Please use this identifier to cite or link to this item:
Title: Data-driven battery health monitoring
Authors: Liu, Xiaoyu
Keywords: Engineering::Electrical and electronic engineering::Electric power
Issue Date: 2020
Publisher: Nanyang Technological University
Abstract: With the development of machine learning technology, data-driven methods are widely applied in researching complex systerms. The extreme learning machine (ELM) is one of the most advanced data-driven methods nowadays because of its high accuracy and efficiency. Besides, as the key factors in electric vehicles, the battery degradation is hard to model and estimate in real application because the battery is a complicated system. Thus, this paper uses ELM to solve the battery health monitoring problem.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Amended Dissertation LiuXY.pdf
  Restricted Access
1.26 MBAdobe PDFView/Open

Page view(s)

Updated on Jul 19, 2024


Updated on Jul 19, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.