Ryan Tae

Using Multiple Regression Analysis to Understand Li-ion Battery Performance

Started June 9, 2023

Abstract or project description

Fossil fuels, which power 80% of the world’s energy, emit greenhouse gases, the primary cause of global warming and climate change, which has consequently caused researchers to flock to solve the climate catastrophe with an efficient alternative fuel source by expanding lithium-ion battery research. The pursuit of an optimal power source has thus fueled the rapid expansion of lithium-ion battery research, which has diversified into several subfields focusing on improving the anode, cathode, electrolyte, and types of batteries in order to acquire the ultimate energy source. Cathode studies are interesting because cathode materials such as lithium cobalt oxide (LCO), lithium iron phosphate (LFP), lithium nickel cobalt aluminum oxide (NCA), and lithium nickel manganese cobalt oxide (NMC) have gained widespread popularity in commercial applications. However, most of the tests that these batteries undergo are tested under standard conditions, which has caused a major gap in the current research concerning the absence of studies that observe batteries in alternative conditions. Fortunately, multiple regression analysis, a statistical technique, uses real-world data to predict battery capacity retention using multiple variables. In this study, I will use multiple regression analysis to prove which commercial batteries, containing varying cathode materials, perform the best in adverse environments.