Mr. TaeYoung Kim Profile

TaeYoung Kim

at US Military Academy

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Proceedings Article | 12 April 2021 Presentation + Paper

Using AI-ML to develop energy and exergy flow characterizations for multi-domain operations

Robert Jane, Corey James, Tae Kim

Proceedings Volume 11746, 1174628 (2021) https://doi.org/10.1117/12.2585775

KEYWORDS: Space operations, Evolutionary algorithms, Energy efficiency, Artificial intelligence, Algorithm development, Thermal modeling, Sensors, Operational intelligence, Neural networks, Machine learning

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Current and future military operations rely on the use of multiple dissimilar platforms ranging from unmanned and manned ground, aerial, naval, and space vehicles, the aggregation of which leads to Multi-Domain Operations (MDOs). The individual vehicular-based platforms are characterized by the presence of three components: (1) power/energy generation, (2) energy storage, and (3) mechanical/electrical/chemical/thermal-based energy demands. Inadequate understanding of the energy consumption, generation, and demand in combination with the energy reserves may limit mission effectiveness and efficiency, which in turn can reduce the effectiveness and flexibility of military operations. To mitigate energy degradation and maximize mission capabilities, energy and exergy characterizations for each individual component, platform, and system must be established and used to develop operational intelligence. Using Artificial Intelligence (AI) and Machine Learning (ML), energy and exergy flow characterizations may be developed and exploited. Using MATLAB/Simulink, vehicular models have been developed in combination with prediction-based algorithms using Artificial Neural Networks (ANNs) and Long-Short-Term-Memory (LSTMs) networks. The neural networks are used to; (1) approximate energy flow characterizations from on-board sensors which link dissimilar energy flows (electrical to thermal, or electricalmechanical to thermal, etc.), and (2) predict future energy flow characterizations which could be incorporated into a Model Predictive Control (MPC) Energy Management Strategy (EMS) in which energy degradation and efficiency can be altered, flexibly managing mission capabilities while improving resilience and survivability. The full paper will include a more thorough analysis of the individual predictive algorithms and the MPC-based control results.

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