The authors of [1] write:
[A physics-agnostic model] presents limited value in informing researchers and engineers on design opportunities to improve the cells’ performance.
Physics-based or physics-inspired models can make researches aware of the gaps in their understanding of the cell physics specifically when these models will fail to capture the cell's voltage response to the electrical current input. Therefore, it's important not to add arbitrary "learned components" to the physics-based model, or to build physics-guided neural networks. Such models can easily compensate for some wrong assumptions about the cell's physics, which would essentially be overfitting.
In real operation, the battery management system should include as accurate model of a cell as possible, regardless of how "physical" it is. So this could be a neural network, i. e. a different model than the one used by the cell designers.
[Machine-learning models] are fundamentally interpolative and cannot be expected to predict behaviors outside of the training data envelope; alternatively, a well-parameterized physics-based model should be able to extrapolate if the correct physics has been selected. [1]
I'm more defensive here: in my opinion, Li-ion cells are predictable enough for interpolative algorithms to cover most of the ways in which cells can fail. It's highly unpredictable whether any given cell will fail and when, but overall, failures should fall into a limited number of recognisable scenarios.
It seems to me that the most practical approach is to leverage our understanding of the cell's electrochemistry "lightly" by estimating the features of a cell from the cell telemetry, instead of hoping a deep neural network to figure out all Cell parameters by itself.
But then, after we Estimate most cell parameters at once, I think we can use more generic statistical algorithms and pattern recognition to estimate the cell's state-of-health and Estimate the risk of cell failure.
See also:
[1] The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety
[2] Analysis of the effect of resistance increase on the capacity fade of Li-ion batteries