Self-Discharge Effects in Lithium-Sulfur Equivalent Circuit Networks for State Estimation

Abstract

This study considers application-oriented models of lithium-sulfur (Li-S) cells. Existing ECN models often neglect self-discharge, but this can be important in applications. After describing the context in which control-oriented models and estimators are based, the self-discharge phenomenon is investigated for a new 21 Ah Li-S cell. As a contribution of this study, an equivalent-circuit-network (ECN) model was extended to account for cells' self-discharge. Formal system identification techniques were used to parameterize a model from experimental data. The original model was then extended by adding terms to represent a self-discharge resistance. To obtain the self-discharge resistance, a particular new series of experiments were designed and performed on the Li-S cell at various temperature and initial state-of-charge (SoC) levels. The results demonstrate the dependency of self-discharge rate on the SoC and temperature. The self-discharge rate is much higher at high SoC levels and it increases as temperature decreases. Much of today's research into lithium-sulfur (Li-S) batteries concerns the development and understanding of materials, construction and the fundamental scientific understanding of cell behavior. Many will recognize the importance of this, but lithium-sulfur is beginning to reach maturity, and there is a need to develop the engineering science and techniques necessary for deployment in practical applications. In particular, there is a need to devise algorithms that can be used to estimate state-of-charge and state-of-health measures in operando. Electrochemistry is of course key here, but it is equally vital to draw from other disciplines: in particular, control theory has much to offer, particularly in respect of state estimation.When electrochemists create models, they usually do so 'as scientists': the aim of a scientific model is to enhance understanding. Of course, no model is perfect, and the pure scientist uses model imperfections to identify gaps in present knowledge and as the inspiration for further research. The aim is to improve understanding and get a 'better model'. However, at some point, cells may be put to practical use, and at this point, the application engineer will often have to make do with the best models available at that time, despite the model's imperfections.Control systems engineers are well accustomed to dealing with model errors and 'uncertainty': there are many excellent text books on control, and any of them will give a short overview of the key principles of control; Aström and Murray's work 1 is a good example. Typically, a dynamic system is modelled as a set of dynamic equations: x, u, w) [1]where u and y represent observed system input and outputs (voltages, currents and temperatures, for example), x comprehensively accounts for past history by representing the system's non-or partlymeasurable dynamic 'states' (perhaps including state of charge in a simple model, or concentrations of chemical species in a complex one), h(·) and f (·) are known functions describ...

Comparison of the state of Lithium-Sulphur and lithium-ion batteries applied to electromobility

“…Moreover, Li-S batteries have 8-15% self-discharge rate per month (Kolosnitsyn and Karaseva, 2008) due to polysulfide shuttle (Mikhaylik and Akridge, 2004) and collector corrosion (Song et al, 2013), (Vaclav Knap et al, 2016), (Marinescu et al, 2015), which is between 10 and 15 times higher than the selfdischarge of Li-ion batteries (Table 1). Yousif et al conducted tests at different conditions on a 21 Ah Li-S cell, which showed that self-discharge is more significant as SOC increases (Yousif et al, 2018). Thus it is recommendable to store Li-s batteries at low SOC.…”

An improved adaptive spherical unscented Kalman filtering method for the accurate state‐of‐charge estimation of lithium‐ion batteries

“…The SOC can reflect the remaining capacity of the battery, and the battery usage strategy can be planned through the known remaining capacity, so that the lithium‐ion battery can be operated in the best working condition and the longest service life by adjusting the voltage and current 3 . Therefore, real‐time and accurate estimation of the SOC will provide significant help to battery management 4–6 . However, due to the complex internal structure of the lithium‐ion battery itself, the SOC of the lithium‐ion battery is affected by various complex factors such as self‐discharge current, self‐discharge effect, internal temperature, external ambient temperature, and battery aging, which makes it difficult to accurately estimate out the value of SOC 7,8 .…”

“…3 Therefore, real-time and accurate estimation of the SOC will provide significant help to battery management. [4][5][6] However, due to the complex internal structure of the lithium-ion battery itself, the SOC of the lithium-ion battery is affected by various complex factors such as self-discharge current, self-discharge effect, internal temperature, external ambient temperature, and battery aging, which makes it difficult to accurately estimate out the value of SOC. 7,8 In order to accurately estimate the SOC of lithium-ion batteries, relevant scholars have conducted a lot of research and proposed many methods for SOC estimation.…”

Análisis de ciclo de vida de sistemas innovadores de almacenamiento eléctrico en litio-azufre (Li-S) para vehículos

“…realizaron pruebas en diferentes condiciones en una celda de Li-S de 21 Ah, que mostraron que la autodescarga es más significativa a medida que aumenta la SOC (Yousif et al, 2018). Por lo tanto, es recomendable almacenar las baterías de Li-S a un nivel bajo de SOC.…”