Abstract
A new study on Machine Learning is now available. According to news reporting out of Vancouver, Canada, by NewsRx editors, research stated, “Data-driven methods have attracted much attention in capacity estimation and remaining useful life (RUL) prediction of lithium-ion batteries. However, existing studies rely on complex machine learning models (e.g., Gaussian process regression, neural networks, and so on.) that are applicable to specific observed operating conditions, and the prediction accuracy can be affected by different usage scenarios.” Financial supporters for this research include CGIAR, Natural Sciences and Engineering Research Council of Canada (NSERC), Natural Sciences and Engineering Research Council of Canada (NSERC). Our news journalists obtained a quote from the research from the University of British Columbia, “This paper proposes to adopt a linear and robust machine learning technique, partial least-squares regression, for battery capacity estimation, and RUL prediction based on the partial incremental capacity curve. The features can be easily obtained by interpolation of the measured charging profiles without data smoothing, and the bootstrapping technique is used to give confidence intervals of the predictions, which helps to evaluate the robustness and reliability of the model. The proposed method is validated on three battery data sets with different operating conditions provided by NASA. We train the model on one battery and test its performance on the other two batteries without changing the model weights.”
NETL
NSTL
Amer Chemical Soc