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Abstract Investigation of vehicle resistant forces and power losses is of crucial importance owing to current state of energy consumption in transport sector. Meanwhile, considerable portion of resistant forces in a ground vehicle is traced back to tires. Pneumatic tires are known to be a source of energy dissipation as a consequence of their viscoelastic nature. The current study aims to provide a modification to tire resistance by considering the power loss in a tire due to longitudinal slip. The modified tire resistance is comprised of rolling resistance and a newly introduced resistance caused by tire slip, called slip resistance. The physical model is chosen for parameters sensitivity study since the tractive force is described in this model via tangible physical parameters, e.g. tire tangential stiffness, coefficient of friction, and contact patch length.

Abstract Rolling-resistance is leading the direction of numerous tire developments due to its significant effect on fuel consumption and CO2 emissions considering the vehicles in use globally. Many attempts were made to reduce rolling-resistance in vehicles, but with no or limited success due to tire complexity and trade-offs. This article investigates the concept of multiple chambers inside the tire as a potential alternative solution for reducing rolling-resistance. To accomplish that, novel multi-chamber designs were introduced and numerically simulated through finite-element (FE) modeling. The FE models were compared against a standard design as the baseline. The influences on rolling-resistance, grip, cornering, and mechanical comfort were studied. The multi-chambers tire model reduced rolling-resistance considerably with acceptable trade-offs. Independent air volumes isolating tread from sidewalls would maintain tire’s profile effectively.

Abstract To reduce fuel consumption and carbon dioxide (CO2) emissions from mobile air conditioning (A/C) systems, “U.S. Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards” identified solar/thermal technologies such as solar control glazings, solar reflective paint, and active and passive cabin ventilation in an off-cycle credit menu. National Renewable Energy Laboratory (NREL) researchers developed a sophisticated analysis process to calculate U.S. light-duty A/C fuel use that was used to assess the impact of these technologies, leveraging thermal and vehicle simulation analysis tools developed under previous U.S. Department of Energy projects. Representative U.S. light-duty driving behaviors and weighting factors including time-of-day of travel, trip duration, and time between trips were characterized and integrated into the analysis.

Abstract This article presents a comprehensive investigation for the interaction between vehicle ride vibration control and braking control using two degrees of freedom (2DOF) quarter vehicle model. A typical limited bandwidth active suspension system with nonlinear spring and damping characteristics of practical hydraulic and pneumatic components is controlled to regulate both suspension and tire forces and therefore provide the optimum ride comfort and braking performance of an anti-lock brake system (ABS). In order to design a suitable controller for this nonlinear integrated system, various control techniques are followed including state feedback tuned using Linear Quadratic Regulator (LQR), state feedback tuned using Genetic Algorithm (GA), Proportional Integrated (PI) tuned genetically, and Fuzzy Logic Control (FLC). The ABS control system is designed to limit skid ratio below threshold of 15%.

Abstract In this article, compared with traditional Remote Parameter Control (RPC), the iterative process is improved based on linear transfer function (TF) estimation of the nonlinear dynamic system. In the improved RPC, the iteration coefficient is designed according to the convergence condition of the nonlinear iterative process, so that the convergence level, convergence speed, and iteration stability could be improved. The difference between the traditional and the improved RPC iterative process is discussed, the RPC iterative process of the nonlinear system is analyzed, and channel decoupling for Multi-Input Multi-Output (MIMO) system based on eigen-decomposition of the system TF and linear TF estimation is introduced. It assumes that the eigenvector matrix of the system TF remains the same, and the linear TF in the iterative process is estimated and updated, which is used for iterative calculation.

Abstract Path tracking is an essential stage for vehicle safety control. It is more newsworthy than ever in the automotive context and especially for autonomous vehicle. The study proposes an optimal control method for path tracking problem in inverse vehicle handling dynamics. The proposed method generates an expected trajectory which guarantees minimum clearance to the prescribed path by identifying the optimal steering torque input. Based on this purpose, the path tracking problem, which is treated as an optimal control problem, is then solved by local collocation method and mesh refinement. Finally, a real vehicle test is executed to verify the rationality of the proposed model and methodology. The results show that using control variables as a mesh refinement function can capture the dramatic changes in state variables, and the efficiency improvement is more significant as the number of the grid points increases.

Abstract Vehicle cabin air quality depends on various parameters such as number of passengers, fan speed, and vehicle speed. In addition to controlling the temperature inside the vehicle, HVAC control system has evolved to improve cabin air quality as well. However, there is no standard test method to ensure reliable and repeatable comparison among different cars. The current study defined Cabin Air Quality Index (CAQI) and proposed a test method to determine CAQI. CAQIparticles showed dependence on the choice of metrics among particle number (PN), particle surface area (PS), and particle mass (PM). CAQIparticles is less than 1 while CAQICO2 is larger than 1. The proposed test method is promising but needs further improvement for smaller coefficient of variations (COVs).

