Meta Learning Based State of Health Estimation of Lithium-Ion Batteries With Small Sampling Retraining

Accurately estimating state of health (SOH) for lithium-ion batteries based on machine learning methods usually entails large requirement of training data, bringing difficulties for practical applications. To address this challenge, this study proposes a novel SOH estimation method integrating meta-learning, temporal convolutional networks (TCNs), and transformers with limited sampling data. First, by dividing constant current charging curves into multiple segments, the capacity increment sequences for each segment are extracted as health features. A parallel hybrid network is developed, which combines the strengths of TCNs, transformers, and attention mechanisms to effectively capture both local and global patterns in health features. In addition, meta-learning is employed with small sampling retraining data to improve the model adaptability acrossvarying temperatures, different charging currents and battery chemistries. Experimental validations conducted on different temperatures and charging currents show that the proposed method achieves the maximum estimation error of 3%. Moreover, when applied to different types of batteries, the proposed method requires only a small amount of target battery data for retraining to achieve performance comparable to traditional methods, thereby reducing the need for aging data. These results underscore the robust generalizability, high accuracy, and strong potential for real-world applications of the proposed method.