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Articles 1 - 11 of 11
Full-Text Articles in Military Vehicles
Integrated Power, Energy, And Thermal Management For Autonomous Hybrid Off-Road Vehicles: Multi-Timescale Modeling, Predictive Control, And Resilient Thermal Design, Anirudh Sundar
All Dissertations
Electrification and autonomy are increasingly important for off-road vehicles operating in construction, agriculture, mining, defense, and other demanding environments. These platforms must deliver high tractive power under unstructured terrain, harsh ambient conditions, low-speed operation, and large auxiliary loads. While hybrid and electrified powertrains offer improved efficiency, mobility, and auxiliary power capability, they also introduce significant challenges in managing tightly coupled power, energy, and thermal subsystems. In particular, lithium-ion batteries, power electronics, electric machines, and internal combustion engine-generator systems operate across different temperature limits and timescales, requiring coordinated management to ensure efficiency, durability, and safe operation. This dissertation develops modeling, planning, …
Development Of A Mixing-Controlled Combustion Model For 0d Engine Modeling: Using High-Order Models To Guide The Formulation Of Reduced-Order Models, James Gohn
All Dissertations
Vehicles are becoming increasingly complex to comply with the increasingly stringent regulations placed on all market sectors while still maintaining performance requirements. This increased complexity leads to increases in the cost and time it takes to develop and produce these next generation vehicles. To meet demand therefore, it becomes necessary to evaluate numerous design iterations using computer models. There are multiple levels of models that may be necessary for different purposes or use cases. All levels of modeling for virtual prototyping, however, require a level of validation and predictive ability to be useful. To this end, the following thesis presents …
Bayesian Assembly Tool For Intelligent Digital Twin Selection, Shishir Sharma
Bayesian Assembly Tool For Intelligent Digital Twin Selection, Shishir Sharma
All Theses
Digital twins can be used for system performance optimization, health monitoring, predictive maintenance, and investigating system anomalies. A ground vehicle digital twin may be assembled using representations of the subsystem models (tires, suspension system, powertrain, etc.) of different fidelity. Lower fidelity Digital Twins have relatively simpler design and physics, hence are generally computationally less expensive, but may not represent the physical system accurately. On the contrary, higher fidelity Digital Twins are generally more precise in their ability to represent the physical system, but due to complexity, are usually computationally more demanding. This thesis describes the application of Bayesian Classification within …
Tradespace Exploration For Multiple Collaborative Vehicles, Mrunal Deshmukh
Tradespace Exploration For Multiple Collaborative Vehicles, Mrunal Deshmukh
All Theses
Modern military operations demand systems that adapt to uncertain, rapidly changing missions across diverse terrains. Traditional single-platform vehicle design is insufficient for such complexity. This research introduces a hierarchical tradespace exploration framework for designing and evaluating families of heterogeneous ground vehicles under a System-of-Systems (SoS) architecture. The framework treats vehicle design as a co-optimization problem, where a “parent” vehicle (e.g., a Squad Multipurpose Equipment Transport) coordinates specialized “child” vehicles for reconnaissance, amphibious tasks, terrain traversal, and stealth missions. Unlike conventional approaches that optimize vehicles individually, this study emphasizes collaborative performance, resource sharing, and adaptability at the family level. Central to …
Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar
Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar
All Dissertations
This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …
Secure Control And Trust Evaluation Framework For Autonomous Transportation Systems, Grace Muriithi
Secure Control And Trust Evaluation Framework For Autonomous Transportation Systems, Grace Muriithi
All Dissertations
This dissertation advances the cybersecurity of hybrid tracked vehicles (HTVs) and ship power systems (SPSs) by developing innovative cyber-attack models and corresponding defence frameworks. First, we formulate stealthy false-data-injection attacks (FDIAs) on HTV energy-management systems as a partially observable Markov decision process (POMDP) solved via deep reinforcement learning. A novel sniffing-based reward function guides the attacker to covertly degrade battery capacity and energy efficiency, which we evaluate using custom stealth–impact metrics and a sliding-window anomaly detector (Isolation Forest with Dynamic Time Warping). Additionally, we model sophisticated control-layer attacks in HTVs, including reinforcement-learning-optimised replay attacks and denial-of-service (DoS) attacks targeting generator-speed …
