首页|Investigators from University of Connecticut Release New Data on Machine Learnin g (Physics-informed Machine Learning for Battery Degradation Diagnostics: a Comp arison of State-of-the-art Methods)
Investigators from University of Connecticut Release New Data on Machine Learnin g (Physics-informed Machine Learning for Battery Degradation Diagnostics: a Comp arison of State-of-the-art Methods)
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 – Current study results on Machine Learn ing have been published. According to news reporting out of Storrs, Connecticut, by NewsRx editors, research stated, “Monitoring the health of lithium-ion batte ries’ internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typic ally involves aging many cells and destructively analyzing them throughout the a ging test, limiting the scope of quantifiable degradation to the test conditions and duration.” Our news journalists obtained a quote from the research from the University of C onnecticut, “Fortunately, recent advances in physics-informed machine learning ( PIML) for modeling and predicting the battery state of health demonstrate the fe asibility of building models to predict the long-term degradation of a lithium-i on battery cell’s major components using only shortterm aging test data by lever aging physics. In this paper, we present four approaches for building physicsinf ormed machine learning models and comprehensively compare them, considering accu racy, complexity, ease-of-implementation, and their ability to extrapolate to un tested conditions. We delve into the details of each physics-informed machine le arning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantab le-grade lithium-ion cells subjected to varying temperatures and C-rates over fo ur years.”
StorrsConnecticutUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningUnivers ity of Connecticut
NETL
NSTL
