• Energy Research

Abstract The effect of different structural designs on the performance of electrical double layer capacitors (EDLCs) has been studied through a mathematical model that considers the mass transfer and conservation of charge equations. The structural design parameters considered in this study are the electrode thickness, electrode porosity, and initial electrolyte concentration. The performance parameters studied are the cell capacitance, specific energy, specific power, and electrolyte concentration for a range of discharge rates. The results of this study can be used to optimize EDLCs at various operating conditions.

Due to their safety and high power density, one of the most promising types of all-solid-state lithium batteries is the one made with the argyrodite solid electrolyte (ASE). Although substantial efforts have been made toward the commercialization of this battery, it is still challenged by some technical issues. One of these issues is to prevent the side reactions at the interface of the ASE and the cathode active material (CAM). A solution to address this issue is to coat the CAM particles with a material that is compatible with both ASE and CAM. Prior studies show that the lithium niobate, LiNbO3, (LNO) is a promising material for coating CAM particles to reduce the interfacial side reactions. However, no systematic study is available in the literature to show the effect of coating LNO on CAM performance. This paper aims to quantify the effect of LNO coating on the electrochemical performance of a nickel-rich CAM. The electrochemical performance parameters that are studied are the capacity, cycling performance, and rate performance of the coated-CAM; and the effectiveness of the coating to prevent the side reactions at the ASE and CAM interface is out of the scope of this study. To eliminate the effect of side reactions at the ASE and CAM interface, we conduct all tests in the organic liquid electrolyte (OLE) cells to solely present the effect of coating on the CAM performance. For this purpose, 0.5 wt.% and 1 wt.% LNO are used to coat the LiNi0.6Mn0.2Co0.2O2 (NMC-60) CAM through two synthesizing methods. Consequently, the effects of the synthesizing method and the coating weight percentage on the NMC-60 performance are presented.

Abstract A thermodynamic model is developed to determine the fuels that would yield an identical maximum cell voltage (MCV) for solid oxide fuel cells (SOFCs) at a given operating condition. These fuels make a continuous curve in the ternary coordinate system. A fuel map is established by developing the continuous fuel curves for different MCVs at the same operating condition and representing them in the carbon–hydrogen–oxygen (C–H–O) ternary diagram. Using the fuel map, the effect of the composition of a fuel containing carbon, hydrogen, oxygen, and inert gas atoms on the MCV of SOFCs can be easily studied. In addition to the effect of the fuel composition, the graphical representation of fuel maps can be applied to study the effect of the fuel processors on the MCV of SOFCs. As a general result, among fuels that can be directly utilized in SOFCs, at the same temperature and pressure, the one located at the intersection of the H–C axis and the carbon deposition boundary (CDB) curve in the C–H–O ternary diagram, provides the highest MCV for SOFCs. The results also show that for the fuels that cannot be directly utilized in SOFC, the steam reforming fuel processor always yields a higher MCV than the autothermal reforming or the partial oxidation fuel processors at the same inlet fuel temperature.

A well-designed battery management system along with a set of voltage and current sensors is required to properly measure and control the battery cell operational variables for Hybrid Electric Aircrafts (HEAs). Some critical functions of the battery including State-Of-Charge (SOC) and State-Of-Health (SOH) estimations, over-current, and over-/under-voltage protections are mainly related to current and voltage sensor measurements. Therefore, in case of battery faults occur in HEA, designing a reliable and robust diagnostic procedure is essential. In this study, for Li-ion batteries, a new and fast fault diagnosis technique via collecting data is proposed. Finally, the effectiveness of the proposed diagnostic method is validated, and the results show how overcharge, over-discharge and sensor faults can be accurately detected.

Solid-state argyrodite electrolytes are promising candidate materials to produce safe all-solid-state lithium batteries (ASSLBs) due to their high ionic conductivity. These batteries can be used to power electric vehicles and portable consumer electronics which need high power density. Atomic-scale modeling with ab initio calculations became an invaluable tool to better understand the intrinsic properties and stability of these materials. It is also used to create new structures to tailor their properties. This review article presents some of the recent theoretical investigations based on atomic-scale modeling to study argyrodite electrolytes for ASSLBs. A comparison of the effectiveness of argyrodite materials used for ASSLBs and the underlying advantages and disadvantages of the argyrodite materials are also presented in this article.

SummaryReliable operation and control of battery packs can lead to increasing applications of batteries as energy sources for mobile power systems such as electric/hybrid electric aircraft. If the operation of a battery pack is controlled and monitored thoroughly, the safety in the battery system of an electrified aircraft can be guaranteed. The battery model has many applications in battery management systems such as battery performance analysis and fault detection. To achieve an accurate fault diagnosis for electric aircraft, an intelligent fault detection scheme within an accurate battery cell model is required. In this study, an adaptive lithium‐ion battery model is proposed in which models' parameters are estimated by a supervised machine learning paradigm. This adaptive battery model is developed based on a second order equivalent circuit model, which has a good representation of lithium‐ion batteries dynamics. Comparative verification experiments show good accuracy and robustness of the machine learning‐based parameter estimator lead to an accurate battery model with an average error less than 0.4%. Moreover, to see the effectiveness of this machine learning‐based model in fault detection applications, a model‐based fault diagnosis scheme is developed. Finally, the analysis of fault diagnosis tests under different test conditions proves that the proposed adaptive battery model can significantly improve the fault diagnosis accuracy of batteries.