Real-Time Degradation Abatement Framework For Energy Storage System In Automotive Application Using Data-Driven Approaches, Laxman Timilsina
Real-Time Degradation Abatement Framework For Energy Storage System In Automotive Application Using Data-Driven Approaches, Laxman Timilsina
All Dissertations
The increasing popularity of electric vehicles (EVs) is driven by their compatibility with sustainable energy goals. However, the decline in the performance of energy storage systems, such as batteries, due to their degradation puts EVs and hybrid electric vehicles (HEVs) at a disadvantage compared to traditional internal combustion engine (ICE) vehicles. The batteries used in these vehicles have limited life. The degradation of the battery is accelerated by the operating conditions of the vehicle, which further reduces its life and increases the reliability and economic concerns for the vehicle’s operation. The aging mechanism inside a battery cannot be eliminated but …
Cfrp Delamination Density Propagation Analysis By Magnetostriction Theory, Brandon Eugene Williams
Cfrp Delamination Density Propagation Analysis By Magnetostriction Theory, Brandon Eugene Williams
All Dissertations
While Carbon Fiber Reinforced Polymers (CFRPs) have exceptional mechanical properties concerning their overall weight, their failure profile in demanding high-stress environments raises reliability concerns in structural applications. Two crucial limiting factors in CFRP reliability are low-strain material degradation and low fracture toughness. Due to CFRP’s low strain degradation characteristics, a wide variety of interlaminar damage can be sustained without any appreciable change to the physical structure itself. This damage suffered by the energy transfer from high- stress levels appears in the form of microporosity, crazes, microcracks, and delamination in the matrix material before any severe laminate damage is observed. This …
Accelerating The Derivation Of Optimal Powertrain Control Strategies Using Reinforcement Learning And Virtual Prototypes, Daniel Egan
All Dissertations
The push for improvements in fuel economy while reducing tailpipe emissions has resulted in significant increases in automotive powertrain complexity, subsequently increasing the resources, both time and money, needed to develop them. Powertrain performance is heavily influenced by the quality of their controller/calibration with modern powertrains reaching levels of complexity where using traditional design of experiment-based methodologies to develop them can take years. Recently, reinforcement learning (RL), a machine learning technique, has emerged as a method to rapidly create optimal controllers for systems of unlimited complexity directly which creates an opportunity to use RL to reduce the overall time and …
Supporting Us Army Next-Generation Ground Vehicle Simulation Modeling Using An Integrated Ontology Suite, Edward Louis
Supporting Us Army Next-Generation Ground Vehicle Simulation Modeling Using An Integrated Ontology Suite, Edward Louis
All Theses
The development of the US Army’s next-generation of ground vehicle systems is supported by simulations that predict vehicle performance and inform engineering design choices in the design and acquisition process. In this paper, we describe the development of an Integrated Ontology Suite to support modeling and simulation of next generation military ground vehicles. The ontology suite is intended to address model reuse challenges and increase the shared understanding of ground vehicle system simulations. It is intended to function in a Model Library structure that stores, documents, assembles, and executes vehicle simulations. The ontology suite consists of four domain ontologies: Vehicle …
Modeling, Estimation, And Optimal Control Of Lithium-Ion Battery At Cell And System Level, Hamidreza Mirzaei
Modeling, Estimation, And Optimal Control Of Lithium-Ion Battery At Cell And System Level, Hamidreza Mirzaei
All Dissertations
Lithium-ion batteries (LIBs) have been regarded as a crucial technology for electrifying a variety of applications, ranging from powering computers, phones, and hybrid electric vehicles (HEVs) to being a critical part of the modern centralized and distributed power grids. Battery systems performance is governed by embedded battery management systems (BMSs). The BMS includes battery state estimation algorithms and control rules, and it acts as the brain of battery-powered systems. The purpose of this research is to address unresolved difficulties associated with control algorithms in BMSs at all levels, from single battery cells to battery packs in hybrid electric vehicles (HEVs).\\ …