The Case for DeepSOH: Addressing Path Dependency for Remaining Useful Life

TL;DR

This paper introduces deepSOH, a novel approach that captures individual degradation mechanisms in batteries to improve RUL prediction, addressing the limitations of existing SOH metrics and electrode-specific estimates.

Contribution

The paper proposes deepSOH states that incorporate multiple degradation mechanisms, enhancing the accuracy of battery remaining useful life predictions beyond current methods.

Findings

DeepSOH states effectively capture individual degradation contributions.

Cell expansion measurement improves deepSOH estimation.

DeepSOH outperforms traditional SOH metrics in RUL prediction.

Abstract

The battery state of health (SOH) based on capacity fade and resistance increase is not sufficient for predicting Remaining Useful life (RUL). The electrochemical community blames the path-dependency of the battery degradation mechanisms for our inability to forecast the degradation. The control community knows that the path-dependency is addressed by full state estimation. We show that even the electrode-specific SOH (eSOH) estimation is not enough to fully define the degradation states by simulating infinite possible degradation trajectories and remaining useful lives (RUL) from a unique eSOH. We finally define the deepSOH states that capture the individual contributions of all the common degradation mechanisms, namely, SEI, plating, and mechanical fracture to the loss of lithium inventory. We show that the addition of cell expansion measurement may allow us to estimate the deepSOH and predict the remaining useful life.