Comparative Analysis of Modified PI and Fuzzy Logic Controllers Based High Current Density DC–DC Converter for EV Charging Applications

Abstract

Two control techniques—modified proportional-integral (PI) controller and fuzzy logic controller (FLC)—used in a high current density DC-DC converter for EV charging systems are compared in this work. Selecting a suitable control method is essential given the growing need for quick, trustworthy EV charging. Under given operating conditions, the modified PI controller is meant to lower output overshoot and power losses while preserving thermal efficiency. By contrast, without exact system modelling, the proposed FLC offers an adaptive, rule-based control approach that can dynamically react to variances in load and input voltage. Under both ideal and non-ideal environments, simulation and experimental evaluations were conducted to investigate converter performance factors including output voltage control, power loss, overshoot, and junction temperature. The FLC beats the modified PI controller in terms of adaptability, thermal stress reduction, and superior efficiency over a wider range of operating conditions even if it stays steady under set settings. The opportunities of fuzzy logic-based control for next-generation, fast-charging electric car infrastructure are underlined by this comparison study.

Introduction

Electric vehicles (EVs) are gaining popularity as environmentally friendly alternatives to conventional internal combustion vehicles due to their lower emissions, higher efficiency, and quieter operation. The key component of EVs is the battery, which stores energy for propulsion. EV charging systems typically involve two stages: AC-DC conversion (connecting to the grid) and DC-DC conversion (battery charging). Conventional AC-DC converters suffer from issues like high power losses, thermal stress, and poor power quality, which are mitigated by power factor correction techniques and filters. DC-DC converters face efficiency challenges and risks of voltage/current overshoot that can damage lithium-ion batteries.

To improve this, the text proposes a novel hybrid DC-DC converter controlled by a fuzzy logic controller (FLC). This FLC dynamically adjusts to varying input voltages and load conditions without needing extra sensors by using fuzzy rules based on input/output membership functions. This approach optimizes charging efficiency, prevents battery overcharging or deep discharge, and enhances overall system flexibility and reliability.

The system uses an LCL filter near the AC source to control total harmonic distortion and power factor, with MOSFETs and diodes managing DC output voltage. The AC-DC converter operates in two modes during the AC cycle to ensure a stable, unidirectional DC output.

Fuzzy Logic Control offers a powerful method for managing nonlinear, complex systems like EV chargers. It works by fuzzifying input errors, applying expert-based IF-THEN rules, aggregating results, and defuzzifying to produce control signals. The Mamdani-type fuzzy inference system used here is adaptable, intuitive, and effective for high-tech power applications such as battery charging in EVs.

Conclusion

In this study, a comprehensive comparison between the Fuzzy Logic Controller (FLC) and the modified PI controller was performed, focusing on critical performance parameters such as State of Charge (SoC) dynamics, terminal voltage regulation, charging current stability, transient response, and Total Harmonic Distortion (THD). The Fuzzy Logic Controller outperformed the modified PI controller in all aspects evaluated. While the redesigned PI controller showed a slower climb, the FLC displayed better dynamic behaviour and achieved a constant increase in SoC within a fraction of seconds. Under these fuzzy logic techniques, voltage control was more steady with fewer oscillations than in the modified PI controller, which showed more variations. Moreover, the FLC kept a very constant current profile with fast settling times and little ripples, which is absolutely important for reliable and effective battery charging. By contrast, demonstrating its poor performance in current control, the modified PI controller displayed notable current ripples and instability. After comparing the FLC to the modified PI controller, the harmonic distortion study confirmed its advantages.When compared to other control strategies, the fuzzy logic controller is superior because it improves energy management, voltage and current quality, transient reaction time, and significantly reduces harmonic distortions. These results bring attention to the FLC as a strong option for high-performance battery charging applications, highlighting its possibilities for uses that demand fast and dependable regulation and very high power quality standards.

