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An entropy-based, self-adaptive predictive algorithm for battery degradation
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AI
DOI：10.1016/j.jpowsour.2025.237920.png)
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Abstract
En 中文
• Method proposed uses a hybrid model that uses physics and information based entropy. • This approach is tested with 3 different ML types ranging from regression to a NN. • Method obtains results on par with the current state of the art in BMS SOH prediction. • Method only needs limited data, and is very quick to train, as it is lightweight. • This work explores self-adaptive modeling for 2nd-life batteries.
Keywords:
hybrid model
entropy
machine learning
battery management system
state of health
self-adaptive modeling
Journal
IF:
7.9
Papers:
3.7W
Citations:
15.0W