  An accurate estimation of the state of health (SOH) of batteries is critical
to ensuring the safe and reliable operation of electric vehicles (EVs).
Feature-based machine learning methods have exhibited enormous potential for
rapidly and precisely monitoring battery health status. However, simultaneously
using various health indicators (HIs) may weaken estimation performance due to
feature redundancy. Furthermore, ignoring real-world driving behaviors can lead
to inaccurate estimation results as some features are rarely accessible in
practical scenarios. To address these issues, we proposed a feature-based
machine learning pipeline for reliable battery health monitoring, enabled by
evaluating the acquisition probability of features under real-world driving
conditions. We first summarized and analyzed various individual HIs with
mechanism-related interpretations, which provide insightful guidance on how
these features relate to battery degradation modes. Moreover, all features were
carefully evaluated and screened based on estimation accuracy and correlation
analysis on three public battery degradation datasets. Finally, the
scenario-based feature fusion and acquisition probability-based practicality
evaluation method construct a useful tool for feature extraction with
consideration of driving behaviors. This work highlights the importance of
balancing the performance and practicality of HIs during the development of
feature-based battery health monitoring algorithms.
