Multi-objective optimisation for cell-level disassembly of waste power battery modules in human-machine hybrid mode

Abstract

The rapid global growth in the production of electric vehicles (EVs) will produce numerous waste power battery modules (WPBMs) in the future, which will create significant challenges concerning waste disposal. Therefore, measures to disassemble and recycle WPBMs before using them in other fixed scenarios provide an opportunity for research. First, considering battery components' hazards and complex properties, a human-machine collaborative cell-level disassembly model of WPBMs is proposed. Second, the WPBMs from the Tesla Model S are selected as the case study to verify reliability and validity. Finally, two different disassembly schemes are obtained by solving the proposed model using NSGA-II based on the actual data from resource-recycling companies. The results show that: 1) The proposed model and method can realize the cell-level disassembly of WPBMs and assign the disassembly tasks of hazard components to robots and the disassembly tasks of complex components to humans. 2) The two disassembly schemes obtained are two solutions that do not dominate each other, and the four objectives (number of workstations, workstation idle time, number of workers, and disassembly cost) are optimized simultaneously. 3) The proposed model can provide decision-makers with additional options when incorporating the number of workers into enterprise risk indicators.

Keywords: Disassembly plan; Human-machine collaborative; Intelligent algorithm; Multi-objective optimisation; Waste power batteries.