Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 339-343· 0 citations· 22 references
Abstract
Compact design, fast regulation, and low-loss operation at high efficiency make direct current-to-direct current (DC-DC) conversion increasingly important for renewable energy interfaces, electric vehicle (EV) subsystems, microgrids, storage units, and Internet of Things (IoT) devices. This work aims to bridge the gap between conventional converter operation and intelligent control under varying source and load conditions by implementing an artificial intelligence (AI)-based control structure with buck and boost converter topologies. The main purpose is to enhance the performance of voltage regulation, tracking accuracy, and conversion efficiency while decreasing ripple, dynamic error, and switching-induced performance limitations. Closed-loop converter operation includes voltage and current sensing, signal conditioning, intelligent duty-cycle regulation, pulse width modulation (PWM) generation, and gate driving all of which are implemented in a numerical simulation platform using the control architecture described. It shows that the voltage gain across the boost converter is 2 over an input of 24 V and a load current of 1 A, delivering an output power of 48 W at an efficiency squeeze of 96% along with maintaining a tracking error in voltage of approximately 0.8 V, duty cycle updates are common, which soon relates to a required updated value where constant effort remains on the duty cycle to lock it into the control stage, hence obtaining an updated duty cycle value as nearly around 0.538. This performance is evident in the buck converter waveforms, which maintain well-regulated output near 12 V, and the ripple response from the boost converter settles at around 48 V after transient fluctuation—ideal for energy management and smart power conversion applications.
Fuel cells are being used more frequently in electric cars and renewable energy systems because to the growing need for energy-efficient and ecologically friendly energy systems. However, a DC-DC converter with a large voltage gain is needed for power conversion because of the low and erratic output voltages. To boost the voltage output for fuel cell applications, a multi-stage voltage enhancement-based DC-DC converter has been created in this work. To increase voltage gain, reduce ripple voltage, boost efficiency, and offer stability, the converter uses cascaded boost stages with PI-controlled PWM. Simulink software was used to construct and analyses the DC-DC converter in order to determine voltage gain, efficiency, ripple reduction, and dynamic response. Under typical working conditions, the intended converter increased the input voltage from 12V to 120V. Additionally, the PI-controlled system offered minimal steady state error and a quick transient response with a settling time of 6–10 ms. These findings indicate that the designed converter provides better voltage gain, efficiency, ripple reduction and dynamic performance as compared to traditional boost converter configurations.
S. Zeeshan, C. Basha, Damarapally Sunil Kumar et al.· 2026 International Conferenc...· 0 citations
The rapid growth of electric vehicles and energy storage systems requires efficient two-way power conversion systems, such as bidirectional VSIs operating in Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. Unfortunately, conventional Hysteresis Current Control (HCC) methods lead to unstable switching frequencies, degrading the system's power quality and performance. This study proposes a single-phase two-way Voltage Source Inverter (VSI) with a full bridge topology controlled by an Artificial Neural Network (ANN) based Adaptive Hysteresis Current Control (AHCC) scheme. ANN is used to define hysteresis bands adaptively to keep the switching frequency stable. The system consists of a two-way VSI connected to the grid and a two-way DC-DC converter that connects the battery via DC-Link. The simulation results showed that the proposed method produced a narrower instantaneous frequency switching range of 5.263 kHz-25 kHz compared to Fixed-HCC of 11,111kHz-50 kHz, so that AHCC-ANN produced an average frequency switching value of 13.37 kHz, close to the desired frequency switching of 15 kHz, while Fixed-HCC was 20,25 kHz. On the other hand, the% of THD for AHCC-ANN (2.72%) is lower than that for Fixed-HCC (3.2%). On the other hand, the DC-link voltage can also be maintained at 400 V during charging and discharging. These results show that AHCC with ANN can stabilize switching frequencies and DC-link voltages and support effective bidirectional power flow.
Ludviatul Amanah, F. Pamuji, Mochamad Ashari· International Seminar on Int...· 0 citations
The growing incorporation of renewable energy sources in modern power systems has intensified the need for efficient and dependable power electronic converters. DC–DC converters are crucial in applications such as solar systems, fuel cells, and battery energy storage for voltage management and minimizing power loss. This study details the design and evaluation of a high-efficiency DC–DC converter intended for renewable energy applications. The suggested system utilizes an efficient switching approach, synchronous rectification, and carefully selected passive components to improve performance across a wide operational range. A mathematical model is created to examine essential characteristics like voltage conversion ratio, inductor current ripple, capacitor voltage ripple, and overall efficiency under diverse load situations. The converter delivers consistent output voltage, less switching losses, enhanced transient responsiveness, and superior thermal efficiency. The design is validated by MATLAB/Simulink simulations across diverse input and load fluctuations characteristic of renewable settings. The findings indicate improved efficiency, greater voltage control, less ripple, and dependable dynamic performance relative to traditional converters, making the suggested design ideal for contemporary renewable energy systems.
