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SUMMARY:Identifying degradation patterns of Li-ion batteries from impedanc
 e spectroscopy using machine learning - Yunwei Zhang 	
DTSTART:20200120T170000Z
DTEND:20200120T173000Z
UID:TALK137578@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Forecasting the state of health and remaining useful life of L
 i-ion batteries is yet an unsolved challenge that limits technologies such
  as consumer electronics and electric vehicles. We build an accurate batte
 ry forecasting system by combining electrochemical impedance spectroscopy 
 – a real-time\, non-invasive and information-rich measurement that is hi
 therto underused in battery diagnosis – with Gaussian process machine le
 arning. We collected over 20\,000 EIS spectra of commercial Li-ion batteri
 es at different states of health (SoH)\, states of charge (SoC) and temper
 atures – the largest dataset to our knowledge of its kind. Our Gaussian 
 process model takes the entire spectrum as input\, without manual feature 
 engineering\, and automatically determines which spectral features predict
  degradation. Our model significantly outperforms the state of the art\, a
 ccurately predicting remaining useful life (RUL) even when the past operat
 ing conditions of the battery are unknown to the user. The model can be in
 terpreted to shed light on the physical mechanisms of battery degradation.
  Our results are uniquely able to design the next-generation intelligent b
 attery management systems which would enable a considerably safer operatio
 n of Li-ion batteries.
LOCATION:Mott Seminar (531) room\, top floor of the Mott Building\, in the
  Cavendish Laboratory\, West Cambridge.
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