Degradation 12-Month Review Summary Cla Clare Grey (P (PI) I) Rhodri i Je Jervi vis (P (PL) L)
Ov Over erview view • Recap of the Proje ject • Cycli ling and Materials • Scie ientific ic Hig ighlights • Year Two Pla lans
De Degrada adation tion Suite of characterisation techniques to study battery degradation across multiple time and length scales Connect degradation processes to electrochemical signatures Learn via AI methods Integrate into BMS systems Connect to modelling activities
The T he Team eam
The o he over erar arching hing goals of goals of this pr this prog ogramme amme ar are e to: to: • Identify stress-induced degradation processes • Study synergistic effects in full cells • Obtain correlative signatures for degradation • Determine how cycling programs and materials solutions , mitigate degradation • Feedback fundamental understanding and provide insights into how they can be improved. • Provide insight into and help provide mitigation strategies for issues and challenges being identified across the UK by various partners
Str Structur ucture e of of the Pr the Project oject WP1: Chemical Degradation (Clare Grey) WP2: Materials Degradation (Paul Shearing) WP3: Electrochemical Degradation (Ulrich Stimming) WP4: Materials Design & Supply (Serena Corr) Project Leader: Rhod Jervis
Cell Cycling/Materials
Materia Ma terials: Ov ls: Over erall S all Str trate tegy • Year 1: 811 + graphite • Year 2 and Beyond: Coated 811 and Si/SiO Strategy 1. Purchase materials from recognized suppliers world wide • Targray pristine material, coated electrodes (by ANL initially, then WMG) • NEI – pristine material and coated electrodes • BTR – pristine materials: graphites and coated 811 • Small scale testing across the consortium • Identified challenge re. moisture sensitivity very early on – cannot scale- up uncoated materials outside dry room • Use of dry room in Cambridge • Developed protocols for optimal full scale electrode construction at small • O1s/C1s NAP XPS => suggest scale rapid growth of LiOH & slower • Larger scale electrode fabrication in Warwick conversion to Li 2 CO 3 • Electrode fabrication in QinetiQ – this week ! Rob Weatherup, Chris Sole 2. Synthesize materials in-house (WP4) for bespoke experiments, coatings and (MAN/Diamond) eventually scale-up Page 8 Overview - Degradation Fast Start
Benchmarking and Cell Development Multiple scales of cells are needed: bespoke in situ cells with in house processing, stable performance from commercial materials, larger scale processing for post-mortem analysis 3-electrode cells allow Improved Formulations Commercially sourced separation of anode and and processing lead to electrodes and large scale cathode polarisation stable cycling performance electrode coating Page 9
Susceptibili Susce ptibility ty Measur Measurement ements: s: A A Simpl Simple e Metho Method d for or Scree Scr eening ning (Bulk) V (Bulk) Var aria iation tions s in Samples in Samples Results (1) – dc magnetometry on 3 separate batches of pristine Li NMC 811 0.08 0.12 Li NMC 811 Li NMC 811_v2 Targray, ANL Targray, WMG ZFC ZFC FC FC 0.06 Field = 100 Oe Field = 100 Oe c (emu Oe -1 mol -1 ) c (emu Oe -1 mol -1 ) 0.08 T irr = 122 K 0.04 T cusp = T irr = 8 K • cluster glass 0.04 0.02 spin glass, 0.00 0.00 0 50 100 150 200 250 300 0 50 100 150 200 250 300 T(K) T(K) Li NMC 811_v3 LiFun ZFC 0.3 FC Field = 100 Oe Difference in ZFC and FC is measure c (emu Oe -1 mol -1 ) of Ni occupancy in Li layers T irr = 148 K 0.2 • cluster glass 0.1 N. Chernova, M. S. Whittingham et al. 0.0 0 50 100 150 200 250 300 T(K) 10
Scie ientific Hig ighlights
A Summary of Key Year 1 Achievements • Materials: secured a supply chain, synthesised high performance materials, scaled up, understanding processing, consistency • Method development: refined in situ and operando techniques, predictive machine learning algorithms, use of large scale facilities • Advances in mechanistic understanding: Li mobility, gas evolution, spectroscopic understanding metal dissolution, EPR of radicals Page 12
Chemical Information Li i Mobilit ity, , Raman
In In-situ situ 7 Li Li so solid lid-st state te NMR NMR: : Iden Identifies tifies Op Optimu timum m Wind indow with w with Highe Highest st Li Li-ion ion Con Condu duct ctivity ivity Li in the Li metal electrolyte Li in NMC811 Room temperature 55 C Room temperature Page 14
