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SUMMARY:EE Distinguished Lecturer Seminar: Squeezing more out of Lithium-i
 on battery grid energy storage systems by accurately modelling battery deg
 radation
DTSTART:20190927T133000
DTEND:20190927T143000
DTSTAMP:20260408T025815Z
UID:e2a103cc396ed20b903c10678798df2c69f6212568f0da7cb68eaa27
CATEGORIES:Conferences - Seminars
DESCRIPTION:David Howey is an Associate Professor in the Department of Eng
 ineering Science at the University of Oxford\, where he leads a research g
 roup focused on modelling\, diagnostics and control of electrochemical ene
 rgy devices and systems\, with a particular focus on batteries. He has act
 ive current research on degradation\, thermal and electrochemical modellin
 g\, data driven health prediction\, parameter estimation\, and control of 
 grid energy storage\, sponsored by EPSRC\, the Faraday Institution\, Innov
 ateUK and companies including Siemens and Continental AG. He is an IEEE Se
 nior Member\, ECS Member\, and editor of IEEE Transactions on Sustainable 
 Energy. He is also a co-founder of battery management system company http
 s://www.brillpower.com/ and technical advisor to grid storage optimisatio
 n company http://www.habitat.energy/. His group website http://epg.eng.o
 x.ac.uk/howey\nAbstract: The lifetime of lithium-ion batteries is a key e
 lement in the business case for grid-connected energy storage. Battery deg
 radation is the result of many different processes. This presentation will
  discuss a range of different models that existing for simulating degradat
 ion\, and how these can be incorporated into optimisation of usage for ene
 rgy arbitrage applications. Various battery degradation models exist\, of 
 differing complexity\, accuracy and data-requirements. The simplest model 
 is a linear degradation model assuming a maximum energy throughput over th
 e battery’s lifetime. A second class are the empirical degradation model
 s\, interpolating test data sets. Different empirical models include diffe
 rent operating conditions\, depending on the dominant degradation mechanis
 ms appearing in the data set. Thirdly\, electrochemical degradation models
  try to capture the physics of the degradation processes. A comprehensive 
 overview of different modelling approaches will be given\, using the singl
 e particle model (SPM) as a framework for investigating behaviour. The dif
 ferent degradation models are implemented as add-ons to the SPM leading to
  a flexible framework\, which allows to assess the effects of individual a
 ssumptions\, as well as how different mechanisms interact. The SPM with a 
 side reaction at the negative electrode is then used within an optimisatio
 n algorithm to perform ‘degradation aware’ energy arbitrage over one y
 ear. Results show that the profit in this application can be increased sub
 stantially and degradation can be reduced by using more realistic models. 
 This illustrates that using a simplistic battery model in a techno-economi
 c assessment of grid-connected batteries might substantially underestimate
  the business case and lead to erroneous conclusions. These results have a
 lso been recently validated with experimental cycling tests of battery cel
 ls using different profiles\, and measured data will be shown from these t
 ests. References: (1) Reniers\, J. M.\, Mulder\, G.\, Ober-Blöbaum\, S.\,
  & Howey\, D. A. (2018). “Improving optimal control of grid-connected li
 thium-ion batteries through more accurate battery and degradation modellin
 g”. Journal of Power Sources\, 379\, 91-102. (2) Reniers\, J.\, Howey\, 
 D.\, & Mulder\, G. (2019)\, ECSArXiv pre-print “Review and performance c
 omparison of mechanical-chemical degradation models for lithium-ion batter
 ies”\, DOI 10.1149/osf.io/zdwsu. (3) Software https://github.com/davidh
 owey/SLIDE
LOCATION:MXF 1 https://plan.epfl.ch/?room==MXF%201
STATUS:CONFIRMED
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