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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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2 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Electrical short circuit and current overload tests on aircraft wiring

The findings of electrical short circuit and current overload tests performed on commercial aircraft wiring are presented. A series of bench-scale tests were conducted to evaluate circuit breaker response to overcurrent and to determine if the wire showed any visible signs of thermal degradation due to overcurrent. Three types of wire used in commercial aircraft were evaluated: MIL-W-22759/34 (150 C rated), MIL-W-81381/12 (200 C rated), and BMS 1360 (260 C rated). A second series of tests evaluated circuit breaker response to short circuits and ticking faults. These tests were also meant to determine if the three test wires behaved differently under these conditions and if a short circuit or ticking fault could start a fire. It is concluded that circuit breakers provided reliable overcurrent protection. Circuit breakers may not protect wire from ticking faults but can protect wire from direct shorts. These tests indicated that the appearance of a wire subjected to a current that totally degrades the insulation looks identical to a wire subjected to a fire; however the 'fire exposed' conductor was more brittle than the conductor degraded by overcurrent. Preliminary testing indicates that direct short circuits are not likely to start a fire. Preliminary testing indicated that direct short circuits do not erode insulation and conductor to the extent that ticking faults did. Circuit breakers may not safeguard against the ignition of flammable materials by ticking faults. The flammability of materials near ticking faults is far more important than the rating of the wire insulation material.

Cahill, Patricia↗
NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