The rapid proliferation of electric vehicles (EVs) has intensified the demand for advanced charging infrastructure capable of delivering reliable, efficient, and grid-aware power management. Conventional unidirectional chargers are inadequate for modern smart-grid applications, as they preclude the utilization of stored battery energy for grid support functions. This paper presents the design, mathematical modeling, and simulation-based validation of a non-isolated bidirectional DC–DC buck–boost converter rated at 20 kW for EV charging station integration. The proposed converter operates on a 700 V DC bus and interfaces with a 50 kWh lithium-ion (Li-ion) EV battery pack having a nominal voltage of 400 V (range: 320–450 V). A dual-loop control architecture comprising an outer voltage regulation loop and an inner current tracking loop is implemented, with proportional-integral (PI) controllers designed via frequency-domain methods to guarantee closed-loop stability. The converter supports bidirectional power flow, enabling both Grid-to-Vehicle (G2V) charging and Vehicle-to-Grid (V2G) discharging modes. Simulation results obtained in MATLAB/Simulink (Simscape Electrical) demonstrate a peak power conversion efficiency of 96.2% at rated load, battery current settling time below 18 ms, and DC bus voltage regulation within ±1.5% under step-load perturbations. Grid support capabilities including peak shaving, load leveling, and reactive power assistance are analyzed and discussed. The proposed system exhibits stable mode transitions with minimal transient overshoot, confirming its suitability for practical smart EV charging station deployment.

Keywords - Bidirectional DC–DC converter; EV charging; V2G; Grid support; Power electronics; Dual-loop control.

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1. Vosoughi A, Samimi MH. Evaluation of the image processing technique in interpretation of polar plot characteristics of transformer frequency response. In2022 International Conference on Machine Vision and Image Processing (MVIP) 2022 Feb 23 (pp. 1-6). IEEE.
2. Zhao X, Wu G, Yang D, Xu G, Xing Y, Yao C, Abu-Siada A. Enhanced detection of power transformer winding faults through 3D FRA signatures and image processing techniques. Electric Power Systems Research. 2025 May 1;242:111433.
3. Godase V, Pawar P, Nagane S, Kumbhar S. Automatic railway horn system using node MCU. Journal of Control & Instrumentation. 2024 Jan;15(1).
4. Aljohani O, Abu-Siada A. Application of digital image processing to detect transformer bushing faults and oil degradation using FRA polar plot signature. IEEE Transactions on Dielectrics and Electrical Insulation. 2017 Mar 7;24(1):428-36.
5. Aljohani O, Abu-Siada A. Application of DIP to detect power transformers axial displacement and disk space variation using FRA polar plot signature. IEEE Transactions on Industrial Informatics. 2016 Nov 8;13(4):1794-805.
6. Abu‐Siada A, Radwan I, Abdou AF. 3D approach for fault identification within power transformers using frequency response analysis. IET Science, Measurement & Technology. 2019 Aug;13(6):903-11.
7. Zhao Z, Liu J, Tang C, Zhou Q, Gui Y. Classification of transformer winding deformation fault types by FRA polar plot and multiple SVM classifiers. In2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE) 2020 Sep 6 (pp. 1-4). IEEE.
8. Zhou L, Zang J, Zhao C, Qian G, Fu A, Wang D, Guo L, Jiang F. Detection for axial displacement fault of winding in autotransformer based on dynamic frequency division of FRA and HHO-RF. IEEE Sensors Journal. 2024 Sep 2;24(20):32619-29.
9. Li A, Zhang L, Liu Y, Zhu C. Exploring frequency-inspired optimization in transformer for efficient single image super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2025 Jan 24;47(4):3141-58.
10. Ou X, Jin Y, Yang X, Cai L, Ma Z, Lin C, Liu J. Classifying transformer winding deformation type by combination of FRA polar plot texture feature and multiple SVM classifiers. InEighth International Conference on Energy System, Electricity, and Power (ESEP 2023) 2024 May 13 (Vol. 13159, pp. 1365-1376). SPIE.
11. Abu-Siada A, Mosaad MI, Kim D, El-Naggar MF. Estimating power transformer high frequency model parameters using frequency response analysis. IEEE Transactions on Power Delivery. 2019 Aug 28;35(3):1267-77.
12. Sasagawa K, Okada R, Mizuno M, Takehara H, Haruta M, Tashiro H, Ohta J. Enhancing image reconstruction method in high-frequency electric field visualization systems using a polarized light image sensor. Sensors. 2025 Mar 5;25(5):1596.
