Data-Driven Prediction and Feature Impact Analysis of Power Converter Performance in Electric Vehicles

Guang  Zhu; Guanghui  Fan; Yajuan  Liu

doi:10.6180/jase.202607_30.015

Data-Driven Prediction and Feature Impact Analysis of Power Converter Performance in Electric Vehicles

Guang Zhu¹This email address is being protected from spambots. You need JavaScript enabled to view it., Guanghui Fan², and Yajuan Liu¹

¹Zhengzhou University of Science and Technology; Zhengzhou Henan, 450064, China

²Henan Province Engineering Research Center of Digitization Intelligent Equipment; Zhengzhou Henan, 450064, China

Received: June 16, 2025
Accepted: October 12, 2025
Publication Date: December 21, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202607_30.015

Power converters, the backbone of electric vehicles (EVs), guarantee energy transfer and system reliability. Optimal design and operation depend on accurate prediction performance. An optimal Machine Learning (ML) regression model based on eXtreme Gradient Boosting (XGB) for forecasting power converter performance was developed. For closer prediction accuracy, three nature-inspired optimization algorithms, Crocodile Hunting Strategy (CHS), Chimp Optimization Algorithm (COA), and Weevil Damage Optimization (WDO), were combined. These algorithms iteratively tuned the hyperparameters of XGB, leading to better convergence and lower prediction errors. A thorough feature selection procedure identified the most significant parameters that induced model performance. The most influential factor affecting model performance was switching frequency (f-sw), with a feature importance score of 102.607, highlighting its decisive influence on converter efficiency. Efficiency ranked as the second most influential factor, with a score of 87.633, demonstrating its role in energy efficiency. Iteration count (iter) likewise exhibited immense influence with a score of 55.570 and highlighted computational iterations’ role in model stability. The integration of XGB and CHS, COA, and WDO resulted in superior prediction accuracy compared to conventional approaches. The optimized model advances understanding of decisive factors influencing converters’ performance and enables more efficient and more reliable power electronics for EVs. These results emphasize the hybrid ML and nature-inspired optimizations’ capability to push EV technology further. Future work must examine more features and real-world testing to support further the model’s viability in various EV systems towards a wider implementation of sustainable transportation technologies.

