Dr. Hanan Haddad Thwany
Ph.D. in Systems Engineering | Oakland University
Integration Engineer Lead · Detroit Metropolitan Area, Michigan, USA
Hanan Haddad Thwany holds a Ph.D. in Systems Engineering from Oakland University. Her research applies machine learning to predictive electric vehicle charging management, integrating driver behavior, charging infrastructure and market data. Her publications explore EV charging prediction, charging management and intelligent transportation systems.
Doctoral research (2026): Multi-Domain Machine Learning for Predictive Electric Vehicle Charging Management: A Framework Integrating Behavioral, Infrastructure, and Market Data
Education
- B.S. in Mechanical Engineering — Oakland University
- M.S. in Industrial and Systems Engineering — Oakland University
- Ph.D. in Systems Engineering — Oakland University, 2026
Research statistics
Verified ICCC collection
- Documented research records
- 3
- Publication records
- 2
- Degree research records
- 1
Indexed citation figures
- Indexed research works
- 1
- Citations to indexed works · all years
- 0
- Citations received · 2024–2026
- 0
- 20240
- 20250
- 20260
Annual citations: 2024: 0 · 2025: 0 · 2026: 0. 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
2 publications · 1 degree thesis · 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.
Multi-Domain Machine Learning Framework for Electric Vehicle Charging Prediction
View PublicationOpenAlex: 0 citations Source ↗
Start here · New researcher guide
A reading workflow, not an author-verified summary. Confirm all details in the original publication.
- Check the fit. Read the abstract and introduction. Write down the research question and explain how it relates to your idea.
- Understand the method. Identify the approach, assumptions, data and evaluation measures in the paper.
- Check reproducibility. Look for linked code, datasets and supplementary material. Availability has not been verified by ICCC.
- Find your next question. Read limitations and future work. Propose one extension and check newer literature before claiming a research gap.
- Plan a first experiment. Define a baseline, the change you will test and a measurable outcome. Discuss feasibility with your supervisor.
For questions or collaboration, visit the member profile and use a listed contact channel. Authorship and collaboration require agreement.
Multi-Domain Machine Learning for Predictive Electric Vehicle Charging Management: A Framework Integrating Behavioral, Infrastructure, and Market Data
View DissertationStart here · New researcher guide
A reading workflow, not an author-verified summary. Confirm all details in the original publication.
- Check the fit. Read the abstract and introduction. Write down the research question and explain how it relates to your idea.
- Understand the method. Identify the approach, assumptions, data and evaluation measures in the paper.
- Check reproducibility. Look for linked code, datasets and supplementary material. Availability has not been verified by ICCC.
- Find your next question. Read limitations and future work. Propose one extension and check newer literature before claiming a research gap.
- Plan a first experiment. Define a baseline, the change you will test and a measurable outcome. Discuss feasibility with your supervisor.
For questions or collaboration, visit the member profile and use a listed contact channel. Authorship and collaboration require agreement.
Machine Learning Approaches for EV Charging Management: A Systematic Literature Review
View PublicationOpenAlex: 6 citations Source ↗
Start here · New researcher guide
A reading workflow, not an author-verified summary. Confirm all details in the original publication.
- Check the fit. Read the abstract and introduction. Write down the research question and explain how it relates to your idea.
- Understand the method. Identify the approach, assumptions, data and evaluation measures in the paper.
- Check reproducibility. Look for linked code, datasets and supplementary material. Availability has not been verified by ICCC.
- Find your next question. Read limitations and future work. Propose one extension and check newer literature before claiming a research gap.
- Plan a first experiment. Define a baseline, the change you will test and a measurable outcome. Discuss feasibility with your supervisor.
For questions or collaboration, visit the member profile and use a listed contact channel. Authorship and collaboration require agreement.
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