Ashraf Shanaq
Ph.D. Candidate · Electrical & Computer Engineering
Oakland University
Ashraf Shanaq is a Ph.D. candidate in Electrical and Computer Engineering at Oakland University. His doctoral research focuses on reliable hybrid nonlinear dual-time-constant modeling of lithium iron phosphate (LiFePO₄) batteries, with particular emphasis on parameter identification, state-of-charge estimation, hysteresis characterization, and measurement uncertainty management. His research combines dynamic system modeling, optimization, signal processing, and computational techniques to improve battery model accuracy and reliability. His broader research interests include intelligent control systems, embedded systems, artificial intelligence, autonomous vehicles, and advanced battery management systems.
Education
- Bachelor’s degree in Computer Engineering Technology — Eastern Michigan University, 2018
- Master’s degree in Electrical and Computer Engineering — Oakland University, 2021
- Ph.D. Candidate in Electrical & Computer Engineering — Oakland University — In progress
Research statistics
Verified ICCC collection
- Documented research records
- 1
- Publication records
- 1
- Degree research records
- 0
Indexed citation figures
- Indexed research works
- 6
- Citations to indexed works · all years
- 6
- Citations received · 2024–2026
- 6
- 20241
- 20254
- 20261
Annual citations: 2024: 1 · 2025: 4 · 2026: 1. Retrieved 2026-10-24. Source 1Checked 2026-10-24
ICCC counts cover the verified collection. Citation figures cover only the displayed indexed source records, not guaranteed career totals. OpenAlex coverage and author clustering can be incomplete. These figures differ from Google Scholar and include self-citations. Compare available member metrics →
Research & Publications
1 research work · Newest first
Authored and co-authored publications, with authors shown in the source order. This list reflects the records currently verified by ICCC and may not be complete.
Enhance Parameter Identification Accuracy for State-of-Charge Optimization in Lithium-Ion Batteries
View PublicationOpenAlex: 0 citations Source ↗
Start here · New researcher guide
Suggested first step: Read the model and parameter-identification method, then identify the datasets, baseline methods and error measures needed to reproduce one comparison. Check newer literature before proposing an extension.
Future work named in the abstract: Computational optimization, scalability and applicability across different battery chemistries.
ICCC reading suggestion based on the abstract; feasibility and reproducibility have not been verified.
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