IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining Useful Life Prediction

• URL: http://arxiv.org/abs/2403.18379v1
• Date: Wed, 27 Mar 2024 09:17:50 GMT
• Title: IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining Useful Life Prediction
• Abstract summary: We propose a simple-Mixer-based architecture named 'Intra-Inter Patch Mixer' (IIP-Mixer) IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time-series frameworks.
• Score: 2.6024829904078994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 involved. 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 simple MLP-Mixer-based architecture named 'Intra-Inter Patch Mixer' (IIP-Mixer), which is an architecture based exclusively on multi-layer perceptrons (MLPs), extracting information by 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

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