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The current work investigates a method in 1D modeling of cooling systems including discretized cooling package with non-uniform boundary conditions. In a stacked cooling package the heat transfer through each heat exchanger depends on the mass flows and temperature fields. These are a result of complex three-dimensional phenomena, which take place in the under-hood and are highly non-uniform. A typical approach in 1D simulations is to assume these to be uniform, which reduces the authenticity of the simulation and calls for additional calibrations, normally done with input from test measurements. The presented work employs 3D CFD simulations of complete vehicle in STAR-CCM+ to perform a comprehensive study of mass-flow and thermal distribution over the inlet of the cooling package of a Volvo FM commercial vehicle in several steady-state operating points.

An advanced catalytic exhaust after-treatment system addresses the problem of NOX emissions from heavy-duty diesel trucks, relying on real-time catalyst modelling. The system consists of de-NOX catalysts, a device for injection of a reducing agent (diesel fuel) upstream the catalysts, and computer programmes to control the injection of the reducing agent and to model the engine and catalysts in real time. Experiments with 5 different air-assisted injectors were performed to determine the effect of injector design on the distribution of the injected diesel in the exhaust gas stream. A two-injector set-up was investigated to determine whether system efficiency could be increased without increasing the amount of catalyst or the amount of reducing agent necessary for the desired outcome. The results were verified by performing European standard transient cycle tests as well as stationary tests.

Combustion with knock is an abnormal phenomenon which constrains the engine performance, thermal efficiency and longevity. The advance timing of the ignition system requires it to be updated with respect to fuel octane number variation. The production series engines are calibrated by the manufacturer to run with a special fuel octane number. In the experiment, the engine was operated at different speeds, loads, spark advance timings and consumed commercial gasoline with research octane numbers (RON) 95, 97 and 100. A 1-dimensional validated engine combustion model was run in the GT-Power software to simulate the engine conditions required to define the knock envelope at the same engine operation conditions as experiment. The knock intensity investigation due to spark advance sweep shows that combustion with noise was started after a specific advance ignition timing and the audible knock occur by increasing the advance timing.

The three-way-catalyst (TWC) is an essential part of the exhaust aftertreatment system in spark-ignited powertrains, converting nearly all toxic emissions to harmless gasses. The TWC’s conversion efficiency is significantly temperature-dependent, and cold-starts can be the dominating source of emissions for vehicles with frequent start/stops (e.g. hybrid vehicles). In this paper we develop a thermal TWC model and calibrate it with experimental data. Due to the few number of state variables the model is well suited for fast offline simulation as well as subsequent on-line control, for instance using non-linear state-feedback or explicit MPC. Using the model could allow an on-line controller to more optimally adjust the engine ignition timing, the power in an electric catalyst pre-heater, and/or the power split ratio in a hybrid vehicle when the catalyst is not completely hot.

An engine model for use in computer simulation of transient behavior in drivetrain and vehicle systems is presented. Two elements, important for deviation (e.g. turbo-lag) from steady state characteristics, are the inertia of the supercharging unit (turbo shaft) and the fuel injection control system. No extensive combustion calculations are carried out within the model. Instead it uses condensed results from existing combustion models and measurements. The model is semi-empirical. Some of the engine specific properties needed for simulation are (e.g. for a turbocharged diesel): engine data in steady state operation, mappings of compressor and turbine performance, inertia of the engine components condensed to the crankshaft, turbo shaft inertia, displacement, compression ratio and the essentials of the fuel injection control strategy. Input parameters to the computer program based on the model are accelerator pedal position and external torque acting on the flywheel.

Squeak and rattle (S&R) are nonstationary annoying and unwanted noises in the car cabin that result in considerable warranty costs for car manufacturers. Introduction of cars with remarkably lower background noises and the recent emphasis on electrification and autonomous driving further stress the need for producing squeak- and rattle-free cars. Automotive manufacturers use several road disturbances for physical evaluation and verification of S&R. The excitation signals collected from these road profiles are also employed in subsystem shaker rigs and virtual simulations that are gradually replacing physical complete vehicle test and verification. Considering the need for a shorter lead time and the introduction of optimisation loops, it is necessary to have efficient and inclusive excitation load cases for robust S&R evaluation.

