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Abstract This study aims to take the first step in bridging the gap between vehicle dynamics systems and autonomous control strategies research. More specifically, a nested method is employed to evaluate the collision avoidance ability of autonomous vehicles in the primary design stage theoretically based on both dynamics and control parameters. An integrated model is derived from a half car mathematical model in the lateral direction, consisting of two degrees of freedom, lateral deviation and yaw angle, with a traction mathematical model in the longitudinal direction, consisting of two degrees of freedom, the longitudinal velocity and rolling velocity of the wheel. The integrated model uses a mathematical power train model to generate the torque on the wheel and connects the two systems via the magic formula tyre model to represent the tyre non-linearity during augmented longitudinal and lateral dynamic attitudes.

Abstract With increasing demands for higher performance along with lower vehicle emissions, lightweight vehicle system construction is key to meet such demands. Suspension and transmission assemblies being the key areas for weight-reduction, we have designed a revolutionary new type of suspension system which combines the suspension links with the powertrain assembly and thus completely eliminates one suspension member. Less weight means lower fuel-consumption with improved passenger-comfort and road-holding due to reduction in unsprung mass. Elimination of a suspension link reduces the overall cost of material, machining & fabrication making our design cost-effective than existing setups. This paper deals with the design and implementation of of our concept. A working prototype is also constructed and tested which completely validates our design.

Abstract The brake discs are subjected to thermal load due to sliding by the brake pad and fluctuating loads because of the braking load. This combined loading problem requires simulation using coupled thermo-mechanical analysis for design evaluation. This work presents a combined thermal and mechanical finite element analysis (FEA) and evolutionary optimization-based novel approach for estimating the optimal design parameters of the ventilated brake disc. Five parameters controlling the design: inboard plate thickness, outboard plate thickness, vane height, effective offset, and center hole radius were considered, and simulation runs were planned. A total of 27 brake disc designs with design parameters as recommended by the Taguchi method (L27) were modeled using SolidWorks, and the FEA simulation runs were carried out using the ANSYS thermal and structural analysis tool.

Abstract Obstacle avoidance is an important function in self-driving vehicle control. When the vehicle move from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. There are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. In this paper reinforcement learning is applied to the problem to form effective strategies. There are two major challenges that make self-driving vehicle different from other robotic tasks. Firstly, in order to control the vehicle precisely, the action space must be continuous which can’t be dealt with by traditional Q-learning. Secondly, self-driving vehicle must satisfy various constraints including vehicle dynamics constraints and traffic rules constraints. Three contributions are made in this paper.

Abstract ISO 26262 needs to be considered when developing safety-relevant E/E systems within the automotive industry. One part of the development process according to ISO 26262 is the derivation of the safety requirements for component functions. Here, one attribute of the safety requirements is the Automotive Safety Integrity Level (ASIL). The ASIL at a component level can be determined using ASIL allocation and decomposition. Considering complex systems such as vehicle electrical systems, countless possibilities can be identified for how the ASILs at a component level can be assigned in line with safety goals. In terms of efficiency, manual assignment is not expedient. Therefore, an algorithm for automated assignment of the ASILs will be introduced which considers constraints based on a fault tree analysis. The function of the approach will be demonstrated using the example of a vehicle electrical system from an automated vehicle.

Abstract The development, analysis, and optimization of battery electric class-7 heavy-duty trucks equipped with multi-speed transmissions are discussed in this paper. The designs of five new traction motors-fractional-slot, concentrated winding machines-are proposed for use in heavy-duty electric trucks. The procedure for gear-ratio range selection is outlined and ranges of gear ratios for three-to six-speed transmission powertrains are calculated for each of the proposed electric traction motors. The simulation and gear-ratio optimization tasks for class-7 battery electric trucks are formulated. The energy consumption of the e-truck with the twenty possible powertrain combinations is minimized over the four driving cycles and the most efficient powertrain layouts that meet the performance criteria are recommended.

Abstract This research developed methods for a virtual operator model (VOM) to learn the optimal control inputs for operation of a virtual excavator. Virtual design, used to model, simulate, and test new features, has often been limited by the fidelity of the virtual model of human operators. Human operator learns, over time, the capability, limits, and control characteristics of new vehicles to develop the best strategy to maximize the efficiency of operation. However, VOMs are developed with fixed strategies and for specific vehicle models (VMs) and require time-consuming re-tuning of the VOM for each new vehicle design. Thus, there typically is no capability to optimize strategies, taking account of variation in vehicle capabilities and limitations. A VOM learning capability was developed to optimize control inputs for the swing-to-pile task of a trenching operation. Different control strategies consisted of varied combinations of speed control, position control, and coast.

