In 2019, Severson et al. demonstrated that ML, combined with a large dataset, is effective for predicting battery lifetime by employing data-driven feature engineering. The authors achieved accurate early lifetime predictions for 124 commercial lithium iron phosphate (LFP)/graphite cells using observations from the first 100 aging cycles. The cells were charged under different fast charging protocols, but the discharge protocol was identical for all cells. Thus, the charging data explicitly encoded the cycling conditions, while the discharging data (for a given cycle) did not. Following this work, several battery datasets have been published . Features inspired by Severson's work have been used to create models that can be transferred to different datasets or different chemistries . Other feature engineering methods and more complex ML algorithms such as neural networks have also been deployed for lifetime and SOH estimation . These complex architectures can be used to improve lifetime prediction accuracy or to enable predictions with fewer aging cycles . Both of these objectives have been used as metrics to benchmark model performance. While ML approaches for battery lifetime predictions are powerful, they also have pitfalls . Among them are "nonlegitimate features" , which are those linked or correlated with the outcome.