Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Ying Zheng, Senior Ryan Taylor, Senior Teshome Teshome, Senior
Lead Presenter's Name
Ying Zheng
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Mihhail Berezovski
Abstract
Lithium-ion batteries have widespread and varied use in modern technology, but are susceptible to degradation. This degradation cannot be avoided due to the mechanism these batteries use to charge and discharge, and because of this the accurate prediction of battery life is a valuable asset; especially when they are critical components of equipment. This task is made difficult due to the diversity present in operating conditions and charging cycle conditions produced by real-world usage of these batteries. Using real-time data collected from 10 flights of three separate Cessna C-700 aircraft, we develop a meaningful prediction model to improve the reliability of these critical components. Using the given data set to produce a set of discharge cycles which are classified based on discharge voltage curves, we predict the cell life after the first 100 cycles and for cells within the first 10 cycles. This research conducted in collaboration with Textron Aviation.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Battery Health and Monitoring System
Lithium-ion batteries have widespread and varied use in modern technology, but are susceptible to degradation. This degradation cannot be avoided due to the mechanism these batteries use to charge and discharge, and because of this the accurate prediction of battery life is a valuable asset; especially when they are critical components of equipment. This task is made difficult due to the diversity present in operating conditions and charging cycle conditions produced by real-world usage of these batteries. Using real-time data collected from 10 flights of three separate Cessna C-700 aircraft, we develop a meaningful prediction model to improve the reliability of these critical components. Using the given data set to produce a set of discharge cycles which are classified based on discharge voltage curves, we predict the cell life after the first 100 cycles and for cells within the first 10 cycles. This research conducted in collaboration with Textron Aviation.