隨著電動車及消費型產品大量的生產，電池的需求及數量與日俱增。然而，這也意味著在經過幾年的使用後，電池剩餘容量低於額定的70‒80% 時，以安全性考量下就需要進行淘汰。未來將會有不計其數的汰役電池要被處理。對於這些電池不該只有分解以淬取可回收再利用的材料一途，對於這些汰役電池有部分仍有其殘餘的價值可以降階層級來應用。藉由梯次利用的特性評估幫這些電池找尋到可用的路。因此，本論文開發一套以應用規格為導向需求的汰役電池篩選分類系統，結合機器視覺外觀檢測與內部電氣特性測試一致性篩選的流程性系統。此系統規畫了四個階段: 使用者自訂應用分類需求之電池芯規格輸入、機器視覺檢測、電氣特性測試、電池篩選分類結果。第一階段的輸入介面為由使用者將要分類的電池芯規格輸入系統中，第二階段利用機器視覺檢測在中來對所需求的電池芯尺寸大小、相同生產商進行挑選，其辨識度都為95% 以上，此外對於電池表面有瑕疵者，例如附著物、破皮、電解液外漏等也會挑出來離開系統，第三階段為電氣特性測試，採用在不同的電荷狀態(State of charge, SOC)下由電壓電流曲線擬合法來建構電池的動態等效電路模型。從實驗中觀察這些汰役電池的剩餘容量大致分佈在36‒96% 之間，能量密度介於68‒185 Wh/kg，內阻是介於0.073‒0.13 Ω，電池電容為8899‒13524 F之間，第四階段為篩選分類，系統會將所有電池芯在第三階段所運算後的參數資訊進行與應用規格及電池間一致性的比對，最後會依需求規格將電池分類，而少數電池內阻超出0.1 Ω以上者，其容量、能量密度、電池電容不良電池則予以汰除。

With the mass productions of electric vehicles and consumer products, the demands and quantities of battery are gradually increasing. However, this means that the batteries with remaining capacities less than 70 to 80% of the rated capacities after a few years of employing in application needs to be retired for the safety considerations. There will be countless retired batteries to be dealt with in the future. For these batteries, there should not be only one way to decompose them in order to extract recycling materials. There are still some residual values for some of them to be utilized in the lower hierarchies of applications. It is found that the appropriate applications for these retired batteries can be evaluated by performance assessments of echelon utilizations. Thus, this study aims to develop a retired battery classification system through application-oriented requirements. It is a process system integrating both the machine vision with external appearance inspection and the consistency screening of the internal electrical characteristics testing. This system is planned in four stages: data input based on user-defined battery cell specification for the classified applications, battery size inspections by using the machine vision, testing of the electrical characteristics, and result output of screening and sorting. First, user can input the sorting battery cell specifications into the system through the input interface. Secondly, the machine vision inspection will select the required battery cells according to their size and the same manufacturers. The recognition rates are more than 95%. Besides, those batteries with defects on their surfaces such as attachments, broken skins, electrolyte leakage, etc. can be picked out to leave the system. Thirdly, electrical voltages and currents based on different states of charge (SOC) are obtained by electrical characteristics testing, and a curve fitting method is used to build a dynamic equivalent model of the batteries. The experiment results show that the remaining capacities of these retired batteries are distributed between 36 to 96% of rated capacities. The energy densities are between 68 to 185 Wh/kg. The internal resistances are between 0.073 to 0.13 Ω. The battery capacitances are between 8899 to 13524 F. Finally, the system will compare the parameter information of all battery cells calculated in the third stage with application specifications and the battery consistencies. Moreover, the batteries will be classified according to the required specifications. For a small number of batteries with internal resistances greater than 0.1 Ω, poor capacities, poor energy densities, and poor battery capacitances, they will be eliminated if they are not classified into any categories.

[1] E. Martinez-Laserna, E. Sarasketa-Zabala, D.-I. Stroe, M. Swierczynski, A. Warnecke, J.M. Timmermans, S. Goutam, P. Rodriguez, “Evaluation of lithium-ion battery second life performance and degradation,” 2016 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2016.
[2] Y. Hua, X.H Liu, S.D. Zhou, Y. Huang, H.P. Ling, S.C. Yang, “Toward sustainable reuse of retired lithium-ion batteries from electric vehicles,” Resources, Conservation and Recycling 168 (2021): 105249.
[3] X.Y. Li, T.S. Wang, L.Pei, C.B. Zhua, B.L. Xu, “A comparative study of sorting methods for lithium-ion batteries,” 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific). IEEE, 2014.
