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Machine learning approach in exploring the electrolyte additives effect on cycling performance of LiNi0.5Mn1.5O4 cathode and graphite anode‐based lithium‐ion cell
International Journal of Energy Research ( IF 4.6 ) Pub Date : 2020-11-15 , DOI: 10.1002/er.6074
Minh Van Duong 1, 2 , Man Van Tran 1, 2, 3 , Akhil Garg 4 , Hoang Van Nguyen 1, 2 , Tuyen Thi Kim Huynh 2, 3 , My Loan Phung Le 1, 2, 3
Affiliation  

LiNi0.5Mn1.5O4 (LNMO), a high‐voltage spinel, has attracted great attention owing to its low cost and high operating voltage. Many great efforts have been devoted to developing full‐cell of LNMO/graphite because of electrolyte oxidation issues at such high voltage. In this work, the effect of additives including vinylene carbonate (VC) and lithium bis(oxalate)borate (LiBOB) in carbonate‐based electrolytes was investigated on LNMO/Li and graphite/Li to find out the optimized electrolyte composition. The use of additives in 1.0 M LiPF6 in EC:DMC (1:1 v/v) seemed not to give any improvement on the electrochemical behavior of the cell. In the case of 1.2 M LiPF6 in EC:EMC (3:7 v/v), however, both half‐cells of LNMO and graphite exhibited stability of discharge capacity during long cycling with the presence of LiBOB or VC additives. Based on the artificial neural network (ANN) simulation, the best electrochemical performance would obtain for LNMO/Li in the electrolyte of 0.67 M LiPF6 in EC:DMC:EMC (2.9:3.5:3.6 (v/v) with 0.41 wt% LiBOB and 1.17 wt% VC additives cell, which delivered 141.1 mAh.g−1 and remained 90% capacity after 100 cycles when using. Also, it predicted that the graphite/Li in the electrolyte of 1.34 M LiPF6 in EC:DMC:EMC (6.2:1.2:2.6) (v/v) with a 0.08 wt% LiBOB additive would achieve 342.2 mAh.g−1 and 85% of capacity retention after 100 cycles. The machine learning approach is efficient in exploring the effect of additive and simultaneously searching an optimization of many design parameters and thus saving the significant cost of time‐consuming experiments.

中文翻译:

机器学习方法探索电解质添加剂对LiNi0.5Mn1.5O4阴极和石墨阳极基锂离子电池循环性能的影响

高压尖晶石LiNi 0.5 Mn 1.5 O 4(LNMO)由于其低成本和高工作电压而备受关注。由于在如此高的电压下存在电解质氧化问题,因此人们已投入大量精力来开发LNMO /石墨全电池。在这项工作中,研究了在碳酸酯类电解质中包括碳酸亚乙烯酯(VC)和双(草酸)硼酸锂(LiBOB)的添加剂对LNMO / Li和石墨/ Li的影响,从而找到了优化的电解质组成。在EC:DMC(1:1 v / v)中的1.0 M LiPF 6中使用添加剂似乎对电池的电化学行为没有任何改善。对于1.2 M LiPF 6然而,在EC:EMC(3:7 v / v)中,在存在LiBOB或VC添加剂的情况下,LNMO和石墨的半电池在长时间循环中均表现出稳定的放电容量。基于人工神经网络(ANN)模拟,在EC:DMC:EMC(2.9:3.5:3.6(v / v)和0.41 wt%)的0.67 M LiPF 6电解质中,LNMO / Li可获得最佳的电化学性能LiBOB和1.17 wt%VC添加剂电池,在使用100次循环后,其输出功率为141.1 mAh.g -1,并保持90%的容量,并且还预测了EC:DMC中1.34 M LiPF 6电解液中的石墨/ Li :具有0.08 wt%LiBOB添加剂的EMC(6.2:1.2:2.6)(v / v)将达到342.2 mAh.g -1100次循环后,容量保留率为85%。机器学习方法可有效地探索添加剂的作用,并同时搜索许多设计参数的优化,从而节省大量耗时的实验成本。
更新日期:2020-11-15
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