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The modern automotive industry is facing challenges of ever-increasing complexity in the electrified powertrain era. On-board diagnostic (OBD) systems must be thoroughly validated and calibrated through many iterations to function effectively and meet the regulation standards. Their development and design process are more complex when prototype hardware is not available and therefore virtual testing is a prominent solution, including Software-in-the-loop (SiL) and Hardware-in-the-loop (HIL) simulations. Virtual prototype testing relying on real-time simulation models is necessary to design and test new era’s OBD systems quickly and in scale. The new fuel cell powertrain involves new and preciously unexplored fail modes. To make the system robust, simulations are required to be carried out to identify different fails.

Combustion engines in hybrid vehicles turn on and off several times during a typical passenger car trip. Each engine restart may pose a risk of excessive tailpipe emissions in real-drive conditions if the after-treatment system fails to maintain an adequate temperature level during zero flow. In view of the tightening worldwide tailpipe emissions standards and real-world conformity requirements, it is important to detect and resolve such risks via cost-effective engineering tools relying on accurate 3d analysis of the thermal and chemical behavior of exhaust systems. In this work, we present a series of experiments to examine the impact of zero-flow duration on the exhaust system cooling and subsequent emissions risk. We also present a catalyst model calibrated to predict the 3d thermal and chemical behavior under normal and zero flow conditions. Particular emphasis is given to the phenomena of free convection and thermal radiation dominating the heat transfer at zero flow.

The global transition to alternative power sources, particularly fuel cells, hinges on the cost-effective production and distribution of hydrogen fuel. While green hydrogen produced through water electrolysis using renewable energy sources holds immense promise, it currently falls short of meeting the burgeoning demand for hydrogen. To address this challenge, alternative methods, such as steam reforming and partial oxidation of hydrocarbon fuels with integrated carbon capture, are poised to bridge the gap between supply and demand in the near to midterm. Steam reforming of methane is a well-established technology with a proven track record in the chemical industry, serving as a dependable source of hydrogen feedstock for decades. However, to meet the demand for efficient hydrogen storage, handling, and onboard reforming, researchers are increasingly exploring liquid hydrocarbon fuels at room temperature, such as methanol and ethanol.

The growing global adoption of electric vehicles (EVs) emphasizes the pressing need for a comprehensive understanding of thermal runaway in lithium-ion batteries. Prevention of the onset of thermal runaway and its subsequent propagation throughout the entire battery pack is one of the pressing challenges of lithium-ion batteries. In addition to generating excess heat, thermal runaway of batteries also releases hazardous flammable gases, posing risks of external combustion and fires. Most existing thermal runaway models in literature primarily focus on predicting heat release or the total amount of vent gas. In this study, we present a model capable of predicting both heat release and the transient composition of emitted gases, including CO, H2, CO2, and hydrocarbons, during thermal runaway events. We calibrated the model using experimental data obtained from an 18650 cell from the literature, ensuring the accuracy of reaction parameters.

Hydrogen-based fuel cell electric vehicles are a promising alternative to pure battery electric vehicles (BEV) in heavy-duty (HD) truck applications, due to lower weight penalty on the cargo mass, a higher range, and a lower refueling time. The overall drivetrain optimization (including battery and fuel cell sizing) requires an efficient and robust energy management concept, capable of exploiting the maximum system fuel saving potential, while considering critical component health metrics. In recent years, the Equivalent Consumption Minimization Strategy (ECMS) has demonstrated its capability to meet those requirements when applied to passenger car hybrid powertrains. In a traditional implementation, the ECMS-based control policy is typically calculated a-priori, based on steady state operating conditions. The solutions are then implemented as look up tables in the final dynamic model.

Fuel economy improvement of Class 8 long-haul trucks has been a constant topic of discussion in the commercial vehicle industry due to the significant potential it offers in reducing GHG emissions and operational costs. Among the different vehicle categories in on-road transportation, Class 8 long-haul trucks are a significant contributor to overall GHG emissions. Furthermore, with the upcoming 2027 GHG emission and low-NOx regulations, advanced powertrain technologies will be needed to meet these stringent standards. Connectivity-based powertrain optimization is one such technology that many fleets are adopting to achieve significant fuel savings at a relatively lower technology cost. With advancements in vehicle connectivity technologies for onboard computing and sensing, the full potential of connected vehicles in reducing fuel consumption can be realized through V2X (Vehicle-to-Everything) communication.

