Jelena Andric

Impact of Conventional and Electrified Powertrains on Fuel Economy in Various Driving Cycles

Many technological developments in automobile powertrains have been implemented in order to increase efficiency and comply with emission regulations. Although most of these technologies show promising results in official fuel economy tests, their benefits in real driving conditions and real driving emissions can vary significantly, since driving profiles of many drivers are different than the official driving cycles. Therefore, it is important to assess these technologies under different driving conditions and this paper aims to offer an overall perspective, with a numerical study in simulations. The simulations are carried out on a compact passenger car model with eight powertrain configurations including: a naturally aspirated spark ignition engine, a start-stop system, a downsized engine with a turbocharger, a Miller cycle engine, cylinder deactivation, turbocharged downsized Miller engine, a parallel hybrid electric vehicle powertrain and an electric vehicle powertrain. These are tested in seven driving cycles including the NYCC, FTP75, NEDC, WLTC, US06, HWFET and CADC. The impacts of different technologies on fuel economy and CO₂ emissions are analyzed, with respect to different operating conditions. Results reveal that a combination of certain driving cycles and vehicle configurations have a large influence on fuel consumption and CO₂ emissions. In general, Miller and downsized engines offer some improvements in all cycles while the start-stop system has benefits in city cycles with frequent stops. The HEV and EV configurations offer a substantial improvement compared to conventional technologies in lower speed conditions like city cycles, but their benefits are reduced at cycles including higher speeds.

Development and Calibration of One Dimensional Engine Model for Hardware-in-the-Loop Applications

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. Therefore, the presented methodology shows a great potential for testing and validation of new control strategies in Engine Management System (EMS) and for optimizing engine performance using HiL systems. The model has been successfully used in powertrain testing and calibration in the VIRtual TEst Cell (VIRTEC) system at Volvo Penta.

Toward an Effective Virtual Powertrain Calibration System

Due to stricter emission regulations and more environmental awareness, the powertrain systems are moving toward higher fuel efficiency and lower emissions. In response to these pressing needs, new technologies have been designed and implemented by manufacturers. As a result of increasing complexity of the powertrain systems, their control and optimization become more and more challenging. Virtual powertrain calibration, also known as model-based calibration, has been introduced to transfer a part of test bench testing into a virtual environment, and hence considerably reduce time and cost of product development process while increasing the product quality. Nevertheless, virtual calibration has not yet reached its full potential in industrial applications. Volvo Penta has recently developed a virtual test cell named VIRTEC, which is used in an ongoing pilot project to meet the Stage V emission standards. The integrated powertrain system includes engine, Exhaust Aftertreatment System (EATS), and Engine Management System (EMS). The objective of this paper is to describe the essential aspects required to increase the contribution of virtual testing in powertrain calibration activities. These aspects comprise the following: Hardware-in-the-Loop (HiL) system, simulation models, and working process for joint virtual and physical testing to facilitate efficient powertrain development process. The current paper describes the design, test and verification of a calibration platform based on the requirements of the project. The future phases in the current project (Virtual Calibration at Volvo Penta) will cover validation of the platform by performing calibrations in industrial scales on the virtual system.

Heavy Duty Diesel Engine Modeling with Layered Artificial Neural Network Structures

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. To improve the accuracy in predicting exhaust emissions, the ANNs were arranged into two layers, such that engine temperature and pressure output signals and their average rate of change act as extra inputs for exhaust emission signals. The simulation results show that the ANN-E model accurately predicts engine performance, engine temperatures and pressures along the flow path, as well as exhaust emissions. In addition, the modular nature of ANN-E makes it possible for fast rebuild of the model if engine components are changed. Therefore, the layered modular ANN modeling approach represents a powerful tool for virtual engine testing and calibration optimization.

A numerical investigation of thermal engine encapsulation concept for a passenger vehicle and its effect on fuel consumption

Increasingly tough regulations for emission levels and a growing demand for an environmentally clean motor industry impose high requirements in modern automotive development. During recent decades, carmakers have been utilizing various strategies to minimize energy losses in the powertrain to meet legislative and market demands. A great part of research efforts has been focused on improving engine performance during cold starts characterized by increased friction losses. Thermal engine encapsulation is an effective design choice to reduce engine friction in applications with frequent cold starts. In the present work, a coupled 1-D–3-D system-level approach is used to investigate the effects of a novel engine-mounted encapsulation concept featuring air shutters on fuel consumption in a Volvo S80 passenger vehicle. Simulations are performed for sequences of the Worldwide harmonized light vehicles test cycle (WLTC) drive cycle, which include different time intervals of engine inactivity when the car is parked in air of an quiescent ambient temperature. The results show that engine encapsulation with high area coverage (97%) can retain engine oil temperature above 19°C for up to 16 h after engine shutdown at an ambient temperature of 5°C, leading to 2.5% fuel saving during engine warm-up when cold starts occur between 2 and 8 h after key-off. Encapsulations with a lower area coverage (90%) have proven to be less effective, with fuel saving of 1.25% as the temperatures of the oil and engine structures decrease more quickly after key-off compared to the fully enclosed encapsulation.

Particle-level simulations of flocculation in a fiber suspension flowing through a diffuser

We investigate flocculation in dilute suspensions of rigid, straight fibers in a decelerating flow field of a diffuser. We carry out numerical studies using a particle-level simulation technique that takes into account the fiber inertia and the non-creeping fiber-flow interactions. The fluid flow is governed by the Reynolds-averaged Navier-Stokes equations with the standard k-omega eddy-viscosity turbulence model. A one-way coupling between the fibers and the flow is considered with a stochastic model for the fiber dispersion due to turbulence. The fibers interact through short-range attractive forces that cause them to aggregate into flocs when fiber-fiber collisions occur. We show that ballistic deflection of fibers greatly increases the flocculation in the diffuser. The inlet fiber kinematics and the fiber inertia are the main parameters that affect fiber flocculation in the pre-diffuser region.

Numerical investigation of fiber flocculation in the air flow of an asymmetric diffuser

A particle-level rigid fiber model is used to study flocculation in an asymmetric planar diffuser with a turbulent Newtonian fluid flow, resembling one stage in dry-forming process of pulp mats. The fibers are modeled as chains of rigid cylindrical segments. The equations of motion incorporate hydrodynamic forces and torques from the interaction with the fluid, and the fiber inertia is taken into account. The flow is governed by the Reynolds-averaged Navier Stokes equations with the standard k-omega turbulence model. A one-way coupling between the fibers and the flow is considered. A stochastic model is employed for the flow fluctuations to capture the fiber dispersion. The fibers are assumed to interact through short-range attractive forces, causing them to interlock as the fiber-fiber contacts occur during the flow. It is found that the formation of fiber flocs is driven by both the turbulenceinduced dispersion and the gradient of the averaged flow field