Keywords: Power converters, EVs, ML, Metaheuristic Algorithm

[1] W.-L. Shang, J. Zhang, K. Wang, H. Yang, and W. Ochieng, (2024) “Can financial subsidy increase electric vehicle (EV) penetration—evidence from a quasi-natural experiment" Renewable and Sustainable Energy Re views 190: 114021. DOI: 10.1016/j.rser.2023.114021.
[2] H.Jiang,H.Xu,Q.Liu,L.Ma,andJ.Song,(2024)“An urban planning perspective on enhancing electric vehicle (EV) adoption: Evidence from Beijing" Travel behaviour and society 34: 100712. DOI: 10.1016/j.tbs.2023.100712.
[3] B. B. Gicha, L. T. Tufa, and J. Lee, (2024) “The electric vehicle revolution in Sub-Saharan Africa: Trends, challenges, and opportunities" Energy Strategy Reviews 53: 101384. DOI: 10.1016/j.esr.2024.101384.
[4] J. Niu, X. Li, Z. Tian, and H. Yang, (2024) “Uncertainty analysis of the electric vehicle potential for a household to enhance robustness in decision on the EV/V2H technologies" Applied Energy 365: 123294. DOI: 10.1016/j.apenergy.2024.123294.
[5] G. Adu-Gyamfi, A. N. Asamoah, B. Obuobi, E. Nke tiah, and M. Zhang, (2024) “Electric mobility in an oil-producing developing nation: Empirical assessment of electric vehicle adoption" Technological Forecasting and Social Change 200: 123173. DOI: 10.1016/j.techfore.2023.123173.
[6] D.F. HakamandS. Jumayla, (2024) “Electric vehicle adoption in Indonesia: Lesson learned from developed and developing countries" Sustainable Futures 8: 100348. DOI: 10.1016/j.sftr.2024.100348.
[7] P. Ramesh, P. K. Gouda, K. Lakshmikhandan, G. Ramanathan, and C. Bharatiraja, (2022) “A three port bidirectional DC-DC converter for PV–Battery–DC microgrid application using fuzzy logic control" Materials Today: Proceedings 68: 1898–1905. DOI: 10.1016/j.matpr.2022.08.066.
[8] R. Gopalasami, B. Chokkalingam, R. Verma, and J. L. Munda, (2024) “A dual-stage high-gain converter with dual inputs and dual outputs for electric vehicle charging" Heliyon 10: DOI: 10.1016/j.heliyon.2024.e38048.
[9] Y.-K. Chen, Y.-C. Wu, C.-C. Song, and Y.-S. Chen, (2012) “Design and implementation of energy management system with fuzzy control for DC microgrid systems" IEEE Transactions on power electronics 28: 1563–1570. DOI: 10.1109/TPEL.2012.2210446.
[10] Q. Tian, G. Zhou, R. Liu, X. Zhang, and M. Leng, (2020) “Topology synthesis of a family of integrated three-port converters for renewable energy system applications" IEEE Transactions on Industrial Electronics 68: 5833–5846. DOI: 10.1109/TIE.2020.2994864.
[11] O. Alatise, R. Wu, A. Deb, and J. O. Gonzalez, (2025) “Second life potential of electric vehicle power electronics for more circular economies" Renewable and Sustainable Energy Reviews 210: 115238. DOI: 10.1016/j.rser.2024.115238.
[12] J. Rocha, S. Amin, S. Coelho, G. Rego, J. L. Afonso, and V. Monteiro, (2025) “Design and Implementation of a DC–DC resonant LLC converter for electric vehicle fast chargers" Energies 18: 1099. DOI: 10.3390/en18051099.
[13] V.SudevandM.R.Sindhu,(2025)“State-of-the-art and future trends in electric vehicle charging infrastructure: A review" Engineering Science and Technology, an International Journal 62: 101946. DOI: 10.1016/j. jestch.2025.101946.
[14] N. Sinenian, E. Urban, and M. Kuykendal. “Advances in power converters”. In: Elsevier, 2025, 1 41. DOI: 10.1016/B978-0-443-13442-5.00006-5.
[15] H. Li, J. Qiu, K. Zhang, and B. Zheng, (2025) “Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives" Frontiers of Environmental Science & Engineering 19: 2. DOI: 10.1007/s11783-025-1922-x.
[16] D. Kumari, S. Shashwat, P. K. Verma, and A. K. Giri, (2025) “Examining the nexus between carbon dioxide emissions, economic growth, fossil fuel energy use, urbanization and renewable energy towards achieving environmental sustainability in India" International Journal of Energy Sector Management 19: 731–746. DOI: 10.1108/IJESM-05-2024-0007.
[17] Y. Miao, A. A. A. Bukhari, W. A. A. Bukhari, S. Ah mad, andN.Hayat, (2025) “Why fossil fuels stifle green economic growth? An environmental management perspective in assessing the spatial spillover impact of energy consumption in South Asia" Journal of Environmental Management 373: 123471. DOI: 10.1016/j.jenvman. 2024.123471.
[18] T. Abedin, J. Pasupuleti, J. K. S. Paw, Y. C. Tak, M. Mahmud, M. P. Abdullah, and M. Nur-E-Alam, (2025) “Proton exchange membrane fuel cells in electric vehicles: Innovations, challenges, and pathways to sus tainability" Journal of Power Sources 640: 236769. DOI: 10.1016/j.jpowsour.2025.236769.
[19] I. E. Anufriev, B. Khusainov, A. Tick, T. Devezas, A. Sarygulov, and S. Kaimoldina, (2025) “Mathematical model for assessing new, non-fossil fuel technological prod ucts (li-Ion batteries and electric vehicle)" Mathematics 13: 143. DOI: 10.3390/math13010143.
[20] E. Yamini, M. Zarnoush, M. Jalilvand, S. M. Zolfaghari, F. Esmaeilion, A. Taklifi, D. A. Garcia, and M. Soltani, (2025) “Integration of emerging technologies in next-generation electric vehicles: Evolution, advancements, and regulatory prospects" Results in Engineering: 104082. DOI: 10.1016/j.rineng.2025.104082.
