Data-driven battery health monitoring

• Main Author: Liu, Xiaoyu
• Other Authors: Xu Yan
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Electrical and electronic engineering::Electric power
• Online Access: https://hdl.handle.net/10356/143507
• Institution: Nanyang Technological University

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.