Document Type
Thesis
Date of Award
12-31-2019
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
Edip Niver
Second Advisor
Ali N. Akansu
Third Advisor
Marek Sosnowski
Fourth Advisor
Carlos Pereira
Abstract
The Nobel Prize in Chemistry 2019 was just recently awarded to John B. Goodenough, M. Stanley Whittingham, and Akira Yoshino for the development of lithium-ion batteries. Lithium-ion batteries have seen use in many different industries and applications such as in portable devices, power grids, and electric vehicles. As lithium-ion batteries become more commonplace they will need to be modeled more extensively. The magnetic field effect on lithium-ion batteries has not been studied significantly since they were first discovered.
Modeling these batteries is still difficult because of the many complexities of the operation of a battery. Lithium-ion batteries are commonly modeled through equivalent circuit models (ECM's) in where experimental data is replicated through the use of parallel and series resistors and capacitors. The values of these resistance and capacitances are tuned to the experimental data. The other route for modeling lithium-ion batteries involves looking at the fundamental electrochemistry that governs them. This involves solving the differential equations for conservation of mass and conservation of charge. These equations are very nonlinear, dependent on each other, and do not have closed-form solutions.
One-dimensional and two-dimensional batteries were modeled based on the underlying physics of a lithium-ion battery. Magnetic fields were injected into the batteries to see the effect on their voltage and current charge/discharge characteristics. It was observed that external magnetic fields result in reduced times during charging and discharing of lithium-ion batteries due to the paramagnetic nature of lithium ions.
Recommended Citation
Mahon, Kevin, "Magnetic field effects on lithium ion batteries" (2019). Theses. 1743.
https://digitalcommons.njit.edu/theses/1743
