Design and Simulation of an Electric Vehicle (EV) Charger with a DC-DC Converter using MATLAB and Proteus

  1. Introduction

Electric Vehicles (EVs) are transforming the transportation sector due to their environmental benefits and energy efficiency. As the adoption of EVs increases globally, the demand for fast, reliable, and efficient EV charging solutions has become critical. The design of an EV charger involves complex interactions between power conversion topologies, control strategies, and system-level protection.

This article focuses on developing a simulation-based EV charger using a DC-DC buck converter, optimized for an output of 800V suitable for modern EV battery packs. The input power is assumed to be derived from a three-phase AC grid, which is rectified and processed through multiple stages to achieve the desired DC output. Key considerations include voltage regulation, power loss minimization, and component selection based on thermal and electrical ratings [4] [6].

MATLAB is used to simulate the system’s control and power conversion characteristics, while Proteus provides a platform for hardware-level design and real-time behavior testing. The charger incorporates advanced features such as PWM control, voltage feedback loops, and resonant circuit elements to maintain output stability under variable input conditions [5].

This article elaborates on the step-by-step development process, including theoretical design, circuit implementation, simulation outputs, and parameter analysis. The proposed system is scalable and serves as a foundation for practical EV charging applications in residential and commercial infrastructures.

2. System Overview

2.1 Specifications

ParameterValue
Input Voltage200V–450V DC (post rectification)
Output Voltage800V DC
Output Power80 kW
Output Current100 A
Efficiency Target>95%
Switching Frequency50 kHz

The target system operates from a 3-phase 220V AC supply, stepped up and rectified to produce a stable DC bus. The output DC voltage of 800V is tailored for high-voltage EV battery packs, supporting fast-charging applications.

3. Component Selection and Justification

3.1 Switching Devices

  • IGBTs were selected due to their high voltage handling and lower conduction losses at high power levels.

3.2 Passive Components

  • Inductors and Capacitors were selected based on calculated ripple constraints and resonant behavior at 50 kHz [3].
  • Transformers with a turn ratio of 0.25 handle galvanic isolation and voltage scaling.

3.3 Control Circuit

  • A PWM-based feedback control implemented in MATLAB ensures tight voltage regulation and dynamic load response [4].

3.4 Protection

  • Overvoltage, overcurrent, and thermal protections were incorporated in the Proteus simulation model to enhance reliability.

4. System Design and Calculations

4.1 Voltage Ratio

The buck converter topology steps down voltage with the ratio [7] [8]:

4.2 Load and Reflected Resistance

4.3 Resonant Tank Design

  • Gmax = 0.876
  • Quality Factor Q = 0.2018

You can download the Project files here: Download files now. (You must be logged in).

Main Circuit diagrams

Circuit Design:

  • Buck Converter Topology: Since EV chargers typically step down the voltage from the grid to the battery voltage, a buck converter is commonly used.
  • Control Scheme: Implement a pulse-width modulation (PWM) control scheme to regulate the output voltage and current [9].
  • Feedback Circuit: Include a feedback loop using voltage and current sensors to regulate the output parameters [5].
  • Snubber Circuit: To reduce switching losses and voltage spikes, add snubber circuits across the switching devices.

5. Circuit Design in Proteus

Figure 1: Circuit diagram of Electric Vehicle charger with DC DC converter
Figure 2: Three-phase AC to DC conversion through bridge rectifier

Three phases 220 volt for each phase (total will be 440V) to DC conversion will be 220V.

Transformer ratio:

Calculation of equivalent load electric and reflection resistance at the output end: [2][4]

Take excitation inductance and the ratio of resonant inductance for 3 or K = 3; [3][4] Calculate the quality factor:

Where, Vd is the pressure drop of the switch tube= -98.846  [4]

The calculated resonant element parameters are as follows:

Resonant Frequency (fr) = 50 KHz

Table for the List of components

ComponentsValues
Resonant Frequency (fr)50 KHz
Vin200 – 450V
Vout800 V
C1 and C26.4uF
L1 and L239.56uF
Transformer ratio (n)0.25

You can download the Project files here: Download files now. (You must be logged in).

