CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems

CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems

Accurate predictions of the temperature of battery energy storage systems (BESSs) are crucial for ensuring their efficient and safe operation. Effectively addressing both the long-term historical periodic features embedded within long look-back windows and the nuanced short-term trends indicated by...

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Bibliographic Details
Main Authors: Yafei Li, Kejun Qian, Qiuying Shen, Qianli Ma, Xiaoliang Wang, Zelin Wang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Energies
Subjects:
battery energy storage systems
temperature prediction
Kolmogorov–Arnold network
convolutional neural network
patch embedding
attention mechanism
Online Access:https://www.mdpi.com/1996-1073/18/12/3095
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