Lead acid battery (LAB) recycling benefits from a long history and a well-developed processing network across most continents. Yet, LAB recycling is subject to continuous optimization efforts because of increasingly stringent regulations on process discharge and emissions.
In the process of disassembling battery cells, various components, including cathodes, anodes, compounds, separators, etc., also necessitate the utilisation of cutting operations for separation.
According to the degree of automation, the battery disassembly process can be divided into several categories, namely manual disassembly, semi-automatic disassembly, and fully automated disassembly. Automated disassembly has gradually become a significant trend since there are certain safety risks in the disassembly process.
In response to this pressing issue, this review presents a comprehensive analysis of the role of artificial intelligence (AI) in improving the disassembly processes for EV batteries, which is integral to the practical echelon utilization and recycling process.
As reported in , even using modules with a limited residual charge, thermal runaway, with gas emission, is possible in case of short circuits that can easily happen during the disassembly. The gas mixture released from LiB can create an ATEX zone around the battery pack.
The review concludes with insights into the future integration of electric vehicle battery (EVB) recycling and disassembly, emphasizing the possibility of battery swapping, design for disassembly, and the optimization of charging to prolong battery life and enhance recycling efficiency.