A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions
Open accessAbstract
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.
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Many studies have investigated the 'closed loop' supply chain.
“Since one of the advantages of mineral supply chains is their ability to be reused in the supply chain, many studies have investigated the 'closed loop' supply chain [84-90].”
From Lithium Supply Chain Optimization: A Global Analysis of Critical Minerals for Batteries (2024) · cites this paper as [86]
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Cited by
Sorted by citations
Lithium Supply Chain Optimization: A Global Analysis of Critical Minerals for Batteries
Jones, E. C. (2024) · Energies · doi:10.3390/en17112685 · Cited by 41 · see citing works
- Authors
- Yan, W., Wang, X., Liu, Y., Zhang, X.-M., Jiang, Z.-G., & Huang, L.
- Affiliations
- Wuhan University of Science and Technology (CN)
- Cardiff University (GB)
- Corresponding author
- Wang, Xiao
- Venue
- Scientific Reports
- Published
- 2024-01-09
- Volume
- 14
- Issue
- 1
- Pages
- 876-876
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10776577
- OpenAlex
- W4390727187
- Cited by
- 31
- References
- 51
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 4
- FWCI
- 10.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (NO.51975432, 52075396)
- “The 14th Five Year Plan” Hubei Provincial advantaged characteristic disciplines(groups) project of Wuhan University of Science and Technology (2023B0405)
- the Logistics Education Reform and Research Project (NO. JZW2023252)
- the Chunhui Plan of the Ministry of Education, grant number (HZKY2022033)
- the science and technology Project of Zhejiang Province (No. 2023C35088)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Sustainable Supply Chain Management
- Physical Sciences › Engineering › Mechanical Engineering › Extraction and Separation Processes
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- stochastic programming, multi-objective optimization, CO2 emissions, economic efficiency, genetic algorithm
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