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The treatment of end-of-life (EOL) electric vehicle battery systems (EVBS) according to the circular economy principle will be a challenge in the next decades. Today, a high uncertainty exists for the EOL battery market, involving the quantities, qualities, and revenues of EOL EVBS, which may influence the optimal reverse logistics network structures and technologies to apply. To cope with these uncertainties, a two-stage stochastic programming model that optimizes a multi-period, multi-technology disassembly reverse logistics network is proposed. In the conducted case study, the economic effort is reduced by at least 16% through the combination of distinct disassembly technologies with subsequently alternative options for circular EOL treatment. It is shown that cathode type and EOL quality significantly influence the technology chosen for treatment. The case study results underline that the best approach is to set up reverse logistics networks with various disassembling options, considering the unique characteristics of each battery. • Multiperiod model: Decisions on network and capacity adjustments. • Modeled EOL battery features: Cathode types (NMC and LFP generations), size, quality. • Modeling of technology substitution for EOL treatment. • Case study result: Combination of distinct technologies is economically optimal.
Rosenberg et al. (Sat,) studied this question.