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

• Published in: IEEE transactions on transportation electrification Vol. 7; no. 3; pp. 1324 - 1338
• Main Authors: Shateri, Neda, Auger, Daniel J., Fotouhi, Abbas, Brighton, James
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aging; Aging (artificial); Algorithms; Batteries; Circuits; Curve fitting; Degradation; Driving conditions; Estimation; Experimental test; Flux density; Gravimetry; Lithium; Lithium sulfur batteries; Lithium-ion batteries; lithium–sulfur (Li–S) battery; Mathematical model; Mathematical models; Parameter identification; Power management; Prototypes; Real-time systems; State estimation; state-of-health (SoH) estimation; Sulfur; support vector machine (SVM) classifier; Support vector machines
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2021.3059738

Lithium-sulfur (Li-S) batteries offer potential for higher gravimetric energy density in comparison to lithium-ion batteries. Since they behave quite different from lithium-ion batteries, distinctive approaches to state estimation and battery management are required to be developed specifically for them. This article describes an experimental work to model and perform real-time estimation of the progression of use-induced aging in prototype Li-S cells. To do that, state-of-the-art 19-Ah Li-S pouch cells were subject to cycling tests in order to determine progressive changes in parameters of a nonlinear equivalent-circuit-network (ECN) model due to aging. A state-of-health (SoH) estimation algorithm was then designed to work based on identifying ECN parameters using forgetting-factor recursive least squares (FFRLS). Two techniques, nonlinear curve fitting and support vector machine (SVM) classification, were used to generate SoH values according to the identified parameters. The results demonstrate that Li-S cell's SoH can be estimated with an acceptable level of accuracy of 96.7% using the proposed method under realistic driving conditions. Another important outcome was that the "power fade" in Li-S cells happens at a much slower rate than the "capacity fade" which is a useful feature for applications where consistency of power delivery is important.