Sodium Nickel Chloride Battery Design and Testing

Author(s):  
Kris Frutschy ◽  
Troy Chatwin ◽  
Leng Mao ◽  
Chris R. Smith ◽  
Roger Bull

The main subsystems in a GE Durathon™ sodium nickel chloride battery are cells, Battery Management System (BMS), and packaging (see Figure 1). This cell chemistry requires an internal operating temperature of about 300°C, which provides unique challenges for battery thermal insulation and cooling. Electrical insulation between the cells and enclosures is another challenge, because traditional polymer-based insulations cannot operate at this elevated temperature range. Similarly, mechanical support structures must be high-temperature capable and also provide adequate protection to the cells and BMS during abusive loading conditions. This paper covers the basic battery design, thermal management, and the testing that has been conducted to ensure reliable and safe performance. Reliability testing includes battery mechanical shock and vibration plus accelerated life tests on specific components such as the heater.

2016 ◽  
Vol 2 (3) ◽  
pp. 243-252 ◽  
Author(s):  
E. Chiodo ◽  
D. Lauria ◽  
N. Andrenacci ◽  
G. Pede

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2163
Author(s):  
Tarek Berghout ◽  
Mohamed Benbouzid ◽  
Leïla-Hayet Mouss

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.


2004 ◽  
Vol 126 (6) ◽  
pp. 1047-1054 ◽  
Author(s):  
Timothy Krantz ◽  
Clark Cooper ◽  
Dennis Townsend ◽  
Bruce Hansen

Hard coatings have potential for increasing gear surface fatigue lives. Experiments were conducted using gears both with and without a metal-containing, carbon-based coating. The gears were case-carburized AISI 9310 steel spur gears. Some gears were provided with the coating by magnetron sputtering. Lives were evaluated by accelerated life tests. For uncoated gears, all of 15 tests resulted in fatigue failure before completing 275 million revolutions. For coated gears, 11 of the 14 tests were suspended with no fatigue failure after 275 million revolutions. The improved life owing to the coating, approximately a sixfold increase, was a statistically significant result.


Author(s):  
LOON-CHING TANG

We present two alternative perspectives to the current way of planning for constant-stress accelerated life tests (CSALTs) and step-stress ALT (SSALT). In 3-stress CSALT, we consider test plans that not only optimize the stress levels but also optimize the sample allocation. The resulting allocations also limit the chances of inconsistency when data are plotted on a probability plot. For SSALT, we consider test plans that not only optimize both stress levels and holding times, but also achieve a target acceleration factor that meets the test time constraint with the desirable fraction of failure. The results for both problems suggest that the statistically optimal way to increase acceleration factor in an ALT is to increase lower stress levels and; in the case of CSALT, to decrease their initial sample allocations; in the case of SSALT, to reduce their initial hold times. Both problems are formulated as constrained nonlinear programs.


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