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Li-ion batteries (LIBs) optimum performance and lifetime depend on temperature, with the commonly suggested operating temperature being in the range of 25 to 40 °C. It's also crucial to keep the temperature difference between battery cells below 5°C. Operation at different temperature ranges can adversely affect or degrade the performance and lifetime of LIBs. A battery thermal management system (BTMS) is essential for keeping the battery temperature within the optimum range. This paper aims to develop and analyze the BTMS for an electric heavy-duty truck. To achieve this aim, battery cells and modules are modelled in ANSYS Fluent software. Validation with experimental results and mesh sensitivity studies are also performed to increase confidence in simulation data. The model is then analyzed for a specific cooling systems to investigate its effect on battery thermal performance during the operation.

Small diesel engines are a common primer for micro and mini-grid systems, which can supply affordable electricity to rural and remote areas, especially in developing countries. These diesel generators have no exhaust after-treatment system thus exhaust emissions are high. This paper investigates the potential of introducing simulated synthetic gas (syngas) to diesel in a small diesel engine to explore the opportunities of widening fuel choices and reducing emissions using a 5.7kW single cylinder direct injection diesel generator engine. Three different simulated syngas blends (with varying hydrogen content) were prepared to represent the typical syngas compositions produced from downdraft gasification and were injected into the air inlet. In-cylinder pressure, ignition delay, premixed combustion, combustion stability, specific energy consumption (SEC), and gaseous and particle emissions were measured at various power settings and mixing ratios.

A Gas to liquid (GTL) fuel was investigated for its combustion and emission performance in an IVECO EURO5 DI diesel engine with a DOC (Diesel Oxidation Catalyst) and DPF (Diesel Particle Filter) installed. The composition of the GTL fuel was analyzed by GC-MS (gas chromatography-mass spectrometry) and showed the carbon distribution of 8-20. Selected physical properties such as density and distillation were measured. The GTL fuel was blended with standard fossil diesel fuel by ratios of diesel/GTL: 100/0, 70/30, 50/50, 30/70 and 0/100. The engine was equipped with a pressure transducer and crank angle encoder in one of its cylinders. The properties of ignition delay and maximum in-cylinder pressure were studied as a function of fraction of the GTL fuel. Particle emissions were measured using DMS500 particle size instrument at both upstream (engine out) and downstream of the DPF (DPF out) for particle number concentrations and size distribution from 5 nm to 1000 nm.

Heat Release Rate (HRR) analysis is indispensable in engine research. The HRR of Internal Combustion Engines (ICEs) is most sensitive to gamma (γ). The proposed HRR models in literature were largely based on γ expressed as functions of temperature. However, γ is depended on temperature as well as the excess air ratio (λ). In this work, an improved HRR model based on γ(T, λ) was used to investigate the combustion behaviour of standard diesel, Gas-to-Liquid (GTL) diesel and Hydrotreated Vegetable Oil (HVO) diesel in a 96 kW, multiple fuel injection, Euro V, Direct Injection (DI) engine. The improved HRR model (Leeds HRR model) was validated for the alternative fuels by comparing the fuel masses predicted by the model to the measured fuel masses. The fuel masses predicted by the Leeds HRR model were also compared to the predictions from four HRR models that were based on γ(T).

Due to the variability of real traffic conditions for vehicle testing, real-world vehicle performance estimation using simulation method become vital. Especially for heavy duty vehicles (e.g. 40 t trucks), which are used for international freight transport, real-world tests are difficult, complex and expensive. Vehicle simulations use mathematical methods or commercial software, which take given driving cycles as inputs. However, the road situations in real driving are different from the driving cycles, whose speed profiles are obtained under specific conditions. In this paper, a real-world vehicle performance estimation method using simulation was proposed, also it took traffic and real road situations into consideration, which made it possible to investigate the performance of vehicles operating on any roads and traffic conditions. The proposed approach is applicable to all kind of road vehicles, e.g. trucks, buses, etc. In the method, the real-road network includes road elevation.

Vehicle surface contamination can degrade not only soldier vision but also the effectiveness of camera and sensor systems mounted externally on the vehicle for autonomy and situational awareness. In order to control vehicle surface contamination, a better understanding of dust particle generation, transport and accumulation is necessary. The focus of the present work is simulation of vehicle surface contamination on the rear part of the vehicle due to the interaction of the combat vehicle track with the ground and dust in the surrounding ambient atmosphere. A notional tracked military vehicle is used for the Computational fluid dynamics (CFD) simulation. A CFD methodology with one-way-coupled Lagrangian particle modeling is used. The simulation is initially run with only air flow to solve the air pressure, velocity, and turbulence quantities in a steady state condition.

