State Estimation of Li-ion Batteries Using Machine Learning Algorithms

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dc.contributor.advisorRezaei Zare, Afshin
dc.contributor.authorBabaeiyazdi, Iman
dc.date.accessioned2023-03-28T21:24:04Z
dc.date.available2023-03-28T21:24:04Z
dc.date.copyright2023-01-16
dc.date.issued2023-03-28
dc.date.updated2023-03-28T21:24:04Z
dc.degree.disciplineElectrical Engineering & Computer Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractLithium-ion batteries are mainly utilized in electric vehicles, electric ships, etc. due to their virtue of high energy density, low self-discharge, and low costs. Electric vehicles are prone to accelerated battery degradation due to the high charging/discharging cycles and high peak power demand. Hence, efficient management of the batteries is a dire need in this regard. Battery management systems (BMS) have been developing to control, monitor, and measure the variables of the battery such as voltage, current, and temperature, to estimate the states of charge (SOC) and state of health (SOH) of the battery. This study is divided into three parts; in the first part, the SOC of the battery is estimated utilizing electrochemical impedance spectroscopy (EIS) measurements. The EIS measurements are obtained at different SOC and temperature levels. The highly correlated measurements with the SOC are then extracted to be used as input features. Gaussian process regression (GPR) and linear regression (LR) are employed to estimate the SOC of the battery. In the second part of this study, the EIS measurements at different SOC and temperature levels are employed to estimate the SOH of the battery. In this part, transfer learning (TL) along with deep neural network (DNN) is adopted to estimate the SOH of the battery at another outrange temperature level. The effect of the number of fixed layers is also investigated to compare the performance of various DNN models. The results indicate that the DNN with no fixed layer outclasses the other DNN model with one or more fixed layers. In the third part of this dissertation, the co-estimation of SOC and SOH is conducted as SOC and SOH are intertwined characteristics of the battery, and a change in one affects the other variation. First, the SOH of the battery is estimated using EIS measurements by GPR and DNN. The estimated SOH, along with online-measurable variables of the battery, i.e., voltage and current, are then utilized as input features for long-short term memory (LSTM) and DNN algorithms to estimate the SOC of the battery.
dc.identifier.urihttp://hdl.handle.net/10315/41045
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectElectrical engineering
dc.subject.keywordsLi-ion battery
dc.subject.keywordsElectric vehicle
dc.subject.keywordsMachine learning
dc.subject.keywordsState of charge (SOC)
dc.subject.keywordsState of health (SOH)
dc.titleState Estimation of Li-ion Batteries Using Machine Learning Algorithms
dc.typeElectronic Thesis or Dissertation

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