Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1781618

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Smith, Kandler (ORCID:0000000170110377)·Gasper, Paul (ORCID:0000000188349458)·Schiek, Andrew·Usseglio-Viretta, Francois (ORCID:0000000275598874)·Dufek, Eric·Kunz, Ross·Gering, Kevin
View original source ↗Read source document ↗Export metadata ↓

Abstract

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

Explore related subjects

47 OTHER INSTRUMENTATIONartificial intelligencebatterydesignimage recognitionlife predictionlithium-ionmachine learningmicroscopyneural networktesting

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Smith, Kandler (ORCID:0000000170110377), Gasper, Paul (ORCID:0000000188349458), Schiek, Andrew, Usseglio-Viretta, Francois (ORCID:0000000275598874), Dufek, Eric, Kunz, Ross, Gering, Kevin. 2021-04-28. Tutorial: Machine Learning and Artificial Intelligence in Batteries. https://www.osti.gov/biblio/1781618

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

High Multiplicity Trigger for long-lived particles in CMS detector

Searches for long-lived particles (LLPs) at the CMS experiment often involve unconventional event topologies that are difficult to efficiently select using standard trigger strategies. To improve sensitivity to such signatures during LHC Run 3 operation, a dedicated High Multiplicity Trigger (HMT) has been developed and deployed in the CMS trigger system. The trigger targets events containing unusually large numbers of hits in the CMS cathode strip chamber (CSC) muon detectors, a characteristic signature of several LLP scenarios involving displaced decays in the muon system. The HMT implementation, trigger logic, rate dependence with pileup, and operational stability are described. Optimized hit multiplicity thresholds are used to maintain acceptable trigger rates under high-luminosity and high-pileup conditions while preserving high efficiency across a broad range of LLP lifetimes and kinematic regimes. The trigger performance is evaluated using both simulated event samples and proton-proton collision data collected during Run 3 of the LHC. The HMT substantially extends the CMS sensitivity to non-standard signatures associated with LLP decays and provides a flexible platform for future searches for physics beyond the Standard Model.

47 OTHER INSTRUMENTATION↗