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

Yafei Li, Kejun Qian (), Qiuying Shen, Qianli Ma, Xiaoliang Wang and Zelin Wang
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Yafei Li: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Kejun Qian: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Qiuying Shen: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Qianli Ma: State Grid Suzhou Power Supply Company, Suzhou 215004, China
Xiaoliang Wang: School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Zelin Wang: School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Energies, 2025, vol. 18, issue 12, 1-23

Abstract: 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 shorter windows are key factors in enhancing prediction accuracy. In this paper, we propose a BESS temperature prediction model based on a convolutional neural network (CNN), patch embedding, and the Kolmogorov–Arnold network (KAN). Firstly, a CNN block was established to extract multi-scale periodic temporal features from data embedded in long look-back windows and capture the multi-scale correlations among various monitored variables. Subsequently, a patch-embedding mechanism was introduced, endowing the model with the ability to extract local temporal features from segments within the long historical look-back windows. Next, a transformer encoder block was employed to encode the output from the patch-embedding stage. Finally, the KAN model was applied to extract key predictive information from the complex features generated by the aforementioned components, ultimately predicting BESS temperature. Experiments conducted on two real-world residential BESS datasets demonstrate that the proposed model achieved superior prediction accuracy compared to models such as Informer and iTransformer across temperature prediction tasks with various horizon lengths. When extending the prediction horizon from 24 h to 72 h, the root mean square error (RMSE) of the proposed model in relation to the two datasets degraded by only 11.93% and 19.71%, respectively, demonstrating high prediction stability. Furthermore, ablation studies validated the positive contribution of each component within the proposed architecture to performance enhancement.

Keywords: battery energy storage systems; temperature prediction; Kolmogorov–Arnold network; convolutional neural network; patch embedding; attention mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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