Research on Optimization of Power Battery Recycling Logistics Network
DOI:
https://doi.org/10.4108/ew.5790Keywords:
power battery, recycling logistics, logistics networkAbstract
With the popularity and development of electric vehicles, the demand for power batteries has increased significantly. Power battery recycling requires a complex and efficient logistics network to ensure that used batteries can be safely and cost-effectively transported to recycling centers and properly processed. This paper constructs a dual-objective mathematical model that minimizes the number of recycling centers and minimizes the logistics cost from the service center to the recycling center, and designs the power battery disassembly and recycling process and the recycling logistics network, and finally uses a genetic algorithm to solve it. Finally, this article takes STZF Company as an example to verify the effectiveness of this method. The verification results show that the logistics intensity of the optimized power battery recycling logistics network has been reduced by 36.2%. The method proposed in this article can provide certain reference for power battery recycling logistics network planning.
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Liu YL, Yang PP, Ding XC. Central-regional industrial policy coordination and new energy vehicle industry development: based on the perspective of innovation ecosystem. Chinese soft science. 2023; 11: 38-53.
Rao WC, Chang Y, Liu P. C Research on collaborative recycling mode and operation method of new energy vehicle power battery. Chinese management science. 2023; 34: 114-135.
Moo CS, Jian JY, Wu TH, Yu LR. Battery power system with arrayed battery power modules. IEEE international conference on system science and engineering. 2013; 13: 437-441. DOI: https://doi.org/10.1109/ICSSE.2013.6614705
Monteiro V, Afonso JA, Afonso JL. Bidirectional power converters for ev battery chargers. Energies. 2023; 16(4): 1694. DOI: https://doi.org/10.3390/en16041694
Toro L, Moscardini E, Baldassari L, Forte F. A Systematic Review of Battery Recycling Technologies: Advances, Challenges, and Future Prospects. Energies. 2023; 11(18): 6571. DOI: https://doi.org/10.3390/en16186571
Zhao YL, Lu JS, Yan Q. Research on cell manufacturing facility layout problem based on improved NSGA-II. Computers, materials & continua. 2020; 62(1): Computers, Materials & Continua. DOI: https://doi.org/10.32604/cmc.2020.06396
Zhao YL. Manufacturing cell integrated layout method based on rns-foa algorithm in smart factory. Processes. 2023; 10: 1759. DOI: https://doi.org/10.3390/pr10091759
Desticioglu B, Calipinar H, Ozyoruk B, Koc E. Model for reverse logistic problem of recycling under stochastic demand. Sustainability. 2022; 14(8): 4640. DOI: https://doi.org/10.3390/su14084640
Gao ZH, Ye CY. Reverse logistics vehicle routing optimization problem based on multivehicle recycling. Mathematical problems in engineering. 2021; 2021: 23-46. DOI: https://doi.org/10.1155/2021/5559684
Pereira N, Antunes J, Barreto L. Impact of management and reverse logistics on recycling in a war scenario. Sustainability. 2023; 15(4): 3835. DOI: https://doi.org/10.3390/su15043835
Roudbari ES, Ghomi SMTF, Sajadieh MS. Reverse logistics network design for product reuse, remanufacturing, recycling and refurbishing under uncertainty. Journal of manufacturing systems. 2021; 60: 473-486. DOI: https://doi.org/10.1016/j.jmsy.2021.06.012
Singh M, Jauhar SK, Pant M, Paul SK. Modeling third-party reverse logistics for healthcare waste recycling in the post-pandemic era. International journal of production research. 2023; 26(1): 125-138. DOI: https://doi.org/10.1080/00207543.2023.2269584
Gemechu A, Abebe A, Anna D. Role of reverse logistics activities in the recycling of used plastic bottled water waste management. Sustainability. 2022; 14(13): 7650. DOI: https://doi.org/10.3390/su14137650
Chen ZY, Lu JH, Yang Y, Xiong R. Online estimation of state of power for lithium-ion battery considering the battery aging. Chinese automation congress. 2017; 269(185): 3112-3116. DOI: https://doi.org/10.1109/CAC.2017.8243310
Lamsal D, Sreeram V, Mishra Y, Kumarv D. Achieving a minimum power fluctuation rate in wind and photovoltaic output power using discrete kalman filter based on weighted average approach. IET renewable power generation. 2018; 12(6): 633-638. DOI: https://doi.org/10.1049/iet-rpg.2017.0346
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Funding data
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Natural Science Foundation of Sichuan Province
Grant numbers 24NSFSC7602