A Computational Model for Sodium Sulfur Battery Analysis. (9th September 2015)

• Record Type: Journal Article
• Authors: Sezer, Hayri; Aygun, Mehmet; Mason, Jerry Hunter; Baran, Engin; Celik, Ismail Bektas
• Is Part Of: ECS transactions. Volume 69:Number 1(2015)
• Page Start: 91
• Page End: 100
• Publication Date: 2015-09-09
• Subjects: Electrochemistry -- Periodicals; Electrochemistry; Periodicals; Electronic journals; Electronic journal; 541.37
• DOI: 10.1149/06901.0091ecst
• Languages: English
• ISSNs: 1938-5862

Abstract

Abstract : Energy storage from the renewable energy sources is one of the challenging issue of clean energy technologies. Recently, sodium sulfur (Na-S) battery has gained considerable attention as a viable candidate of energy storage application. However, Na-S battery technology comes with the disadvantages of relatively high temperature operating conditions, brittle insulator and safety concern with regarded to temperature management. In this study, a two-dimensional heat transfer model is integrated with a zero-dimensional, transient electrochemical model for Na-S batteries to predict the temperature distribution inside the Na-S battery under cell operating conditions. Properties required for solving the heat transfer governing equation and electrochemical model, such as electronic and ionic conductivity, species concentrations and heat conductivity are calculated and updated as a function of time. Additionally, the instant outputs of these combined computational tools for single sodium sulfur battery, temperature, open circuit voltage, polarization and ohmic losses are used as input values for a designed Simulink model to analyze a stack of Na-S batteries. These computational tools aim to enable the thermal management and to a complete design procedure of storage systems made of Na-S batteries. The present model is tested against experimental results found in literature. Good agreement is achieved between current model and the available experiments. Validated model is used to perform a parametric study.