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
Research & Publications
3 verified research records · Newest first
Selected publications and doctoral research records. This list may not include every publication.
Multi-Domain Machine Learning Framework for Electric Vehicle Charging Prediction
View Publication ↗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 Doctoral Record ↗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.
Machine Learning Approaches for EV Charging Management: A Systematic Literature Review
View Publication ↗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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