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

Yan, Wei, Wang, Xiao, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Zhang, Xu-mei, Jiang, Zhi-gang and Huang, Lin 2024. A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions. Scientific Reports 14 (1) , 876. 10.1038/s41598-024-51169-6

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Nature Research
ISSN:	2045-2322
Date of First Compliant Deposit:	10 January 2024
Date of Acceptance:	1 January 2024
Last Modified:	10 Jan 2024 10:15
URI:	https://orca.cardiff.ac.uk/id/eprint/165393

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