13. Beheshti Asl M, Fofana I, Meghnefi F, Brahami Y, Souza JP. A comprehensive review of transformer winding diagnostics: Integrating frequency response analysis with machine learning approaches. Energies. 2025 Mar 1;18(5):1209.
14. Akhmetov Y, Nurmanova V, Bagheri M, Zollanvari A, Gharehpetian GB. A bootstrapping solution for effective interpretation of transformer winding frequency response. IEEE Transactions on Instrumentation and Measurement. 2022 Mar 11;71:1-1.
15. Shao M, Qiao Y, Meng D, Zuo W. Uncertainty-guided hierarchical frequency domain transformer for image restoration. Knowledge-Based Systems. 2023 Mar 5;263:110306.
16. Li Z, He Y, Xing Z, Chen M. Minor fault diagnosis of transformer winding using polar plot based on frequency response analysis. International Journal of Electrical Power & Energy Systems. 2023 Oct 1;152:109173.
17. Sharada KA, Kathiravan A, Mannam P, Akila D, Kumar BS, Godase VV. Advanced neural network models for optimal energy management in microgrids with integrated electric vehicles. In2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM) 2025 Apr 7 (pp. 1869-1874). IEEE.
18. Lowry SN, Flood JM, Cheeran GR, Reid ME, Collier CM. Spatial polarization modulation for terahertz single-pixel imaging. IEEE Transactions on Terahertz Science and Technology. 2024 Apr 11;14(3):386-94.
19. Pan Y, Zhao H. Color polarization image fusion algorithm based on wavelet transform. InFourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024) 2024 Sep 20 (Vol. 13269, pp. 22-26). SPIE.
20. Wang Y, Li JW, Chen J, Xu HP, Sun B. A parameter-adjusting polar format algorithm for extremely high squint SAR imaging. IEEE transactions on geoscience and remote sensing. 2013 Mar 13;52(1):640-50.
21. Dange R, Attar E, Ghodake P, Godase V. Smart agriculture automation using ESP8266 NodeMCU. J. Electron. Comput. Netw. Appl. Math,(35). 2023 Jul:1-9.
22. Godase V, Modi S, Misal V, Kulkarni S. LoRaEdge-ESP32 synergy: Revolutionizing farm weather data collection with low-power, long-range IoT. Advance Research in Analog and Digital Communications. 2025 Jul;2(2):1-1.

The rapid proliferation of electric vehicles (EVs) has intensified the demand for advanced charging infrastructure capable of delivering reliable, efficient, and grid-aware power management. Conventional unidirectional chargers are inadequate for modern smart-grid applications, as they preclude the utilization of stored battery energy for grid support functions. This paper presents the design, mathematical modeling, and simulation-based validation of a non-isolated bidirectional DC–DC buck–boost converter rated at 20 kW for EV charging station integration. The proposed converter operates on a 700 V DC bus and interfaces with a 50 kWh lithium-ion (Li-ion) EV battery pack having a nominal voltage of 400 V (range: 320–450 V). A dual-loop control architecture comprising an outer voltage regulation loop and an inner current tracking loop is implemented, with proportional-integral (PI) controllers designed via frequency-domain methods to guarantee closed-loop stability. The converter supports bidirectional power flow, enabling both Grid-to-Vehicle (G2V) charging and Vehicle-to-Grid (V2G) discharging modes. Simulation results obtained in MATLAB/Simulink (Simscape Electrical) demonstrate a peak power conversion efficiency of 96.2% at rated load, battery current settling time below 18 ms, and DC bus voltage regulation within ±1.5% under step-load perturbations. Grid support capabilities including peak shaving, load leveling, and reactive power assistance are analyzed and discussed. The proposed system exhibits stable mode transitions with minimal transient overshoot, confirming its suitability for practical smart EV charging station deployment.

Keywords - Bidirectional DC–DC converter; EV charging; V2G; Grid support; Power electronics; Dual-loop control.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Vosoughi A, Samimi MH. Evaluation of the image processing technique in interpretation of polar plot characteristics of transformer frequency response. In2022 International Conference on Machine Vision and Image Processing (MVIP) 2022 Feb 23 (pp. 1-6). IEEE.
2. Zhao X, Wu G, Yang D, Xu G, Xing Y, Yao C, Abu-Siada A. Enhanced detection of power transformer winding faults through 3D FRA signatures and image processing techniques. Electric Power Systems Research. 2025 May 1;242:111433.
3. Godase V, Pawar P, Nagane S, Kumbhar S. Automatic railway horn system using node MCU. Journal of Control & Instrumentation. 2024 Jan;15(1).
