Selection of Electric Vehicle Battery using multi attribute decision making method

Authors

  • Kaustubh Shinde School of Mechanical and Civil Engineering, MIT Academy of Engineering, Pune
  • Pratik Bendre School of Mechanical and Civil Engineering, MIT Academy of Engineering, Pune
  • Pratik Kundargi School of Mechanical and Civil Engineering, MIT Academy of Engineering, Pune
  • Aditya Sanap School of Mechanical and Civil Engineering, MIT Academy of Engineering, Pune
  • Avinash Kamble School of Mechanical and Civil Engineering, MIT Academy of Engineering, Pune

DOI:

https://doi.org/10.61467/2007.1558.2026.v17i1.383

Keywords:

electric vehicle battery, Multi-Objective Optimization

Abstract

An electric vehicle (EV) battery provides the energy required to power vehicles, ranging from passenger cars to buses. Among EV components, the battery is widely regarded as one of the most critical, as it directly supports vehicle operation. In parallel, the adoption of electric vehicles has increased, largely due to their potential to reduce carbon emissions and contribute to environmental protection initiatives. Consequently, selecting an appropriate battery for electric vehicles represents a significant decision-making challenge. To identify a suitable battery option, a multi-criteria decision-making (MCDM) approach is employed, taking into account several fundamental performance specifications. These criteria include battery lifespan, efficiency, durability, recharge time, temperature dependence, cost, and weight. The present work applies a structured framework for EV battery selection through the use of multiple decision-making techniques, namely the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the Complex Proportional Assessment (COPRAS) method, and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA). Collectively, these methods are used to evaluate and rank battery alternatives, thereby providing a systematic and comparative basis for informed decision-making.

 

Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.383

Dimensions.

Open Alex.

References

Luzon, B., & El-Sayegh, S. M. (2016). Evaluating supplier selection criteria for oil and gas project in the UAE using AHP and Delphi. International Journal of Construction Management, 16(2), 175–183. https://doi.org/10.1080/15623599.2016.1146112

Jianping, L. U., Zhang, S., Wu, J., & Wei, Y. (2021). COPRAS method for multiple attribute group decision making under picture fuzzy environment and their application to green supplier selection. Technological and Economic Development of Economy, 27(2), 369–385. https://doi.org/10.3846/tede.2021.14211

Tahriri, F., Osman, M. R., Ali, A., Yusuff, R. M., & Esfandiary, A. (2008). AHP approach for supplier evaluation and selection in a steel manufacturing company. Journal of Industrial Engineering and Management, 1(2), 54–76.

Zhang, H., Lu, M., Ke, X., Yu, S., Zhao, J., & Cheng, L. (2021). Evaluation model of black start schemes based on optimal combination weights and improved VIKOR method. International Journal of Electrical Power & Energy Systems, 129, 106762. https://doi.org/10.1016/j.ijepes.2021.106762

Dragincic, J., & Vransevic, M. (2013). AHP-based group decision making approach to supplier selection of irrigation equipment. Water Resources, 41(6), 782–791. https://doi.org/10.1134/S0097807814060050

Zolfani, S. H., Rezaeiniya, N., Aghdate, M. H., & Zavadskas, E. K. (2012). Quality control manager selection based on AHP-COPRAS-G methods: A case in Iran. Economic Research-Ekonomska Istraživanja, 25(1), 72–86. https://doi.org/10.1080/1331677X.2012.11517495

Albayrak, E., & Erensal, Y. C. (2004). Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision-making problem. Journal of Intelligent Manufacturing, 15, 491–503. https://doi.org/10.1023/B:JIMS.0000034112.00652.4c

Ersoy, Y. (2020). Green machine selection in a manufacturing company using TOPSIS method. Academia Journal of Nature and Human Sciences, 6, 45–56. https://dergipark.org.tr/en/download/article-file/1356759

Zhongyou, X. (2012). Study on the applications of TOPSIS method to the introduction of foreign players in CBA games. Physics Procedia, 33, 2034–2039. https://doi.org/10.1016/j.phpro.2012.05.320

Pei, Z. (2015). A note on the TOPSIS method in MADM problems with linguistic evaluations. Applied Soft Computing, 36, 24–35. https://doi.org/10.1016/j.asoc.2015.06.042

Jahanshahloo, G. R., Lotfi, F. H., & Izadikhah, M. (2006). Extension of TOPSIS method for decision making problems with fuzzy data. Applied Mathematics and Computation, 181(2), 1544–1551. https://doi.org/10.1016/j.amc.2006.02.057

Zavadskas, E. K., Kaklauskas, A., Peldschus, F., & Turskis, Z. (2007). Multi-attribute assessment of road design solutions by using the COPRAS method. The Baltic Journal of Road and Bridge Engineering, 2(4), 195–203. https://bjrbe-journals.rtu.lv/bjrbe/article/view/1822-427X.2007.4.195–203

Liou, J. H., Tamosaitiene, J., Zavadskas, E. K., & Tzeng, G. H. (2016). New hybrid COPRAS MADM method for improving and selecting suppliers in green supply chain management. International Journal of Production Research, 54(1), 114–134. https://doi.org/10.1080/00207543.2015.1010747

Krishankumar, R., Garg, H., Arun, K., Saha, A., Ravichandran, K. S., & Kar, S. (2021). An integrated decision-making COPRAS approach to probabilistic hesitant fuzzy set information. Complex & Intelligent Systems, 7, 2281–2298. https://doi.org/10.1007/s40747-021-00387-w

Organ, A., & Yalcin, E. (2016). Performance evaluation of research assistants by COPRAS method. Social Science and Humanities, 1, 111–119. https://core.ac.uk/reader/328025543

Zheng, G., & Wang, X. (2019). The comprehensive evaluation of renewable energy system schemes in tourist resorts based on VIKOR method. Energy, 193, 116676. https://doi.org/10.1016/j.energy.2019.116676

Hashemi, A., Dowlatshahi, M. B., & Nezamabadi-pour, H. (2021). VMFS: A VIKOR-based multi-target feature selection. Expert Systems with Applications, 182, 115224. https://doi.org/10.1016/j.eswa.2021.115224

Hezer, S., Gelmez, E., & Ozceylan, E. (2021). Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 regional safety assessment. Journal of Infection and Public Health, 14(6), 775–786. https://doi.org/10.1016/j.jiph.2021.03.003

Kim, J. H., & Ahn, B. S. (2019). Extended VIKOR method using incomplete criteria weights. Expert Systems with Applications, 126, 124–132. https://doi.org/10.1016/j.eswa.2019.02.019

Downloads

Published

2026-01-02

How to Cite

Shinde, K., Bendre, P., Kundargi, P., Sanap, A., & Kamble, A. (2026). Selection of Electric Vehicle Battery using multi attribute decision making method. International Journal of Combinatorial Optimization Problems and Informatics, 17(1), 239–252. https://doi.org/10.61467/2007.1558.2026.v17i1.383

Issue

Vol. 17 No. 1 (2026)

Section

Articles

License

Copyright (c) 2025 International Journal of Combinatorial Optimization Problems and Informatics

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.