow modelling

2.4.2: In water quality modelling

2.4.3: In groundwater modelling

2.5: Case Studies: Application of ANN in Water Resources 2.5.1: Rainfall-runoff (RR) process

2.5.2: Surface runoff

2.5.3: Rainfall-runoff (RR) modelling approaches

2.5.4: Physically based RR models

2.5.5: Conceptual RR models

2.5.6: Empirical RR models

2.6: ANN Application in Water Energy Harvesting

2.7: Rainfall-runoff (RR) Modelling using ANNs

2.8: Implementation of ANN-RR (Rainfall-Runoff) Models

2.9: Conclusion

Chapter 3: Artificial Intelligence (AI) in Electrical Vehicles

3.1: Introduction

3.2: Advantages of Electric Vehicles

3.3: Opportunities for Energy Harvesting on Electric Vehicles 3.4: The Electric Vehicle Industry and Artificial Intelligence

3.5: How AI Is Accelerating the Power of Electric Vehicle Batteries

3.6: Internal Combustion Engine (ICE) Sales Ban Proposals

3.7: Electric Vehicle Charging

3.8: ML and Predictive Analytics

3.9: Supervised Learning

3.10: Unsupervised Learning and Statistical Models

3.11: Electric Vehicle Battery Makers

3.12: Is Lithium the New Gasoline?

3.13: Enabling High-Energy Dense Batteries

3.14: Future Scope of AI in the EV Industry

3.15: Conclusion Chapter 4: Advances in Maximum Power Point Tracking of Solar Photovoltaic Systems Under Partially Shaded Conditions with Swarm Intelligence Techniques

4.1: Introduction

4.2: Model Description of PV Source

4.3: Partial Shading and Its Effects

4.4: Model Description of MPPT Controller

4.5: Overview of MPPT Techniques for Solar Photovoltaic Systems

4.6: Working Principles of MPPT Techniques

4.6.1: Gradient-based MPPT techniques

4.6.2: Soft computing-based MPPT techniques

4.7: Classification of MPPT Techniques

4.7.1: Gradient-based MPPT techniques.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

ISBN:

1-5231-5643-0

1-00-344038-X

1-003-44038-X

87-7022-880-9

9781003440383