Multicriteria assessment of technical performance in battery electric vehicles

Paulina Szterlik-Grzybek; Aleksandra Bartosiewicz; Adam Kucharski

doi:10.61089/aot2026.hct7pq13

Authors

Paulina Szterlik-Grzybek Department of Operational Research and Logistics, University of Łódź, Łódź, Poland Author https://orcid.org/0000-0003-1792-4691
Aleksandra Bartosiewicz Department of Operational Research and Logistics, University of Łódź, Łódź, Poland Author https://orcid.org/0000-0002-6464-9277
Adam Kucharski Department of Operational Research and Logistics, University of Łódź, Łódź, Poland Author https://orcid.org/0000-0001-8699-7566

DOI:

https://doi.org/10.61089/aot2026.hct7pq13

Keywords:

Battery electric vehicles, Technological competitiveness, Multicriteria decision-making, TOPSIS, Chinese electric vehicles

Abstract

The rapid electrification of road transport is reshaping the automotive market, particularly in Europe, where battery-electric vehicles (BEVs) are becoming increasingly prevalent. Evaluating vehicle-level technological performance is critical for understanding competitiveness in this evolving landscape. This study aims to systematically assess and rank 174 BEV models in the B and C segments, considering technical and performance criteria, to identify technologically competitive vehicles and to compare the technological performance of Chinese manufacturers with that of established global brands. Eight technical indicators were selected to capture key dimensions of competitiveness: driving range, battery capacity, energy efficiency, fast-charging speed, maximum speed, trunk capacity, acceleration, and full-charging time. An integrated multicriteria decision-making (MCDM) framework was applied, combining Shannon’s entropy, the CRITIC method for objective weighting, the TOPSIS method for ranking, and the Borda count for consensus aggregation. The proposed MCDM framework offers a replicable and robust approach for benchmarking technological performance across heterogeneous BEV models. The analysis reveals that European manufacturers, particularly German brands, continue to dominate the highest-ranking positions due to well-established engineering capabilities, long-term investment in innovation, and patent-based technological leadership. Chinese producers included in the sample, while representing a growing share of the market, display strong performance in battery technology, cost efficiency, and manufacturing scale. Yet, their presence in the European B and C segments remains limited. The results further highlight that BEV competitiveness is multidimensional, reflecting trade-offs between performance, efficiency, and usability. C-segment vehicles tend to prioritise extended driving range and larger battery capacity, whereas B-segment models focus on energy efficiency and compact design, catering to urban mobility requirements. The study demonstrates that a single attribute cannot determine technological competitiveness; rather, it depends on a balanced combination of multiple criteria. These findings provide policymakers, manufacturers, and investors with insights into the technological strengths and weaknesses of contemporary BEV models, supporting evidence-based discussions on sustainable mobility and industrial development.

References

1. Adnan, N., Nordin, S. M., Amini, M. H., & Langove, N. (2018). What make consumer sign up to PHEVs? Predicting Malaysian consumer behavior in adoption of PHEVs. Transportation Research Part A. Policy and Practice, 113, 259–278. https://doi.org/10.1016/j.tra.2018.04.007

2. Aiginger, K. (2006). Competitiveness: From a dangerous obsession to a welfare-creating ability with positive externalities. Journal of Industry, Competition and Trade, 6(2), 161–177. https://doi.org/10.1007/s10842-006-9475-6

1. Alanazi, F. (2023). Electric vehicles: Benefits, challenges, and potential solutions for widespread adaptation. Applied Sciences, 13(10), 6016. https://doi.org/10.3390/app13106016

2. Altenburg, T., Corrocher, N., & Malerba, F. (2022). China's leapfrogging in electromobility. A story of green transformation driving catch-up and competitive advantage. Technological Forecasting and Social Change, 183: 121914. https://doi.org/10.1016/j.techfore.2022.121914

3. Ambastha, A., & Momaya, K. (2004). Competitiveness of firms: Review of theory, frameworks and models. Singapore Management Review, 26(1), 45–61.

4. Ashraf, F., Equbal, A., & Khan, O. (2025). Assessment and ranking of different vehicles carbon footprint: A comparative study utilising entropy and TOPSIS methodologies. Green Technologies and Sustainability, 3(1), 100128. https://doi.org/10.1016/j.grets.2024.100128

5. Bartosiewicz, A., & Jadczak, R. (2023). Multicriteria analysis of the competitiveness of major Baltic Sea container terminals. Comparative Economic Research. Central and Eastern Europe, 26(2), 163–178. https://doi.org/10.18778/1508-2008.26.18

6. Bencivelli, L., Jorra, M., Lajer Baron, A., Suárez-Varela, M., & Vuletic, M. (2024). The rise of electric vehicles in China and their impact in the EU. Economic Bulletin, 4(03). https://doi.org/10.53479/37853

7. Costa, H. G. (2017). AHP-De Borda: A hybrid multicriteria ranking method. Journal of Operations & Production Management, 14(3), 281–290. https://doi.org/10.14488/BJOPM.2017.v14.n3.a1

