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To enable smooth and low-risk autonomous driving in the presence of other road users, such as cyclists and pedestrians, appropriate predictive safe speed control strategies relying on accurate and robust prediction models should be employed. However, difficulties related to driving scene understanding and a wide variety of features influencing decisions of other road users significantly complexifies prediction tasks and related controls. This paper proposes a hierarchical neural network (NN)-based prediction model of pedestrian crossing behavior, which is aimed to be applied within an autonomous vehicle (AV) safe speed control strategy. Additionally, different single-level prediction models are presented and analyzed as well, to serve as baseline approaches.

Design and optimization of a plug-in hybrid electric vehicle (PHEV) control strategy is typically based on a backward-looking (BWD) powertrain model, which ensures a high computational efficiency by neglecting the powertrain dynamics. However, the control strategy developed for BWD model may considerably underperform when applied to a forward-looking (FWD) powertrain model, which includes a dynamic driver model, powertrain dynamics, and corresponding low-level controls. This paper deals with bond-graph based modelling and energy balance analysis of BWD and FWD powertrain models for a P2 parallel PHEV-type city bus equipped with a 12-speed automated manual transmission. The powertrain consists of a motor/generator (M/G) machine supplied by the lithium-ion battery and placed at the transmission input shaft, and an internal combustion engine which can be disconnected from the rest of the powertrain by a main clutch placed between the engine and M/G machine.

The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent.

A promising strategy for increasing thermal efficiency and decreasing emissions of a spark ignited (SI) internal combustion engine is the application of lean mixtures. The flammability limit of lean mixtures can be increased by using an active pre-chamber containing an injector and a spark plug, resulting in a combustion mode commonly called Turbulent Jet Ignition (TJI). The optimization of the combustion chamber shape and operating parameters for TJI combustion can be a demanding task, since the number of design parameters is significantly increased and is today supported by numerical simulations. In this paper, the process of the development of a numerical framework based on 3D CFD and 1D/0D numerical models that will support the research of the pre-chamber design and optimization of operating parameters will be shown. For 3D CFD modelling the AVL Fire™ code is employed, where the full combustion chamber model with intake and exhaust ports of the experimental engine is prepared.

Automatic transmission (AT) upshift control performance in terms of shift duration and comfort can be improved during the inertia phase by coordinating the off-going clutch together with oncoming clutch and engine torque. The performance improvement is highest in low gear shifts (i.e., for high ratio steps), which are typically performed with open torque converter. In this paper, a discrete-time, linear quadratic regulation (LQR) is applied during the upshift inertia phase, as it provides an optimal multi-input/multi-output control action with respect to the prescribed cost function. The LQR law is based on a reduced-order drivetrain model, which is applicable to actual transmissions characterized by a limited number of available state measurements. The reduced-order model includes the linearized torque converter model. The shift duration is ensured by precise tracking of a linear-like oncoming clutch slip speed reference profile.

A step-ratio automatic transmission alters torque paths for gearshifting through engagement and disengagement of clutches. It enables torque sources to run efficiently while meeting driver demand. Yet, clutch thermal energy during gearshifting is one of the contributors to the overall fuel loss. In order to optimize drivetrain control strategy, including the frequency of shifts, it is important to understand the cost of shift itself. In a power-on upshift, clutch thermal energy is primarily dissipated during inertia phase. The interaction between multiple clutches, coupled with input torque truncation, makes the decomposition of overall energy loss less obvious. This paper systematically presents the mathematical analysis of clutch thermal energy during the inertia phase of a typical single-transition gearshift. In practice, a quicker shift is generally favored, partly because the amount of energy loss is considered smaller.