Abstract Catalytic converters, a primary component in most automotive emissions control systems, do not function well until they are heated substantially above ambient temperature. As the primary energy for catalyst heating comes from engine exhaust gases, plug-in hybrid electric vehicles (PHEVs) that have the potential for short and infrequent use of their onboard engine may have limited energy available for catalytic converter heating. This article presents a comparison of multiple hybrid supervisory control strategies to determine the ability to avoid engine cold starts during a blended charge-depleting propulsion mode. Full vehicle and catalytic converter simulations are performed in parallel with engine dynamometer testing in order to examine catalyst temperature variations during the course of the US06 City drive cycle. Emissions and energy consumption (E&EC) calculations are also performed to determine the effective number of engine starts during the drive cycle.

Abstract During certain driving scenarios, low-speed engine vibrations get propagated to the driveline and affect the drivability of a vehicle. To reduce the impact of these vibrations, a locked torque converter lockup clutch (TCC) is allowed to temporarily slip to increase the damping in the driveline. However, the initial slow dynamics of the fluid path of the torque converter cause the vehicle to feel sluggish. In this article, we design a model predictive controller (MPC) that optimally controls the torque request from the actuator (i.e., engine or e-motor) and the lockup clutch capacity for reducing this sluggishness. The study is conducted for a light-duty vehicle and uses an experimentally validated, detailed full-order model (FOM) for developing and validating a computationally efficient, reduced-order driveline model (ROM).

Abstract A critical component of vehicle dynamic control systems is the accurate and real-time knowledge of the vehicle’s key states and parameters when running on the road. Such knowledge is also essential for vehicle closed-loop feedback control. Vehicle state and parameter estimation has gradually become an important way to soft-sense some variables that are difficult to measure directly using general sensors. In this work, a seven degrees-of-freedom (7-DOF) nonlinear vehicle dynamics model is established, where consideration of the Magic formula tire model allows us to estimate several vehicle key states using a hybrid algorithm containing an unscented Kalman filter (UKF) and a genetic algorithm (GA). An estimator based on the hybrid algorithm is compared with an estimator based on just a UKF. The results show that the proposed estimator has higher accuracy and fewer computation requirements than the UKF estimator.

Abstract Fuel consumption is at an all-time high, with crude oil set to get depleted in the next two decades. Drag force is one of the major components responsible for decreasing mileage and thus increasing fuel consumption in vehicles. Using passive modifications such as spherical depressions on the body surface, aerodynamic drag experienced by passenger vehicles can be significantly reduced. Spherical depressions are designed to delay flow separation, following which the wake size is reduced, resulting in a decrease in drag force. In this study, computer-aided design (CAD) models of generalized lightweight vehicles are made with dimples at the sides of the car, having a diameter of 60 mm and a center-to-center distance of 90 mm. Several models are created having depression aspect ratios (ARs) of 2, 4, 6, and 8, and each model is simulated to velocities of 22 m/s, 24 m/s, 26 m/s, 28 m/s, and 30 m/s.

Abstract The principal issue in choosing an energy management strategy (EMS) for hybrid electric vehicles (HEVs) has been the way of determining the optimal share of electric energy in hybrid drive. In this article, a novel EMS is proposed that, along with maximum engine efficiency in the hybrid drive, can optimize the share of battery energy for the maximum efficiency of vehicle power train expanded with an imaginary power plant that, by delivering the electric energy to a grid, feeds the vehicle battery. It is proved that the expanded power train efficiency has the local maximum for a wide range of wheel power demand. The relation between the wheel power demand in hybrid drive, the share of battery energy, and the maximum efficiency of the expanded power train is conducted offline. Downloaded to the onboard control system, it enables the operation with the instantaneously optimal share of battery energy and the control system to operate with the low computational load.

Abstract In recent years, lean-burn gasoline Spark-Ignition (SI) engines have been a major subject of investigations. With this solution, in fact, it is possible to simultaneously reduce NOx raw emissions and fuel consumption due to decreased heat losses, higher thermodynamic efficiency, and enhanced knock resistance. However, the real applicability of this technique is strongly limited by the increase in cyclic variation and the occurrence of misfire, which are typical for the combustion of homogeneous lean air/fuel mixtures. The employment of a Pre-Chamber (PC), in which the combustion begins before proceeding in the main combustion chamber, has already shown the capability of significantly extending the lean-burn limit. In this work, the potential of an ultralean PC SI engine for a decisive improvement of the thermal efficiency is presented by means of numerical and experimental analyses.

