Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi0.86Co0.11Al0.03O2-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi0.83Co0.11Mn0.07O2-based positive electrodes and batteries with the blend of Li(NiCoMn)O2 - Li(NiCoAl)O2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.
We present a machine
learning (ML) framework to optimize the specificity
and speed of liquid crystal (LC)-based chemical sensors. Specifically,
we demonstrate that ML techniques can uncover valuable feature information
from surface-driven LC orientational transitions triggered by the
presence of different gas-phase analytes (and the corresponding optical
responses) and can exploit such feature information to train accurate
and automatic classifiers. We demonstrate the utility of the framework
by designing an experimental LC system that exhibits similar optical
responses to a stream of nitrogen containing either 10 ppmv dimethyl-methylphosphonate
(DMMP) or 30% relative humidity (RH). The ML framework is used to
process and classify thousands of images (optical micrographs) collected
during the LC responses and we show that classification (sensing)
accuracies of over 99% can be achieved. For the same experimental
system, we demonstrate that traditional feature information used in
characterizing LC responses (such as average brightness) can only
achieve sensing accuracies of 60%. We also find that high accuracies
can be achieved by using time snapshots collected early in the LC
response, thus providing the ability to create fast sensors. We also
show that the ML framework can be used to systematically analyze the
quality of information embedded in LC responses and to filter out
noise that arises from imperfect LC designs and from sample variations.
We evaluate a range of classifiers and feature extraction methods
and conclude that linear support vector machines are preferred and
that high accuracies can only be achieved by simultaneously exploiting
multiple sources of feature information.
We present a clustering-based preconditioning strategy for KKT systems arising in stochastic programming within an interior-point framework. The key idea is to perform adaptive clustering of scenarios (inside-the-solver) based on their influence on the problem at hand. This approach thus contrasts with existing (outside-the-solver) approaches that cluster scenarios based on problem data alone. We derive spectral and error properties for the preconditioner and demonstrate that scenario compression rates of up to 94 % can be obtained, leading to dramatic computational savings. In addition, we demonstrate that the proposed preconditioner can avoid scalability issues of Schur decomposition in problems with large first-stage dimensionality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.