Seongmin Heo | Searcharxiv

arXiv2024

Gaussian Process Regression-Based Lithium-Ion Battery End-of-Life Prediction Model under Various Operating Conditions

For the efficient and safe use of lithium-ion batteries, diagnosing their current state and predicting future states are crucial. Although there exist many models for the prediction of battery cycle life, they typically have very complex input structures, making it very difficult and expensive to develop such models. As an alternative, in this work, a model that predicts the nominal end-of-life using only operating conditions as input is proposed. Specifically, a total of 100 battery degradation data were generated using a pseudo two-dimensional model with three major operating conditions: charging C-rate, ambient temperature and depth-of-discharge. Then, a Gaussian process regression-based model was developed to predict the nominal end-of-life using these operating conditions as the inputs. To improve the model accuracy, novel kernels were proposed, which are tailored to each operating condition. The proposed kernels reduced the lifetime prediction error by 46.62% compared to the conventional kernels.

eess.SP↗

arXiv2024

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

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.

cs.LG↗