In a significant stride towards sustainable energy solutions, a team of researchers from the Department of Electrical and Electronic Engineering has unveiled a groundbreaking innovation. Their paper titled “A Novel Multi-Port High-Gain Bidirectional DC–DC Converter for Energy Storage System Integration with DC Microgrids” has been accepted in the prestigious Q1 Journal of Energy Storage, boasting an impressive impact factor of 9.4. The study focuses on addressing the critical challenges associated with energy storage systems (ESS) in direct current (DC) microgrids. Dr Ramanjaneya Reddy, Assistant Professor, Dr Tarkeshwar Mahto, Assistant Professor, and Mrs Maya Vijayan, a dedicated PhD Scholar, collaborated to design a multi-port high-gain bidirectional DC-DC converter. This innovative converter facilitates seamless integration of energy storage systems with DC microgrids, enhancing overall system efficiency and reliability.

Abstract

Bidirectional converters have often been used in numerous applications like DC microgrids, renewable energy, hybrid energy storage systems, electric vehicles, etc. The paper proposes a novel multi-port high-gain (NMPHG) bidirectional DC-DC converter that supports DC microgrid (DC-MG) applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, multiple input ports, lower switch voltage stress, and lower component count owing to the single converter with multiple input ports for DC microgrid applications.

The detailed operational principle, analysis, and design considerations of proposed NMPHG bidirectional DC-DC converters are discussed. Furthermore, the loss analysis, detailed comparison with similar works, and efficiency analysis with non-modalities during forward power flow (LV to HV) and reverse power flow (HV to LV) modes are presented. The efficiency of the proposed converter is found to be 93.8% in forward power flow and 92.9% in reverse power flow modes at rated power. Finally, a hardware prototype of the proposed NMPHG bidirectional DC-DC converter is implemented with 100 W in FPF mode and 200 W in RPF mode with a TMS320F28335 processor and validated with theoretical counterparts.

Explanation of Research in Layperson’s Terms

The proposed converter is a 200W bidirectional topology used in DC microgrid applications such as renewable energy, hybrid energy storage systems, and electric vehicles. The converter can accept two or more sources to supply the load. Thus, it is suitable for various applications of traction vehicles. It exhibits a lower switch stress and reduces the component ratings to lower values.

Title of Research Paper in the Citation Format

A NOVEL MULTI-PORT HIGH-GAIN BIDIRECTIONAL DC-DC CONVERTER FOR ENERGY STORAGE SYSTEM INTEGRATION WITH DC MICROGRIDS

Vijayan, Maya, Ramanjaneya Reddy Udumula, Tarkeshwar Mahto, and Ravi Eswar KM. “A novel multi-port high-gain bidirectional DC-DC converter for energy storage system integration with DC microgrids.” Journal of Energy Storage 87 (2024): 111431.

Practical Implementation or the Social Implications Associated with it

The features include port expandability on the source side, lower switch voltage stress, bidirectional property, and fewer components. It is most suitable for electric vehicles, Unmanned ariel vehicles, and energy storage systems at renewable power plants, etc. It improves the reliability of the grid system whereas hybrid energy storage systems with battery or supercapacitor will improve system stability.

It can be used in various on-grid and off-grid applications like hospitals, offices, and educational institutions, especially where energy backup is very important. These types of converters are more specific for use in fast power transition required such as EVs, drones, aircraft, space vehicles, etc. The major advantage is the reduction in the size of the converter due to multiple source capability and ease of control.

Future Research Plans

We plan to work on a bidirectional converter with better efficiency and ultra-high gain. That should be able to reduce the size of the converter and the source ratings too. Design and implement bidirectional multi-port converters for various applications of DC microgrids, such as renewable and hybrid storage integration.

Link to the Article

It is with great pleasure that we announce the publication of a research paper titled “Self-Learning Controller Design for DC-DC Power Converters with Enhanced Dynamic Performance,” jointly authored by Dr Tousif Khan N, Associate Professor, Department of Electrical and Electronics Engineering, and Dr Ramanjaneya Reddy & Dr Arghya Chakravarty, Assistant Professors, Department of Electrical and Electronics Engineering. The research paper introduces a novel self-learning control for precise output voltage tracking in DC-DC buck power converters.

Abstract:

This article introduces a self-learning robust control approach for accurate output voltage tracking in DC-DC buck power converters, focusing on scenarios with high precision requirements and significant load uncertainties. The method employs a simple online neural network to swiftly estimate unexpected load changes and disturbances across a wide range. Operating within a backstepping framework, the controller utilises neural network-learned uncertainties to enhance stability and improve dynamic and steady-state performance of both output voltage and inductor current. Extensive numerical simulations and practical experiments on a laboratory prototype demonstrate substantial enhancements in dynamic performance with a 94% reduction in settling time and precise steady-state tracking. The reliability of the proposed controller is further supported by the consistency between computational and experimental outcomes, showcasing its potential for real-world applications.

Practical implementations:

The proposed controller can be implemented/used for robotics applications, industrial processes, and medical equipment where precise control is needed.

Future research plans:

The following are the potential future directions of the proposed work;

(i) Design and development of the proposed self-learning neural network-based control for DC-DC buck converter systems with real-time DC sources, such as solar PV and fuel cells, experiencing highly intermittent input voltage changes.

(ii) Incorporating inductor current constraints and output voltage limitations into the proposed controller would also be an avenue worth exploring.

We congratulate the professors for their valuable contribution and look forward to future breakthroughs in this area.