_edite.jpg)
RESEARCH PROFESSION
Research Assistant
"Hierarchical MPC-Based Energy Management for Electric Cars with Hybrid Energy Storage"
As a Research Assistant at the University of Transport and Communications, I worked with Associate Professor Dr. Vo Thanh Ha on the topic "Hierarchical MPC-Based Energy Management for Electric Cars with Hybrid Energy Storage." This was my first experience in a real academic research setting. Here, all results must be based on and verifiable. I was in charge of gathering academic documents, constructing reference models from over 25 international studies, and simulating energy control strategies using MATLAB and Simulink.
I processed data from battery and supercapacitor systems directly. I also ran tests to optimize energy distribution, aiming to reduce loss and extend battery life. In addition, I helped the research team edit scientific manuscripts, particularly in analyzing results and checking the validity of comparative models. Because of these contributions, I was honored to be listed as a co-author on the work that was submitted for international review. This experience taught me to cultivate a serious scientific mindset, patience, and a commitment to pursuing knowledge.
ABSTRACT
"This paper introduces a Hierarchical Model Predictive Control (H-MPC) method for managing energy in electric vehicles (EVs) that utilise a hybrid energy storage system (HESS) comprising lithium-ion batteries and supercapacitors. The suggested H-MPC system has two levels of control: the top level uses a predictive control method to best distribute power between the battery and supercapacitor by considering future energy needs, the state of charge (SOC), and system limits; the bottom level makes sure that current is tracked accurately using a quick inner-loop PI controller. We integrate a degradation-aware supercapacitor model into the MPC optimisation to account for long-term system performance. The control objective is to enhance energy efficiency, reduce battery current stress, and maintain DC-link voltage stability during dynamic driving conditions. Simulation studies are conducted in MATLAB/Simulink using a representative electric vehicle (EV) powertrain. Results demonstrate that the H-MPC approach surpasses conventional PI-based and passive strategies by reducing peak battery current by 24%, enhancing supercapacitor usage by 31%, and improving system efficiency by 18%. These findings confirm the effectiveness of H-MPC for real-time, constraint-aware energy management in HESS-powered electric vehicles (EVs)."
Keywords: Hierarchical Model Predictive Control (H-MPC); Hybrid Energy Storage System (HESS); Lithium-ion Battery; Supercapacitor; Energy Management Strategy.
Independent Author
"Quantum and Classical Methods for Solving Linear Systems: A High School Exploration"
In my independent research, I chose to explore the topic “Quantum and Classical Methods for Solving Linear Systems: A High School Exploration.” I didn't have a lab or a research group to support me; I only had curiosity and determination to dive into a new field: Quantum Computing. I began from scratch with the math foundations of linear algebra. I learned how to solve systems of equations using Gaussian Elimination, LU Decomposition, and Cramer’s Rule. Then, I expanded my research to the HHL (Harrow-Hassidim-Lloyd) algorithm in quantum computing. I taught myself bra-ket notation, Hadamard gates, and Phase Estimation. I also simulated the algorithm in Python using the Qiskit library. My goal was not to prove anything new, but to clarify how quantum methods can be applied and their limitations in practice, particularly regarding computational complexity and scalability. I wrote the research manuscript to meet academic standards and aimed to publish it in a journal for high school students. This project helped me develop independent research skills, improve my ability to synthesize documents, and, most importantly, strengthen my perseverance in pursuing knowledge over the long term.
