Olivia Palombo
Developing machine learning methods for state of health assessments of lithium-ion batteries.
Email: o.palombo1@ncl.ac.uk
Project title
Higher harmonics response analysis as diagnostic and prediction tool for battery state of health
Supervisors
Project description
This research will improve our understanding of irreversible processes in batteries. I will apply a large AC current to lithium-ion batteries (LIB). I will monitor the voltage response beyond linearity to study the non-linear processes.
I will investigate the individual higher harmonics response and total harmonic distortion (THD). We can recognise these by applying nonlinear response analysis (NLRA).
I will establish and identify relevant characteristics and frequencies for processes in LIBs. I will apply this analysis to produce machine learning methods for a Lithium-ion battery state of health (SOH) estimation. This will help to uncover the process of degradation of lithium-ion batteries using novel techniques.
The results will show that the approach of revealing and analysing higher harmonics is effective and reproducible for SOH identification.
Qualifications
BSc in Mathematics and Accounting, Newcastle University