Ongoing development of a CFD framework for the simulation of primary atomization of a high pressure diesel jet is presented in this work. The numerical model is based on a second order accurate, polyhedral Finite Volume (FV) method implemented in foam-extend-4.1, a community driven fork of the OpenFOAM software. A geometric Volume-of-Fluid (VOF) method isoAdvector is used for interface advection, while the Ghost Fluid Method (GFM) is used to handle the discontinuity of the pressure and the pressure gradient at the interface between the two phases: n-dodecane and air in the combustion chamber. In order to obtain highly resolved interface while minimizing computational time, an Adaptive Grid Refinement (AGR) strategy for arbitrary polyhedral cells is employed in order to refine the parts of the grid near the interface. Dynamic Load Balancing (DLB) is used in order to preserve parallel efficiency during AGR.

Syngas produced from biomass gasification is being increasingly considered as a promising alternative to traditional fuels in Spark-Ignition (SI) Internal Combustion Engines (ICEs). Due to the low energy density and extreme variability in the composition of this gaseous fuel, numerical modeling can give an important contribution to assure stable engine performances. The present work intends to give a contribution in this sense in this sense, by proposing a multi-level set of approaches, characterized by an increasing detail, as a tool aimed at the optimization of energy conversion of non-conventional fuels. At first, a specific characterization of the dependency of the syngas laminar flame speed upon its composition is achieved through an iterative approach pursued in the ANSYS ChemkinTM environment, where validated correlations of the flame speed tuning parameters are obtained in a zero-dimensional framework.

In order to reduce air and noise pollution in urban environments, low emission zones (LEZ) are being introduced in many cities worldwide. This paper deals with design of a LEZ-anticipating control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) given in a P2-type parallel powertrain configuration. A control-oriented backward-looking model of the PHEV powertrain is used as a design basis. The core control strategy is based on combining a rule-based (RB) controller including an explicit battery state-of-charge (SoC) controller and an equivalent consumption minimization strategy (ECMS), and it is superimposed by generating an optimal SoC reference trajectory aimed at enabling pure electric driving through forthcoming LEZs and minimizing the overall fuel consumption. The optimal SoC reference trajectory is generated by minimizing its length over travelled distance.

This paper deals with a thorough analysis of using two fundamentally different algorithms for optimization of electric vehicle (EV) fleet charging. The first one is linear programming (LP) algorithm which is particularly suitable for solving linear optimization problems, and the second one is dynamic programming (DP) which can guarantee the global optimality of a solution for a general nonlinear optimization problem with non-convex constraints. Functionality of the considered algorithms is demonstrated through a case study related to a delivery EV fleet, which is modelled through the aggregate battery modeling approach, and for which realistic driving data are available. The algorithms are compared in terms of execution time and charging cost achieved, thus potentially revealing more appropriate algorithm for real-time charging applications.

Commonly, the spray process in Direct Injection (DI) diesel engines is modeled with the Euler Lagrangian discrete droplet approach which has limited validity in the dense spray region, close to the injector nozzle hole exit. In the presented research, a new reactive spray modelling method has been developed and used within the 3D RANS CFD framework. The spray process was modelled with the Euler Eulerian multiphase approach, extended to the size-of-classes approach which ensures reliable interphase momentum transfer description. In this approach, both the gas and the discrete phase are considered as continuum, and divided into classes according to the ascending droplet diameter. The combustion process was modelled by taking into account chemical kinetics and by solving general gas phase reaction equations.

The ever-present pressure on shortening the development cycle of transmission systems requires development of numerical methods and tools that would speed up those processes. This paper contributes to the field by proposing a method for automated generation of the full-order automatic transmission (AT) model from a bond graph model that directly reflects the AT structure. The proposed numerical method is implemented within the 20-sim and MATLAB software environments, where 20-sim is used to draw the bond graph and export it to a MATLAB script (or simulate it). A proposed method relies on a system identification method that extracts the characteristic full-order model state-space matrices from either a 20-sim-Matlab exported script or 20-sim-simulated bond graph model. The automated modeling method is demonstrated on an example of an advanced 10-speed AT.