Figure 3: DC to AC converter and then use transformer to boost the voltage level
Figure 4: After the increase of voltage level bridge rectifier applied to convert the AC to DC voltage
Figure 5: Final Circuit diagram
  1. Design Equations:[2][4]

  1. MATLAB Simulation and its output results
Figure 6: Main circuit block diagram of Electric Vehicle
Figure 7: Front end converter MATLAB Model
Figure 8: FEC control circuit in MATLAB
Figure 9: Lithium Ion battery of Electric vehicles circuit simulation
Figure 10: DC DC Converter Circuit for the Electric Vehicle charger in MATLAB
Figure 11: AC noise filtering circuit in MATLAB simulation
Figure 12: DC output voltage for the EV charger MATLAB Simulation
Figure 13: DC output Current for the EV charger MATLAB Simulation
Figure 14: Final Input and output voltages and currents for the EV charger MATLAB Simulation

You can download the Project files here: Download files now. (You must be logged in).

Conclusion

In conclusion, the development of an Electric Vehicle (EV) charger integrated with a DC-DC converter using MATLAB and Proteus offers a comprehensive solution for efficient and scalable EV charging. By systematically designing the power electronics stages—including AC to DC conversion, high-frequency DC-DC transformation, and output filtering—the system ensures a stable 800V DC output suitable for modern EV batteries [10]. The use of resonant converter topology with calculated parameters such as inductance, capacitance, and transformer ratios enhances the converter’s performance, efficiency, and thermal management. MATLAB simulations validate the design by accurately modeling voltage regulation, current control, and system dynamics. Proteus complements this with real-time circuit behavior visualization, enabling hardware-ready prototyping. The inclusion of protective measures ensures safety and reliability in practical applications. Overall, the project bridges simulation with real-world implementation, addressing key challenges in EV charger design. It demonstrates that a well-optimized converter circuit can achieve high power density, efficiency, and system robustness. This work lays a strong foundation for future innovations in smart EV charging infrastructure.

References:

  1. P. Chen et al., “Study on technical bottleneck of new energy development,” Proc. of CSEE, vol. 37, no. 1, pp. 20–26, 2017.
  2. Song et al., “Overview of research on smart DC distribution networks,” Proc. of CSEE, vol. 33, no. 25, pp. 9–19, 2013.
  3. Y. Jiang, “Research on the bidirectional resonant DC/DC converter,” Zhejiang University, 2015.
  4. Zhao et al., “Bi-directional full-bridge DC-DC converters with dual-phase-shifting control,” Proc. of CSEE, vol. 32, no. 12, pp. 43–50, 2012.
  5. C. Chen, “Key technologies of bidirectional CLLLC resonant DC/DC converters,” Harbin Institute of Technology, 2015.
  6. Zhang, H. Wu, Y. Xing, and J. Luo, “High-efficiency isolated bidirectional DC–DC converter for EV/HEV applications,” IEEE Transactions on Power Electronics, vol. 28, no. 12, pp. 5730–5743, Dec. 2013.
  7. Singh and V. Bist, “Power quality improvements in AC–DC–DC converters for electric vehicle battery charging,” IET Power Electronics, vol. 7, no. 5, pp. 1107–1115, May 2014.
  8. Khaligh and S. Dusmez, “Comprehensive topological analysis of conductive and inductive charging solutions for plug-in electric vehicles,” IEEE Transactions on Vehicular Technology, vol. 61, no. 8, pp. 3475–3489, Oct. 2012.
  9. Ehsani, Y. Gao, S. E. Gay, and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, 2nd ed. CRC Press, 2009.
  10. Jang and M. M. Jovanović, “A new soft-switched PFC boost rectifier with integrated flyback converter for stand-by power,” IEEE Transactions on Power Electronics, vol. 21, no. 1, pp. 66–72, Jan. 2006.

Keywords: DC-DC Converter, Electric Vehicle (EV) Charger, MATLAB, Proteus Circuit design

You can download the Project files here: Download files now. (You must be logged in).

Do you need help with Electronics circuits on Proteus and MATLAB Simulink? Don’t hesitate to contact our Tutors to receive professional and reliable guidance.

