Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks

Zhang, Xingchen; Xiang, Haoxiang; Xiong, Xin; Wang, Yujie; Chen, Zonghai

doi:10.1016/j.applthermaleng.2024.123257

Cited by 2 publications

(1 citation statement)

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“…A fixed sliding window size makes it difficult to capture local patterns and sequence features in the data [22,23]. Adjusting the window size in real time based on the change rate of the sequence can yield better results.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

Sun,

Gu,

Liu

2024

Electronics

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Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method.

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Section: Introductionmentioning

confidence: 99%