(Digital Presentation) Thermal Runaway Initiation, Propagation and the Potential for Propagation Inhibition in Commercial Automotive Lithium-Ion Cells and Modules
Bibliographic record
Abstract
Lithium-ion batteries, containing flammable electrolytes, have become safer in many ways since their invention. As the technology matures, energy and power densities increase: the European Council for Automotive R&D set 2030 (cell level) targets for battery electric vehicle energy density of 1000 Wh L-1, with 450 Wh kg-1 specific energy and 1800 W kg-1 peak specific power. This rise in energy and power densities increases the risk of accidental release of energy from the batteries and thermal runaway (TR). Safety is thus a key concern when designing a lithium-ion battery system for electric vehicles (EV). TR relates to fast heating of a battery cell caused by exothermic chemical decomposition reactions of the materials inside the cells. It develops in a cell when heat is generated and cannot be dissipated quickly enough to the surroundings, giving rise to an abrupt (exponential) temperature increase and subsequent reaction rate increase. During TR, various exothermic side reactions can occur, leading to temperature increase, accompanied by pressure increase as electrolyte evaporates and venting. This can result in the emission of highly flammable gas and the formation of toxic atmosphere, as well as, in fire and, in very rare circumstances, in explosion. The corresponding temperature increase in adjacent cells (or modules) might then be sufficient to cause them to also go into TR – leading to a process known as thermal runaway propagation (TRP). Fit-for purpose testing procedures to assess the risks associated with TRP are therefore of utmost importance and the focus should always be the safety of the EV occupants, bystanders, first responders and property. Some tests in current standards and regulations try to simulate internally driven failures (e.g. internal short circuit), but whether these tests are suitable to represent field failures remains an open question. This work will give an insight into two selected TR initiation methods assessed within JRC’s TR initiation and propagation test campaigns, namely (a) localised rapid external heating, as developed and patented internationally by NRC (National Research Council Canada) and (b) ceramic nail penetration, as described in the IEC TR 62660-4 standard. It examines the response of short-stacks of pouch cells extracted from automotive batteries after TR is triggered in a single cell. Experimental data and analysis to better understand how TR propagates and the potential for TRP inhibition will be reported. The potential of inhibiting TRP is explored on 2- and 5-cell assemblies with a multi-layer, porous, composite insulation material between cells. Separating the cells can delay significantly TRP in adjacent cells in modules constructed with pouch cells, whereas TRP may be slowed and even inhibited as the thickness of the multilayer material varies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.254 | 0.082 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".