Abstract A number of semi-deterministic and stochastic formulations of multi-degree of freedom, frequency domain load models for heavy truck chassis are proposed and evaluated. The semi-deterministic models aim at reproducing the damage of a specific vehicle, while the stochastic ones aim to describe a collection of vehicle loads. The stochastic models are divided into two groups: Monte Carlo based and models based on single spectrum matrices. In both cases, the objective is to provide a load model that may be used to produce a design with a certain probability of survival. The goodness of the models is evaluated through a comparison of their damage outcomes with the corresponding damages of a set of time domain loads. This original time domain load set consists of chassis accelerations collected from seven physical trucks.

Abstract In this study, an attempt is made to deduce the number of teeth in the driven sprocket of a Formula SAE (FSAE) car using Optimum Lap software based on track run simulation of the car, which comes out to be 51 teeth. The sprocket material was selected as Aluminum Alloy AL-7075 T6 because of its strength-to-weight ratio. In addition to it, the generative design strategy by Fusion-360 was utilized to automatically engender the slotted sprocket design on the ground of stress induced on it during operation. Furthermore, the design was verified virtually carrying out static structural and fatigue analysis under the worst-case scenario in CAE software. The overall weight reduction achieved was around 45%. Furthermore, the center-to-center distance between the sprockets and the number of chain links required were also calculated on the basis of space constraints and the wrap angle of the sprocket.

Abstract This article presents the application of the Life Cycle Sustainability Assessment (LCSA) methodology for integrating environmental, economic, and social assessment results by the direct application of Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The aim of this work is to test the applicability of LCSA methodology as a potential tool to support the design phase, providing solutions tailored to its application in the automotive sector. To validate the proposed procedure, two alternative design solutions for a car dashboard are used as case study. In response to the need of methods and tools for evaluating and comparing sustainability of alternative design solutions, LCSA is seen as one of the most promising method, but which needs further testing with real cases to solve some methodological challenges.

Abstract Maximum lifetime and minimum manufacturing cost for new products are the primary goals of companies for competitiveness. These two objectives are contradictory and the geometric dimensions of the products directly control them. In addition, the earlier design errors of new products are predicted, the easier and more inexpensive their rectification becomes. To achieve these objectives, we propose in this article a novel model that makes it possible to solve the problem of optimizing the lifespan and the manufacturing cost of new products during the phase of their design. The prediction of the life of the products is carried out by an energy damage method implemented on the finite element (FE) calculation by using the ABAQUS software. The manufacturing cost prediction is carried out by applying the ABC cost estimation analytical method. In addition, the optimization problem is solved by the method of genetic algorithms.

Abstract The preliminary gas turbine combustor design process uses a huge amount of empirical correlations to achieve more optimized designs. Combustion efficiency, in relation to the basic dimensions of the combustor, is one of the most critical performance parameters. In this study, semi-empirical correlations for combustion efficiencies are examined and correlation coefficients have been revised using an experimental air-blasted tubular combustor that uses JP8 kerosene aviation fuel. Besides, droplet diameter and effective evaporation constant parameters have been investigated for different operating conditions. In the study, it is observed that increased air velocity significantly improves the atomization process and decreases droplet diameters, while increasing the mass flow rate has a positive effect on the atomization—the relative air velocity in the air-blast atomizer increases and the fuel droplets become finer.

Abstract Aerodynamic optimization of the exterior vehicle shape is a highly multidisciplinary task involving, among others, styling and aerodynamics. The often differing priorities of these two disciplines give rise to iterative loops between stylists and aerodynamicists. Reduced-order modeling (ROM) has the potential to shortcut these loops by enabling aerodynamic evaluations in real time. In this study, we aim to assess the performance of ROM via proper orthogonal decomposition (POD) for a real-life industrial test case, with focus on the achievable accuracy for the prediction of fields and aerodynamic coefficients. To that end, we create a training data set based on a six-dimensional parameterization of a Volkswagen passenger production car by computing 100 variants with Detached-Eddy simulations (DES).

