Early Prediction of Lithium-Ion Batteries Lifetime via Few-Shot Learning

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Abstract
Artificial intelligence (AI) has been widely studied for batteries remaining useful lifetime prediction. However, the requirement of big datasets to train a robust AI model limits its practical application, particularly when batteries exhibit diverse degradation behaviors under different working conditions. Collecting sufficient data through laboratory testing can take several years. To tackle these challenges, a few-shot learning-based method for battery early lifetime prediction is proposed where only 6 cycles of charging data are required. The proposed method models batteries with different lengths of cycle life separately, considering that aging features recognized from early cycles might be different for long-life and short-life batteries. First, an auto encoder is trained to group batteries into long-life and short-life classes. The prototypical networks algorithm is employed to learn a metric space where samples from the same class are brought closer together than samples from different classes. Then based on the classification result, different lifetime models are selected, resulting in the final prediction. Few-shot learning technique is utilized to enable accurate and early health assessment of lithium-ion batteries. Compared to building a single model for all batteries throughout their lifetimes, the proposed method reduces the required data size, simplifies AI modeling, and improves prediction accuracy. Finally, the effectiveness of the proposed framework is verified using the accelerated aging dataset from 124 batteries.
Funding Information
  • Villum Foundation (222860)

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