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This paper presents a computational framework for the physics-based simulation of light vehicles operating on discrete terrain. The focus is on characterizing through simulation the mobility of vehicles that weigh 1000 pounds or less, such as a reconnaissance robot. The terrain is considered to be deformable and is represented as a collection of bodies of spherical shape. The modeling stage relies on a novel formulation of the frictional contact problem that requires at each time step of the numerical simulation the solution of an optimization problem. The proposed computational framework, when run on ubiquitous Graphics Processing Unit (GPU) cards, allows the simulation of systems in which the terrain is represented by more than 0.5 million bodies leading to problems with more than one million degrees of freedom.

Improving vehicle response through advanced knowledge of traffic behavior can lead to large improvements in energy consumption for the single isolated vehicle. This energy savings across multiple vehicles can even be larger if they travel together as a cohort in harmonization. Additionally, if the vehicles have enough information about their immediate path of travel, and other vehicles’ in that path (and their respective critical forward-looking information), they can safely drive close enough to each other to share aerodynamic load. These energy savings can be upwards of multiple percentage points, and are dependent on several criteria. This analysis looks at criteria that contributes to energy savings for a cohort of vehicles in synchronous motion, as well as describes a study that allows for better understanding of the potential benefits of different types of cohorted vehicles in different platoon arrangements.

The current system requirements for the power management subsystem and ground combat vehicles for the Future Combat System require higher power and voltages for greater energy efficiency, advanced mobility, lethality and survivability. Efficient and reliable electrical power management is an essential capability within current force ground combat vehicles and will become even more important with the increased electrical power demands of future force vehicles which will exceed the capabilities of onboard power generation/storage technologies. This paper describes how to meet the aforementioned power distribution challenges through the development of a power management software interfaces standard that will provide the flexibility required by various programs and vehicles yet still provide a consistent framework for software development providing a consistent environment for all future Army programs.

A high voltage battery is an essential part of hybrid electric vehicles (HEVs). It is imperative to precisely estimate the state of charge (SOC) and state of health (SOH) of battery in real time to maintain reliable vehicle operating conditions. This paper presents a method of estimating SOC and SOH through the incorporation of current integration, voltage translation, and Ah-throughput. SOC estimation utilizing current integration is inadequate due to the accumulation of errors over the period of usage. Thus voltage translation of SOC is applied to rectify current integration method which improves the accuracy of estimation. Voltage translation data is obtained by subjecting the battery to hybrid pulse power characterization (HPPC) test. The Battery State of Health was determined by semi-empirical model combined with accumulated Ah-throughput method. Battery state of charge was employed as an input to estimate damages accumulated to battery aging through a real-time model.

This paper describes the author's experiences in the design, validation and field-testing of a low cost, online oil condition monitor for diesel driven Army ground vehicles. This online oil condition monitor utilizes a multi-frequency approach to electrochemical impedance spectroscopy to interrogate and evaluate fluid health in near real time. A dual microcontroller processing architecture embedded in the sensor itself executes an oil-health evaluation algorithm and provides estimates of lubricant remaining useful life, as well as identification of the primary mode of degradation of the fluid. These data are transmitted off the sensor via J1939 compliant CAN messages. In this paper the unique application requirements, which formed the foundation of the development process, are discussed, and the technical and design challenges associated with producing a military grade smart-sensor at a sufficiently low price point for widespread adoption in the ground vehicle market are detailed.

Systems engineering is a key discipline to develop, deploy, and sustain military systems. The wide variety of product types and the tendency to leverage emerging technologies require that disciplined technical planning and management processes be used by both engineers and program managers in all phases of a product's life cycle. This paper describes requirements development, management, and architecture in military systems engineering and acquisition be it software or hardware.

The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach “learns” the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them.

The Powertrain Analysis and Computational Environment (PACE) is a forward-looking powertrain simulation tool that is ready for a High-Performance Computing (HPC) environment. The code, written in C++, is one actor in a comprehensive ground vehicle co-simulation architecture being developed by the CREATE-GV program. PACE provides an advanced behavioral modeling capability for the powertrain subsystem of a conventional or hybrid-electric vehicle that exploits the idea of reusable vehicle modeling that underpins the Autonomie modeling environment developed by the Argonne National Laboratory. PACE permits the user to define a powertrain in Autonomie, which requires a single desktop license for MATLAB/Simulink, and port it to a cluster computer where PACE runs with an open-source BSD-3 license so that it can be distributed to as many nodes as needed.

The Powertrain Analysis and Computational Environment (PACE) is a powertrain simulation tool that provides an advanced behavioral modeling capability for the powertrain subsystems of conventional or hybrid-electric vehicles. Due to its origins in Argonne National Lab’s Autonomie, PACE benefits from the reputation of Autonomie as a validated modeling tool capable of simulating the advanced hardware and control features of modern vehicle powertrains. However, unlike Autonomie that is developed and executed in Mathwork’s MATLAB/Simulink environment, PACE is developed in C++ and is targeted for High-Performance Computing (HPC) platforms. Indeed, PACE is used as one of several actors within a comprehensive ground vehicle co-simulation system (CRES-GV MERCURY): during a single MERCURY run, thousands of concurrent PACE instances interact with other high-performance, distributed MERCURY components.