Second-life Lithium-ion batteries: A chemistry-agnostic and scalable health estimation algorithm. (arXiv:2203.04249v1 [eess.SY])

Battery state of health is an essential metric for diagnosing battery
degradation during testing and operation. While many unique measurements are
possible in the design phase, for practical applications often only
temperature, voltage and current sensing are accessible. This paper presents a
novel combination of machine learning techniques to produce accurate
predictions significantly faster than standard Gaussian processes. The
data-driven approach uses feature generation with simple mathematics, feature
filtering, and bagging, which is validated with publicly available aging
datasets of …

algorithm arxiv batteries chemistry health life