The authors of [1] suggest that it's hard to train a physics-agnostic model to Estimate the risk of cell failure from the data because there are too few cell failures and the data to train on. I agree with this.

To get more data for model training, the authors suggest to artificially induce short circuits. I think this is a practical idea.

The authors enumerate several ways of inducing short circuits in cells, or increasing their probability:

• Replace a part of separator with wax with low melting point;
• Strain the separator by dropping, bending, or pressing the cell;
• Insert a small conducting object into the cell and then press the cell to puncture the separator;
• Cycle the cell at high temperature.

I would also add to this list:

• Cycling cells at freezing cold temperatures can lead to rapid Lithium plating and induce failures, too.
• Apply very high charge or discharge current to the cells.

I think mechanically stressing the cell and cycling at extreme temperatures and high currents are the most practical approaches because cells from the main manufacturing line can be used. Also, both are real-life scenarios for how the cells can actually fail.

The downside of these approaches is that cells can still fail only rarely even after such abuses, so to avoid wasting a lot of cells, short circuit induction should be integral part of the general cell modelling and cell parameter verification process.

On a similar idea, we can take the cells that performed poorly during the end-of-line testing and cycle them to death, hoping that these cells are more likely to fail in the process than normal cells.

Intentionally inserting a small conducting object between cell's foils also captures a possible real-life scenario, but the cells should be created bespoke in the lab.

To counter their own ideas, authors write:

However, it can be difficult to confidently relate [results obtained for cells on which failures were artificially induced] back to predictions about normal cells under standard operating conditions. [1]

I can see that the statistics of failures of cells cycled at high temperature might not apply to cells cycled at different temperatures.

However, I think that separator failures should be characterised by a phase transition of one or several cell parameters (such as Cell self-discharge rate and Cell internal resistance). The transition patterns might be different for different types of separator failures (e. g. Lithium dendrite vs. spurring vs. tearing), but I don't think the transition patterns depend on how the separator failures where brought about. Therefore, the models for early detection of cell failures should work well for cells in real operation even though these models are trained on the data from the controlled experiments.

References

[1] The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety