COMPARISON OF AN INTERVAL TYPE-2 FUZZY SETS AND AHP METHODS FOR MATERİAL SELECTION PROBLEM ON LITHIUM-ION BATTERİES

Yıl 2020, Cilt: 3 Sayı: 1, 30 - 46, 30.06.2020

Ahmet Aktürk Seda Türk

Öz

Lithium-ion batteries have become one of the most commercially preferred energy storage devices because of their high energy density, low self-discharge rate, and the ability to be cycled many times with slow capacity fading in comparison with other rechargeable batteries. They have been applied on a wide variety of electrical devices and systems such as consumer electronics, power tools, electric vehicles and aerospace equipment. The characteristics of Li-ion batteries are mainly determined by the materials used for its components which can be categorized into four parts: anode and cathode electrodes, separator, and electrolyte. Over the last decade, there has been a significant increase in the number of studies evaluating battery performance based on various materials used in each battery component. However, few attempts have been made to evaluate materials of Lithium-ion batteries. Thus, in this study, we aim to evaluate different materials for cathode electrode in terms of four main criteria: cost, performance, safety and service life using two methods; AHP and interval type-2 fuzzy sets. It is shown that more reliable results are obtained for selecting the best cathode material of Li-ion battery and based on comparison of two methods, same rank is achieved for both approaches.

Anahtar Kelimeler

Material selection problem, , AHP, , Fuzzy Logic, Interval type-2 fuzzy sets, , Li-ion battery

