@misc{874b94ca4866408d9045bdf35d132d26,
title = "Machine-Learning for Battery Health Diagnosis",
abstract = "Battery health diagnosis often requires time-consuming measurements in laboratory conditions, but these types of measurements are unsuitable for use in real-world application such as electric vehicles. Instead, rapid measurements that can be conducted at varying environmental conditions need to be used to monitor battery health. Machine-learning techniques can then be utilized to connect these rapid measurements to health diagnostic information recorded in the lab. This presentation is a part of a tutorial on using machine-learning methods to analyze and predict battery state.",
keywords = "battery, battery health, degradation, electrochemical impedance spectroscopy, machine-learning",
author = "Paul Gasper and Andrew Schiek and Kandler Smith and Shuhei Yoshida and Yuta Shimonishi",
year = "2022",
language = "American English",
series = "Presented at the MRS Spring Meeting, 8-13 May 2022, Honolulu, Hawaii",
type = "Other",
}