In recent years the U.S. Army Tank-Automotive Research, Development, and Engineering Center (TARDEC) has been investigating the survivability and injury mechanisms of underbody blast and crash, and their effects on personnel, with the use of Anthropomorphic Test Devices (ATD), or crash test dummies. Injury Assessment Reference Values (IARV) for crash have been researched for decades, and the US Army Research Laboratory (ARL), some years ago, also developed IARVs for underbody blast for the Hybrid III 50th percentile ATD. More recently, TARDEC extended these IARVs for the 5th and 95th percentile. With the advent of TARDEC’s Occupant Protection Laboratory large amounts of data were accumulated, which brought an interest in automating the analysis, and so a software tool was developed. The interactive in-house written software, called ICalc, allows the user to open test data files acquired from blast testing, drop tower testing, and crash testing.

We present an Accelerated Life Testing (ALT) methodology along with a design for fatigue approach, using Gaussian or non-Gaussian excitations. The accuracy of fatigue life prediction at nominal loading conditions is affected by model and material uncertainty. This uncertainty is reduced by performing tests at a higher loading level, resulting in a reduction in test duration. Based on the data obtained from experiments, we formulate an optimization problem to calculate the Maximum Likelihood Estimator (MLE) values of the uncertain model parameters. In our proposed ALT method, we lift all the assumptions on the type of life distribution or the stress-life relationship and we use Saddlepoint Approximation (SPA) method to calculate the fatigue life Probability Density Functions (PDFs).

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

Light-duty China-6, which is among the most stringent vehicle exhaust emission standards globally, mandates the monitoring and reporting of real driving emissions (RDE) from July, 2023. In the process of regulation promulgation and verification, more than 300 RDE tests have been performed on over 50 China-5 and China-6 certified models. This technical paper endeavors to summarize the experience of RDE practice in China, and discuss the impacts of some boundary conditions (including vehicle dynamic parameters, data processing methods, hybrid propulsion and testing altitude) on the result of RDE measurement. In general, gasoline passenger cars confront few challenges to meet the upcoming RDE NOx requirement, but some China-5 certified samples, even powered by naturally-aspirated engines may have PN issues. PN emissions from some GDI-hybrid powertrain systems also need further reduction to meet China-6 RDE requirements.

The objective of this study was to optimize the occupant restraint systems for a light tactical vehicle in frontal crashes. A combination of sled testing and computational modeling were performed to find the optimal seatbelt and airbag designs for protecting occupants represented by three size of ATDs and two military gear configurations. This study started with 20 sled frontal crash tests to setup the baseline performance of existing seatbelts, which have been presented previously; followed by parametric computational simulations to find the best combinations of seatbelt and airbag designs for different sizes of ATDs and military gear configurations involving both driver and passengers. Then 12 sled tests were conducted with the simulation-recommended restraint designs. The test results were further used to validate the models. Another series of computational simulations and 4 sled tests were performed to fine-tune the optimal restraint design solutions.

A full-system, end-to-end blast modeling and simulation of vehicle underbody buried blast events typically includes detailed modeling of soil, high explosive (HE) charge and air. The complex computations involved in these simulations take days to just capture the initial 50-millisecond blast-off phase, and in some cases, even weeks. The single most intricate step in the buried blast event simulation is in the modeling of the explosive loading on the underbody structure from the blast products; it is also one of the most computationally expensive steps of the simulation. Therefore, there is significant interest in the modeling and simulation community to develop various methodologies for fast running tools to run full simulation events in quicker turnarounds of time.

In this study, we extend and improve on the methods introduced by Brudnak et al. [1] by adding a second objective to the reduction of test time. This second objective under consideration is to diminish or reduce the number of cuts or deletions to the time histories during an editing process. As discussed in [1], segment-based methods consider each segment for retention or deletion based on its own localized severity, not considering the segments around it. As a result, retained segments can be widely scattered in the time domain depending on signal characteristics and therefore a large number of cuts can be induced unintentionally. Regardless of the joining method, such cuts and joins require artificial signal processing and should therefore be minimized. In this paper we present techniques to minimize these cuts while at the same time maintaining our original goals of time reduction and severity retention.