Abstract The vibration behavior of components exposed to aerodynamic loads must be taken into consideration when designing aerial vehicles. Numerical simulation plays a key role in developing more realistic analytical models for panel flutter analysis. The notable feature of the present research is the use of two methods for the aeroelastic analysis of two-dimensional curved panels with cylindrical bending. In the first approach, the finite volume method (FVM) is used for supersonic viscous flow and nonlinear structural model while full Navier-Stokes equations are discretized. In the second approach, the third-order nonlinear piston theory aerodynamics in addition to mechanical and thermal loads is assumed. Moreover, the semi-analytical weighted residual method for the nonlinear curved panel is utilized. These approaches are concurrently compared with each other for the first time. Furthermore, Hamilton’s principle is used and partial differential equations (PDEs) are derived.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract The suspension system has been shown to have significant effects on vehicle performance, including handling, ride, component durability, and even energy efficiency during the design process. In this study, a new roll-plane hydraulically interconnected suspension (HIS) system is proposed to enhance both roll and lateral dynamics of a two-axle bus. The roll-plane stability analysis for the HIS system has been intensively explored in a number of studies, while only few efforts have been made for suspension tuning, especially considering lateral plane stability. This article aims to explore the integrated lateral and roll dynamics by suspension tuning of a two-axle bus equipped with HIS system. A ten-degree-of-freedom (DOF) lumped-mass vehicle model is integrated with either transient mechanical-hydraulic model for HIS or the traditional suspension components, namely, shock absorber and anti-roll bar (ARB).

Abstract In this article, an adaptive state estimation algorithm for precise air-fuel ratio (AFR) control is presented. AFR control is a critical part of internal combustion engine (ICE) control, and tight AFR control delivers lower engine emissions, better engine fuel economy, and better engine transient performance. The proposed control algorithm significantly improves transient AFR control to eliminate and reduce the amplitude of the lean and rich spikes during transients. The new algorithm is first demonstrated in simulation (using Matlab/SimulinkTM and GT-PowerTM) and then verified on a test engine. The engine tests are conducted using the European Transient Cycle (ETC) with HoribaTM double-ended dynamometer. The developed algorithm utilizes a nonlinear physics-based engine model in the observer and advanced control principles with modifications to solve real industrial control issues.

Abstract Brake systems are one of the essential components of vehicles ensuring the safety of roads and passengers as well as accident prevention. Faulty brake systems, however, can cause inevitable accidents. Fatality analysis reporting system of NHTSA (National Highway Transport Safety Association) has reported that heavy and light trucks, which are obliged to be equipped with dual-circuit air brake system, were, respectively, involved in 8.8% and 38.0% of fatal crashes in the United States, during 2017. Number of heavy vehicle accidents due to complete failure of brake system is far less than accidents due to deficiencies such as worn out brake linings, out-of-adjustment push rod strokes, and leak in the circuits. Severe leakages due to ruptured air hoses or punctured reservoir are highly unlikely to be replenished by compressor and would be distinguished through pressure indicator.

Abstract Identifying and eliminating failure scenarios is critical in the development of autonomous vehicle (AV) systems. However, finding such failures through real-world vehicle-level testing is a difficult task as system disengagements and accidents are rare occurrences. Simulation approaches have been proposed to supplement vehicle-level testing and reduce the costs associated with operating large fleets of autonomous test vehicles. While one can run more vehicles in simulation than in the real world, applying traditional Monte Carlo sampling techniques to find failures still yields an unguided search and a large waste of computing resources. A more directed method than random sampling is needed to identify failure scenarios in a computationally efficient manner. Adaptive Stress Testing (AST) is a method that uses reinforcement learning (RL) paradigms to efficiently find failure scenarios in stochastic sequential decision-making systems.

Abstract Recent research has demonstrated that gasoline compression ignition (GCI) can improve the soot-oxides of nitrogen (NOx) trade-off of conventional diesel engines due to the beneficial properties of light distillate fuels. In addition to air handling and aftertreatment, fuel systems also require further development to realize the potential efficiency and emissions benefits of GCI. Injector one-dimensional (1-D) hydraulic modeling is an important design tool used for this purpose. The current study is a continuation of prior work that used computed physical fuel properties and hydraulic models to accurately simulate high-pressure injection behavior relevant to GCI. With respect to fuel characteristics for the model, physical properties were validated by direct comparison to measurements at temperatures and pressures reaching 150°C and 2500 bar, respectively.