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

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...

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Bibliographic Details
Main Authors: Min-Sung Sim, Do-Yoon Kim, Yong-Jin Yoon, Seok-Won Kang, Jong Dae Baek
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Lithium-ion battery
low-temperature
long short-term memory (LSTM)
particle filter (PF)
worldwide light vehicles test cycle (WLTC)
real-time prediction
Online Access:https://ieeexplore.ieee.org/document/10570406/

Internet

https://ieeexplore.ieee.org/document/10570406/

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