Artificial Intelligence for End-of-Life Electric Vehicle Battery Disassembly: A Comprehensive Review
<p>The rapid growth of the electric vehicle (EV) market has led to a significant increase in end-of-life electric vehicle batteries (EOL-EVBs), posing serious environmental and economic challenges. Efficient and safe disassembly of EOL-EVBs is crucial for enabling high-value material recovery and su...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1236166 |
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| Sumario: | <p>The rapid growth of the electric vehicle (EV) market has led to a significant increase in end-of-life electric vehicle batteries (EOL-EVBs), posing serious environmental and economic challenges. Efficient and safe disassembly of EOL-EVBs is crucial for enabling high-value material recovery and supporting a circular economy. However, traditional manual disassembly methods are labor-intensive, inefficient, and hazardous, while automated disassembly faces difficulties due to the high variability in battery design, structural complexity, data scarcity, and safety risks. Recent advances in artificial intelligence (AI) have shown great potential in addressing these challenges. This review systematically examines the application of AI-driven technologies across three core stages of EOL-EVB disassembly: preprocessing, sequence planning, and operation. AI-enhanced methods, including transformer-based networks, hybrid neural models, and computer vision systems, have significantly improved the accuracy of state estimation (SOH, SOC, RUL), sorting efficiency, and disassembly planning under uncertainty. Vision-based detection and robotic operation systems support high-precision component handling, while AI-aided human-robot collaboration (HRC) enhances both safety and productivity. Despite these advancements, challenges remain in data dependency, computational cost, and real-world deployment. This chapter highlights the current state of intelligent disassembly technologies, discusses persistent limitations, and suggests future directions for achieving fully autonomous, efficient, and scalable EOL-EVB recycling systems.</p> |
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| ISBN: | 978-1-80631-697-7 |
| DOI: | 10.5772/intechopen.1014504 |