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In the development of an FAW SUV, one of the goals is to achieve a state of the art drag level. In order to achieve such an aggressive target, feedback from aerodynamics has to be included in the early stage of the design decision process. The aerodynamic performance evaluation and improvement is mostly based on CFD simulation in combination with some wind tunnel testing for verification of the simulation results. As a first step in this process, a fully detailed simulation model is built. The styling surface is combined with engine room and underbody detailed geometry from a similar size existing vehicle. From a detailed analysis of the flow field potential areas for improvement are identified and five design parameters for modifying overall shape features of the upper body are derived. In a second step, a response surface method involving design of experiments and adaptive sampling techniques are applied for characterizing the effects of the design changes.

Coatings have the potential to improve bearing tribological performance. However, every coating application process and material combination may create different residual stresses and coating microstructures, and their effect on bearing fatigue and wear performance is unclear. The aim of this work is to investigate coating induced residual stress effects on bearing failure indicators using a microstructural contact mechanics (MSCM) finite element (FE) model. The MSCM FE model consists of a two-dimensional FE model of a coated bearing surface under sliding contact where individual grains are represented by FE domains. Interactions between FE domains are represented using contact element pairs. Unique to this layered rolling contact FE model is the use of polycrystalline material models to represent realistic bearing and coating microstructural behavior. The MSCM FE model was compared to a second non-microstructural contact mechanics (non-MSCM) model.

Using the total probability theorem, we propose a method to calculate the failure rate of a linear vibratory system with random parameters excited by stationary Gaussian processes. The response of such a system is non-stationary because of the randomness of the input parameters. A space-filling design, such as optimal symmetric Latin hypercube sampling or maximin, is first used to sample the input parameter space. For each design point, the output process is stationary and Gaussian. We present two approaches to calculate the corresponding conditional probability of failure. A Kriging metamodel is then created between the input parameters and the output conditional probabilities allowing us to estimate the conditional probabilities for any set of input parameters. The total probability theorem is finally applied to calculate the time-dependent probability of failure and the failure rate of the dynamic system. The proposed method is demonstrated using a vibratory system.

A twist beam rear suspension system is modeled, analyzed and optimized in this paper. An ADAMS model is established based on the REC (Rigid-Elastic Coupling) Theory, which is verified by FEM (Finite Element Method) approach, the effects of the geometric parameters on the twist beam suspension performance are investigated. In order to increase the calculation efficiency and improve the simulation accuracy, a neural network model and NSGA II (Non-dominated Sorting Genetic Algorithm II) are adopted to conduct a multi-objective optimization on a twist beam rear suspension system.

New engine controls have been developed for the turbocharged Lexus NX200t to improve driving power by reducing engine torque output lag. Drive power management functions have been centralized in an integrated drive power control system. The newly developed controls minimize the potential reduction in drivability associated with the adoption of a turbocharged engine while improving fuel efficiency. General driveability issues commonly associated with a turbocharged engine include sudden increases in drive power due to the response lag of the turbocharger, and higher shifting frequencies if this response lag triggers a disturbed accelerator operation pattern by the driver. The developed technologies detect and control sudden increases in drive power to create the optimum drive power map, and reduce unnecessary shifts even if the driver's accelerator operation is disturbed.

This paper presents a custom integrated circuit (IC) on which circuit functions necessary for “Active Hydraulic Brake (AHB) system” are integrated, and its key component, “Current-to-Digital Converter” for solenoid current measurement. The AHB system, which realizes a seamless brake feeling for Antilock Brake System (ABS) and Regenerative Brake Cooperative Control of Hybrid Vehicle, and the custom IC are installed in the 4th-generation Prius released in 2015. In the AHB system, as linear solenoid valves are used for hydraulic brake pressure control, high-resolution and high-speed sensing of solenoid current with ripple components due to pulse width modulation (PWM) is one of the key technologies. The proposed current-to-digital converter directly samples the drain-source voltage of the sensing DMOS (double-diffused MOSFET) with an analog-to-digital (A/D) converter (ADC) on the IC, and digitizes it.