In the ANNIE project - Applications of Neural Networks to Integrated Ergonomics - BE96-3433, a tool for integrating ergonomics into the design process is developed. This paper presents some features in the current ANNIE as applied to the design of car interiors. A variant of the ERGOMan mannequin with vision is controlled by a hybrid system for neuro-fuzzy simulation. It is trained by using an Elite system for registration of movements. An example of a trajectory generated by the system is shown. A fuzzy model is used for comfort evaluation. An experiment was performed to test its feasibility and it showed very promising results.

Existing battery parameter model structures are evaluated by estimating model parameters on real driving data applying standard system identification methods. Models are then evaluated on the test data in terms of goodness of fit and RMSE in voltage predictions. This is different from previous battery model evaluations where a common approach is to train parameters using standardized tests, e.g. hybrid pulse-power capability (HPPC), with predetermined charge and discharge sequences. Equivalent linear circuit models of different complexity were tested and evaluated in order to identify parameter dependencies at different state of charge levels and temperatures. Models are then used to create voltage output given a current, state of charge and temperature. The average accuracy of modelling the DC bus voltage provides a model goodness of fit average higher than 90% for a single RC circuit model.

This paper presents results of a parametric CFD modeling study of a prototype Free Piston Engine (FPE), designed for application in a series hybrid electric vehicle. Since the piston motion is governed by Newton's second law, accounting for the forces acting on the piston/translator, i.e. friction forces, electrical forces, and in-cylinder gas forces, having a high-level control system is vital. The control system changes the electrical force applied during the stroke, thus obtaining the desired compression ratio. Identical control algorithms were implemented in a MATLAB/SIMULINK model to those applied in the prototype engine. The ignition delay and heat release data used in the MATLAB/SIMULINK model are predicted by the KIVA-3V CFD code which incorporates detailed chemical kinetics (305 reactions among 70 species).

The transport sector is experiencing a shift to zero-carbon powertrains driven by aggressive international policies aiming to fight climate change. Battery electric vehicles (BEVs) will play the main role in passenger car applications, while diversified solutions are under investigation for the heavy-duty sector. Within this framework, Light Commercial Vehicles (LCVs) impact is not negligible and accountable for about 2.5% of greenhouse gas (GHG) emissions in Europe. In this regard, few LCV comparative assessments on green powertrains are available in the scientific literature and justified by the fact that several factors and limitations should be considered and addressed to define optimal powertrain solutions for specific use cases. The proposed research study deals with a comparative numerical assessment of different zero-carbon powertrain solutions for LCV. BEVs are compared to hydrogen-based fuel cells (FC) and internal combustion engines (ICE) powered vehicles.

The automotive industry makes extensive use of virtual models to increase efficiency during the development stage. The complexity of such virtual models increases with the complexity of the process that they describe, and thus new methods for their development are constantly evaluated. Among many others, data-driven techniques and machine learning offer promising results, creating deep neural networks that map complex input-output relations. This work aims at comparing the performance of two different neural network architectures for the estimation of the engine state and emissions (flow fuel, NOx and soot). More specifically, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) will be evaluated in terms of performance, using different techniques to increase the model generalization. During the learning stage data from different engine cycles are fed to the neural networks.

A design method for ultra-dependable control-by-wire systems is presented here. With a top-down approach, exploiting the system's intrinsic redundancy combined with a scalable software redundancy, it is possible to meet dependability requirements cost-effectively. The method starts with the system's functions, which are broken down to the basic elements; task, sensor or actuator. A task graph shows the basic elements interrelationships. Sensor and actuator nodes form a non-redundant hardware architecture. The functional task-graph gives input when allocating software on the node architecture. Tasks are allocated to achieve low inter-node communication and transient fault tolerance using scalable software redundancy. Hardware is added to meet the dependability requirements. Finally, the method describes fault handling and bus scheduling. The proposed method has been used in two cases; a fly-by-wire aircraft and a drive-by-wire car.

