Advanced Materials for Energy Conversion and Storage 2022: Energy Conversion and Energy Storage Student
Sponsored by: TMS Functional Materials Division, TMS: Energy Conversion and Storage Committee
Program Organizers: Jung Choi, Pacific Northwest National Laboratory; Soumendra Basu, Boston University; Paul Ohodnicki, University of Pittsburgh; Partha Mukherjee, Purdue University; Surojit Gupta, University of North Dakota; Amit Pandey, Lockheed Martin Space; Kyle Brinkman, Clemson University
Wednesday 2:00 PM
March 2, 2022
Location: Anaheim Convention Center
Session Chair: Scott Roberts, Sandia National Laboratory; Johanna Nelson Weker, Slac National Accelerator Laboratory
Experimental and Computational Investigations of the Multiple Impurities Effects on the SOFC Cathode Materials: Rui Wang1; Lucas Parent2; S. Pamir Alpay2; Srikanth Gopalan3; Yu Zhong1; 1Worcester Polytechnic Institute; 2University of Connecticut; 3Boston University
To study the single and multiple impurities poisoning phenomena in the SOFC cathode systems, three common cathode materials, LSM, LSCF, and LNO, were prepared, sintered, and annealed at various temperatures with different impurity-containing atmospheres, respectively. Through X-Ray Diffraction (XRD), Scanning Electron Microscope (SEM) and Transmission electron microscopy (TEM) technique, as well as the CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) methodology, the secondary phases under different conditions (temperatures and the partial pressure of oxygen and impurities) were predicted and experimentally verified correspondingly. Furthermore, comprehensive comparisons among the three candidates under different impurity-containing conditions were also made to recommend promising cathode materials.
Joining and Oxidation of Haynes 282 Superalloy Microtubes for High-performance Heat Exchanger Applications: Narayanan Murali1; Xiaochun Li1; 1University of California, Los Angeles
As heat exchanger technology utilizes more exotic materials, their reliable manufacturing becomes complicated due to the novelty of such materials and the urgent need for their in-depth study, and the availability of this knowledge will undoubtedly aid in future design and manufacturing. To this end, we investigated the joining of drawn Haynes 282 microtubes to wrought Haynes 282 plates using transient liquid-phase (TLP) bonding and evaluated the microstructure and mechanical properties of the assembly. We also studied the oxidation of Haynes 282 microtubes in a carbon dioxide (CO2) environment at an elevated temperature to mimic expected service temperatures. The TLP joint showed coarsening in the microtube’s thickness, while the oxidation study revealed recrystallized grain structures with a bimodal distribution. The results show that microtubes have much potential for superalloy heat exchanger applications.
Magnesium Hydride Slurry Aerospace Fuel with Net-zero or Net-negative Emissions: Yi Jie Wu1; Jake Scarponi1; Jagannath Jayachandran1; Adam Powell1; 1Worcester Polytechnic Institute
This work describes the initial results of a study to determine the potential of a 1:1 Mg:carbon ratio slurry-fuel consisting of magnesium hydride (MgH2) and a surrogate fuel. Magnesium oxide (MgO) reacts in the atmosphere with CO2 to form magnesium carbonate (MgCO3) or magnesium bicarbonate (Mg(HCO3)) thus capturing carbon. When comparing the surrogate fuel chosen for this study, n-dodecane (n-C12H26), MgH2 has ~33% less energy/mass but ~28% higher energy/volume. Using Cantera, models compare the thermal performance of the slurry to pure n-C12H26. The models show that relative to energy content, the slurry requires 2-5% less fuel than n-C12H26 to reach the same temperature. The Breguet equation estimates that the slurry could achieve 8% higher aircraft range than the same volume of n-C12H26. Droplet experiments observed this fuel’s combustion characteristics and MgO combustion product morphology. The presentation will also discuss implications for engine and airframe design.
Prevention of Thermal Runaway in Li-ion Batteries Using Machine Learning Model Prediction: Meghana Sudarshan1; Alexey Serov1; Casey Jones1; Vikas Tomar1; 1Purdue University
Thermal runaway failure of Lithium-ion battery (LIB) pack can be caused by a rise in temperature of a single battery affecting other batteries in the pack, leading to catastrophic results. Thus, it is vital to detect overheating and predict the temperature rise of batteries in the upcoming battery cycles to avoid excessive heat generation. Prediction of temperature can provide a warning for improved safety of batteries by avoiding dangerous situations. In this study, we train, test, and compare machine learning-based models using experimental data of 18650 LIB and publicly available datasets to predict the temperatures of cells for future cycles. A well-trained offline temperature prediction model makes a battery management system online monitoring and prediction of temperature feasible in LIBs. A data-driven model architecture is proposed considering major thermal runaway factors during battery cycling like over-charging, over-discharging, and battery aging during temperature prediction.
3:20 PM Break
Use of Internal Thermal Sensors in Lithium-ion 18650 Battery Packs for Analysis of Individual Cell Temperatures during Cycling: Casey Jones1; Vikas Tomar1; 1Purdue University
This work focuses on the use of modified 18650 Li-ion cells to include internal thermal sensors for monitoring the internal temperatures of individual batteries during pack cycling. A four-cell battery pack was used, with two series of two LiCoO2 cells each. The cells were cycled at a current of 1.3 A (C-rate of 0.5) and through a voltage range of 6.0 – 8.4 V based on the recommendations of the manufacturer. During cycling, the internal and external temperatures of the cells were monitored, as well as the voltages of the individual cells and the overall voltage and current of the battery pack. The results of the experiment show the variations of internal and external temperatures and voltages of the individual cells, as well as their variation over cycling, and how this information can be used for state of health determination in both individual cells and battery packs.