About Me
I’m a PhD candidate in Mechanical Engineering at Ohio State’s Center for Automotive Research, advised by Dr. Qadeer Ahmed. My research uses reinforcement learning and physics-informed deep learning to control hybrid-electric powertrains in a way that doesn’t just minimize fuel consumption — it also slows the aging of the battery, electric machine, and aftertreatment system.
My PhD work is in collaboration with Cummins Inc., where I’m currently interning in the Powertrain Electrification Controls (PTEC) group.
I’m graduating in December 2026 and looking for full-time roles in AI/ML engineering, controls, or applied research starting early 2027.
Research Interests
- Deep reinforcement learning for energy and aging management
- Physics-informed deep learning for system identification and prognostics
- Multi-objective optimization for electrified powertrains
News
- I’m interning at Cummins this summer in the Powertrain Electrification Controls (PTEC) group, working on adaptive cruise control modeling and simulation for heavy-duty electric vehicles.
- Our paper Physics-Aware Deep Reinforcement Learning for Energy and Aging Management was accepted at IEEE Transactions on Transportation Electrification (May 2026).
- Our paper Learning Battery Aging Dynamics using Physics-Informed Transformer was accepted at IEEE TTE (January 2026).
- I’m graduating in December 2026 and starting to look for full-time roles in AI/ML, controls, or applied research for early 2027.
Older News
- Submitted Physics-Aware Deep RL journal paper for review (February 2026).
- Submitted Physics-Informed Transformer journal paper to IEEE TTE (December 2025).
- Presented work on aging model robustness for heavy-duty EVs at IEEE ITEC 2025 in Anaheim, CA (June 2025).
- Presented work on powertrain aging model selection at SAE WCX 2025 in Detroit, MI (April 2025).
- Submitted papers to IEEE ITEC 2025 (December 2024) and SAE WCX 2025 (September 2024).
Selected Publications
Physics-Aware Deep Reinforcement Learning for Energy and Aging Management in Electrified Powertrains
M.R. Rownak, W. Jaleel, A. Hanif, M.Q. Fahim, D.D. Le, H. Anwar, M. Nelson, & Q. Ahmed
IEEE Transactions on Transportation Electrification, 2026 (accepted)
Learning Battery Aging Dynamics Using Physics-Informed Transformer
M.R. Rownak, A. Hanif, M.Q. Fahim, D.D. Le, H. Anwar, W. Jaleel, M. Nelson, & Q. Ahmed
IEEE Transactions on Transportation Electrification, vol. 12, no. 2, pp. 3792–3804, 2026. doi:10.1109/TTE.2026.3658446
Robustness and Sensitivity of Aging Models for Batteries and Electric Machines in Heavy-Duty Electrified Powertrains
M.R. Rownak, A. Hanif, Q. Ahmed, M.Q. Fahim, H. Anwar, H. Li, D.D. Le, & M. Nelson
IEEE ITEC 2025, Anaheim, CA.
Powertrain Components Aging Model Selection for Energy Efficient Vehicles: Selection Strategy and Challenges
M.R. Rownak, A. Hanif, Q. Ahmed, M.Q. Fahim, H. Anwar, H. Li, D. Le, & M. Nelson
SAE WCX 2025, Detroit, MI.
Selected Projects
Aging-Aware Powertrain Control with Reinforcement Learning. Physics-aware RL framework for hybrid-electric powertrains that jointly optimizes fuel economy and component aging on heavy-duty drive cycles.
Physics-Informed Transformer for Battery Degradation. Transformer-based model for battery capacity-fade prediction trained across multi-cell cycling datasets.
Aging Model Development for Electrified Powertrains. Physics-based aging models for batteries, electric machines, and aftertreatment systems, integrated into a forward-looking powertrain simulator.
Teaching
TA for ME 3501: Introduction to Engineering Thermodynamics at Ohio State. Previously a Lecturer in Mechanical Engineering at BAUST in Bangladesh (2019–2020).
Beyond Research
Outside of research, I enjoy traveling, long walks, and photography.
Visitors
A small dot for every visitor.