Perovskite Solar Cells

Modeling the Future of Renewable Energy
1. Auflage 2025
ISBN: 978-1-032-96503-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 410 Seiten, Format (B × H): 156 mm x 234 mm

This book provides a comprehensive overview of the role of modeling in advancing perovskite solar cell technology and its implications for the future of renewable energy. It encompasses various aspects of perovskite solar cell modeling, including computational modeling and simulation techniques, experimental validation methods, optimization strategies, and performance evaluation metrics.

Features:

• Discusses the basic principles, working mechanisms, materials, and designing approaches related to the implementation of perovskite solar cells.

• Covers electron and hole transport models, computational approaches to charge transport, and transport in different perovskite structures.

• Illustrates the crystal structure, composition, optical and electronic properties, stability, and degradation mechanisms of perovskite materials.

• Explains tandem solar cell design principles, interface engineering for tandems, and stability challenges in tandem solar cells.

• Explores the performance parameters related to perovskite solar cells and the implementation of such devices.

It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electrical and communications engineering, energy engineering, renewable energy, and computer science and engineering.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Swart, Arthur James

Pandey, Bishwajeet

Kumar, Keshav

Sharma, Sakshi

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

Chapter 1. An Introduction to the Solar Energy: A Step Towards Sustainability. Chapter 2. Fundamentals of Perovskite Materials. Chapter 3. Technology Advancements in Solar Cells: A Summary. Chapter 4. Recent Advances in Perovskite Tandem Solar Cells for Enhanced Solar Efficiency. Chapter 5. Modelling Techniques of Perovskite Solar Cells. Chapter 6. Modeling the Future of Renewable Energy: Machine Learning in Solar Energy Prediction. Chapter 7. Optimizing Hybrid Electric Vehicle Performance by Deep Learning for Power Distribution and Regenerative Braking Prediction in Urban Driving Conditions. Chapter 8. Optimization of Power for Solar Panel Optimizer Using Different FPGAs. Chapter 9. Advancing Solar Energy with Machine Learning, Perovskite Technology, and Smart Data Systems. Chapter 10. Machine Learning for Performance Prediction and Optimization. Chapter 11. Machine Learning Applications in Solar Energy: Predicting Performance and Efficiency. Chapter 12. A Novel Hybrid LSTM-XGBoost Model for Enhanced Solar PV Power Generation Forecasting. Chapter 13. A Comprehensive Review of Cybersecurity Challenges in Solar Grids. Chapter 14. Harnessing Machine Learning for Solar Energy Forecasting: Advancing Perovskite Solar Cells and Renewable Energy Solutions. Chapter 15. Toward Secure Solar Energy Systems: A Cyber Perspective. Chapter 16. Thermal and Power Efficient Hardware Design of Solar Panel on Reconfigurable Architecture. Chapter 17. Solar Charge Controller Design on FPGA. Chapter 18. Exploring the Role of Solar Energy in Advancing Agricultural Practices. Chapter 19. Machine Learning in Solar Energy Prediction. Chapter 20. Real-Time Solar Panel Performance Monitoring and Energy Forecasting. Chapter 21. Solar Energy to Sustainable Development Goals: A Case Study. Chapter 22. Advancements and Challenges in All-Perovskite Tandem Solar Cells: A Critical Review

Über Autor(innen)

Arthur James Swart is currently working as an Associate Professor in the Department of Electrical Electronics and Computer Engineering at the Central University of Technology, South Africa. His research interests include engineering education and alternative energy. He worked for Telkom SA and De Beers Namaqualand Mines for 4 years. He joined the Vaal University of Technology in 1995 and progressed from a Technician to a Senior Lecturer in 2007. He completed his MEd in 2007 and his DTech in 2011. He has always loved teaching, but his passion for research took time to develop. Research affords one the opportunity to engage in life-long learning, which will always remain his primary goal and which he is currently pursuing at the Central University of Technology.

Keshav Kumar is an Assistant Professor in the Department of Electronics and Communication Engineering at Pranveer Singh Institute of Technology, Kanpur, India. He is pursuing his PhD in the field of Hardware Security from Lingaya’s Vidyapeeth, Faridabad, India. He has previously worked at Chandigarh University, Punjab, India (NIRF 29). He completed his Master of Engineering in ECE with a specialization in Hardware Security from Chitkara University, Punjab, India. He has also worked as a Junior Research Fellow (JRF) at NIT Patna and as an Assistant Lecturer at Chitkara University, Punjab, India. He has authored and co-uthored many books and more than 55 research papers in the fields of hardware security, green communication, low-ower VLSI design, machine learning techniques, and IoT. He has also worked with professors from 20 different countries. His areas of specialization include deep learning, hardware security, green communication, low-ower VLSI design, machine learning techniques, wireless sensor network (WSN), and IoT. He has experience in teaching Python programming, embedded systems, IoT, computer networks, and digital electronics. He is also associated with Gyancity Research Consultancy Pvt Ltd. He is also a member of IAENG. He has more than 600 citations (Google Scholar), 15 H-index (Google Scholar), and 12 H-Index (Scopus).

Bishwajeet Pandey is a Professor at GL Bajaj College of Technology and Management, Greater Noida, India. He has been a Senior Member of IEEE since 2019. He holds an MTech in Computer Science and Engineering from the Indian Institute of Information Technology, Gwalior, India, and a PhD in Computer Science from the Gran Sasso Science Institute, Italy. He has taught at esteemed institutions such as Chitkara University Chandigarh; Birla Institute of Applied Science, Bhimtal; Jain University, Bangalore; Astana IT University, Kazakhstan; Eurasian National University, Kazakhstan (QS World Rank 321); and UCSI University, Malaysia (QS World Rank 265). He is a prolific researcher, with 11 published books, 196 research papers indexed in Scopus, 45 papers in SCIE, and a total of 296 papers. He has garnered over 3,600 citations and holds an H-index of 28. His leadership roles include serving as the Research Head of the School of Computer Science and Engineering at Jain University, Bangalore (2021–2023), and as the Head of the International Global Academic Partnership Committee at Birla Institute of Applied Science, Bhimtal (2020–2021). In 2023, he was honored with the prestigious Professor of the Year Award at Lord’s Cricket Ground by the London Organisation of Skills Development. Beyond his outstanding research output, his greatest strength lies in his global academic network. He has visited 49 countries, participated in 105 international conferences, and co-uthored papers with 218 professors from 93 universities across 42 nations.

Sakshi Sharma is currently working as a Junior Research Fellow at the School of Advanced Engineering, University of Petroleum and Energy Studies, Dehradun, India. She is pursuing her PhD in Photovoltaic Systems from the University of Petroleum and Energy Studies, Dehradun, India. She successfully completed her Master of Engineering in ECE with the specialization in Hardware Security from Chitkara University, Punjab, India. She has also worked as an Assistant Lecturer at Chitkara University, Punjab, India.