Enhancing Data Efficiency and Feature Identification for Lithium-Ion Battery Lifespan Prediction by Deciphering Interpretation of Temporal Patterns and Cyclic Variability Using Attention-Based Models

arXiv:2311.10792v3 Announce Type: replace
Abstract: 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, …

abstract arxiv attention batteries battery cs.ai cs.lg data efficiency feature identification interpretability interpretation lithium lithium-ion batteries patterns prediction risks stat.ap strategies studies temporal type