Integrated Risk-Aware Smart Disassembly Planning for Scrap Electric Vehicle Batteries

Shibo Yang, Xiaojun Zhuo, Wei Ning (), Xing Xia () and Yong Huang
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Shibo Yang: Equipment Research Institute, Changsha Research Institute of Mining and Metallurgy, Changsha 410012, China
Xiaojun Zhuo: Equipment Research Institute, Changsha Research Institute of Mining and Metallurgy, Changsha 410012, China
Wei Ning: Equipment Research Institute, Changsha Research Institute of Mining and Metallurgy, Changsha 410012, China
Xing Xia: Equipment Research Institute, Changsha Research Institute of Mining and Metallurgy, Changsha 410012, China
Yong Huang: Equipment Research Institute, Changsha Research Institute of Mining and Metallurgy, Changsha 410012, China

Energies, 2024, vol. 17, issue 12, 1-17

Abstract: With the increase in the production of electric vehicles (EVs) globally, a significant volume of waste power battery modules (WPBM) will be generated accordingly, posing challenges for their disposal. An intelligent scrap power battery disassembly sequence planning method, integrated with operational risk perception, is proposed to automate the planning process. Taking into consideration the risk coefficients, energy consumption, and costs during disassembly, this method maximizes profits, minimizes energy usage, and ensures safety. Utilizing an extended part priority graph, an optimized model for integrated risk-aware disassembly sequence planning (IRA-DSP) is constructed. With the Guangqi Toyota LB7A-FX1 as a case study, and using real data from resource recovery enterprises, an improved MOPSO-GA algorithm is proposed to solve the model and generate disassembly plans. The results demonstrate the method’s ability to achieve unit-level disassembly of WPBM, avoid high-risk sequences, and optimize profit and energy consumption, exhibiting its practicality and feasibility.

Keywords: scrap power battery; disassembly planning; risk perception; circular economy; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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