Real-Time Predicting the Low-Temperature Performance of WLTC-Based Lithium-Ion Battery Using an LSTM-PF Sequential Ensemble Model

Predicting an abnormally rapid decline in battery capacity in low-temperature environments is important for maintaining battery stability and performance. This study introduces a method that integrates cycling tests under various current conditions with deep neural network algorithms to identify and...

詳細記述

書誌詳細

出版年:	IEEE Access
主要な著者:	Min-Sung Sim, Do-Yoon Kim, Yong-Jin Yoon, Seok-Won Kang, Jong Dae Baek
フォーマット:	論文
言語:	英語
出版事項:	IEEE 2024-01-01
主題:	Lithium-ion battery low-temperature long short-term memory (LSTM) particle filter (PF) worldwide light vehicles test cycle (WLTC) real-time prediction
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10570406/