Model-Driven Feature Engineering for Data-Driven Battery SOH Model

Accurate State of Health (SoH) estimation is indispensable for ensuring battery system safety, reliability, and run-time monitoring. However, as instantaneous runtime measurement of SoH remains impractical when not unfeasible, appropriate models are required for its estimation. Recently, various data-driven models have been proposed, which solve various weaknesses of traditional models. However, the accuracy of data-driven models heavily depends on the quality of the training datasets, which usually contain data that are easy to measure but that are only partially or weakly related to the physical/chemical mechanisms that determine battery aging. In this study, we propose a novel feature engineering approach, which involves augmenting the original dataset with purpose-designed features that better represent the aging phenomena. Our contribution does not consist of a new machine-learning model but rather in the addition of selected features to an existing model. This methodology consistently demonstrates enhanced accuracy across various machine-learning models and battery chemistries, yielding an approximate 25% SoH estimation accuracy improve-ment. Our work bridges a critical gap in battery research, offering a promising strategy to significantly enhance SoH estimation by optimizing feature selection.

I. Introduction

The accuracy of State of Health (SoH) estimation in Battery Management Systems (BMS) is essential for ensuring the reliability of battery systems of a battery-powered device, and in particular, for Electric Vehicles (EV s) [1]. However, the estimation is valuable if it can be done in real-time or at least with a frequency in the timescale of seconds. As there is no practical physical way to instantaneously measure the SoH, such tracking inevitably requires a model.