Tailoring Mission Effectiveness and Efficiency of a Ground Vehicle Using Exergy-Based Model Predictive Control (MPC)

Robert Jane, Tae Young Kim, Emily Glass, Emilee Mossman and Corey James
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Robert Jane: Combat Capabilities Development Command (CCDC), U.S. Army Research Laboratory (ARL), Adelphi, MD 20783, USA
Tae Young Kim: Department of Chemistry and Life Science, United States Military Academy (USMA), West Point, NY 10996, USA
Emily Glass: Department of Chemistry and Life Science, United States Military Academy (USMA), West Point, NY 10996, USA
Emilee Mossman: Department of Chemistry and Life Science, United States Military Academy (USMA), West Point, NY 10996, USA
Corey James: Department of Chemistry and Life Science, United States Military Academy (USMA), West Point, NY 10996, USA

Energies, 2021, vol. 14, issue 19, 1-39

Abstract: To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a methodology which was simulated the energy dynamics of a ground vehicle in parallel to running predictive neural network (NN) based predictive algorithms to address two separate research questions: (1) can energy and exergy flow characterization be developed at a future point in time, and (2) can we use the predictive algorithms to extend the energy and exergy flow characterization and derive operational intelligence, used to inform our control based algorithms or provide optimized recommendations to a battlefield commander in real-time. Using our predictive algorithms we confirmed that the future energy and exergy flow characterizations could be generated using the NNs, which was validated through simulation using two separately created datasets, one for training and one for testing. We then used the NNs to implement a model predictive control (MPC) framework to flexibly operate the vehicles thermal coolant loop (TCL), subject to exergy destruction. In this way we could tailor the performance of the vehicle to accommodate a more mission effective solution or a less energy intensive solution. The MPC resulted in a more effective solution when compared to six other simulated conditions, which consumed less exergy than two of the six cases. Our results indicate that we can derive operational intelligence from the predictive algorithms and use it to inform a model predictive control (MPC) framework to reduce wasted energy and exergy destruction subject to the variable operating conditions.

Keywords: exergy; model predictive control (MPC); neural network (NN); multi-domain operations (MDO) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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