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Full Description
In the pursuit of efficient and reliable energy storage solutions, this book presents a groundbreaking approach to estimating the state of charge (SOC) of lithium-ion batteries. This innovative method combines the strengths of neural networks and Kalman filtering techniques to enhance accuracy and adaptability in SOC estimation, addressing the challenges posed by varying operating conditions and battery aging. By leveraging advanced modeling technique, this research provides a comprehensive analysis of battery behavior, paving the way for improved battery management systems that optimize performance and extend lifespan. As the demand for sustainable energy solutions grows, this hybrid SOC methodology stands at the forefront of battery technology, offering significant implications for electric vehicles, renewable energy systems, and beyond. It explores how this advanced approach can transform the future of battery management and contribute to a greener tomorrow.