The transition towards battery electric vehicles (BEVs) has increased the focus of vehicle manufacturers on energy efficiency. Ensuring adequate airflow through the heat exchanger is necessary to climatize the vehicle, at the cost of an increase in the aerodynamic drag. With lower cooling airflow requirements in BEVs during driving, the front air intakes could be made smaller and thus be placed with greater freedom. This paper explores the effects on exterior aerodynamics caused by securing a constant cooling airflow through intakes at various positions across the front of the vehicle. High-fidelity simulations were performed on a variation of the open-source AeroSUV model that is more representative of a BEV configuration. To focus on the exterior aerodynamic changes, and under the assumption that the cooling requirements would remain the same for a given driving condition, a constant mass flow boundary condition was defined at the cooling airflow inlets and outlets.

Semi-active suspension systems for ground vehicles have been the focus of research for several years as they offer improvements in vehicle comfort and handling. This kind of suspension has attracted more interest compared to active suspension systems especially due to lower cost and energy consumption. In this paper the capabilities of a semi-active front axle suspension are investigated for a commercial vehicle. A half-truck model of a 4x2 tractor and semitrailer combination is developed in Matlab/Simulink for this purpose. Also, a 2 DOF roll plane model is considered to capture the roll motion of the vehicle body mass. Employing the above-mentioned models, results from on-off and continuous variable semi-active damping systems are compared to the ones from the passive suspension system according to ride comfort and handling safety characteristics.

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.

The drag from the underbody, including wheels and wheel housing, constitutes a significant amount of the total aerodynamic drag of heavy vehicles. A correct simulation of the underbody boundary conditions, including rotating wheels and moving ground, has turned out to be of great importance in the minimising of the aerodynamic drag. In the current study several front wheel housing design parameters have been evaluated using Computational Fluid Dynamics (CFD). Design concepts, like enclosed inner wheel housings, underbody panel and wheel housing ventilation, were evaluated by flow analysis and comparison of the drag force contribution. It was shown that changes to the wheel housing geometry had an important impact on the local flow field and force distribution. The total drag of the vehicle decreased with reduced wheel housing volume and wheel housing ventilation can reduce the aerodynamic drag significantly provided it is designed properly.

In the next 20 years the share of small electric vehicles (SEVs) will increase especially in urban areas. SEVs show distinctive design differences compared to traditional vehicles. Thus the consequences of impacts of SEVs with vulnerable road users (VRUs) and other vehicles will be different from traditional collisions. No assessment concerning vehicle safety is defined for vehicles within European L7e category currently. Focus of the elaborated methodology is to define appropriate test scenarios for this vehicle category to be used within a virtual tool chain. A virtual tool chain has to be defined for the realization of a guideline of virtual certification. The derivation and development of new test conditions for SEVs are described and are the main focus of this work. As key methodology a prospective methodical analysis under consideration of future aspects like pre-crash safety systems is applied.

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.

The INCOLL or INertia COLLection system described in this paper, should meet the requirements for a short transient test, without using any chassis dynamometer. To prove this point not only the background of its principles are described, but also results from its application both to S I engines with and without catalytic converters and to truck diesel engines. Special interest has been devoted to the oxygen sensor and converter efficiency and their response both during warm up and under transient conditions. The simplification of the analyzing equipment and the direct interpretation of the results, have been dealt with, as well as the repeativity of the results achieved. The INCOLL test may also have a potential use as quality test at the end of the production line and as a tool for reliability development as well as research and development within the field. The cost for an INCOLL test is estimated to be around one (1) percent of a normal FTP certification procedure.

Bus drivers are a group at risk of often suffering from musculoskeletal problems, such as low-back pain, while bus passengers on the last-row seats experience accelerations of high values. In this paper, the contribution of K-seat in decreasing the above concern is investigated with a detailed simulation study. The K-seat model, a seat with a suspension that functions according to the KDamper concept, which combines a negative stiffness element with a passive one, is benchmarked against the conventional passive seat (PS) in terms of comfort when applied to different bus users’ seats. More specifically, it is tested in the driver’s and two different passengers’ seats, one from the rear overhang and one from the middle part. For the benchmark shake, both are optimized by applying excitations that correspond to real intercity bus floor responses when it drives over a real road profile.