Enhancing Temperature-Dependent Li-Ion Battery Behavior Predictions with Transfer Learning

Abstract

Lithium-ion batteries (LiBs) are crucial in modern energy applications, with their performance significantly influenced by temperature variations. LiBs undergo temperature-dependent chemo-mechanical processes that contribute to capacity fade. Accurate assessment of capacity degradation during cycling necessitates consideration of various factors, including temperature, depth of discharge, cell chemistry, charge/discharge rate, and others. Publicly accessible datasets often encompass only a restricted spectrum of the aforementioned battery health influencers. However, obtaining comprehensive real-world data encompassing diverse temperature conditions poses challenges and requires substantial resources. To address the data scarcity, we propose a novel approach utilizing transfer learning to improve predictive models for temperature-dependent behavior in Li-ion batteries. In this study, we generate synthetic data simulating battery behavior under specific temperature ranges. The pre-training of the machine learning model on this synthetic data enables the capture of complex interplays between temperature and battery phenomena, identifying unique patterns and relationships. Subsequently, we transfer the acquired knowledge to the target domain, facilitating predictions for battery behavior under varying temperature conditions. Fine-tuning the pre-trained model using real-world data from the target domain ensures adaptability to new temperature conditions. The data efficiency gained from synthetic data generation in the source domain allows effective pre-training, even with limited real-world data in specific temperature regimes. Moreover, the model with its transfer learning method acquires a robust understanding of the underlying features governing temperature-dependent battery behavior, enabling better generalization and enhanced predictions for effective battery management across diverse environmental conditions. In addition to the sensitivity analysis to identify the most significant impact on the predictive model’s performance in the focused temperature range, we compare the performance of the fine-tuned model with other predictive models that do not utilize transfer learning. Our findings could offer cost-effective, rapid, and accurate predictions, ensuring robust performance in scenarios with limited real-world data. As the demand for Li-ion batteries grows across various applications, these insights hold significant potential in advancing the development of efficient and reliable energy storage systems.