The development of efficient, reliable, and affordable Hybrid and Electric Vehicles (xEVs) relies on optimized Vehicle Thermal Management System (VTMS) architecture and control strategies. Compared to conventional vehicles, xEVs have more complex VTMS due to additional powertrain components and cooling circuits to meet distinct thermal requirements. The cooling circuits comprise a combination of hoses, straight, and bent pipes to route coolant flow around obstacles between powertrain components at distinct locations in the vehicle. The increased length and geometrical complexity of these piping systems, compared to conventional vehicles, results in increased pressure losses. Thus, accurate predictions of pressure drop within these piping systems is critical for component selection for an optimized VTMS. Numerical simulations are often used to study interactions between components from a system-level perspective allowing early stage rapid assessment of performance.

In recent years, electrification of vehicle powertrains has become more mainstream to meet regulatory fuel economy and emissions requirements. Amongst the many challenges involved with powertrain electrification, developing supervisory controls and energy management of hybrid electric vehicle powertrains involves significant challenges due to multiple power sources involved. Optimizing energy management for a hybrid electric vehicle largely involves two sets of tasks: component level or low-level control task and supervisory level or high-level control task. In addition to complexity within powertrain controls, advanced driver assistance systems and the associated chassis controls are also continuing to become more complex. However, opportunities exist to optimize energy management when a cohesive interaction between chassis and powertrain controls can be realized.

Numerical simulation of in-cylinder processes can significantly reduce the development and refinement costs of engines. While it can be argued that higher fidelity models improve accuracy of prediction, it comes at the expense of high computational cost. In this respect, a 3D analysis of in-cylinder processes may not be feasible for evaluating large number of design and operating conditions. The situation can be more foreboding for transient simulations. In the current work a phenomenological combustion modeling approach is explored that can be implemented in a lower fidelity modeling framework and can approach the accuracy of higher dimensional models with significant reduction in computational cost. The proposed model uses transported probability density function (tPDF) method within a 0D framework to provide a computationally efficient solution while capturing the essential physics of in-cylinder combustion.

The requirements set for the next-generation powertrain systems (e.g. performance and emissions) are becoming increasingly stringent with ever-shortening time-to-markets at reduced costs. To remain competitive automotive companies are progressively relying on model-driven development and virtual testing. Virtual test benches, such as HiL (Hardware-in the-Loop) simulators, are powerful tools to reduce the amount of physical testing and speed up engine software calibration process. The introduction of these technologies places new, often conflicting demands (such as higher predictability, faster simulation speed, and reduced calibration effort) upon simulation models used at HiL test benches. The new models are also expected to offer compliance to industry standards, performance and usability to further increase the usage of virtual tests in powertrain development.

An increasing demand for reducing cost and time effort of the design process via improved CAE (Computer-Aided Engineer) tools and methods has characterized the automotive industry over the past two decades. One of the main challenges involves the effective simulation of a vehicle’s propulsion system dealing with different physical domains: several examples have been proposed in the literature mainly based on co-simulation approach which involves a specific tool for each propulsion system part modeling. Nevertheless, these solutions are not fully suitable and effective to perform statistical analysis including all physical parameters. In this respect, this paper presents the definition and implementation of a new simulation methodology applied to a propulsion subsystem.

The recent automotive industry trend towards electrification has created new challenges for NVH engineers. These challenges stem from new powertrain architectures and their complex interactions, the governing control strategies which aim to optimize energy management, and new unmasked sources of excitation. Additionally, vehicle manufacturers are attempting to reduce hardware testing in order to rapidly satisfy increasing production demand and to minimize its costs. Hence, to meet the above-mentioned challenges up front in the development process of Hybrid Electrical Vehicles (HEVs) while balancing competing design objectives of drivability, durability and NVH, a simulation-led design and optimization is required. NVH problems are often the result of mechanisms that originate through complex interactions between different physical domains (flow, electromagnetic, structural/mechanical, control logic, etc.) and the assembly of individual components into a complete system.

Draining and fuel starvation prediction is of critical importance in designing and approving fuel tanks. Simulation of fluid dynamics to predict draining of a moving tank having multiple fuel compartments and multiple ports is, however, challenging. This is because the dynamics involve multiple fluids which follow distinct thermodynamics - compressible air at the top and nearly incompressible fuel below it. Moreover, for a typical vehicle accelerating transiently in a general trajectory (road profile), the surface angle keeps changing which leads to dynamic fuel covering/uncovering of interior as well as outlet ports. Simulation of these effects often requires 3D multiphase solution, which is computationally expensive especially when it is required to model additional fluid systems such as fuel pipes and jet pumps. We present fast and efficient modeling and simulation of tank draining using the 0D/1D framework of GT-SUITE.

