Discipline: Technology and Engineering
Subcategory: Civil/Mechanical/Manufacturing Engineering
Session: 4
Room: Exhibit Hall
Ana Torres - Missouri State University
Co-Author(s): Joshua Cox, Missouri State University
Electric Vehicles (EVs) are becoming more pervasive in our society, and batteries play a fundamental role in their energy efficiency. Currently, several types of battery chemistries and architectures are considered, such as aqueous lead-acid, nickel cadmium, and vanadium redox batteries. Solid-state batteries have advantages over aqueous batteries including lighter weight and faster charging times, which can significantly reduce their cost. Lithium-ion batteries (LiBs) are often recommended for EV use due to their high capacity, high power, and longer lifespan compared to other battery chemistries. The batteries listed will be compared with those that have already been assembled and continue to do so in our lab. Understanding the aging mechanism for LiBs is crucial for optimizing the battery operation in real-life applications. Although at different levels of utilization, all types of LiBs experience the problems of aging and capacity degradation. LiB aging is a unique and complicated phenomenon influenced by the interdependence between different internal and external factors. There are many important factors that can affect the performance, such as temperature, size, chemical composition, voltage cutoff, and depth of discharge. In this work, the focus is on the battery capacity to obtain a better understanding of the aging of batteries that are meant for EVs. In doing so, different cycling loads (e.g. changing discharging current, and idle times through which self-discharge can occur) are examined to determine differences in aging effects. Our research goal is to influence how these factors affect the aging mechanism based on the composition (e.g. water/binder composition of anode and cathode) and charging cycles. We show how different compositions influence the charging as well as aging of these LiBs by also alternating cutoff voltage and other charging cycle settings. The studies will later be coupled with machine learning techniques to incorporate the physical mechanisms of aging into a model which predicts aging subject to different loads over larger numbers of cycles.
Funder Acknowledgement(s): Missouri Louis Stokes Alliances for Minority Participation
Faculty Advisor: Dr. Daniel Moreno and Dr. Tayo Obafemi-Ajayi, tayoobafemiajayi@missouristate.edu
Role: I started the research from scratch by contacting my advisor about being interested in independent research. After one semester, this research brought attention in by others in the engineering department and became a research in which I research, assemble, test, and change charging cycles.