Related Articles

Design and LTSpice Simulation of a High-Efficiency Bidirectional Single-Phase EV Charger for Vehicle-to-Grid (V2G) Applications

Author: Waqas Javaid

Introduction:

In this project, a groundbreaking single-phase bidirectional current-source AC/DC converter tailored for Vehicle-to-Grid (V2G) applications is unveiled. The converter is ingeniously designed; comprising a line frequency commutated unfolding bridge and an interleaved buck-boost stage. Notably, the semiconductor losses within the line frequency commutated unfolding bridge contribute to the converter’s commendable efficiency. The interleaved buck-boost stage further enhances performance with its dual capabilities of buck and boost operating modes, facilitating seamless operation across a broad battery voltage range [2]. The interleaved structure of this stage significantly reduces battery current ripple. Beyond these advantages, the converter ensures sinusoidal input current, bidirectional power flow, and the capability for reactive power compensation. This project delves into the intricate topology and operational principles of this innovative converter, shedding light on its potential impact in the realm of V2G applications.

Simulating Renewable Energy Systems Using Simulink: A Practical Approach with Design a Large Battery Storage System

This MATLAB Simulink model presents the design and implementation of a Large Battery Energy Storage System (BESS) aimed at alleviating peak power demands in Colombo, Sri Lanka. The system utilizes grid-forming control to facilitate power injection during peak times and incorporates a battery management system (BMS) for efficient operation. Additionally, a photovoltaic (PV) system is integrated to supplement power generation. The model encompasses various components such as converters, filters, and controllers to regulate power flow and ensure seamless integration with the grid. Detailed simulations evaluate system performance, validating the effectiveness of peak shaving strategies and compliance with relevant industry standards like IEEE 1547-2018 and IEEE 2030.2.1-2019. Results indicate successful peak shaving functionality and highlight the impact of time delays on system dynamics.

Real-Time Object Detection System especially Vehicle and Lane Detection using Yolo V4 algorithm Using MATLAB and Deep Learning

Abstract

This article presents the development and implementation of a Real-Time Object Detection System, focusing on Vehicle and Lane Detection using the YOLOv4 algorithm integrated within MATLAB and Deep Learning frameworks. The primary objective of this research is to design, simulate, and evaluate an intelligent driving assistance system capable of detecting vehicles, identifying lane markings, and performing basic trajectory planning and lane change control in a highway driving scenario. The proposed system leverages a pre-trained YOLOv4 model for robust and accurate vehicle detection in real-time video streams. Lane detection is achieved through image pre-processing techniques, including grayscale conversion, edge detection, and Hough transform-based lane line extraction. Furthermore, the system incorporates trajectory planning algorithms and a basic proportional lane change controller, enabling lateral position adjustments based on detected objects and lane boundaries. A key contribution of this work is the seamless integration of object detection and lane detection outputs with control algorithms to simulate decision-making in autonomous highway driving. The performance of the object detection module is quantitatively assessed using standard metrics such as precision, recall, mean Average Precision (mAP), false positives, and false negatives. Lane detection accuracy is evaluated through Intersection over Union (IoU) metrics, demonstrating reliable lane identification even in complex scenarios. The system’s inference time was optimized to meet real-time processing requirements, achieving an average frame processing speed compatible with autonomous driving applications. Visualizations of detected vehicles, lane boundaries, and trajectory adjustments were implemented to enhance interpretability and user understanding. The experimental results validate the efficiency of YOLOv4 in vehicle detection tasks within the MATLAB environment, achieving high precision and recall rates, and demonstrate the feasibility of integrating lane detection and control mechanisms for highway lane management. However, the study also highlights areas for future work, such as enhancing the realism of vehicle dynamics models, integrating advanced decision-making algorithms, and extending the system to more complex urban environments. This research offers a foundational framework for further exploration in the field of autonomous vehicle perception systems, contributing to the development of advanced driver assistance systems (ADAS) and autonomous navigation technologies.

Analysis and Hardware Implementation of 3-Level and 5-Level CHB Multilevel Inverters Using SPWM

Author: Waqas Javaid

ABSTRACT

In this report a brief review on multilevel inverters and different multilevel inverter topologies are discussed. Inverter is a power electronic device that converts DC power into AC power at desired output voltage and frequency. Multilevel inverters nowadays have become an interesting area in the field of industrial applications. This Project mainly involves analysis of Cascaded H-bridge topology, conduction loss and switching loss calculations, LC filter design and different SPWM modulation techniques. It also involves implementing 3-level CHB MLI with and without SPWM on the Arduino UNO board.

Responses

Your email address will not be published. Required fields are marked *

L ading...