[4] K. Lee, D.S. Kum, “Development of cell selection framework for second-life cells with homogeneous properties,” International Journal of Electrical Power & Energy Systems 105 (2019): 429-439.
[5] J.H. Teng, R.J. Chen, P.T. Lee, C.W. Hsu, “Accurate and Efficient SOH Estimation for Retired Batteries,” Energies 16.3 (2023): 1240.
[6] L. Chang, C. Ma, Y.L. Zhang, H.Y. Li, L.J. Xiao, “Experimental assessment of the discharge characteristics of multi-type retired lithium-ion batteries in parallel for echelon utilization,” Journal of Energy Storage 55 (2022): 105539.
[7] Y.L. Zhang, Y. Li, Y.B. Tao, J.L. Ye, A.Q. Pan, X.Z. Li, Q.Q. Liao, Z.Q. Wang, “Performance assessment of retired EV battery modules for echelon use,” Energy 193 (2020): 116555.
[8] N. Yan, B. Zhang, S.H. Ma, X. Pan, “Sorting method for retired power battery module considering consistent static and dynamic characteristics,” 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD). IEEE, 2020.
[9] S. Azizighalehsar, P. Venugopal, D.P. Singh, G. Rietveld, “Performance Evaluation of Retired Lithium-ion Batteries for Echelon Utilization,” IECON 2022–48th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2022.
[10] Z.H. Chen, Y.L. Deng, H.L. Li, W.W. Liu, “An efficient regrouping method of retired lithium-ion iron phosphate batteries based on incremental capacity curve feature extraction for echelon utilization,” Journal of Energy Storage 56 (2022): 105917.
[11] A. Garg, L. Yun, L. Gao, D.B. Putungan, “Development of recycling strategy for large stacked systems: Experimental and machine learning approach to form reuse battery packs for secondary applications,” Journal of Cleaner Production 275 (2020): 124152.
[12] B.Z. Xia, Y.D. Yang, J. Zhou, G.H. Chen, Y.F. Liu, H.W. Wang, M.W. Wang, Y.Z. Lai, “Using self organizing maps to achieve lithium-ion battery cells multi-parameter sorting based on principle components analysis,” Energies 12.15 (2019): 2980.
[13] M.Y. Gao, X. Li, Y.X. Yang, Z.W. He, J.Y. Huang, “An automatic sealing system for battery lid based on machine vision,” 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE). IEEE, 2017.
[14] O. Badmos, A. Kopp, T. Bernthaler, G. Schneider, ”Image-based defect detection in lithium-ion battery electrode using convolutional neural networks,” Journal of Intelligent Manufacturing 31 (2020): 885-897.
[15] H. Shen, X. Wang, J. Wang, Y. Xie, W. Wang, “Vision based battery exchange robots for electric vehicle,” 2012 IEEE International Conference on Mechatronics and Automation. IEEE, 2012.
[16] T. Pingpittayakul, C. Mitsantisuk, “Development of EV battery swapping station using automated vision system,” 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2022.
[17] W. Sterkens, D.J. Diaz-Romero, B.Y. Xiao, B.W. Wu, T. Goedmé, W. Dewulf, J. Peeters, “Computer vision and optical character recognition for the classification of batteries from WEEE,” Procedia CIRP 105 (2022): 110-115.
[18] Z. Sun, W. Han, Y.X. Yang, ”High precision machine vision measurement based on the in situ comparison,” 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020.
[19] Q.Q. Liao, M.M. Mu, S.Q. Zhao, L.H. Zhang, T. Jiang, J.L. Ye, X.W. Shen, G.D. Zhou, “Performance assessment and classification of retired lithium ion battery from electric vehicles for energy storage,” International Journal of Hydrogen Energy 42.30 (2017): 18817-18823.
[20] S.Z. Zhang, G.Z. Tang, “Detection of the Wounds of the Battery Cathode Based on Machine Vision,” 2014 Seventh International Symposium on Computational Intelligence and Design. Vol. 2. IEEE, 2014.
[21] Y.Y. Zhan, J.W. Deng, T.H. Wang, “Lithium battery swollen detection based on computer vision,” 2013 IEEE 4th International Conference on Software Engineering and Service Science. IEEE, 2013.
[22] K.Y. Li, T. Dan, ”Research and design of inspection of LR6 battery negative surface scratches online defects based on computer vision,” 2013 International Conference on Communications, Circuits and Systems (ICCCAS). Vol. 2. IEEE, 2013.
