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

Robert S. Jane; Corey James; Tae Kim

doi:10.1117/12.2585775

12 April 2021 Using AI-ML to develop energy and exergy flow characterizations for multi-domain operations

Robert S. Jane, Corey James, Tae Kim

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 1174628 (2021) https://doi.org/10.1117/12.2585775
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Robert S. Jane, Corey James, and Tae Kim "Using AI-ML to develop energy and exergy flow characterizations for multi-domain operations", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 1174628 (12 April 2021); https://doi.org/10.1117/12.2585775

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available