8. Diakoulaki D., Mavrotas G., & Papayannakis L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-H

9. Dua, R., Almutairi, S., & Bansal, P. (2024). Emerging energy economics and policy research priorities for enabling the electric vehicle sector. Energy Reports, 12, 1836–1847. https://doi.org/10.1016/j.egyr.2024.08.001

10. Dwivedi, P. P., & Sharma, D. K. (2023). Evaluation and ranking of battery electric vehicles by Shannon’s entropy and TOPSIS methods. Mathematics and Computers in Simulation, 212, 457–474. https://doi.org/10.1016/j.matcom.2023.05.013

11. European Alternative Fuels Observatory (EAFO). Dissecting the Drop: How Policy Changes Affect BEV Sales Data in the EU. Available at: https://alternative-fuels-observatory.ec.europa.eu/general-information/news/dissecting-drop-how-policy-changes-affect-bev-sales-data-eu [Accessed 16 August 2025].

12. Eurostat. Passenger cars in the EU. Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Passenger_cars_in_the_EU [Accessed 10 March 2026].

13. Fan, J., Han, Y., & Wu, M. (2025). CRITIC-TOPSIS method based on pairwise preference graph neural similarity and its application in vehicle selection. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-024-01956-0

14. Faraz, A., Ambikapathy, A., Thangavel, S., Logavani, K., & Arun Prasad, G. (2021). Battery Electric Vehicles (BEVs). In: Patel, N., Bhoi, A.K., Padmanaban, S., & Holm-Nielsen, J.B. (eds) Electric Vehicles. Green Energy and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-9251-5_8

15. Golui, S., Mahapatra, B. S., & Mahapatra, G. S. (2023). A new correlation-based measure on Fermatean fuzzy applied on multicriteria decision making for electric vehicle selection. Expert Systems with Applications 237: 121605. https://doi.org/10.1016/j.eswa.2023.121605

16. Gökgöz, F., & Yalçın, E. (2024). Analysing the performances and efficiencies of European electric vehicles in the European Union market using decision analysis methods. Transportation Research Record 2678(10), 1039–1056. https://doi.org/10.1177/03611981241236474

17. Gómez Vilchez, J. J., Smyth, A., Kelleher, L., Lu, H., Rohr, C., Harrison, G., & Thiel, C. (2019). Electric Car Purchase Price as a Factor Determining Consumers’ Choice and Their Views on Incentives in Europe. Sustainability 11(22): 6357. https://doi.org/10.3390/su11226357

18. Gupta, N., Saini, C. P., Dangi, A., & Akhtar, M. T. (2022). Unified AHP-TOPSIS and DEA technique for the adoption and performance evaluation of green transportation alternatives in India. International Journal of Vehicle Design, 88(2–4), 260–282. https://doi.org/10.1504/IJVD.2022.127019

19. Hassan, I., Alhamrouni, I., & Azhan, N. H. (2023). A CRITIC–TOPSIS multicriteria decision-making approach for optimum site selection for solar PV farm. Energies, 16(10), 4245. https://doi.org/10.3390/en16104245

20. International Energy Agency (IEA). Global EV Outlook 2025, Paris. Available at: https://www.iea.org/reports/global-ev-outlook-2025/trends-in-electric-car-markets-2 [Accessed 16 August 2025].

21. Jena, R. (2020). An empirical case study on Indian consumers’ sentiment towards electric vehicles: A big data analytics approach. Industrial Marketing Management, 90, 605–616. https://doi.org/10.1016/j.indmarman.2019.12.012

22. Jurlin, K. (2023). How Efficient and Socially Sensitive Are Fiscal Incentives for Electric Cars in Europe? Journal of Risk and Financial Management 16(6), 283. https://doi.org/10.3390/jrfm16060283

23. Kaczyńska, A., Sulikowski, P., Wątróbski, J., & Sałabun, W. (2023). Enhancing sustainable assessment of electric vehicles: A comparative study of the TOPSIS technique with interval numbers for uncertainty management. Energies, 16(18), 6652. https://doi.org/10.3390/en16186652

24. Khaleel, M., Nassar, Y., El-Khozondar, H., Elmnif, M., Rajab, Z., Yaghoubi, E., & Yaghoubi, E. (2024). Electric Vehicles in China, Europe, and the United States: Current Trends and Market Comparison. IJEES International Journal of Electrical Engineering and Sustainability 2(1): 1-20. https://doi.org/10.65998/ijees.v2i1.70

25. Konka, B., & Veres, A. (2022). Overview of European Patents in Germany, France and Spain, with a Potential Application to the Development of Electric Vehicles. Proceedings of the European Union’s Contention in the Reshaping Global Economy, 265–84. Szeged, Hungary: Szegedi Tudományegyetem Gazdaságtudományi Kar, https://doi.org/10.14232/eucrge.2022.17

26. Kucharski, A., & Lekka-Porębska, I. (2023). Implementation of electric road transport – Poland in comparison with European countries. Scientific Papers of Silesian University of Technology. Organisation and Management, 187, 353–378. https://doi.org/10.29119/1641-3466.2023.187.19

27. Lieven, T. (2015). Policy measures to promote electric mobility–A global perspective. Transportation Research Part A: Policy and Practice, 82, 78–93. https://doi.org/10.1016/j.tra.2015.09.008

