Developed a novel framework for degradation abatement of the battery used in HEV.
Prognostic-based Control Framework (PBCF)
Developed degradation forecasting techniques to predict battery aging
Markov Chain-based Model
Data-Driven based Model
Developed an energy management system for HEV
An optimization technique that considers the rate of battery aging to find an optimal point where the total operating cost of the HEV is minimized, taking into account battery degradation
Implemented the developed framework in the controller hardware-in-the-loop (CHIL) for validation.
Developed a framework (Hardware Integrated Virtual Environment (HIVE)) to interconnect different equipment from geographically separated laboratories
Developed delay compensation techniques to maintain the system stability due to communication delays
Implemented the framework on a series of hybrid electric vehicle models where components were located at three different laboratories
Tested the frameworks using power hardware-in-the-loop (PHIL) for further validation.
Developed several versions of a series hybrid electric vehicle model for Military applications
Implemented different components into a high-performance computing environment
Implemented the developed models in real-time using Speedgoat, a real-time simulator.
In collaboration with the University of North Carolina at Charlotte (UNCC) and several other universities
Developed a framework for data transfer between multiple laboratories in real time using VILLASnode for the Microgrid application
Implemented distributed codes on multiple controllers across multiple locations
Connected multiple laboratories to carry out Microgrid simulation using different real-time simulators (RTDS and Speedgoat).
Working on the Modular Multilevel Converter (MMC) for the SPS application
Working on the thermal management system for power electronics used in SPS
Working on degradation forecasting techniques of the power electronics components used in SPS
Working on integrating the degradation forecasting into the control architecture of the SPS
Working on implementing the whole system into the CHIL experiment to validate the proposed system.
Developing the power system model for the US Navy ship
Simplified system without zone
4-Zone ship power system (SPS)
Developed different energy management algorithms for SPS
Crow Search Algorithm
Federated Learning based energy management system
Model-free based Neural Network based energy management system
Implemented the developed models in real-time using OPAL-RT and Speedgoat, and the algorithms on Raspberry Pis using Python.
Developed the CHIL testbench and validated the developed algorithms.