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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.

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.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

This paper describes the development of an analytical method to assess and optimize halfshaft joint angles to avoid excessive 3rd halfshaft order vibrations during wide-open-throttle (WOT) and light drive-away events. The objective was to develop a test-correlated analytical model to assess and optimize driveline working angles during the virtual design phase of a vehicle program when packaging tradeoffs are decided. A twelve degree-of-freedom (12DOF) system model was constructed that comprehends halfshaft dynamic angle change, axle torque, powertrain (P/T) mount rate progression and axial forces generated by tripot type constant velocity (CV) joints. Note: “tripot” and “tripod” are alternate nomenclatures for the same type of joint. Simple lumped parameter models have historically been used for P/T mount optimization; however, this paper describes a method for using a lumped parameter model to also optimize driveline working angles.

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.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

In order to extend the operability limit of the gasoline compression ignition (GCI) engine, as an avenue for low temperature combustion (LTC) regime, the effects of parametric variations of engine operating conditions on the performance of six-stroke GCI (6S-GCI) engine cycle are numerically investigated, using an in-house 3D CFD code coupled with high-fidelity physical sub-models along with the Chemkin library. The combustion and emissions were calculated using a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel. Authors’ previous study highlighted the effects of the variation of injection timing and split ratio on the overall performance of 6S-GCI engine and the unique mixing-controlled burning mode of the charge mixtures during the two additional strokes. As a continuing effort, the present study details the parametric studies of initial gas temperature, boost pressure, fuel injection pressure, compression ratio, and EGR ratio.

In order to increase the efficiency of automotive turbochargers at low speed without compromising the performance at maximum boost conditions, variable geometry compressor (VGC) systems, based on either variable inlet guide vanes or variable geometry diffusers, have been recently considered as a future design option for automotive turbochargers. This work presents a modeling, analysis and optimization study for a Diesel engine equipped with a variable geometry compressor that help understand the potentials of such technology and develop control algorithms for the VGC systems,. A cycle-averaged engine system model, validated on experimental data, is used to predict the most important variables characterizing the intake and exhaust systems (i.e., mass flow rates, pressures, temperatures) and engine performance (i.e., torque, BMEP, volumetric efficiency), in steady-state and transient conditions.

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers.

To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

It is difficult to improve tire cavity noise since the pressure of cavity resonance acts as a compelling force, and its low damping and high gain characteristics dominate the vibration of both the suspension and body. For this reason, the analysis described in this article aimed to clarify the design factors involved and to improve this phenomenon at the source. This was accomplished by investigating the acoustic coupling vibration mode of the wheel, which is the component that transmits the pressure of cavity resonance at first. In addition, the vibration characteristic of suspension was investigated also. A speaker-equipped sound pressure generator inside the tire and wheel assembly was developed and used to infer that wheel vibration under cavity resonance is a forced vibration mode with respect to the cavity resonance pressure distribution, not an eigenvalue mode, and this phenomenon may therefore be improved by optimizing the out-of-plane torsional stiffness of the disk.

A 2-D CFD model was developed to describe the heat transfer, and reaction kinetics in a honeycomb structured ceramic diesel particulate trap. This model describes the steady state as well as the transient behavior of the flow and heat transfer during the trap regeneration processes. The trap temperature profile was determined by numerically solving the 2-D unsteady energy equation including the convective, heat conduction and viscous dissipation terms. The convective terms were based on a 2-D analytical flow field solution derived from the conservation of mass and momentum equations (Opris, 1997). The reaction kinetics were described using a discretized first order Arrhenius function. The 2-D term describing the reaction kinetics and particulate matter conservation of mass was added to the energy equation as a source term in order to represent the particulate matter oxidation. The filtration model describes the particulate matter accumulation in the trap.

Time-averaged temperatures at critical locations on the piston of a direct-fuel injected, two-stroke, 388 cm3, research engine were measured using an infrared telemetry device. The piston temperatures were compared to data [7] of a carbureted version of the two-stroke engine, that was operated at comparable conditions. All temperatures were obtained at wide open throttle, and varying engine speeds (2000-4500 rpm, at 500 rpm intervals). The temperatures were measured in a configuration that allowed for axial heat flux to be determined through the piston. The heat flux was compared to carbureted data [8] obtained using measured piston temperatures as boundary conditions for a computer model, and solving for the heat flux. The direct-fuel-injected piston temperatures and heat fluxes were significantly higher than the carbureted piston. On the exhaust side of the piston, the direct-fuel injected piston temperatures ranged from 33-73 °C higher than the conventional carbureted piston.

An investigation was conducted into pressure pulsation in the exhaust port, which greatly affects volumetric efficiency and engine performance. From experiments using a single blow-down generator, it was established that the amplitude of the pressure pulsation increases as the manifold branch is lengthened and that large negative pressure synchronized with the timing of valve overlap can be obtained if a proper branch length is used. The performance of a 2ℓ test engine was optimized by varying the length of both the manifold branches and front pipe forks. It was found that whereas front pipe fork length affects engine performance over only a narrow range of engine speed, optimizing manifold branch length results in a considerable improvement over a wide engine speed range. In the course of optimizing the exhaust pipe manifold length of this two-degree-of-freedom exhaust system, abnormal exhaust noises were emitted at specific engine speeds during deceleration.

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.