28. Malik, M. A. I., Kalam, M. A., Ikram, A., Zeeshan, S., & Zahidi, S. Q. R. (2025). Energy transition towards electric vehicle technology: Recent advancements. Energy Reports, 13, 2958–2996. https://doi.org/10.1016/j.egyr.2025.02.029

29. Mariasiu, F., Chereches, I., & Raboca, H. (2023). Statistical Analysis of the Interdependence between the Technical and Functional Parameters of Electric Vehicles in the European Market. Energies 16(7), 2974. https://doi.org/10.3390/en16072974

30. Möring-Martínez, G., Senzeybek, M., Jochem, P. (2024). Clustering the European Union Electric Vehicle Markets: A Scenario Analysis until 2035. Transportation Research Part D: Transport and Environment 135: 104372. https://doi.org/10.1016/j.trd.2024.104372

31. Onat, N. C., Gumus, S., Kucukvar, M., & Tatari, Ö. (2015). Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies. Sustainable Production and Consumption 6, 12–25. https://doi.org/10.1016/j.spc.2015.12.003

32. Pal, K., Saraswat, D., & Budhraja, N. (2023). An integrated entropy-TOPSIS approach for electric vehicle selection. International Journal of Experimental Research and Review, 36, 311–318. https://doi.org/10.52756/ijerr.2023.v36.028

33. Peng, R., Tang, J. H. C. G., Yang, X., Meng, M., Zhang, J., & Zhuge, C. (2024). Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States. Applied Energy 355, 122327. https://doi.org/10.1016/j.apenergy.2023.122327

34. Pichler, M., Krenmayr, N., Schneider, E., & Brand, U. (2021). EU industrial policy: Between modernisation and transformation of the automotive industry. Environmental Innovation and Societal Transitions, 38, 140–152. https://doi.org/10.1016/j.eist.2020.12.002

35. Puška, A. (2025). Application of new method evaluation by distance from ideal solution of alternatives in the assessment of electric vehicles. Advanced Engineering Letters 4(2), 92–103. https://doi.org/10.46793/adeletters.2025.4.2.5

36. Razmjoo, A, Ghazanfari, A., Jahangiri, M., Franklin, E., Denai, M., Marzband, M., Garcia, D.A., & Maheri, A. (2022). A Comprehensive Study on the Expansion of Electric Vehicles in Europe. Applied Sciences 12(22): 11656. https://doi.org/10.3390/app122211656

37. Samaie, F., Javadi, S., Meyar-Naimi, H., & Feshki-Farahani, H. (2020). Environmental sustainability policy on plug-in hybrid electric vehicle penetration utilising fuzzy TOPSIS and game theory. Clean Technologies and Environmental Policy, 22, 787–801. https://doi.org/10.1007/s10098-020-01821-2

38. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x

39. Shao, J. (2024). How does local context matter? Assessing the heterogeneous impact of electric vehicle incentive policies in China. Journal of Cleaner Production, 464, 142770. https://doi.org/10.1016/j.jclepro.2024.142770

40. Sierzchula, W., Bakker, S., Maat, K., & Van Wee, B. (2014). The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68, 183–194. https://doi.org/10.1016/j.enpol.2014.01.043

41. Sinha, G. K., & Chattopadhyay, N. (2024). Multicriteria evaluation of electric vehicles alternatives for sustainable transportation using Fuzzy AHP and Fuzzy TOPSIS. International Journal of Environmental Protection, 44(12), 1069–1080.

42. Song, Y., Li, Y., Zhu, H., & Li, G. (2023). A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior. Journal of Retailing and Consumer Services, 73, 103303. https://doi.org/10.1016/j.jretconser.2023.103303

43. Tadić, D., Lukić, J., Komatina, N., Marinković, D., & Pamučar, D. (2025). A fuzzy decision-making approach to electric vehicle evaluation and ranking. Tehnički Vjesnik 32(3), 1066–1075. https://doi.org/10.17559/TV-20250223002410

44. Tian, Z., Liang, H., Nie, R., Wang, X., & Wang, J. (2023). Data-driven multicriteria decision support method for electric vehicle selection. Computers & Industrial Engineering, 177, 109061. https://doi.org/10.1016/j.cie.2023.109061

45. Zhang, Q., Fan, J., & Gao, C. (2024). CRITID: Enhancing CRITIC with advanced independence testing for robust multicriteria decision-making, Reports, 14, 25094. https://doi.org/10.1038/s41598-024-75992-z

46. Zhao, X., Li, X., Jiao, D., Mao, Y., Sun, J., & Liu, G. (2024). Policy incentives and electric vehicle adoption in China: From a perspective of policy mixes. Transportation Research Part A: Policy and Practice, 190, 104235. https://doi.org/10.1016/j.tra.2024.104235

47. Zhou, X., Zhao, R., Cheng, L., & Min, X. (2019). Impact of policy incentives on electric vehicles development: A system dynamics-based evolutionary game theoretical analysis. Clean Technologies and Environmental Policy, 21(5), 1039–1053. https://doi.org/10.1007/s10098-019-01691-3