[23] J.H. Kim, S.J. Lee, J.M. Lee, B.H. Cho, “A new direct current internal resistance and state of charge relationship for the Li-ion battery pulse power estimation,” 2007 7th Internatonal Conference on Power Electronics. IEEE, 2007.
[24] Y.P. Wang, S.Y. Kim, Y.F. Chen, H.L. Zhang, S.J. Park, “An SMPS-Based Lithium-ion Battery Test System for Internal Resistance Measurement,” IEEE Transactions on Transportation Electrification (2022).
[25] J. Feng, J. Sun, Z.W. Li, Y.L. Yang, “AC internal resistance measurement of batteries,” 2018 Conference on Precision Electromagnetic Measurements (CPEM 2018). IEEE, 2018.
[26] S.H. Chen, S.Q. Lu, S.S. Qiu, Y. Tang, T. Jiang, S.Y. Pan, J.L. Sun, “Battery Internal Resistance Detection Based on AC Injection Method,” 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 2021.
[27] A.R.C. Bredar, A.L. Chown, A.R. Burton, B.H. Farnum, “Electrochemical impedance spectroscopy of metal oxide electrodes for energy applications,” ACS Applied Energy Materials 3.1 (2020): 66-98.
[28] H.Y. Wang, K. Dong, G.H. Li, F. Wu, “Research on the consistency of the power battery based on multi-points impedance spectrum,” International Forum on Strategic Technology 2010. IEEE, 2010.
[29] X. Lai, C. Deng, X.P. Tang, F.R Gao, X.B. Han, Y.J. Zheng, “Soft clustering of retired lithium-ion batteries for the secondary utilization using Gaussian mixture model based on electrochemical impedance spectroscopy,” Journal of Cleaner Production 339 (2022): 130786.
[30] S.M. Mousavi G, M.Nikdel, “Various battery models for various simulation studies and applications,” Renewable and Sustainable Energy Reviews 32 (2014): 477-485.
[31] S. Castano, L. Gauchia, E. Voncila, J. Sanz, “Dynamical modeling procedure of a Li-ion battery pack suitable for real-time applications,” Energy Conversion and Management 92 (2015): 396-405.
[32] X.Y. Liu, W.L. Li, A.G. Zhou, “PNGV equivalent circuit model and SOC estimation algorithm for lithium battery pack adopted in AGV vehicle,” IEEE Access 6 (2018): 23639-23647.
[33] Innolia Energy, Energy Storage System, [Online]. Available: https://www.innoliaenergy.com/products/energy-storage-systems/
[34] 「GoCharger Mobile Plus 電器快充版使用手冊」，睿能創意股份有限公司，2020。
[35] 「LEV 高功率事業(電動自行車電池產品)」，新普科技股份有限公司。https://www.simplo.com.tw/edcontent.php?lang=tw&tb=5&id=73&cid=4
[36] National Instruments, “IMAQ Vision for LabVIEW User Manual,” 2003, ch. 1, pp. 7‒8, ch. 6, pp. 1‒10.
[37] National Instruments, “NI Vision Concepts Manual,” 2005, ch. 12, pp. 7‒8.
[38] X. Zhao, B.J. Xu, G.X. Wu, “Canny edge detection based on Open CV,” 2017 13th IEEE international conference on electronic measurement & instruments (ICEMI). IEEE, 2017.
[39] William H. Hayt, Jr., Jack E. Kemmerly, Steven M. Durbin, “Basic RL and RC Circuits,” in Engineering Circuit Analysis, 8th ed., New York, NY, USA: McGraw-Hill, 2012, ch7. pp. 218, ch8. pp. 272‒274.
[40] Microsoft, “TDS Microsoft LifeCam Studio,” 2016.
[41] Keysight Technologies, “Keysight Series N5700 System DC Power Supply User’s Guide,” 7th ed., 2014, ch. 2, pp. 26‒31, ch. 4, pp. 52‒58, pp. 90‒92.
[42] Prodigit Electronics Co., Ltd, “3310D Series Plug-in Electronic Load module Operation manual,” ch. 1, pp. 2, 8‒9.
[43] Prodigit Electronics Co., Ltd, “3300 Mainframe Operation manual,” ch. 4, pp. 1‒2.
[44] Advantech Co., Ltd, “USB-4718 8-Channel Thermocouple Input Module User Manual,” 2019.
[45] 惠汝生，「LabVIEW 程式與虛擬儀表設計」，旗標科技股份有限公司，2017，ch. 12, pp. 2‒29。
[46] Sanyo Electric Co., Ltd, “Sanyo Lithium Battery specification UR18650F-SCUD-3,” 2005.