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A requiem for the stiff upper lip

A requiem for the stiff upper lip
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Marine life offers green alternative to petroleum products

Marine life offers green alternative to petroleum products
thetimes.co.uk - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from thetimes.co.uk Daily Mail and Mail on Sunday newspapers.

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Former Ithaca chief Les Thomas will join Jersey Oil and Gas board

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Researchers Develop New Machine Learning Method to Estimate Battery Health

Researchers Develop New Machine Learning Method to Estimate Battery Health Written by AZoRoboticsApr 12 2021 Electrical batteries are increasingly crucial in a variety of applications, from integration of intermittent energy sources with demand, to unlocking carbon-free power for the transportation sector through electric vehicles (EVs), trains and ships, to a host of advanced electronics and robotic applications. A key challenge however is that batteries degrade quickly with operating conditions. It is currently difficult to estimate battery health without interrupting the operation of the battery or without going through a lengthy procedure of charge-discharge that requires specialised equipment. In work recently published by

United kingdomCity ofDarius romanSmart systems group at heriotUniversity of marylandWatt university in edinburghNature machine intelligenceSmart systems groupHeriot watt universityஒன்றுபட்டது கிஂக்டம்நகரம் ஆஃப்டேரியஸ் ரோமன்புத்திசாலி அமைப்புகள் குழு இல் ஹெரியட்பல்கலைக்கழகம் ஆஃப் மேரிலாந்துவாட் பல்கலைக்கழகம் இல் எடின்பர்க்இயற்கை இயந்திரம் உளவுத்துறை

New machine learning method accurately predicts battery state of health

New machine learning method accurately predicts battery state of health Researchers from the Smart Systems Group at Heriot-Watt University in Edinburgh, UK, working together with researchers from the CALCE group at the University of Maryland in the US, have developed a new method to estimate battery health irrespective of operating conditions and battery design or chemistry, by feeding artificial intelligence (AI) algorithms with the raw battery voltage and current operational data. A paper describing the method is published in the journal Nature Machine Intelligence. In the reported study, the team designed and evaluated a machine learning pipeline for estimation of battery capacity fade a metric of battery health on 179 cells cycled under various conditions. The pipeline estimates battery state of health (SOH) with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engin

United kingdomCity ofDarius romanCentre for doctoral training in embedded intelligenceSmart systems group at heriotUniversity of marylandWatt university in edinburghSmart systems groupHeriot watt universityNature machineDoctoral trainingEmbedded intelligenceUnited kingdom roboticsArtificial intelligence hubOffshore energy asset integrity managementResponsive flexibility