£67.99
£67.99
This book explores how deep learning enhances the safety and efficiency of electric energy storage systems. It covers core principles, battery assessment, and real-world applications on the grid and user side. A practical guide for engineers, researchers, and students.
This book takes you through the development history and technological classification of the power storage industry, laying the groundwork with core principles and real-world applications of deep learning. It explores the testing and condition assessment of energy storage batteries and how deep learning is applied on both the grid side and the user side. Its goal is to reveal how intelligent technologies can enhance the safety and efficiency of electric energy storage systems. Whether you’re an engineer, researcher, policymaker, or a student passionate about deep learning and energy storage, this book is designed to be your practical and insightful guide.
This volume provides science and environmental educators with examples of learning that contribute to a more eco-socially just and sustainable world. This education moves beyond facts to develop learners’ values and capabilities to envision and work towards preferable futures.
From the unique perspective of a solar space scientist turned oilfield engineer, this book takes an honest look at the energy transition. How are oil giants pivoting on climate change? Is the urgency as dire as we’re told? A must-read to understand the reality of climate change.
Your electricity bills have doubled for a reason. Failed state policy has put our power supply at risk. An engineer reveals the true costs of renewables and the radical solution politicians have missed.
With fossil fuels depleting, reliable and eco-friendly energy is essential. This book discusses renewable resources like wind and solar power, comparing them to non-renewable sources with data from case studies and exploring their advantages over conventional energy.
Human activities have massively accelerated the nitrogen cycle, causing eutrophication, smog, acid rain, and the emission of potent greenhouse gases. This book reviews the biotransformation of nitrogen, its ecological significance, and the consequences of human interference.
This book describes human civilization from an energy perspective, showing how mastering energy catapulted our species to supremacy. It weaves a fascinating story from Stone Age tools to climate change, discussing the dark side and the future of renewables.
This volume contains a selection of the best papers on renewable energies and power quality. It presents state-of-the-art research from leading authorities drawn from across the globe, with contributions from 300 authors from 60 different countries.
Solar PV power is intermittent. This book provides an understanding of this variability and its impacts on power systems. It explores the sources of intermittency, from solar irradiation to dust, and the consequences for power system design and operation.
This book explores people-forest relationships in Kenya through the Indigenous Knowledge of the Agĩkũyũ people. It confronts the history of land dispossession, cements the forest’s role in the struggle for independence, and shows how this wisdom can forge sustainable futures.
Disasters are not always acts of nature. This book opens a new field in disaster research: those caused by mankind. From industrial accidents to political corruption, it explores our vulnerability, resilience, and introduces the new concept of disaster sustainability.
This book presents a groundbreaking approach to estimating the state of charge (SOC) of lithium-ion batteries. This method combines neural networks and Kalman filtering to enhance accuracy and adapt to battery aging, paving the way for improved battery management systems.
Real-time State of Energy (SOE) estimation is crucial for lithium-ion battery safety and predicting the driving range of electric vehicles. This book details an optimized algorithm combining a fuzzy controller and a Kalman filter for fast, accurate, and robust SOE estimation.
This book proposes a method for estimating lithium battery state of charge (SOC) by fusing deep learning and filtering algorithms. It uses BiLSTM neural networks to overcome the limitations of standard LSTM, resulting in improved feature extraction and greater accuracy.
This book focuses on the co-estimation strategies of State-of-Charge (SOC) and State-of-Health (SOH) for lithium-ion batteries. It proposes a collaborative optimization strategy based on neural networks, providing technical references for scholars and engineers.
This book covers core estimation methods, intelligent condition assessment, and management for energy storage systems. It provides a theoretical foundation and practical guide with rich examples for researchers and practitioners seeking to innovate in the field.
Real-time State of Energy (SOE) estimation is crucial for lithium-ion battery safety and predicting the driving range of electric vehicles. This book details an optimized algorithm combining a fuzzy controller and a Kalman filter for fast, accurate, and robust SOE estimation.
This book proposes a method for estimating lithium battery state of charge (SOC) by fusing deep learning and filtering algorithms. It uses BiLSTM neural networks to overcome the limitations of standard LSTM, resulting in improved feature extraction and greater accuracy.
This book focuses on the co-estimation strategies of State-of-Charge (SOC) and State-of-Health (SOH) for lithium-ion batteries. It proposes a collaborative optimization strategy based on neural networks, providing technical references for scholars and engineers.
This book covers core estimation methods, intelligent condition assessment, and management for energy storage systems. It provides a theoretical foundation and practical guide with rich examples for researchers and practitioners seeking to innovate in the field.
Real-time State of Energy (SOE) estimation is crucial for lithium-ion battery safety and predicting the driving range of electric vehicles. This book details an optimized algorithm combining a fuzzy controller and a Kalman filter for fast, accurate, and robust SOE estimation.
This book proposes a method for estimating lithium battery state of charge (SOC) by fusing deep learning and filtering algorithms. It uses BiLSTM neural networks to overcome the limitations of standard LSTM, resulting in improved feature extraction and greater accuracy.
This book focuses on the co-estimation strategies of State-of-Charge (SOC) and State-of-Health (SOH) for lithium-ion batteries. It proposes a collaborative optimization strategy based on neural networks, providing technical references for scholars and engineers.
This book covers core estimation methods, intelligent condition assessment, and management for energy storage systems. It provides a theoretical foundation and practical guide with rich examples for researchers and practitioners seeking to innovate in the field.
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