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Currently, there are no safe and suitable fuel sources with comparable power density to traditional combustible fuels capable of replacing Internal Combustion Engines (ICEs). For the foreseeable future, civilian and military systems are likely to be reliant on traditional combustible fuels. Hybridization of the vehicle powertrains is the most likely avenue which can reduce emissions, minimize system inefficiencies, and build more sustainable vehicle systems that support the United States Army modernization priorities. Vehicle systems may further be improved by the creation and implementation of artificial intelligence and machine learning (AI/ML) in the form of advanced predictive capabilities and more robust control policies. AI/ML requires numerous characterized and complete datasets, given the sensitive nature of military systems, such data is unlikely to be known or accessible limiting the reach to develop and deploy AI/ML to military systems.

The future battlefield will include multiple dissimilar manned and unmanned aerial, ground, sea, and space vehicles working in concert with each other to support fires, logistics, maneuvers, communication, and coordination-based missions. Mission effectiveness and efficiency are often at odds, and due to the distributed and dissimilar energy flows inherent in Multi-Domain Operations (MDO) there is a need to understand, identify, and characterize the energy flows. The ability to analyze the energy flows and effectively maintain adequate energy reserves could provide strategic capabilities to the warfighters, permitting energy informed operations to maximize mission effectiveness and efficiency, while mitigating vulnerabilities. This research focuses on developing energy and exergy characterization through development of Artificial Intelligence (AI), Machine Learning (ML), and Artificial Neural Networks (ANNs) for assessing and analyzing performance of a platform.