Two points are enough

TL;DR

This paper demonstrates that using only two data points and a simple correlation-based feature selection, accurate battery prognosis and diagnosis can be achieved, challenging existing complex methods.

Contribution

It introduces the best two-point feature (BTPF) method, showing minimal data suffices for accurate prognosis and diagnosis across diverse battery datasets.

Findings

Achieves comparable accuracy to state-of-the-art features

Validates the effectiveness across multiple datasets and chemistries

Highlights the potential of minimal data-driven approaches

Abstract

Prognosis and diagnosis play an important role in accelerating the development of lithium-ion batteries, as well as reliable and long-life operation. In this work, we answer an important question: What is the minimum amount of data required to extract features for accurate battery prognosis and diagnosis? Based on the first principle, we successfully extracted the best two-point feature (BTPF) for accurate battery prognosis and diagnosis using the fewest data points (only two) and the simplest feature selection method (Pearson correlation coefficient). The BTPF extraction method is tested on 820 cells from 6 open-source datasets (covering five different chemistry types, seven manufacturers, and three data types). It achieves comparable accuracy to state-of-the-art features in both prognosis and diagnosis tasks. This work challenges the cognition of existing studies on the difficulty of battery prognosis and diagnosis tasks, subverts the fixed pattern of establishing prognosis and diagnosis methods for complex dynamic systems through deliberate feature engineering, highlights the promise of data-driven methods for field battery prognosis and diagnosis applications, and provides a new benchmark for future studies.