  The early prediction of battery life (EPBL) is vital for enhancing the
efficiency and extending the lifespan of lithium batteries. Traditional models
with fixed architectures often encounter underfitting or overfitting issues due
to the diverse data distributions in different EPBL tasks. An interpretable
deep learning model of flexible parallel neural network (FPNN) is proposed,
which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D
CNN, and a dual-stream network. The proposed model effectively extracts
electrochemical features from video-like formatted data using the 3D CNN and
achieves advanced multi-scale feature abstraction through the InceptionBlock.
The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle
tasks of varying complexity in EPBL. The test on the MIT dataset shows that the
FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs
of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10,
20, 30, and 40, respectively. The interpretability of the FPNN is mainly
reflected in its flexible unit structure and parameter selection: its diverse
branching structure enables the model to capture features at different scales,
thus allowing the machine to learn informative features. The approach presented
herein provides an accurate, adaptable, and comprehensible solution for early
life prediction of lithium batteries, opening new possibilities in the field of
battery health monitoring.