In this paper, a fuel tank evaporation/condensation model was developed, which was suitable for calculation of evaporative emissions in a fuel tank. The model uses a diffusion-controlled mass transfer approach in the form of Fick's second law in order to calculate the average concentration of fuel vapor above the liquid level and its corresponding evaporation rate. The partial differential equation of transient species diffusion was solved using a separation of variables technique with the appropriate boundary conditions for a fuel tank. In order to simplify the solution, a quasi-steady assumption was utilized and justified. The fuel vapor pressure was modeled based on an American Petroleum Institute (API) procedure using either a distillation curve or a Reid Vapor Pressure (RVP) as an experimental input for the specific fuel used in the system.

The present paper aims at developing an innovative procedure to create a one-dimensional (1D) real-time capable simulation model for a heavy-duty diesel engine. The novelty of this approach is the use of the top-level engine configuration, test cell measurement data, and manufacturer maps as opposite to common practice of utilizing a detailed 1D engine model. The objective is to facilitate effective model adjustments and hence further increase the application of Hardware-in-the-Loop (HiL) simulations in powertrain development. This work describes the development of Fast Running Model (FRM) in GT-SUITE simulation software. The cylinder and gas-path modeling and calibration are described in detail. The results for engine performance and exhaust emissions produced satisfactory agreement with both steady-state and transient experimental data.

In order to meet emissions and power requirements, modern engine design has evolved in complexity and control. The cost and time restraints of calibration and testing of various control strategies have made virtual testing environments increasingly popular. Using Hardware-in-the-Loop (HiL), Volvo Penta has built a virtual test rig named VIRTEC for efficient engine testing, using a model simulating a fully instrumented engine. This paper presents an innovative Artificial Neural Network (ANN) based model for engine simulations in HiL environment. The engine model, herein called Artificial Neural Network Engine (ANN-E), was built for D8-600 hp Volvo Penta engine, and directly implemented in the VIRTEC system. ANN-E uses a combination of feedforward and recursive ANNs, processing 7 actuator signals from the engine management system (EMS) to provide 30 output signals.

Particulate matter emissions from gasoline direct injection engines are a concern due to the health effects associated with ultrafine particles. This experimental study investigated sources of particulate matter emissions variability observed in previous tests and also examined the effect of ethanol content in gasoline on particle number (PN) concentrations and particle mass (PM) emissions. FTIR measurements of gas phase hydrocarbon emissions provided evidence that changes in fuel composition were responsible for the variability. Exhaust emissions of toluene and ethanol correlated positively with emitted PN concentrations, while emissions of isobutylene correlated negatively. Exhaust emissions of toluene and isobutylene were interpreted as markers of gasoline aromatic content and gasoline volatility respectively.

Metallic open-cell foams have proven to be valuable for many engineering applications. Their success is mainly related to mechanical strength, low density, high specific surface, good thermal exchange, low flow resistance and sound absorption properties. The present work aims to investigate three principal aspects of real foams: the geometrical characterization, the flow regime characterization, the effects of the pore size and the porosity on the pressure drop. The first aspect is very important, since the geometrical properties depend on other parameters, such as porosity, cell/pore size and specific surface. A statistical evaluation of the cell size of a foam sample is necessary to define both its geometrical characteristics and the flow pattern at a given input velocity. To this purpose, a procedure which statistically computes the number of cells and pores with a given size has been implemented in order to obtain the diameter distribution.

Widely used in automotive industry, lightweight metallic structures are a key contributor to fuel efficiency and reduced emissions of vehicles. Lightweight structures are traditionally designed through employing the material distribution techniques sequentially. This approach often leads to non-optimal designs due to constricting the design space in each step of the design procedure. The current study presents a novel Multidisciplinary Design Optimization (MDO) framework developed to address this issue. Topology, topography, and gauge optimization techniques are employed in the development of design modules and Particle Swarm Optimization (PSO) algorithm is linked to the MDO framework to ensure efficient searching in large design spaces often encountered in automotive applications. The developed framework is then further tailored to the design of an automotive Cross-Car Beam (CCB) assembly.

Large-scale emergency or off-grid power generation is typically achieved through diesel or natural gas generators. To meet governmental emission requirements, emission control systems (ECS) are required. In operation, effective control over the generator’s acoustic emission is also necessary, and can be accomplished within the ECS system. Plug flow mufflers are commonly used, as they provide a sufficient level of noise attenuation in a compact structure. The key design parameter is the transmission loss of the muffler, as this dictates the level of attenuation at a given frequency. This work implements an analytically decoupled solution, using multiple perforate impedance models, through the transfer matrix method (TMM) to predict the transmission loss based on the muffler geometry. An equivalent finite element model is implemented for numerical simulation. The analytical results and numerical results are then evaluated against experimental data from literature.