  Accurately predicting the lifespan of lithium-ion batteries is crucial for
optimizing operational strategies and mitigating risks. While numerous studies
have aimed at predicting battery lifespan, few have examined the
interpretability of their models or how such insights could improve
predictions. Addressing this gap, we introduce three innovative models that
integrate shallow attention layers into a foundational model from our previous
work, which combined elements of recurrent and convolutional neural networks.
Utilizing a well-known public dataset, we showcase our methodology's
effectiveness. Temporal attention is applied to identify critical timesteps and
highlight differences among test cell batches, particularly underscoring the
significance of the "rest" phase. Furthermore, by applying cyclic attention via
self-attention to context vectors, our approach effectively identifies key
cycles, enabling us to strategically decrease the input size for quicker
predictions. Employing both single- and multi-head attention mechanisms, we
have systematically minimized the required input from 100 to 50 and then to 30
cycles, refining this process based on cyclic attention scores. Our refined
model exhibits strong regression capabilities, accurately forecasting the
initiation of rapid capacity fade with an average deviation of only 58 cycles
by analyzing just the initial 30 cycles of easily accessible input data.
