Lithium–Sulfur Battery State-of-Charge Observability Analysis and Estimation

Abstract

Lithium-Sulfur (Li-S) battery technology is considered for an application in an electric-vehicle energy storage system in this study. A new type of Li-S cell is tested by applying load current and measuring cell's terminal voltage in order to parameterize an equivalent circuit network model. Having the cell's model, the possibility of state-of-charge (SOC) estimation is assessed by performing an observability analysis. The results demonstrate that the Li-S cell model is not fully observable because of the particular shape of cell's open-circuit voltage curve. This feature distinguishes Li-S batteries from many other types of battery, e.g., Li-ion and NiMH. As a consequence, a Li-S cell's SOC cannot be estimated using existing methods in the literature and special considerations are needed. To solve this problem, a new framework is proposed consisting of online battery parameter identification in conjunction with an estimator that is trained to use the identified parameters to predict SOC. The identification part is based on the well-known prediction-error minimization algorithm; and the SOC estimator part is an adaptive neuro-fuzzy inference system in combination with coulomb counting. Using the proposed method, a Li-S cell's SOC is estimated with a mean error of 4% and maximum error of 7% in a realistic driving scenario.

Lithium-Sulfur Battery Technology Readiness and Applications—A Review

“…There is a breakpoint at around 75% SOC that determines the boundary between the two plateaus. This transition, which is caused by a sudden change in electrochemical reactions inside the Li-S cell, might shift slightly to the right or left under different discharge conditions [56]. The flat shape of the OCV curve at LP makes the system unobservable based on control theory as discussed in [56].…”

“…Closely related to these is a set of techniques from artificial intelligence based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS). In [56], an ANFIS structure is designed to estimate a real Li-S cell's SoC based on real-time cell model parameterization. In this approach, parameters of an ECN cell model were used as an indicator of SoC in real-time.…”

Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters

“…Based on the motive for fast finite time convergence and fast convergence property inherited from NFTA, the NFTA VSI will be able to offer not only good dynamic response (fast convergence), but also higher accuracy control (lower THD and tracking error). However, the load of the VSI is not fixed, and the system still has chattering or steady-state error problems when exposed to large step change in loading conditions, extreme parameter variations or highly non-linear loads; the ANFIS can be suggested as an effective way to resolve the chattering or steady-state error in practical applications [44][45][46][47][48][49][50]. Therefore, an intelligent robust tracking controller with the masterly combination of the NFTA and ANFIS compensation for high performance VSI will be presented and the design of the controller is as follows.…”

Nonlinear Temperature-Dependent State Model of Cylindrical LiFePO4 Battery for Open-Circuit Voltage, Terminal Voltage and State-of-Charge Estimation with Extended Kalman Filter

“…An alternate non-model based approach including neural networks [38][39][40], fuzzy logic [41], neural network-fuzzy [42], support vector machine (SVM) [43,44] and extreme learning machine (ELM) [45][46][47][48][49][50][51] methods was developed to predict the SOC. These machine learning methods require sufficient large dataset and computation time for the training and validating the SOC value.…”