Machine Learning

Cloud-Based Battery Prognostics And Health Management

Machine Learning

Cloud-Based Battery Prognostics And Health Management

Developing a cloud computing pipeline with ML for accurate in-situ battery life prediction In-situ battery life prediction and classification are important to lithium-ion battery prognostics and health management (PHM). This research proposes a cloud-based PHM pipeline with a novel physical features-driven moving-window ML model, which can be used to predict the battery remaining useful life and knee-point. The proposed ML model is validated based on experimental data from a batch of 124 LFP/graphite cells from A123. The results show that the method predicts remaining useful life and knee-point accurately (with extremely small prediction error.

Machine learning is used to capture the relationship between physical features and battery life, demonstrating high accuracy in remaining useful life and knee-point prediction, with low errors of 55 cycles and 3.55% respectively.

Academia | P. 230 Source: Zhang, Yongzhi, and Mingyuan Zhao

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