Intelligent disassembly of electric-vehicle batteries: a forward-looking overview

Kai Meng, Guiyin Xu, Xianghui Peng, Kamal Youcef-Toumi, Ju Li

Research output: Contribution to journalReview articlepeer-review

43 Scopus citations

Abstract

Retired electric-vehicle lithium-ion battery (EV-LIB) packs pose severe environmental hazards. Efficient recovery of these spent batteries is a significant way to achieve closed-loop lifecycle management and a green circular economy. It is crucial for carbon neutralization, and for coping with the environmental and resource challenges associated with the energy transition. EV-LIB disassembly is recognized as a critical bottleneck for mass-scale recycling. Automated disassembly of EV-LIBs is extremely challenging due to the large variety and uncertainty of retired EV-LIBs. Recent advances in artificial intelligence (AI) machine learning (ML) provide new ways for addressing these problems. This study aims to provide a systematic review and forward-looking perspective on how AI/ML methodology can significantly boost EV-LIB intelligent disassembly for achieving sustainable recovery. This work examines the key advances and research opportunities of emerging intelligent technologies for EV-LIB disassembly, and recycling and reuse of industrial products in general. We show that AI could benefit the whole disassembly process, particularly addressing the uncertainty and safety issues. Currently, EV-LIB state prognostics, disassembly decision-making as well as target detection are indicated as promising areas to realize intelligence. The challenges still exist for extensive autonomy due to present AI's inherent limitations, mechanical and chemical complexities, and sustainable benefits concerns. This paper provides the practical map to direct how to implement EV-LIB intelligent disassembly as well as forward-looking perspectives for addressing these challenges.

Original languageEnglish (US)
Article number106207
JournalResources, Conservation and Recycling
Volume182
DOIs
StatePublished - Jul 2022

All Science Journal Classification (ASJC) codes

  • Waste Management and Disposal
  • Economics and Econometrics

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