A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation

Fotouhi, Abbas; Shateri, Neda; Auger, Daniel J.; Longo, Stefano; Propp, Karsten; Purkayastha, Rajlakshmi; Wild, Mark

doi:10.1109/smacd.2016.7520715

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…As the input of these tests, a typical EV (i.e., Nissan Leaf) was simulated on UDDS drive cycle. More details of EV modeling and simulation can be found in [34] and [38]. The power demand signal was, then, scaleddown to be applied to a single cell.…”

Section: Case Study: Simulation and Test Of Li-s Cell Basedmentioning

confidence: 99%

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

Fotouhi

Auger

Propp

et al. 2018

IEEE Trans. Power Electron.

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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.

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Section: Case Study: Simulation and Test Of Li-s Cell Basedmentioning

confidence: 99%

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

Fotouhi

Auger

Propp

et al. 2018

IEEE Trans. Power Electron.

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“…Another difference between Li-ion and Li-S is that the last one, the voltage profile is substantially distinct between charge and discharge. (Fotouhi et al, 2016d) As a general conclusion, models for Li-ion are not transferable to Li-S model due to their differences in performance. The automotive sector is interested in simple and fast models such as ECN.…”

Section: Modellingmentioning

confidence: 99%

“…The essential configuration of an ECN battery model is using only one resistor and one voltage source to make a circuit Figure 7a) which simulate exclusively the internal resistance of the battery. Adding RC ( Figure 7 b,c) pairs to the model increments its accuracy by contemplating the battery polarization characteristics (Fotouhi et al, 2016d). It is essential to accurately parametrize the different elements in the ECN model to obtain reliable results.…”

Section: Modellingmentioning

confidence: 99%

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

Benveniste

Rallo

Casals

et al. 2018

Journal of Environmental Management

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The market share in electric vehicles (EV) is increasing. This trend is likely to continue due to the increased interest in reducing CO emissions. The electric vehicle market evolution depends principally on the evolution of batteries capacity. As a consequence, automobile manufacturers focus their efforts on launching in the market EVs capable to compete with internal combustion engine vehicles (ICEV) in both performance and economic aspects. Although EVs are suitable for the day-to-day needs of the typical urban driver, their range is still lower than ICEV, because batteries are not able to store and supply enough energy to the vehicle and provide the same autonomy as ICEV. EV use mostly Lithium-ion (Li-ion) batteries but this technology is reaching its theoretical limit (200-250 Wh/kg). Although the research to improve Li-ion batteries is very active, other researches began to investigate alternative electrochemical energy storage systems with higher energy density. At present, the most promising technology is the Lithium-Sulphur (Li-S) battery. This paper presents a review of the state of art of Li-Sulphur battery on EVs compared to Li-ion ones, considering technical, modelling, environmental and economic aspects with the aim of depicting the challenges this technology has to overcome to substitute Li-ion in the near future. This study shows how the main drawbacks for Li-S concern are durability, self-discharge and battery modelling. However, from an environmental and economic point of view, Li-S technology presents many advantages over Li-ion.

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“…The essential configuration of an ECN battery model uses a circuit containing only one voltage source and one resistor simulating the internal battery resistance. Adding RC pairs to the model increases its accuracy by considering the battery polarization characteristics [30]. It is needed to use the accurate parameterization of the elements of the ECN technique to achieve reliable results.…”

Section: Differences Occurring During Modelling Of Li-ion and Li-s Ba...mentioning

confidence: 99%

The Comparative Study on the Li-S and Li-ion Batteries Cooperating with the Photovoltaic Array

Siczek

Piersa

et al. 2020

Energies

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The stationary photovoltaic array can be used to charge the different vehicle batteries and, in parallel, be used as a power source for the utility grid or standalone devices placed such as in campers. The main objective of the study was to compare chosen electrical characteristics of two assemblies with each containing the same PV array, boost converter and inverter, and a different battery, such as the Li-S one and the Li-ion one, respectively. Differences occurring during modelling of Li-ion and Li-S batteries were discussed. The model of the chosen photovoltaic array was used during analysis. The models based on electrical equivalent circuits for Li-ion battery and of Li-S battery were utilized during calculations. The models of the boost converter and boost inverter of known topology parameters were utilized during simulations. In the chosen performances (courses of voltages and currents versus time) obtained from the simulation of the sets composed of the Li-S battery cooperating with the boost inverter or the boost converter, only small differences or no differences occurred when compared to the case of the Li-ion battery.

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A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation

Cited by 12 publications

References 14 publications

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

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

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

The Comparative Study on the Li-S and Li-ion Batteries Cooperating with the Photovoltaic Array

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