Military vehicles are typically armored, hence the open surface area for heat rejection is limited. Hence, the cooling parasitic load for a given heat rejection can be considerably higher and important to consider upfront in the system design. Since PEMFCs operate at low temp, the required cooling flow is larger to account for the smaller delta temperature to the air. This research aims to address the combined problem of optimal sizing of the lithium ion battery and PEM Fuel Cell stack along with development of the scalable power split strategy for small a PackBot robot. We will apply scalable physics-based models of the fuel cell stack and balance of plant that includes a realistic and scalable parasitic load from cooling integrated with existing scalable models of the lithium ion battery. This model allows the combined optimization that captures the dominant trends relevant to component sizing and system performance.

Hydrogenated Vegetable Oil (HVO) diesel fuels have the potential to provide a reduced carbon footprint for diesel engines and reduce exhaust emissions. Therefore, it is a strong candidate for transport and diesel powered machines including electricity generators and other off-road machines. In this research, a waste cooking oil derived HVO diesel was investigated for its combustion and emission performance including ignition delays, size segregated particulate number emissions and gaseous emissions. The results were compared to the standard petroleum diesel. A EURO5 emission compliant three litre, direct injection, intercooled IVECO diesel engine equipped with EGR was used which has a maximum power output of 96kW. The engine was equipped with an integrated DOC and DPF aftertreatment system. Both the upstream and downstream of the aftertreatment emissions were measured. The tests were conducted at different RPM and loads at steady state conditions.

This paper investigates the optimization of the aerodynamic design of a police car, BMW 5-series which is popular police force across the UK. A Bezier curve fitting approach is proposed as a tool to improve the existing design of the warning light cluster in order to reduce drag. A formal optimization technique based on Computational Fluid Dynamics (CFD) and moving least squares (MLS) is used to determine the control points for the approximated curve to cover the light-bar and streamline the shape of the roof. The results clearly show that improving the aerodynamic design of the roofs will offer an important opportunity for reducing the fuel consumption and emissions for police vehicles. The optimized police car has 30% less drag than the non-optimized counter-part.

Mercury is a high-fidelity, physics-based object-oriented software for conducting simulations of vehicle performance evaluations for requirements and engineering metrics. Integrating cutting-edge, massively parallel modeling techniques for soft, cohesive and dry granular soil that will integrate state-of-the-art soil simulation with high-fidelity multi-body dynamics and powertrain modeling to provide a comprehensive mobility simulator for ground vehicles. The Mercury implements the Chrono::Vehicle dynamics library for vehicle dynamics, which provides multi-body dynamic simulation of wheeled and tracked vehicles. The powertrain is modeled using the Powertrain Analysis Computational Environment (PACE), a behavior-based powertrain analysis based on the U.S. Department of Energy’s Autonomie software. Vehicle -terrain interaction (VTI) is simulated with the Ground Contact Element (GCE), which provides forces to the Chrono-vehicle solver.

This paper considers optimal power management during the establishment of an expeditionary outpost using battery and vehicle assets for electrical generation. The first step in creating a new outpost is implementing the physical protection and barrier system. Afterwards, facilities that provide communications, fires, meals, and moral boosts are implemented that steadily increase the electrical load while dynamic events, such as patrols, can cause abrupt changes in the electrical load profile. Being able to create a fully functioning outpost within 72 hours is a typical objective where the electrical power generation starts with batteries, transitions to gasoline generators and is eventually replaced by diesel generators as the outpost matures. Vehicles with power export capability are an attractive supplement to this electrical power evolution since they are usually on site, would reduce the amount of material for outpost creation, and provide a modular approach to outpost build-up.

The US Army Tank Automotive Research, Development and Engineering Center (TARDEC) has developed a unique physics based modeling & simulation (M&S) capability using Computational Fluid Dynamics (CFD) techniques to optimize automatic fire extinguishing system (AFES) designs and complement vehicle testing for both occupied and unoccupied spaces of military ground vehicles. The modeling techniques developed are based on reduced global kinetics for computational efficiency and are applicable to fire suppressants that are being used in Army vehicles namely, bromotrifluoromethane (Halon 1301), heptafluoropropane (HFC-227ea, trade name FM200), sodium-bicarbonate (SBC) powder, water + potassium acetate mixture, and pentafluoroethane (HFC-125, trade name, FE-25). These CFD simulations are performed using High Performance Computers (HPC) that enable the Army to assess AFES designs in a virtual world at far less cost than physical-fire tests.

Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example.