In the present work, an artificial intelligence (AI)-based direct current to alternating current converter is developed for fast power conversion with reduced harmonic distortion in three-phase power systems. The system that comprises a DC-link source, inverter switching network, pulse width modulation (PWM) control method, output filter and AI-based adaptive controller minimise voltage fluctuation and improves quality waveforms. An adaptive switching signal for inverter generation based on the error in voltage, rate of change of error and variations in power along with DC-link voltage is processed as a part of the control strategy. Mathematical modelling is established using inverter pole-voltage equations, neutral-point voltage compensation, Clarke and Park transformations, sinusoidal reference generation, the modulation index ratio to duty ratio for filter dynamics, and root mean square (RMS) estimation, along with total harmonic distortion (THD). The performance metric is a weighted sum of voltage tracking error, current variation and harmonic distortion, which aims to provide a stable low-distortion operation for the converter. Results of the simulation demonstrate accuracy in converter response, including input DC voltage, switching pulse patterns, unfiltered output voltage, filtered three-phase AC voltage and THD. The emulated DC-AC converter can be used for renewable energy systems, grid-connected inverter applications, electric vehicle power interfaces and various configuration units that require stable voltage output and reduced distortion.
S. Venkatakiran, N. B, V. S. Reddy et al.· 2026 6th International Confe...· 0 citations
Electric vehicles (EVs) require power converters that are efficient, compact, and capable of managing hybrid energy storage systems. Conventional designs use separate DC-DC converters for the supercapacitor and battery, which increases hardware count and losses. This paper proposes an integrated converter topology that combines the supercapacitor interface with the motor-side inverter through a modified auxiliary resonant commutated pole (ARCP) converter. The integration eliminates multiple DC-DC converters while enabling bidirectional energy transfer between the supercapacitor and the drivetrain during acceleration and regenerative braking, and providing soft-switching for the inverter during normal cruise operation. Simulation and experimental validation confirm the stability of DC-link functionality and the efficiency of energy transfer for various driving modes, such as acceleration, constant-speed operation, and regenerative braking. The converter provides soft-switching conditions for the main and auxiliary switches, resulting in a 51% reduction in total losses relative to a conventional hard-switched inverter. The results validate the design’s efficiency and practicality. By simplifying hardware, reducing passive components, and enhancing regenerative energy recovery, the topology provides a compelling option for next-generation EV propulsion systems with the potential to extend driving range, lower cost, and facilitate the wider utilization of sustainable transportation.
Esmaeil Kiani Dehkian, Seyed M. Madani, E. Adib· IEEE Access· 0 citations
This paper presents the modelling, simulation, and control of an isolated Dual Active Bridge (DAB) DC–DC converter fed DC motor drive for bidirectional electric vehicle (EV) applications. The proposed system consists of a lithium-ion battery, an isolated bidirectional DAB converter with a high-frequency transformer, an LC output filter, and a separately excited DC motor representing the traction motor. Unlike conventional isolated converters that employ fixed-gain Proportional–Integral (PI) controllers, the proposed system employs an Artificial Neural Network (ANN) controller to regulate the converter output voltage by processing the voltage error and change in error through a trained feed-forward neural network. The ANN output determines the optimal phase-shift angle between the two full bridges, which is converted into switching pulses by a discrete PWM generator for the DAB converter switches. The proposed control strategy adapts to the nonlinear characteristics of the converter and motor without requiring manual gain tuning, providing faster transient response, reduced overshoot, lower steady-state error, and improved bidirectional power transfer during motoring and regenerative braking. MATLAB/Simulink results demonstrate stable motor speed regulation, smooth torque and current characteristics, regulated battery voltage and current, and efficient state-of-charge management, confirming the suitability of the proposed ANN-based isolated DAB converter-fed DC motor drive for next-generation electric vehicle application.
Keywords— Dual Active Bridge (DAB) Converter, Artificial Neural Network (ANN) Controller, Isolated DC–DC Converter, Bidirectional Power Transfer, Electric Vehicle (EV), DC Motor Drive, Regenerative Braking, Lithium-Ion Battery.
Bade Abhishek Bade Abhishek, Dr. P. Ram Kishore Kumar Reddy Dr. P. Ram Kishore Kumar Reddy, Dr. P. Nagasekhara Reddy Dr. P. Nagasekhara Reddy· International Journal of Cre...· 0 citations