Abstract This article aims to identify the best combination of intake port angle (IPA) and cylinder head pentroof angle (PA) of a gasoline direct injection (GDI) engine to achieve a simultaneous reduction in the fuel consumption and the exhaust emissions using computational fluid dynamics (CFD) and optimization techniques. The present study is carried out on a single-cylinder, four-stroke GDI engine. The design space is bound by the range of the IPA (35°, 80°) and the PA (5°, 20°). The initial data set consists of 80 design points, which are generated using the uniform Latin hypercube (ULH) algorithm. CFD simulations were carried out at all the points in the initial data set using CONVERGE at engine speed of 2,000 rev/min and the overall equivalence ratio of 0.7 ± 0.05.

Abstract The aim of this study is to achieve the target transient posture of a vehicle according to the user’s steering operation. The target behavior was hypothesized to be a roll mode in the diving pitch, even during steering inputs on rough surfaces, in order to improve subjective evaluation. As a result of organizing the issues of feedforward control (FF) and feedback control (FB), we hypothesized that it would be appropriate to follow the ideal posture. The model following damping control (MFDC) was newly proposed by the authors as a solution to a control algorithm based on model-following control. The feature employs skyhook control (SH), which follows the deviation between the behavior of the reference model, which generates a target behavior with no input from the road surface, and the actual behavior of the vehicle. Numerical analyses were performed to verify the followability of the target behavior and the effect of roll damping performance.

Abstract Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10−8 probability of failure per hour of operation. We then define the geometry of the problem where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on.

Abstract In order to further improve the active safety protection of the vehicle’s active collision avoidance system for vulnerable road users, consider the limitations of on-board sensors, a pedestrian active collision avoidance control strategy based on vehicle-to-vehicle (V2V) communication technology is proposed for the blind-spot dangerous scenario where pedestrians pass through the front of a stationary obstacle vehicle and collide with the host vehicle. Firstly, the relative position relationship model between the host vehicle and the pedestrian is established according to the pedestrian information detected by the obstacle vehicle sensor and the global positioning system (GPS) position information of the obstacle vehicle and the host vehicle so that the host vehicle can obtain the state information of the pedestrian in front of the obstacle vehicle through V2V communication.

Abstract Due to the transition of the driver to a passenger as well as the option of non-driving tasks, automated driving will necessitate adjustments of driving dynamics. In order to face higher comfort requirements and mitigate motion sickness not only horizontal dynamics but also vertical dynamics should be concerned. Therefore, we developed a novel control algorithm for active suspension systems, which takes the requirements of autonomous vehicles into account. Due to safety, cost reasons, and the unavailability of automated test vehicles, the controller was built up, tested, and tuned in simulation before final in-car testing. In this article we introduce a combined simulation and testing process for suspension control systems with focus on comfort measures. We successfully apply the method to the mentioned active suspension control algorithm with good accordance between simulation and measurement for low-frequency excitation.

Abstract This article proposes a disturbance observer-based event-triggered H ∞ controller for a semi-active seat suspension that equips an advanced electromagnetic damper (EMD) system. Automated driving is one of the leading technologies of the automotive industry. However, automated vehicles (AVs) may increase the incidence of motion sickness (MS) and deteriorate motion comfort. This article investigates a semi-active seat suspension system and an advanced controller to improve the motion comfort of AVs. The disturbance force of the seat suspension has considerable influence on the system dynamic, and applying a constant model to describe the real-time disturbance force is unreliable. Therefore, a disturbance observer is designed to estimate the seat suspension disturbance force, and it is used to compensate the controller. The Bouc-Wen model is selected to compare with the disturbance observer and validate its effectiveness.

Abstract In this work, the three-setting switchable damper (SD) is selected because of its cost-effectiveness compared with semi-active (SA) and active suspension systems. This article aims to compare fuzzy (FLC) and linear quadratic regulator (LQR) control strategies for mechatronic three-setting SD and active suspensions in terms of ride comfort considering the switching dynamics nonlinearities of the SD. A four-degree-of-freedom half-car model is utilized for this study. The switchable inerter (SI) is included in these systems to investigate its effect on the ride performance. A comparison between the active suspension and three-setting SD suspension systems with and without SI is assessed. The optimal parameters for passive suspension are evaluated. Results showed that the fuzzy control strategy gives a better ride comfort than the LQR up to 6.9% and 14% for three-setting SD and active suspension systems, respectively.