首页|University of Oslo Researcher Publishes New Data on Machine Learning (Comparativ e Study of Machine Learning Methods for State of Health Estimation of Maritime B attery Systems)
University of Oslo Researcher Publishes New Data on Machine Learning (Comparativ e Study of Machine Learning Methods for State of Health Estimation of Maritime B attery Systems)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Oslo, Norway, by NewsRx correspondents, research stated, “This paper tests tw o data-driven approaches for predicting the state of health (SOH) of lithium-ion -batteries (LIBs) for the purpose of monitoring maritime battery systems.” The news correspondents obtained a quote from the research from University of Os lo: “First, nonsequential approaches are investigated and various models are te sted: ridge, lasso, support vector regression, and gradient boosted trees. Binni ng is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumula ted time the LIB has been within various voltage, temperature, and current range s. Further binning to combine these histograms into 2D or 3D histograms is explo red in order to capture relationships between voltage, temperature, and current. Second, a sequential approach is explored where different deep learning archite ctures are tried out: long short-term memory, transformer, and temporal convolut ional network. Finally, the various models and the two approaches are compared i n terms of their SOH prediction ability.”
University of OsloOsloNorwayEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
