Battery chemistry and thermal runaway
Cell chemistries are compared for energy density, cycle life and thermal-runaway behaviour, using published abuse tests and incident investigations.
GCRI · Energy Nexus
Materials science for energy assets
Materials science and chemistry explain how energy equipment ages and fails, and which materials the transition depends on: battery chemistries and their thermal behaviour, corrosion of pipelines and towers, insulation ageing in transformers, and the critical minerals in magnets, cells and electrolysers.
01Contents
Materials science for energy assets in five parts.
02Application
What materials science and chemistry does here.
Materials science and chemistry study the composition, structure and properties of substances and how they react and degrade. They inform the safety of industrial processes, the durability of infrastructure and the availability of critical materials. In energy, critical infrastructure and climate and hazards, materials and chemical knowledge supports hazard classification, infrastructure lifetime assessment and substitution planning for scarce inputs. It links industrial accidents and pollution to health and supply chain risk. The canonical description is on the STEM Commons.
Ageing and corrosion of grid assets
Transformer insulation ageing, conductor corrosion and concrete degradation are related to temperature, humidity and salt exposure to estimate remaining life with stated uncertainty.
Critical mineral dependence
The material content of batteries, turbines, panels and grid equipment is linked to production and processing concentration by country.
03Monitor
Field sampling.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Field sampling
Energy · Materials science for energy · Run R-5294
Three sites overdueGCRI · Energy Nexus
Sampling sites
red: result above guideline
Latest results
| S-27 | Reserve | 0.8 | 9 | Below |
| S-15 | Frequency | 11.1 | 10 | Above |
| S-13 | Unserved | 3.1 | 8 | Near |
| S-27 | Hydro | 5.4 | 7 | Below |
Trend at selected site
04Data
What the method consumes.
Datasets and records used by materials science and chemistry on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Materials property databases | Performance and degradation parameters | Published statistics |
| Battery abuse test and incident reports | Thermal-runaway behaviour | REPORTED |
| Mineral production statistics from USGS and national surveys | Supply concentration | Published statistics |
| Asset condition monitoring | Field ageing evidence | OBSERVED |
05Simulator
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 13%
- Assets still working
- 54%
- Amplification of the initial damage
- 3.6 times
- Steps to settle
- 2
Numbers, methods and sources
| Step | Power working (%) | Gas working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 89 | 83 | 89 |
| 1 | 50 | 72 | 78 |
| 2 | 44 | 56 | 61 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Energy Nexus in Foundry
- Tested at Energy Nexus in Universe
- Codified in Energy and Critical Infrastructure in Standards
- Published in Energy Nexus in Reports
- Taught in Faculty of Infrastructure, Energy and Industry in Academy
06Validation
How results are checked.
Measurements are traceable to reference materials and verified through interlaboratory comparison. Simulations are benchmarked against experimental data.
| Check | How it is done |
|---|---|
| Laboratory to field comparison | Accelerated ageing results are compared with field failure records before a lifetime is quoted |
| Traceable measurement | Material data are traced to laboratories assessed against ISO/IEC 17025 or to reference materials |
| Supply data cross-check | Mineral production figures are compared across national and international sources |
Laboratory ageing tests may not reproduce long-term field conditions. Combined exposures and novel substances lack characterisation.
07Sector signature
The sector signature of materials science for energy assets.
The sectors materials science for energy assets touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Energy and its partners
Energy gives power for transport and telecoms to infrastructure (0.8), pumping and treatment to water (0.5) and fertiliser, machinery and cold chains to food (0.5).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Energy → Infrastructure · power for transport and telecoms0.8
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Infrastructure → Water · pipes, dams and treatment works0.6
08Connections
Topics that rely on this method.
09Methods and evidence
How materials science for energy assets is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in materials science for energy assets: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
10Stewardship
Who maintains the method.
Advanced Industry and Robotics stewards materials science and chemistry for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
Questions and answers.
What are the limits of materials science and chemistry in this field?
Laboratory ageing tests may not reproduce long-term field conditions. Combined exposures and novel substances lack characterisation.
How are results checked?
Measurements are traceable to reference materials and verified through interlaboratory comparison. Simulations are benchmarked against experimental data.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIEnergy NexusGlobal Sector Risk and ResilienceStrategic SystemsScenario and Stress-Test
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
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