Contact Shehzar Sheikh
Areas of expertise
- Electric and Hybrid Vehicles
- Electric Power Machines
Background
Shehzar Shahzad Sheikh is a PhD researcher in Battery Systems Engineering at Cranfield University, working under the supervision of Professor Daniel J. Auger. He has a background in electrical engineering, battery diagnostics, and machine‑learning‑based estimation for energy‑storage systems. His research focuses on developing advanced state‑of‑health estimation, fast model‑calibration techniques, and electrochemical‑data‑driven diagnostic methods for real‑time battery management. By integrating laboratory experimentation, large‑scale data analysis, and control‑oriented modelling, he aims to develop reliable diagnostic and prognostic tools suitable for next‑generation applications in sustainable mobility, aerospace platforms, and intelligent power systems.
Shehzar is currently working on machine‑learning‑supported battery health estimation, impedance‑spectroscopy‑based diagnostic modelling, physics‑informed characterisation, and hybrid prognostic approaches for predicting battery ageing and remaining useful life. His previous research at NUST—conducted in collaboration with Arizona State University—resulted in multiple peer‑reviewed publications on lithium‑ion degradation modelling, microgrid analysis, and sustainable energy systems. Before joining Cranfield, he served as an Engineering Officer and Quality Assurance Officer in the Pakistan Air Force, managing high‑reliability electrical and avionics systems and leading technical teams in demanding operational environments. He is a Senior Member of IEEE and a qualified Professional Electrical Power Engineer, bringing more than seven years of combined research and engineering experience to emerging challenges in battery technology, aviation electrification, and smart power infrastructures.
Research opportunities
Battery State Estimation • Machine‑Learning Diagnostics • Electrochemical Modelling • Energy Storage Systems
Publications
Articles In Journals
- Sheikh SS, Shah FA, Athar SO & Khalid HA. (2023). A Data-Driven Comparative Analysis of Lithium-Ion Battery State of Health and Capacity Estimation. Electric Power Components and Systems, 51(1)
- Sheikh SS, Javed A, Anas M & Ahmed F. (2018). Solar Based Smart Irrigation System Using PID Controller. IOP Conference Series: Materials Science and Engineering, 414(1)
- Sheikh SS, Anjum M, Khan MA, Hassan SA, Khalid HA, .... A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach. Energies, 13(14)
Conference Papers
- Arif A, Sheikh SS, Ali MH, Arif D & Wshah S. (2025). Data-Driven Capacity Degradation Modeling for Commercial Lithium-Iron Phosphate Batteries Under Fast-Charging Conditions Using LSTM
- Sheikh SS, Iqbal S, Kazim M & Ulasyar A. (2019). Real-Time Simulation of Microgrid and Load Behavior Analysis Using FPGA
- Ahmed AB, Abbas Kazmi SA, Ameer U & Shehzad S. (2019). Cleaning Mechanism to Improve Efficiency and Sustainability of Desert Solar Plant
- Shah FA, Shahzad Sheikh S, Mir UI & Owais Athar S. (2019). Battery Health Monitoring for Commercialized Electric Vehicle Batteries: Lithium-Ion
- Khalid M, Sheikh SS, Janjua AK & Khalid HA. (2018). Performance Validation of Electric Vehicle’s Battery Management System under state of charge estimation for lithium-ion Battery
- Danial , Khan MH, Moiz Siddiqui A, Sheikh SS & Owais Athar S. (2018). State of The Art Tidal Energy Systems: Issues, Challenges, and Possible Solutions