Driving behavior-guided battery health monitoring for electric vehicles using machine learning

TL;DR

This paper introduces a machine learning approach for battery health monitoring in electric vehicles that considers real-world driving behaviors and feature practicality to improve estimation accuracy and reliability.

Contribution

It proposes a scenario-based feature fusion and acquisition probability evaluation method to enhance battery SOH estimation by balancing accuracy and feature accessibility in real-world conditions.

Findings

Improved battery SOH estimation accuracy using the proposed method.

Effective feature screening based on correlation and estimation performance.

Enhanced practicality of battery health monitoring in real-world driving scenarios.

Abstract

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.