Kaynakça

• Nitta, N., Wu, F., Lee, J. T., and Yushin, G., Li-ion battery materials: present and future. Materials Today, 2015, 18(5):252–264.
• Nishi, Y., Lithium ion secondary batteries; past 10 years and the future, Journal of Power Sources, 2001, 100(1-2): 101-106.
• Sarkar, A., Shrotriya, P., Chandra, A., Parametric Analysis of Electrode Materials on Thermal Performance of Lithium-Ion Battery: A Material Selection Approach Batteries and Energy Storage, J. Electrochem. Soc., 2018, 165(9): A1587-A1594.
• Mishra, A., Mehta, A., Basu, S., Malode, S. J, Nagaraj P. Shetti, N. P., Shukla, S., Nadagouda, M. N., Aminabhavi, T. M., Electrode materials for lithium-ion batteries, Materials Science for Energy Technologies, 2018, 1(2): 182-187.
• Panday, A. , and Bansal, H. O. , Multi-Objective Optimization in Battery Selection for Hybrid Electric Vehicle Applications. Journal of Electrical Systems, 2016, 12(2):325–343.
• Kaa, G., Fens, T., Rezaei, J., Residential grid storage technology battles: a multi-criteria analysis using BWM, Technology Analysis and Strategic Management, 2019, 31(1):40-52.
• Sangwan, S. K., Jindal, A., An integrated fuzzy multi-criteria evaluation of lithium-ion battery recycling processes, International Journal of Sustainable Engineering, 2013, 6(4): 359-371.
• Warner, J. T., The Handbook of Lithium-Ion Battery Pack Design: Chemistry, Components, Types and Terminology, Elsevier, Grand Blanck, MI,, 2015, 1–80.
• Graf, C., Cathode materials for lithium-ion batteries, Lithium-ion batteries: basics and applications. Edited by Korthauer, R., and Wuest, M., Springer, 2018, 29-40.
• Deng, D., Li-ion batteries: Basics, Progress, and Challenges, Energy Science & Engineering, 2015, 3(5): 385–418.
• Scrosati, B., and Garche, J., Lithium Batteries: Status, Prospects and Future. Journal of Power Sources, 2010, 195(9):2419–2430.
• Gwon, H., Hong, J., Kim, H., Seo, D.-H., Jeon, S., and Kang, K., Recent Progress on Flexible Lithium Rechargeable Batteries. Energy Environ. Sci., 2014, 7(2):538-551.
• Yuan, M., Erdman, J., Tang, C., and Ardebili, H., 2014, “High Performance Solid Polymer Electrolyte with Graphene Oxide Nanosheets,” RSC Adv., 4(103), pp. 59637–59642.
• Doeff, M. M., Chapter 2: Battery Cathodes. Batteries for Sustainability: Selected Entries from the Encyclopedia of Sustainability Science and Technology. Edited by Brodd J. R., Springer, New York, NY, 2013, 5–49.
• Manthiram, A., Materials Challenges and Opportunities of Lithium Ion Batteries. The Journal of Physical Chemistry Letters, 2011, 2(3):176–184.
• Wang, Y., and Huang, H.-Y. S., An Overview of Lithium-Ion Battery Cathode Materials. MRS Proceedings, 2011, 1363.
• Doughty, D. H., and Roth, E. P., A General Discussion of Li Ion Battery Safety. The Electrochemical Society Interface, 2012, 21(2):37–44.
• Schmuch, R., Wagner, R., Hörpel, G., Placke, T., & Winter, M., Performance and cost of materials for lithium-based rechargeable automotive batteries. Nature Energy, 2018, 3(4): 267–278.
• Al-Hallaj, S., Wilk, G., Crabtree, G., and Eberhard, M., Overview of distributed energy storage for demand charge reduction. MRS Energy & Sustainability, 2018, 5(1): 1–18.
• Lazim, A., Norsyahida Z., Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management, Expert Systems with Applications, 2015, 42(9): 4397-4409.
• Ying-Chyi C., Chia-Chi S., Hsin-Yi Y., Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach, Applied Soft Computing, 2012, 12(1):64-71.
• Marbini A.H. and Tavana M., An extension of the Electre I method for group decision-making under a fuzzy environment, Omega, 2011, 39 (4): 373-386.
• Sen, C. G., Cinar, G., Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and max–min approach, Expert Systems with Applications, 2010, 37 (3): 2043-2053.
• Ling W., Jian C., Jun W., Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process, International Journal of Production Economics, 2007, 107(1): 151-163.
• Yeap, J.A.L., Ignatius J., Ramayah, T., Determining consumers’ most preferred eWOM platform for movie reviews: A fuzzy analytic hierarchy process approach, Computers in Human Behavior, 2014, 31: 250-258.
• Saaty, R.,The analytic hierarchy process–what it is and how it is used, Math. Model.,1987,9:161–176.
• Dweiri, F., Al-Oqla, F. M., Material selection using analytical hierarchy process. Int. J. Comput. Appl. Technol., 2006, 26 (4):182-189.
• Kiong, S. C., Lee, L. Y., Chong, S. H., Azlan, M. A., Nor, N. H. M., Decision Making with the Analytical Hierarchy Process (AHP) for Material Selection in Screw Manufacturing for Minimizing Environmental Impacts, Applied Mechanics and Materials, 2013, 315: 57-62.
• Kühn, F., Rehra, J., May, D., Schmeer, S., Mitschang, P., Dry fiber placement of carbon/steel fiber hybrid preforms for multifunctional composites, Advanced Manufacturing: Polymer & Composites Science, 2019,5(1):37-49.
• Zadeh, L., Fuzzy sets. Information and Control, 1965, 8:338- 353.
• Zadeh, L., The consept of a linguistic variable and its applications to approximate reasoning. Inform Science, 1975, 8:199- 249.
• Hagras, H., Type-2 flcs: A new generation of fuzzy controllers. IEEE Computational Intelligence Magazine, 2007, 2:30-43.
• Greenfield, S., Chiclana, F., John, R., Coupland, S., The sampling method of defuzzification for type-2 fuzzy sets: Experimental evaluation, Information Sciences, 2012, 189: 77- 92.
• Mendel, J.M., John, R., Liu, F., Interval type-2 fuzzy logic systems made simple. IEEE T. Fuzzy Systems, 2006, 14:808- 821.
• Young, K., Wang,, C., Wang, L. Y., and Strunz, K., Electric Vehicle Battery Technologies. Electric vehicle integration into modern power networks. Edited by R. Garcia-Valle, and J.A.P. Lopes, SPRINGER-VERLAG NEW YORK, 2013, 15–56.
• W. Reaugh, Larry. Re-Cycling Spent Electric Vehicle Batteries Potentially Recovers Significant Amounts of Lithium, Cobalt and Other Cathode Metals. American Manganese Inc., 19 Jan. 2017, https://americanmanganeseinc.com/re-cycling-spent-electric-vehicle-batteries-potentially-recovers-significant-amounts-of-lithium-cobalt-and-other-cathode-metals/.(accessed September 10, 2019).
• Sen, C. G., Cinar, G., Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and max–min approach, Expert Systems with Applications, 2010, 37 (3): 2043-2053.
• Bhushan, N., & Rai, K. (2004). Strategic Decision Making: Applying the Analytic Hierarchy Process. Decision Engineering. Springer London.
• Türk, S. John, R. and Özcan, E., Interval type-2 fuzzy sets in supplier selection, 14th UK Workshop on Computational Intelligence (UKCI), Bradford, 2014, pp. 1-7.
• Chen, S., Lee, L., Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method, Expert Systems with Applications, 2010, 37 (4): 2790-2798.