  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
