IIP-Mixer: Intra–Inter-Patch Mixing Architecture for Battery Remaining Useful Life Prediction
Guangzai Ye,
Li Feng (lfeng@must.edu.mo),
Jianlan Guo and
Yuqiang Chen
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Guangzai Ye: School of Computer Science and Engineering, Macau University of Science and Technology, Macau SAR, China
Li Feng: School of Computer Science and Engineering, Macau University of Science and Technology, Macau SAR, China
Jianlan Guo: Dongguan Polytechnic, Dongguan 523808, China
Yuqiang Chen: Dongguan Polytechnic, Dongguan 523808, China
Energies, 2024, vol. 17, issue 14, 1-15
Abstract:
Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics. Recently, attention-based networks, such as Transformers and Informer, have been the popular architecture in time series forecasting. Despite their effectiveness, these models with abundant parameters necessitate substantial training time to unravel temporal patterns. To tackle these challenges, we propose a straightforward MLP-Mixer-based architecture named “Intra–Inter Patch Mixer” (IIP-Mixer), which leverages the strengths of multilayer perceptron (MLP) models to capture both local and global temporal patterns in time series data. Specifically, it extracts information using an MLP and performs mixing operations along both intra-patch and inter-patch dimensions for battery RUL prediction. The proposed IIP-Mixer comprises parallel dual-head mixer layers: the intra-patch mixing MLP, capturing local temporal patterns in the short-term period, and the inter-patch mixing MLP, capturing global temporal patterns in the long-term period. Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed. Our experiments demonstrate that IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time series frameworks, such as Informer and DLinear, with relative reductions in mean absolute error (MAE) of 24% and 10%, respectively.
Keywords: lithium-ion batteries; remaining useful life; multivariate time series; multilayer perceptron; time series prediction; patch mixing (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: 2024
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