Lithium batteries are increasingly used in electric vehicle applications. However, different manufacturing processes and technical constraints lead to battery inconsistency, even for batteries in the same production batch. High-rate discharging negatively affects battery consistency and results in service life reduction. A multi-parameter sorting method at high-rate operation was proposed in this study. The method was applied to sort batteries for cars. The sorted datasets were compared and analyzed by the fuzzy C-mean clustering method, the K-means clustering method, and the simulated annealing genetic algorithm. The comparisons proved that the genetic annealing algorithm was more suitable for battery classification. The clustered batteries were assembled into modules in series and parallel for experimental validation. The test results showed that the battery module cycle life was improved.
GaoYJiangJZhangC, et al. Lithium-ion battery aging mechanisms and life model under different charging stresses. J Power Sources2017; 356: 103–114.
2.
ChaudhariKUkilAKumarK N, et al. Hybrid optimization for economic deployment of ESS in PV-integrated EV charging stations. IEEE Trans Industr Inform2018; 14(1): 106–116.
3.
ChangLMaCLuanC, et al. Influence of the assembly method on the cell current distribution of series–parallel battery packs based on connector resistance. Front Energy Res2022; 10: 27.
4.
SawLHYeYTayAA, et al. Computational fluid dynamic and thermal analysis of Lithium-ion battery pack with air cooling. Appl Energy2016; 177(7): 783–792.
5.
BerecibarMGarmendiaMGandiagaI, et al. State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application. Energy2016; 103: 784–796.
KumarASSatyavaniTVSenthilkumarM. Effect of temperature and charge stand on performance of lithium-ion polymer pouch cell. J Energy Storage2016; 6: 239–247.
8.
SanthanagopalanSWhiteRE. Quantifying cell-to-cell variations in lithium ion batteries. Int J Electrochem2012; 2012; 395838.
9.
KenneyBDarcovichKMacNeilDD, et al. Modelling the impact of variations in electrode manufacturing on lithium-ion battery modules. J Power Sources2012; 213: 391–401.
10.
DubarryMVuillaumeNLiawBY. Origins and accomodation of cell variations in Li-ion battery pack modeling. Int J Energy Res2010; 34: 216–231.
11.
SchusterSFBrandMJBergP, et al. Lithium-ion cell-to-cell variation during battery electric vehicle operation. J Power Sources2015; 297: 242–251.
LiWALeqiongXIGanZH, et al. Research progress in the consistency screening of li-ion batteries. Energy Storage Sci Technol2018; 7(2): 194–202.
14.
JinWPangJTangL, et al. Research progress of conformance evaluation methods for lithium-ion power batteries, in Chinese. Battery Bimonthly2014; 44(1): 53–56.
15.
RuiZZhouYRanL. A battery sorting scheme based on fuzzy C-mean Clustering, taking advantage of the flatness of discharge voltage curve, in Chinese. Automot Eng2017; 39: 864.
16.
XiaBYangYZhouJ, et al. Using self organizing maps to achieve lithium-ion battery cells multi-parameter sorting based on principle components analysis. Energies2019; 12(15): 2980.
17.
ZhouZRanAChenS, et al. A fast screening framework for second-life batteries based on an improved bisecting K-means algorithm combined with fast pulse test. J Energy Storage2020; 31: 101739.
18.
AnFZhaoHLiP. Self-discharge rates in cells have a critical effect on the cycle life of parallel lithium-ion batteries. RSC Adv2018; 8(54): 30802–30812.
19.
BaiXTanJWangX, et al. Study on distributed lithium-ion power battery grouping scheme for efficiency and consistency improvement. J Clean Product2019; 233: 429–445.
20.
LyuCSongYWangL, et al. A new method for lithium-ion battery uniformity sorting based on internal criteria. J Energy Storage2019; 25: 100885.
21.
NingSYHanhongQYanjunW, et al. Battery screening technology based on feature extraction and unsupervised clustering. Power Technol2020; 44(11): 1650–1653.
22.
LaiXDengCLiJ, et al. Rapid sorting and regrouping of retired lithium-ion battery modules for echelon utilization based on partial charging curves. IEEE Trans Veh Technol2021; 70(2): 1246–1254.
23.
ChenZDengYLiH, et al. An efficient regrouping method of retired lithium-ion iron phosphate batteries based on incremental capacity curve feature extraction for echelon utilization. J Energy Storage2022; 56: 105917.
24.
LiRYaoJZhouY, et al. Study on sorting method of zinc silver battery based on multi-step FCM clustering algorithm. IEICE Electron Expr2019; 16(7): 20190120.
25.
ZhouJ. Research on the method of second use battery screening and comprehensive performance evaluation. Master Thesis, Beijing Jiaotong University, China, 2021.
26.
YunLMaddilaSGaoL, et al. An integrated framework for minimization of inter lithium-ion cell temperature differences and the total volume of the cell of battery pack for electric vehicles. Energy Storage2019; 1(2): e41.
27.
GogoanaRPinsonMBBazantMZ, et al. Internal resistance matching for parallel-connected lithium-ion cells and impacts on battery pack cycle life. J Power Sources2014; 252: 8–13.
28.
GB/T 31486-2015. Electrical performance requirements and test methods for traction battery of electric vehicle.
29.
FulimeniLd’ApolitoLBellesiP, et al. How to get the best performance from lithium cells in Telecommunications battery module. In: 2011 IEEE 33rd International Telecommunications Energy Conference, Amsterdam, Netherlands, 9–13 October 2011. New York: IEEE.
30.
JiangTSunJWangT, et al. Sorting and grouping optimization method for second-use batteries considering aging mechanism. J Energy Storage2021; 44: 103264.
31.
ShaoPShiWHeP, et al. Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sens2016; 8(3): 264.
32.
VermaHVermaDTiwariPK. A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image. Expert Syst Appl2021; 167: 114121.
33.
LinB. Study on influence of balanced effect with voltage difference as control parameter on battery system charging in Chinese. Bus Coach Technol Res2020; 42(3): 20–23.
34.
ShuaiWZhongdongYZhongZ, et al. Representation of influence factors for battery module consistency on discharge voltage curves in Chinese. Trans China Electrotech Soc2020; 35(8): 1836–1847.
35.
LiuXChangGTianJ, et al. Regrouping strategy of retired batteries considering SOC consistency. Energy Rep2022; 8: 218–228.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
