Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

Abstract

Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.

Recent Progress and Emerging Application Areas for Lithium–Sulfur Battery Technology

“…Examples are the general framework describing Li–S cell SoH in terms of capacity fade and resistance growth which have been presented in a study by Wild et al [ 7 ] and the SoH estimation technique presented in a study by Knap. [ 89 ] Looking at the literature, Li–S cell degradation mechanism has been investigated by electrochemists in a number of studies; however, the literature suffers from lack of studies where Li–S cell SoH estimation is investigated for BMS application. Although an insight into understanding the degradation mechanism in Li–S cells using electrochemical models is quite helpful, those models are hardly useable in real‐time applications mainly due to their complexity.…”

Lithium-Sulfur Cell State of Charge Estimation Using a Classification Technique

“…Although there are a number of studies in the literature in which Li-ion battery models are used beside a SoC estimator, few similar studies exist for Li-S battery. Looking at the literature, a first version of Kalman filter-based SoC estimators have been recently developed for Li-S cells in [29]- [31]. An alternative body of work has used Adaptive Neuro-Fuzzy Inference Systems (ANFIS) [3].…”

An Experimental Study on Prototype Lithium–Sulfur Cells for Aging Analysis and State-of-Health Estimation

“…19 Ah) Li-S pouch cell. In another study presented in [15], a fresh 3.4 Ah Li-S cell is compared with a similar type aged cell to investigate the effect of ageing on cell's capacity and internal resistance. Although the results of that study are promising but they suffer from limited test data.…”