[21] W. Pan and E. Shittu, (2025) “Assessment of mobility decarbonization with carbon tax policies and electric vehicle incentives in the US" Applied Energy 379: 124838. DOI: 10.1016/j.apenergy.2024.124838.
[22] M. Waseem, G. S. Lakshmi, M. Ahmad, and M. Suhaib, (2025) “Energy storage technology and its impact in electric vehicle: Current progress and future outlook" Next Energy 6: 100202. DOI: 10.1016/j.nxener.2024. 100202.
[23] B.M.Patel,N.R.Patel,K.L.Mokariya,andK.I.Patel. “Challenges to EV Adoption with Future Forecasting Sales Market in India”. In: Springer, 2025, 145–169. DOI: 10.1007/978-981-96-0361-9_8.
[24] S. B. Verma and A. K. Pandey, (2025) “Optimized Charging Scheme Using Resonant Converter Technology for Electric Vehicles (EV) Battery Charging System." International Energy Journal 25: DOI: 10.64289/iej.25.01a05.8309892.
[25] G. A. Samawi, O. M. Bwaliez, M. Jreissat, and A. Kandas, (2025) “Advancing sustainable development in Jordan: A business and economic analysis of electric vehicle adoption in the transportation sector" World Electric Vehicle Journal 16: 45. DOI: 10.3390/wevj16010045.
[26] Y. Tong, I. Salhi, Q. Wang, G. Lu, and S. Wu, (2025) “Bidirectional DC-DC Converter Topologies for Hybrid Energy Storage Systems in Electric Vehicles: A Com prehensive Review" Energies 18: 2312. DOI: 10.3390/en18092312.
[27] S. Meraj, S. Mekhilef, M. Mubin, H. K. Ramiah, M. Seyedmahmoudian, and A. Stojcevski, (2025) “Bidi rectional Wireless Charging System for Electric Vehicles: A Review of Power Converters and Control Techniques in V2GApplication"IEEEAccess: DOI:10.1109/ACCESS.2025.3561396.
[28] U. Sharma and B. Singh, (2025) “A bidirectional charging system with the capability to charge electric vehicles with low-voltage powered batteries" Electric Power Systems Research 243: 111501. DOI: 10.1016/j.epsr.2025.111501.
[29] H. Zhang, Z. Li, X. Wang, and H. Deng, (2025) “Modeling and flexible power control of electric vehicle wireless charging applied to building side AC/DC power distribution and consumption on building side" Journal of Power Electronics: 1–10. DOI: 10.1007/s43236-025 00991-w.
[30] S. Sruthi, K. Karthikumar, and P. Chandrasekar, (2025) “Efficient energy management system for AC–DC microgrid and electric vehicles utilizing renewable energy with HCO approach" Energy Storage 7: e70054. DOI: 10.1002/est2.70054.
[31] B. Liu, (2025) “Research on the Management of Hybrid AC/DC Microgrids for Electric Vehicles Based on Innovative Algorithms" International Journal of High Speed Electronics and Systems: 2540279. DOI: 10.1142/ S0129156425402797.
[32] S. F. Tie and C. W. Tan, (2013) “A review of energy sources and energy management system in electric vehicles" Renewable and sustainable energy reviews 20: 82–102. DOI: 10.1016/j.rser.2012.11.077.
[33] N. Jinrui, S. Fengchun, and R. Qinglian. “A study of energy management system of electric vehicles”. In: 2006 IEEE vehicle power and propulsion conference. IEEE, 2006, 1–6. DOI: 10.1109/VPPC.2006.364301.
[34] B. Siddique, T. S. Alomar, M. H. Tahir, N. Al Masoud, and Z. M. El-Bahy, (2025) “Designing of small molecule donors with the help of machine learning for organic so lar cells and performance prediction" Journal of Photochemistry and Photobiology A: Chemistry 459: 116026. DOI: 10.1016/j.jphotochem.2024.116026.
[35] A. Maoucha, T. Berghout, F. Djeffal, and H. Ferhati, (2025) “Machine learning-guided analysis of CIGS solar cell efficiency: Deep learning classification and feature importance evaluation" Solar Energy 287: 113251. DOI: 10.1016/j.solener.2025.113251.
[36] K. A. Nirmal, T. D. Dongale, S. S. Sutar, A. C. Khot, and T. G. Kim, (2025) “Machine learning empowers efficient design of ternary organic solar cells with PM6 donor" Journal of Energy Chemistry 100: 337–347. DOI: 10.1016/j.jechem.2024.08.052.
[37] S. M. K. Alam, P. Li, M. Rahman, M. Fida, and V. Elumalai, (2025) “Key factors affecting groundwater nitrate levels in the Yinchuan Region, Northwest China: Research using the eXtreme Gradient Boosting (XGBoost) model with the SHapley Additive exPlanations (SHAP) method" Environmental Pollution 364: 125336. DOI: 10.1016/j.envpol.2024.125336.
[38] Z. Hussain, (2025) “Employing crocodile hunting search approaches to manage renewable energy-based microgrid" NTU Journal of Renewable Energy 8: 24–32. DOI: 10.56286/ezj1zd38.
[39] S. Arunachalam, A. K. V. Kumar, D. N. Reddy, H. Pathipati, N. I. Priyadarsini, and L. N. B. Ramisetti, (2025) “Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks" Int J Reconfigurable & Embedded Syst 14: 221–230. DOI: 10.11591/ijres.v14.i1.pp221-230.
[40] F. Gao, Q. Zhang, A. H. Hamadou, and B. Xu, (2025) “Enhanced wheat flour safety through impact treatment: Effective control of Tribolium castaneum (Herbst)(Coleoptera: Tenebrionidae)" Journal of Stored Products Research 111: 102513. DOI: 10.1016/j.jspr. 2024.102513.

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