Staff Profile
Dr Ma'd El Dalahmeh
Research Associate - Modeling
- Email: ma'd.el-dalahmeh@ncl.ac.uk
- Personal Website:
Summary
I am a postdoctoral research associate data driven modelling and optimization of lithium-ion batteries. I focus on developing data-driven statistical mathematical models based on neural networks and tools to aid decision-making on recycling and reuse actions to be taken once lithium-ion battery reach end of their life in electric vehicles. Also, I focus on developing hybrid real-time data driven approach to estimate battery health and remaining useful life based on neural network to help manufacturers, users and recyclers in their decision making on what to do with battery, (recycle or reuse) and which application to reuse in and at what conditions.
Prior to joining Â鶹´«Ã½, I studied for a PhD at Teesside University, where I developed algorithms for the state of health estimation and degradation trajectory prediction of lithium-ion batteries (LiB's). My research primarily focused on developing rapid diagnostic, prognostic, and accurate methods for estimating the state of health of LiBs to maximise their lifetime. To achieve that, I developed a novel framework to estimate LiB's state of health and remaining useful life prediction based on time-frequency analysis and data-driven mathematical models such as convolutional neural networks, long short-term memory, and nonlinear autoregressive neural network. During my PhD, I worked as a Research Assistant on a project funded by Lloyd's Register Foundation, I developed data-driven algorithms for fault diagnosis and detection of electrical machines. This work was a collaboration between Teesside University and Middle East Technical University in Turkey.
Qualifications
- PhD in Energy- Teesside University.
- MSc in Electrical Engineering and Renewable Energy Systems- Teesside University.
I am currently working on developing data-driven statistical mathematical models based on neural networks and tools to aid decision-making on recycling and reuse actions to be taken once lithium-ion battery reach end of their life in electric vehicles as a part of the project.