Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7736291 | Journal of Power Sources | 2014 | 7 Pages |
Abstract
This short paper presents a recently reported dynamic data-driven method, Symbolic Dynamic Filtering (SDF), for real-time estimation of the state-of-health (SOH) and state-of-charge (SOC) in lead-acid batteries, as an alternative to model-based analysis techniques. In particular, SOC estimation relies on a k-NN regression algorithm while SOH estimation is obtained from the divergence between extracted features. The results show that the proposed data-driven method successfully distinguishes battery voltage responses under different SOC and SOH situations.
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Authors
Yue Li, Zheng Shen, Asok Ray, Christopher D. Rahn,