4. Aljohani O, Abu-Siada A. Application of digital image processing to detect transformer bushing faults and oil degradation using FRA polar plot signature. IEEE Transactions on Dielectrics and Electrical Insulation. 2017 Mar 7;24(1):428-36.
5. Aljohani O, Abu-Siada A. Application of DIP to detect power transformers axial displacement and disk space variation using FRA polar plot signature. IEEE Transactions on Industrial Informatics. 2016 Nov 8;13(4):1794-805.
6. Abu‐Siada A, Radwan I, Abdou AF. 3D approach for fault identification within power transformers using frequency response analysis. IET Science, Measurement & Technology. 2019 Aug;13(6):903-11.
7. Zhao Z, Liu J, Tang C, Zhou Q, Gui Y. Classification of transformer winding deformation fault types by FRA polar plot and multiple SVM classifiers. In2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE) 2020 Sep 6 (pp. 1-4). IEEE.
8. Zhou L, Zang J, Zhao C, Qian G, Fu A, Wang D, Guo L, Jiang F. Detection for axial displacement fault of winding in autotransformer based on dynamic frequency division of FRA and HHO-RF. IEEE Sensors Journal. 2024 Sep 2;24(20):32619-29.
9. Li A, Zhang L, Liu Y, Zhu C. Exploring frequency-inspired optimization in transformer for efficient single image super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2025 Jan 24;47(4):3141-58.
10. Ou X, Jin Y, Yang X, Cai L, Ma Z, Lin C, Liu J. Classifying transformer winding deformation type by combination of FRA polar plot texture feature and multiple SVM classifiers. InEighth International Conference on Energy System, Electricity, and Power (ESEP 2023) 2024 May 13 (Vol. 13159, pp. 1365-1376). SPIE.
11. Abu-Siada A, Mosaad MI, Kim D, El-Naggar MF. Estimating power transformer high frequency model parameters using frequency response analysis. IEEE Transactions on Power Delivery. 2019 Aug 28;35(3):1267-77.
12. Sasagawa K, Okada R, Mizuno M, Takehara H, Haruta M, Tashiro H, Ohta J. Enhancing image reconstruction method in high-frequency electric field visualization systems using a polarized light image sensor. Sensors. 2025 Mar 5;25(5):1596.
13. Beheshti Asl M, Fofana I, Meghnefi F, Brahami Y, Souza JP. A comprehensive review of transformer winding diagnostics: Integrating frequency response analysis with machine learning approaches. Energies. 2025 Mar 1;18(5):1209.
14. Akhmetov Y, Nurmanova V, Bagheri M, Zollanvari A, Gharehpetian GB. A bootstrapping solution for effective interpretation of transformer winding frequency response. IEEE Transactions on Instrumentation and Measurement. 2022 Mar 11;71:1-1.
15. Shao M, Qiao Y, Meng D, Zuo W. Uncertainty-guided hierarchical frequency domain transformer for image restoration. Knowledge-Based Systems. 2023 Mar 5;263:110306.
16. Li Z, He Y, Xing Z, Chen M. Minor fault diagnosis of transformer winding using polar plot based on frequency response analysis. International Journal of Electrical Power & Energy Systems. 2023 Oct 1;152:109173.
17. Sharada KA, Kathiravan A, Mannam P, Akila D, Kumar BS, Godase VV. Advanced neural network models for optimal energy management in microgrids with integrated electric vehicles. In2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM) 2025 Apr 7 (pp. 1869-1874). IEEE.
18. Lowry SN, Flood JM, Cheeran GR, Reid ME, Collier CM. Spatial polarization modulation for terahertz single-pixel imaging. IEEE Transactions on Terahertz Science and Technology. 2024 Apr 11;14(3):386-94.
19. Pan Y, Zhao H. Color polarization image fusion algorithm based on wavelet transform. InFourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024) 2024 Sep 20 (Vol. 13269, pp. 22-26). SPIE.
20. Wang Y, Li JW, Chen J, Xu HP, Sun B. A parameter-adjusting polar format algorithm for extremely high squint SAR imaging. IEEE transactions on geoscience and remote sensing. 2013 Mar 13;52(1):640-50.
21. Dange R, Attar E, Ghodake P, Godase V. Smart agriculture automation using ESP8266 NodeMCU. J. Electron. Comput. Netw. Appl. Math,(35). 2023 Jul:1-9.
22. Godase V, Modi S, Misal V, Kulkarni S. LoRaEdge-ESP32 synergy: Revolutionizing farm weather data collection with low-power, long-range IoT. Advance Research in Analog and Digital Communications. 2025 Jul;2(2):1-1.