A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions

With the development of the electric vehicle industry, the number of power batteries has increased dramatically. Establishing a recycling EOL (end-of-life) battery network for secondary use is an effective way to solve resource shortage and environmental pollution. However, existing networks are cha...

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Bibliographic Details
Published in:Scientific reports 2024-01, Vol.14 (1), p.876-876, Article 876
Main Authors: Yan, Wei, Wang, Xiao, Liu, Ying, Zhang, Xu-mei, Jiang, Zhi-gang, Huang, Lin
Format: Article
Language:English
Subjects:
639/166
639/4077
704/172
704/844
Carbon
Economics
Electric vehicles
Emissions
Humanities and Social Sciences
multidisciplinary
Recycling
Science
Science (multidisciplinary)
Stochasticity
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Summary:With the development of the electric vehicle industry, the number of power batteries has increased dramatically. Establishing a recycling EOL (end-of-life) battery network for secondary use is an effective way to solve resource shortage and environmental pollution. However, existing networks are challenging due to the high uncertainty of EOL batteries, e.g., quantity and quality, resulting in a low recycling rate of the recovery network. To fill this gap, this paper proposes a stochastic programming approach for recovery network design under uncertain conditions of EOL batteries. Firstly, a multi-objective model for battery recovery network is established, considering carbon emissions and economic benefits. Secondly, a stochastic programming approach is proposed to clarify the model. Subsequently, the genetic algorithm is employed to solve the proposed model. Finally, a recovery network case of Region T is given to verify the credibility and superiority of the proposed method. The results demonstrate that the proposed model reduces carbon emissions by 20 metric tons and increases overall economic benefits by 10 million yuan in Region T compared to the deterministic model. Furthermore, the two portions affecting the optimization results are also discussed to provide a reference for reducing carbon emissions and improving economic efficiency in recycling networks.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-51169-6