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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.

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.

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.

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.

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.

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.

The paper proposes an energy management control strategy for a Extended Range Electric Vehicle comprising an internal combustion engine, two electrical machines, and three clutches. The control strategy smoothly combines a rule-based strategy, extended with a battery state-of-charge (SoC) controller, with an instantaneous optimization algorithm based on equivalent consumption minimization strategy (ECMS). In addition to engine on/off logic, the rule based controller includes rules which are extracted from the global dynamic programming-based off-line optimization results. The control strategy is verified by means of computer simulation for different operating modes and certification driving cycles, and the simulation results are compared with the dynamic programming optimization results which are considered as globally optimal.

A dynamic programming-based algorithm is developed and used for off-line optimization of range extended electric vehicle power train control variables over standardized certification driving cycles. The aim is to minimize the fuel consumption subject to battery state-of-charge constraints and physical limits of different power train variables. The control variables to be optimized include engine torque and electric machine speed, as well as a variable that selects the power train operating mode. The optimization results are presented for four characteristic certification driving cycles and characteristic vehicle operating regimes including electric driving during charge depleting mode, hybrid driving during charge sustaining mode, and combined/blended regime.

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.

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.

In order to support active limited slip differential (ALSD) modeling work, a test rig of a DC motor-actuated ALSD has been developed. The test rig is equipped with a torque servomotor that provides a precise closed-loop control of the clutch slip speed, as well as with sensors of clutch torque, and DC motor position and current. In addition to the test rig, a precise wet clutch experimental setup has been developed by using the differential hardware. The setup provides direct measurements of the clutch pack axial force, the separator plate temperature, and the press plate axial position. The paper describes the ALSD test rig and the wet clutch experimental setup, presents and analyzes characteristic experimental results, and outlines the main ALSD modeling results.

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