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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.

Driving cycles are usually defined by vehicle speed as a function of time and they are typically used to estimate fuel consumption and pollutant emissions. Currently, certification driving cycles are mainly used for this purpose. Since they are artificially generated, the resulting estimates and analyzes can generally be biased. In order to address these shortcomings, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded real-world data. To this end, this paper focuses on synthesis of multidimensional driving cycles using the Markov chain-based method and particularly on their validation. The synthesis is based on Markov chain of fourth order, where the road slope is accounted, as well. The corresponding transition probability matrix is implemented in the form of a sparse matrix parameterized with a rich set of recorded city bus driving cycles.

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.

Driving cycles are typically used for estimation of vehicle fuel/energy consumption and CO2 emissions. In most of applications only the vehicle velocity vs. time profile is considered as a driving cycle, while a road slope is typically omitted. Since the road slope significantly impacts the fuel consumption, it should be included into realistic driving cycles for hilly roads. As a part of wider research of multidimensional driving cycle synthesis, this paper focuses on analysis of a broad city bus driving cycle dataset recorded in the city of Dubrovnik. The analysis is aimed at revealing the impact of road slope on velocity and acceleration distributions, and clustering the recorded data into several groups reflecting various driving and traffic congestion characteristics. Finally, the Markov chain method is employed to synthesize 3D driving cycles for the selected data clusters, where the Markov chain states include vehicle velocity, vehicle acceleration, and road slope.

Modern automotive heating, ventilation, and air-conditioning (HVAC) systems have multiple and often redundant actuators. Design of a control system that optimally synthesizes multiple control actions while satisfying control set points and system hardware-related constraints is necessary to maximize HVAC efficiency. To this end, an optimization approach to control system design is proposed in this paper and demonstrated for a generic air-conditioning (A/C) system. The paper first outlines a nonlinear 12th-order A/C dynamics model based on the moving-boundary method. Then, the A/C control system is defined, which combines feedback controllers commanding the compressor speed and expansion valve opening, and open-loop actions of condenser and blower fans. Next, a three-stage, multi-objective genetic algorithm-based approach of control system optimization is proposed.

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.

This paper considers using linear quadratic regulation (LQR) for multi-input control of the Automatic Transmission (AT) upshift inertia phase. The considered control inputs include the transmission input/engine torque, oncoming clutch torque, and traditionally not used off-going clutch torque. Use of the off-going clutch has been motivated by discussed Control Trajectory Optimization (CTO) results demonstrating that employing the off-going clutch during the inertia phase along with the main, oncoming clutch can improve the upshift control performance in terms of the shift duration and/or comfort by trading off the transmission efficiency and control simplicity to some extent. The proposed LQR approach provides setting an optimal trade-off between the conflicting criteria related to driving comfort and clutches thermal energy loss.

Since the Extended range electric vehicle (EREV) powertrain structure is based on different power sources, a key vehicle design activity is related to development of an optimal control strategy for achieving a high fuel economy potential. The central role in developing an optimized energy management strategy is related to availability of computationally-efficient, high-fidelity EREV powertrain model. This paper proposes a method for developing an extended quasi-static backward-looking EREV powertrain model, which when compared to traditional backward model accounts for powertrain transient effects through additional fuel and battery state-of-charge consumptions. The effects of powertrain transients are characterized by means of extensive simulations of dynamic forward-looking EREV powertrain model covering a wide array of possible powertrain transient scenarios.

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.

This paper proposes a method for multi-objective parameter optimization of piecewise linear time profiles for control of Automatic Transmission (AT) shifts and presents results obtained on an example of a powertrain with a 10-speed automatic transmission. The paper first outlines the powertrain dynamics model. Then, the AT control trajectory optimization approach is outlined and employed with the aim of getting insights into the optimal shift control profiles and related performance. The parameter optimization problem is to find parameters of piecewise linear shift control profiles, which provide a trade-off between the shift comfort and performance. The optimization problem is solved by using the multi-objective genetic algorithm MOGA-II incorporated within modeFRONTIER environment.

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.

New generation of torque converter automatic transmissions (AT) include a large number of gears for improved fuel economy. Control requirements for such transmissions become more demanding, which calls for the development of new shift optimization and analysis tools. This paper presents two contributions to the field of transmission dynamics analysis: (i) bond graph method-based shift transient analysis, and (ii) deriving a unique set of conditions for beneficial use of a third (normally-open) clutch for any upshift or downshift, with emphasis on inertia phase. The derived conditions are examined on an example of 10-speed AT based on the clutch torque input trajectory optimization results. The examination results point out that the extra clutch has a potential of significant performance improvement for any single-transition upshift in the inertia phase, in terms of reduced vehicle jerk RMS value due to the suppressed inertia bump effect.

This paper presents the extended Kalman filter-based sideslip angle estimator design using a nonlinear 5DoF single-track vehicle dynamics model with stochastic modeling of tire forces. Lumped front and rear tire forces have been modeled as first-order random walk state variables. The proposed estimator is primarily designed for vehicle sideslip angle estimation; however it can also be used for estimation of tire forces and cornering stiffness. This estimator design does not rely on linearization of the tire force characteristics, it is robust against the variations of the tire parameters, and does not require the information on coefficient of friction. The estimator performance has been first analyzed by means of computer simulations using the 10DoF two-track vehicle dynamics model and underlying magic formula tire model, and then experimentally validated by using data sets recorded on a test vehicle.

Clutch wear is dominantly manifested as the reduction of friction plate thickness. For dry dual clutch with position-controlled electromechanical actuators this affects the accuracy of normal force control because of the increased clutch clearance. In order to compensate for the wear, dry dual clutch is equipped with wear compensation mechanism. The paper presents results of experimental characterization and mathematical modeling of two clutch wear related effects. The first one is the decrease of clutch friction plate thickness (i.e. increase of clutch clearance) which is described using friction material wear rate experimentally characterized using a pin-on-disc type tribometer test rig. The second wear related effect, namely the influence of the clutch wear compensation mechanism activation at various stages of clutch wear on main clutch characteristics, was experimentally characterized using a clutch test rig which incorporates entire clutch with related bell housing.

In an authors' previous SAE publication, an energy management control strategy has been proposed for the basic, charge-depleting/charge-sustaining (CD/CS) regime of an Extended Range Electric Vehicle (EREV). The strategy is based on combining a rule-based controller, including a state-of-charge regulator, with an equivalent consumption minimization strategy. This paper presents an extension of the control strategy, which can provide a favorable vehicle behavior in the more general blended (BLND) operating regime, as well. Dynamic programming-based control variables optimization is first conducted to gain an insight into the vehicle optimal behavior in the BLND regime, facilitate the feedback control strategy development/extension, and serve as a benchmark for the control strategy verification. Next, a parameter optimization method is applied to find optimal values of critical engine on/off thresholds.

Thermal expansion of a clutch pack with position-controlled actuation can affect the accuracy of clutch normal torque control, because it causes an increase of the clutch normal force for the given actuator position. The paper presents an experimental characterization and mathematical modeling of the dry dual clutch thermal expansion effects. The experimental data have been collected by using a clutch/transmission test rig. The acquired data point to two separate, mutually opposite thermal expansion effects. The first effect relates to increase of the clutch clearance with temperature growth, while the second one includes decrease of press plate and engagement bearing positions for a given clutch torque and a rising temperature (i.e. the clutch torque rises with temperature growth and a constant actuator position). In order to explain and describe these two effects, a geometry analysis of the clutch, focused on thermal expansion, is carried out.