The paper deals with the design of shift scheduling maps based on dynamic programing (DP) optimization algorithm. The recorded data related to a delivery vehicle fleet are used, along with a model of delivery truck equipped with a 12-gear automated manual transmission, for an analysis and reconstruction of the truck-implemented shift scheduling patterns. The same map reconstruction procedure has been applied to a set of DP optimization-based operating points. The cost function of DP optimization is extended by realistic clutch energy losses dissipated during shift transients, in order to implicitly introduce hysteresis in the shift scheduling maps for improved drivability. The different reconstructed shift scheduling maps are incorporated within the truck model and validated by computer simulations for different driving cycles.

Dual Clutch Transmissions with dry electromechanically actuated clutches have emerged on the market recently. In order to provide their favorable operation in terms of the clutch torque control, it is very important to have a good knowledge on the system behavior related to the actuator dynamics, the dry friction coefficient behavior, and the thermal dynamics. This paper describes two test rigs developed to support the research work on a dry dual clutch with a lever-based electromechanical actuation system. The first test rig (actuation system test rig) provides a basis for a comprehensive multi-step identification of the actuation system parameters and characterization of the overall system behavior. This test rig includes a modified dual clutch assembly including a built-in sensor for the purpose of direct normal force measurement.

This paper presents an adaptive extended Kalman filter (EKF)-based sideslip angle estimator, which utilizes a sensor fusion concept that combines the high-rate inertial sensors measurements with the low-rate GPS velocity measurements. The sideslip angle estimation is based on a vehicle kinematic model relying on the lateral accelerometer and yaw rate gyro measurements. The vehicle velocity measurements from low-cost, single antenna GPS receiver are used for compensation of potentially large drift-like estimation errors caused by inertial sensors offsets. Adaptation of EKF state covariance matrix ensures a fast convergence of inertial sensors offsets estimates, and consequently a more accurate sideslip angle estimate.

The vehicle dynamics control systems are traditionally based upon utilizing wheel brakes as actuators. However, there has been recently strong interest in the automotive industry for introduction of other vehicle dynamics actuators, in order to improve the overall vehicle stability, responsiveness, and agility features. This paper considers various actuators such as active rear and central differentials and active front and rear steering, and proposes design of related yaw rate control systems. Different control subsystems such as reference model, feedback and feedforward control, allocation algorithm, and time-varying controller limit are discussed. The designed control systems are verified and compared by computer simulation for double lane change and slalom maneuvers.

Magnetorheological fluid (MRF) clutches are expected to be used in several automotive systems such as auxiliary engine devices, active differentials, and automatic transmissions. An experimental MRF clutch has been developed at the University of Zagreb, in order to support MRF clutch modeling and control research. The paper first presents calculation of the main clutch design parameters and describes the clutch mechatronic system. Next, the clutch static and dynamic behaviors are experimentally characterized. Finally, a model of MRF clutch dynamics is outlined, and characteristic model validation results are presented.

The paper presents the estimator design procedures for automotive power train systems based on the adaptive Kalman filter. The Kalman filter adaptation is based on a simple and robust algorithm that detects sudden changes of power train variables. The adaptive Kalman filter has been used to estimate the SI engine load torque and air mass flow, and also the tire traction force and road condition. The presented experimental results indicate that proposed estimators are characterized by favorable response speeds and good noise suppression abilities.

This paper presents a detailed experimental analysis of potentials for road condition estimation based on the tire static curve gradient in the low-slip region, and the damping of the 40 Hz tire vibration mode. An experimental vehicle equipped with four standard ABS sensors (44 pulses/rev), two special high-resolution wheel speed sensors (512 pulses/rev), and a halfshaft torque sensor was utilized for collecting the experimental data. The test data were recorded on a test track characterized by different road conditions such as dry concrete, wet and dry ice, and wet and dry snow.

The paper presents a detailed design procedure of an SI engine load torque estimator based on an adaptive Kalman filter. The adaptation mechanism is based on a simple and robust load torque change detection algorithm. The presented simulation and experimental results point out that the estimator is characterized by a fast response and good noise suppression. The proposed estimator is used to establish a fast load torque compensation path in an idle speed control system. The presented simulation and experimental results show that this yields a significant improvement of idle speed control performance