dc.contributor.author |
Asaad, Amal Mohammad, |
dc.date.accessioned |
2017-12-11T16:30:48Z |
dc.date.available |
2017-12-11T16:30:48Z |
dc.date.issued |
2017 |
dc.date.submitted |
2017 |
dc.identifier.other |
b1918346x |
dc.identifier.uri |
http://hdl.handle.net/10938/20964 |
dc.description |
Thesis. M.E. American University of Beirut. Department of Electrical and Computer Engineering, 2017. ET:6592 |
dc.description |
Advisor : Dr. Sami Karaki; Professor, Electrical and Computer Engineering ; Members of Committee : Dr. Ali Chehab, Professor, Electrical and Computer Engineering ; Dr. Rabih Jabr, Professor, Electrical and Computer Engineering |
dc.description |
Includes bibliographical references (leaves 93-94) |
dc.description.abstract |
Batteries have played a crucial role in power systems of different sizes, ranging from small portable electronic devices, to hybrid electric vehicle (HEV), as well as storage devices in renewable systems. The two main challenges of renewable energy sources are their intermittency and unpredictability, which are two drawbacks that can be mitigated by the usage of battery storage in the system. Many power engineers continuously thrive to understand the performance of batteries and their ageing process by modeling. The uncertainty of the expected lifetime of a battery has a significant impact on the cost of a given project as well as its effectiveness. This uncertainty in the life time of a battery can be limited by modeling it using circuit parameters and identifying how these parameters vary over time, which is the essential focus of this thesis. The ageing process of the battery is simulated in an experimental setup that charges and discharges the battery in a cyclic manner through the use of a programmable power supply and an electronic load. Three different approaches were attempted to estimate the battery lifetime. The first relies on using the total energy supplied by a battery through all the discharge cycles of the experiment as a reference for the energy supplied, obtained by integration of delivered power over time while the battery is being used. The second approach relies on discharging the battery for 20 minutes while measuring its terminal voltage; if it drops to below 1.75V per cell then the battery is to be replaced. The third method is based on the use of electrochemical impedance spectroscopy (EIS) to measure the variation in internal model parameters as the battery is cycled and thus aged. The models used were of the single and double Randle cell which consists of a resistance in series with one or two polarization circuits formed by one or more resistance in parallel with one or more double layer capacitance. Other models based on an infinite Warburg impedance element as well as a finite Wa |
dc.format.extent |
1 online resource (xiii, 119 leaves) : color illustrations |
dc.language.iso |
eng |
dc.relation.ispartof |
Theses, Dissertations, and Projects |
dc.subject.classification |
ET:006592 |
dc.subject.lcsh |
Lead-acid batteries. |
dc.subject.lcsh |
Impedance spectroscopy. |
dc.subject.lcsh |
Neural networks (Computer science) |
dc.subject.lcsh |
Support vector machines. |
dc.title |
Battery modeling for life time assessment - |
dc.type |
Thesis |
dc.contributor.department |
Faculty of Engineering and Architecture. |
dc.contributor.department |
Department of Electrical and Computer Engineering, |
dc.contributor.institution |
American University of Beirut. |