دانلود کتاب پیشرفت‌ها در ساخت باتری و راهکارهای مبتنی بر هوش مصنوعی و فیلترینگ برای تحلیل وضعیت عملکرد

As the global energy transition accelerates, battery technology has become central to achieving clean energy goals. The performance, safety, and longevity of batteries are critical for the widespread adoption of new energy technologies. However, the increasing complexity of battery systems presents significant challenges in managing and optimizing their manufacturing, operation, and health status. Traditional methods, while foundational, are limited when dealing with the complexity, noise, and uncertainty inherent in modern battery systems. This book addresses this gap by integrating advanced filtering techniques with artificial intelligence (AI) strategies, driving the smart development of battery technology. We provide a unified framework that combines data-driven algorithms with conventional battery management and state estimation technologies, offering new solutions for optimizing the entire battery lifecycle. The book covers key areas of battery manufacturing and management. We begin with AI applications in battery grouping and manufacturing capacity forecasting, introducing strategies based on genetic algorithms and Q-learning to improve production efficiency and consistency. We then explore AI-driven optimization of state of charge (SOC) estimation, even in the presence of significant noise, ensuring greater accuracy and stability. As the book progresses, we delve into advanced filtering methods, including zonotopic Kalman filtering (ZKF) and particle swarm optimization (PSO), which tackle the complexities of battery systems and improve the precision of battery status analysis. The integration of AI and filtering techniques for predicting remaining useful life (RUL) is also emphasized, demonstrating how hybrid models can accurately predict battery health and failure modes, enabling predictive maintenance. These methods provide powerful support for efficient and reliable battery management, even under challenging operational conditions. This book is primarily aimed at senior undergraduate students, graduate students, and researchers in the fields of control theory, automation, intelligent manufacturing, and related disciplines. Additionally, for engineers and technicians involved in battery management system design and optimization, the theoretical analysis and practical case studies presented in this book will also serve as valuable references.