In real-world automotive control, there are many constraints to be considered. In order to explicitly treat the constraints, we introduce a model-prediction-based algorithm called a reference governor (RG). The RG generates modified references so that predicted future variables in a closed-loop system satisfy their constraints. One merit of introducing the RG is that effort required in control development and calibration would be reduced. In the preceding research work by Nakada et al., only a single reference case was considered. However, it is difficult to extend the previous work to more complicated systems with multiple references such as the air path control of a diesel engine due to interference between the boosting and exhaust gas recirculation (EGR) systems. Moreover, in the air path control, multiple constraints need to be considered to ensure hardware limits. Hence, it is quite beneficial to cultivate RG methodologies to deal with multiple references and constraints.

Global surrogate modeling aims to build surrogate model with high accuracy in the whole design domain. A major challenge to achieve this objective is how to reduce the number of function evaluations to the original computer simulation model. To date, the most widely used approach for global surrogate modeling is the adaptive surrogate modeling method. It starts with an initial surrogate model, which is then refined adaptively using the mean square error (MSE) or maximizing the minimum distance criteria. It is observed that current methods may not be able to effectively construct a global surrogate model when the underlying black box function is highly nonlinear in only certain regions. A new surrogate modeling method which can allocate more training points in regions with high nonlinearity is needed to overcome this challenge. This article proposes an efficient global surrogate modeling method based on a multi-layer sampling scheme.

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

Computer-aided engineering (CAE) is an important tool routinely used to simulate complex engineering systems. Virtual simulations enhance engineering insight into prospective designs and potential design issues and can limit the need for expensive engineering prototypes. For complex engineering systems, however, the effectiveness of virtual simulations is often hindered by excessive computational cost. To minimize the cost of running expensive computer simulations, approximate models of the original model (often called surrogate models or metamodels) can provide sufficient accuracy at a lower computing overhead compared to repeated runs of a full simulation. Metamodel accuracy improves if constructed using space-filling designs of experiments (DOEs). The latter provide a collection of sample points in the design space preferably covering the entire space.

This paper presents typical flow structures around a 60%-scale wind-tunnel model of a Formula One (F1) car, using planar particle image velocimetry (PIV). The customized PIV system is permanently installed in a wind tunnel to help aerodynamicists in the development loop. The PIV results enhance the understanding of the mean velocity field in the two-dimensional plane in some important areas of the car, such as the front-wheel wake and the underfloor flow. These real phenomena obtained in the wind tunnel also help maintain the accuracy of simulations using computational fluid dynamics (CFD) by allowing regular checking of the correlation with the real-world counterpart. This paper first surveys recent literature on unique flow structures around the rotating exposed wheel, mostly that on the isolated wheel, and then gives the background to F1 aerodynamics in the late 2000s.

Conventional CFD-based shape optimization technology that uses parametric shape modification and optimal solutions searching algorithms has the two problems: (1) outcome of optimized shapes depend on the selection of design parameters made by the designer, and (2) high computational costs. To resolve those problems, two innovative inverse analysis technologies based on the Adjoint Method were developed in previous study: surface geometry deformation sensitivity analysis to identify the locations to be modified, and topology optimization to generate an optimal shape for maximizing the cost function in the constrained design space. However, these technologies are only applicable to steady flows. Since most flows in a vehicle (such as engine in-cylinder flow) are transient, a practical technology for surface geometry sensitivity analysis has been developed based on the Transient Adjoint Method.

A new concept of an electromagnetic torque converter for hybrid electric vehicles is proposed. The electromagnetic torque converter, which is an electric system comprised of a set of double rotors and a stator, works as a high-efficiency transmission in the driving conditions of low gear ratio including a vehicle moving-off and as a starting device for an internal combustion engine. Moreover, it can be used for an electric vehicle driving as well as for a regenerative braking. In this concept, a high-efficiency drivetrain system for hybrid electric vehicles is constructed by replacing a fluid-type torque converter with the electromagnetic torque converter in the automatic transmission of a conventional vehicle. In this paper, we present the newly developed electromagnetic torque converter with a compact structure that enables mounting on a vehicle, and we evaluate its transmission efficiency by experiment.

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions.