References

[1] Phillips, M., Jankovic, M., and Bailey, K. E. (2000). Vehicle system controller design for a hybrid electric vehicle. in Proceedings of the 2000. IEEE International Conference on Control Applications. Conference Proceedings (Cat. No. 00CH37162), pp. 297–302. [2] Lü, X et al., (2020). Energy management of hybrid electricvehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm,” Energy Convers. Manag., vol. 205, p. 112474. [3] Hemavathi, S., and A. Shinisha. \"A study on trends and developments in electric vehicle charging technologies.\" Journal of Energy Storage 52 (2022): 105013. [4] Shariff, Samir M., Mohammad Saad Alam, Furkan Ahmad, Yasser Rafat,M. Syed Jamil Asghar, and Saadullah Khan. \"System design and realization of a solar-powered electric vehicle charging station.\" IEEE Systems Journal 14, no. 2 (2019): 2748-2758. [5] A. Alassi, A. Al-Aswad, A. Gastli, L. B. Brahim, and A. Massoud,‘‘Assessmentofisolatedandnon-isolatedDC–DCconvertersformedium-voltagePVapplications,’’inProc.9thIEEE-GCCConf.Exhib.(GCCCE),May 2017, pp. 1–6 [6] S. Das, M. R. Haque, and M. A. Razzak, ‘‘Development of one-kilowattcapacity single phase pure sine wave off-grid PV inverter,’’ in Proc.IEEE Region Symp. (TENSYMP), Jun. 2020, pp. 774–777 [7] M.M.Faruk,N.T.Khan,andM.A.Razzak,‘‘AnalysisoftheimpactofEVchargingonTHD,powerfactorandpowerqualityofdistributiongrid,’’inProc.Innov.PowerAdv.Comput.Technol.(i-PACT),Nov.2021,pp.1–6 [8] A.KumarandP.Kumar,‘‘Powerqualityimprovementforgrid-connectedPV system based on distribution static compensator with fuzzy logic con-troller and UVT/ADALINE-based least mean square controller,’’J. Mod-ern Power Syst. Clean Energy, vol. 9, no. 6, pp.1289–1299, 2021 [9] V. Nagamalleswari, S. R. Arya, S. Mallikharjun, and G. Sridhar, ‘‘Improve-ment in power quality for distribution system using momentum algorithm,’’in Proc. IEEE 2nd Int. Conf. Sustain. Energy Future Electric Transp.(SeFeT), Aug. 2022, pp. 1–5 [10] F.ZhengandW.Zhang,‘‘Longtermeffectofpowerfactorcorrectionontheindustrialload:Acasestudy,’’inProc.Australas.UniversitiesPowerEng.Conf.(AUPEC),Nov.2017,pp.1–5 [11] J.YeandH.B.Gooi,‘‘Phaseanglecontrolbasedthree-phaseDVRwithpowerfactorcorrectionatpointofcommoncoupling,’’J.Mod-ern Power Syst. Clean Energy, vol. 8, no. 1, pp.179–186, 2020 [12] S.S.SayedandA.M.Massoud,‘‘Reviewonstate-of-the-artunidirectionalnon-isolated power factor correction converters for short-/long-distanceelectric vehicles,’’ IEEE Access, vol. 10, pp.11308–11340, 2022 [13] K.Cao,X.Liu,M.He,X.Meng,andQ.Zhou,‘‘Active-clampresonant power factor correction converter with output ripplesuppression,’’ IEEE Access, vol. 9, pp.5260–5272, 2021 [14] J.-Y. Lin, P.-H. Liu, H.-Y. Yueh, and Y.-F. Lin, ‘‘Design of boost-typepower factor correction with stepped air-gap ferrite inductor for peak-power-loadcondition,’’IEEEAccess,vol.10,pp.57655–57664,2022 [15] S.Abdelhady,A.Osama,A.Shaban,andM.Elbayoumi,‘‘Areal-time optimization of reactive power for an intelligent system usinggenetic algorithm,’’ IEEE Access, vol. 8, pp.11991–12000, 2020 [16] G.Zhang,J.Zeng,S.S.Yu,W.Xiao,B.Zhang,S.-Z.Chen,andY.Zhang,‘‘Control design and performance analysis of a double-switched LLCresonantrectifierforunitypowerfactorandsoft-switching,’’IEEEAccess,vol. 8, pp. 44511–44521, 2020 [17] L.Sarker,M.Nazir,andM.A.Razzak,‘‘Harmonicsreductionandpowerfactor correction for electric vehicle charging system,’’ in Proc. Innov.PowerAdv.Comput.Technol.(i-PACT),Nov.2021,pp.1–6 [18] Castro, José FC, Davidson C. Marques, Luciano Tavares, Nicolau KL Dantas, Amanda L. Fernandes, Ji Tuo, Luiz HA de Medeiros, and Pedro Rosas. \"Energy and Demand Forecasting Based on Logistic Growth Method for Electric Vehicle Fast Charging Station Planning with PV Solar System.\" Energies 15, no. 17 (2022): 6106. [19] Alrubaie, Ali Jawad, Mohamed Salem, Khalid Yahya, Mahmoud Mohamed, and Mohamad Kamarol. \"A comprehensive review of electric vehicle charging stations with solar photovoltaic system considering market, technical requirements, network implications, and future challenges.\" Sustainability 15, no. 10 (2023): 8122. [20] Huang, Zhiyu, Zhilong Xie, Caizhi Zhang, Siew Hwa Chan, Jaros?aw Milewski, Yi Xie, Yalian Yang, and Xiaosong Hu. \"Modeling and multi-objective optimization of a stand-alone PV-hydrogen-retired EV battery hybrid energy system.\" Energy conversion and management 181 (2019): 80-92.

Copyright

Copyright © 2025 CH. Sajan, N. Anila, MD. Ameer Khan, B. Madhu, SD. Feroz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.