State Estimation for a Zero-Dimensional Electrochemical Model of Lithium-Sulfur Batteries

TL;DR

This paper presents a novel state estimation method for lithium-sulfur batteries using a zero-dimensional electrochemical model integrated with an extended Kalman filter, enabling accurate internal state estimation from voltage and current data.

Contribution

First application of electrochemical model-based observers for Li-S batteries, addressing complex dynamics with a zero-dimensional DAE model and EKF for practical state estimation.

Findings

Effective estimation of battery states demonstrated through numerical simulations.

The approach enables real-time monitoring of internal battery conditions.

Potential for improved safety and efficiency in Li-S battery management.

Abstract

Lithium-sulfur (Li-S) batteries have become one of the most attractive alternatives over conventional Li-ion batteries due to their high theoretical specific energy density (2500 Wh/kg for Li-S vs. $\sim$250 Wh/kg for Li-ion). Accurate state estimation in Li-S batteries is urgently needed for safe and efficient operation. To the best of the authors' knowledge, electrochemical model-based observers have not been reported for Li-S batteries, primarily due to the complex dynamics that make state observer design a challenging problem. In this work, we demonstrate a state estimation scheme based on a zero-dimensional electrochemical model for Li-S batteries. The nonlinear differential-algebraic equation (DAE) model is incorporated into an extend Kalman filter. This observer design estimates both differential and algebraic states that represent the dynamic behavior inside the cell, from voltage and current measurements only. The effectiveness of the proposed estimation algorithm is illustrated by numerical simulation results. Our study unlocks how an electrochemical model can be utilized for practical state estimation of Li-S batteries.