Weight reduction is very important in automotive design because of stringent demand on fuel economy. Structural optimization of dynamic systems using finite element (FE) analysis plays an important role in reducing weight while simultaneously delivering a product that meets all functional requirements for durability, crash and NVH. With advancing computer technology, the demand for solving large FE models has grown. Optimization is however costly due to repeated full-order analyses. Reanalysis methods can be used in structural vibrations to reduce the analysis cost from repeated eigenvalue analyses for both deterministic and probabilistic problems. Several reanalysis techniques have been introduced over the years including Parametric Reduced Order Modeling (PROM), Combined Approximations (CA) and the Epsilon algorithm, among others.

With the goal of improving drivability, this research aimed to clarify the mechanism of vehicle longitudinal acceleration, focusing on tip-in acceleration. Conventional typical analysis methods include experimental modal and model-based analysis. However, since the former requires the measurement of impulses and other input forces while the vehicle is stopped, measurement under actual driving conditions is difficult. The latter requires characteristic values such as the stiffness and damping coefficients to be identified in advance, which cannot be achieved either easily or precisely. Therefore, this paper proposes a new experiment-based analysis method. This method enables the acquisition of engine torque and transmission torque/force by measuring only the acceleration values of some components under driving conditions.

To improve fuel economy, there is a trend in automotive industry to use light weight, high strength materials. Automotive body structures are composed of several panels which must be downsized to reduce weight. Because this affects NVH (Noise, Vibration and Harshness) performance, engineers are challenged to recover the lost panel stiffness from down-gaging in order to improve the structure borne noise transmitted through the lightweight panels in the frequency range of 100-300 Hz where most of the booming and low medium frequency noise occurs. The loss in performance can be recovered by optimized panel geometry using beading or damping treatment. Topography optimization is a special class of shape optimization for changing sheet metal shapes by introducing beads. A large number of design variables can be handled and the process is easy to setup in commercial codes. However, optimization methods are computationally intensive because of repeated full-order analyses.

High frequency wind noise caused by turbulent flow around the front pillars of a vehicle is an important factor for customer perception of ride comfort. In order to reduce undesirable interior wind noise during vehicle development process, a calculation and visualization method for exterior wind noise with an acceptable computational cost and adequate accuracy is required. In this paper an index for prediction of the strength of exterior wind noise, referred to as Exterior Noise Power (ENP), is developed based on an assumption that the acoustic power of exterior wind noise can be approximated by the far field acoustic power radiated from vehicle surface. Using the well-known Curle’s equation, ENP can be represented as a surface integral of an acoustic intensity distribution, referred to as Exterior Noise Power Distribution (ENPD). ENPD is estimated from turbulent surface pressure fluctuation and mean convective velocity in the vicinity of the vehicle surface.

One way to improve the fuel efficiency of HVs is to reduce the losses and size of the Power Control Unit (PCU). To achieve this, it is important to reduce the losses of power devices (such as IGBTs and FWDs) used in the PCU since their losses account for about 20% of the total loss of an HV. Furthermore, another issue when reducing the size of power devices is ensuring the thermal feasibility of the downsized devices. To achieve the objectives of the 4th generation PCU, the following development targets were set for the IGBTs: reduce power losses by 19.8% and size by 30% compared to the 3rd generation. Power losses were reduced by the development of a new Super Body Layer (SBL) structure, which improved the trade-off relationship between switching and steady-state loss. This trade-off relationship was improved by optimizing the key SBL concentration parameter.

In order to improve the fuel efficiency of Hybrid Electric Vehicles (HEVs), it is necessary to reduce the size and power loss of the HEV Power Control Units (PCUs). The loss of power devices (IGBTs and FWDs) used in a PCU accounts for approximately 20% of electric power loss of an HEV. Therefore, it is important to reduce the power loss while size reduction of the power devices. In order to achieve the newly developed PCU target for compact-size vehicles, the development targets for the power device were to achieve low power loss equivalent to its previous generation while size reduction by 25%. The size reduction was achieved by developing a new RC-IGBT (Reverse Conducting IGBT) with an IGBT and a FWD integration. As for the power loss aggravation, which was a major issue due to this integration, we optimized some important parameters like the IGBT and FWD surface layout and backside FWD pattern.