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
AI-Based System Inspects Defects in Wind Turbine Blades More Accurately
Written by AZoRoboticsApr 1 2021
While the demand for wind power has increased considerably, the accompanying need to assess wind turbine blades and recognize flaws may have an impact on their operational efficiency.
Crack detection using the AI tool. Image Credit: Loughborough University.
From ultrasound to visual thermography, a broad range of blade inspection methods have been tested, but these techniques have demonstrated certain limitations.
There are many inspection procedures that still need engineers to perform manual examinations in which a vast number of high resolution images are captured. Such analyses consume time and are affected by light conditions, and added to this, they are also dangerous.