In Situ Raman spectr In Situ Raman spectroscop oscopy can pr y can probe obe chemical c hemical changes dur hanges during c ing cycling ling embedded Goal: fibre-optics Study of degradation processes in Lithium Ion Batteries (LIBs) using in-situ Li-Ion (pouch) cell and in-operando Raman Raman spectroscopy Operando Raman – Cambridge Interpretation of data – Liverpool and Cambridge Kerr Gated Raman – Liverpool New cell designs Page 15
Electrochemical Observations Gas formation, , AI I EIS IS
Detect Detecting ing Volume olume Changes Changes on Cy on Cycling ling via via Pr Pressur essure e Measur Measurements ements: : NMC811 NMC811 vs. vs. Li cells Li cells Li electrolyte NMC 811 • Fast cycling at C/2 • Cyclic volumetric changes due to lithium plating/stripping • Overall changes due to electrolyte decomposition and gas evolution • => extremely sensitive set-up *corrected for temperature fluctuations Page 17 Niamh Ryall, Nuria Garcia Araez (Sot)
Gas Gas evolution fr olution from om NMC811 NMC811 vs. vs. graphite phite cells cells Li 0.5 FePO 4 Graphite electrolyte NMC 811 • Three electrode cell with a reference electrode • ca. 3 mmol of gas evolved per mole of Li + inserted on graphite in the first cycle due to SEI formation on graphite • Gases consumed on rest – reaction with cathode? *: corrected from temperature fluctuations Page 18 Niamh Ryall, Nuria Garcia Araez (Sot)
Can we use machine learning to detect degradation with EIS? • Experimental EIS spectra do not perfectly fit the classic capacitor-resistor model. We can fit it to more complex equivalent circuits, but the fit can be ill-posed • However, the spectrum changes with cycle number, thus it is an indicator of degradation, but why and how? • Can we use machine learning to detect persistent but subtle features in the EIS that correlate with degradation? Alpha Lee, Yunwei Zhang (Cam), Qiaochu Tang, Page 19 Ulrich Stimming (New)
AI × EIS: Subtle but persistent correlation between impedance and cycle number at the “magic frequency” The bode plots of Im[Z] during cycling One “magic frequency” in the imaginary part of EIS was identified as the key predictor of cycle number. Alpha Lee, Yunwei Zhang (Cam), Qiaochu Tang, Page 20 Ulrich Stimming (New)
Morphological Degradation Mic icroscopy and Cry rystallography
Post ost-mor mortem tem anal analysis r ysis reveals eals subst substantial antial par partic ticle f le fractu acturing ring After 201 cycles Pristine NMC (by WMG Warwick) C/2 cycling ~ 14.3 % capacity loss 1 μm 1 μm How is particle cracking affected by voltage window limits? holding at specific SOCs? Ref 1 shapes and sizes of particles? C/20 cycles Post-mortem analysis: EIS , XRD, SEM/EDX and ssNMR (K Marker) on cathode and anode, solution NMR on cycled electrolyte (J Allen, C O’Keefe) Ryu, H.-H. et al. Sun, Y.-K. Chem. Mater. 2018, 30, 1155- Page 22 Chao Xu, Katharina Marker (Cam) 1163.
Page 23 Anisha Patel, Mel Loveridge (WMG)
Imaging the cross sectioned coin cell Page 24 Anisha Patel, Mel Loveridge (WMG)
NMC MC 811: r 811: reduc educing ing par partic ticle siz le size e for or in in- an and d ex-situ situ TE TEM M an anal alysis ysis Sub- μ m particle size and high precision printing are essential for in situ electron microscopy (b) Aerosol printing Targray secondary particles are too large (a) Ball milling in a planetary mill Mass transfer to substrate, for TEM work so: – 60 min, 350 rpm compatible with TEM e-chip Efficient fragmentation of secondary particles, prep crystallography and composition are preserved Standard electrodes can be studied with SEM and FIB Page 25 Team: Cate, Jedrzej, Georgina and Amogh (Cam)
Operando XRD CT Technique Development Bespoke Cell Housing Electrochemical cell In-situ and operando imaging Potentiostat terminal Pin Radiation Li metal Separator Electrode Potentiostat Current Collector PEEK terminal housing Pin Tom Heenan, Chun Tan, Andy Leach, Rhodri Jervis, Paul Shearing (UCL), ID15 ESRF
Operando XRD CT allows Sub-particle Spatially Resolved XRD 1 µm resolution Potentiostat terminal Li-ion electrode particles (NMC) Radiation Normalised intensity Potentiostat terminal 1 st projection … n th projection Reconstruction FOV 400 µm x 400 µm 1 µm voxel length Tom Heenan, Chun Tan, Andy Leach, Rhodri 400 µm FOV Jervis, Paul Shearing (UCL), ID15 ESRF
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