Search Results

In recent years, swift changes in market demands toward achieving carbon neutrality have driven significant developments within the automotive industry. Consequently, employing computer simulations in the early stages of vehicle development has become imperative for a comprehensive understanding of performance characteristics. Of particular importance is the cooling performance of vehicles, which plays a vital role in ensuring safety and overall performance. It is crucial to predict optimal cooling performance, particularly about the heat generated by the powertrain during the initial phases of vehicle development. However, the utilization of thermal analysis models for assessing vehicle cooling performance demands substantial computational resources, rendering them less practical for evaluating performance associated with design changes in the planning phase.

When developing an off-road vehicle, it is essential to create excellent drivability that enables the vehicle to be driven on all surfaces while ensuring passenger comfort. Since durability is another indispensable performance aspect for these vehicles, the development method must be capable of considering a high-level combination of a wide range of performance targets. This paper proposes a method to identify the region in which each performance aspect is realized through a complex domain combination problem. The proposed method is helpful in the initial design stage when the detailed specifications of the target vehicle are not determined because it is capable of considering both the specifications and usage method of the target vehicle, such as the selection of road profiles and driving speeds as design variables. The proposed method has the advantage of enabling efficient concurrent studies to search for feasible regions.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

The purpose of this study is to propose braking characteristics that are easy for drivers to handle in a system in which braking and driving operations are performed by hand. Genetic algorithm optimization of braking characteristics showed that the best deceleration tracking was achieved by an FG diagram with a logarithmic function shape. In contrast, the slope of the optimal FG diagram tended to decrease as the driver's proportional gain increased.

In the 1st generation Toyota "MIRAI" fuel cell stack, carbon protective surface coating is deposited after individual Ti bipolar plate being press-formed into the desired shape. Such a process has relatively low production speed, not ideal for large scale manufacturing. A new coating concept, consisting of a nanostructured composite layer of titanium oxide and carbon particles, was devised to enable the incorporation of both the surface treatment and the press processes into the roll-to-roll production line. The initial coating showed higher than expected contact resistance, of which the root cause was identified as nitrogen contamination during the annealing step that inhibited the formation of the composite film structure. Upon the implementation of a vacuum furnace chamber as the countermeasure, the issue was resolved, and the improved coating could meet all the requirements of productivity, conductivity, and durability for use in the newer generation of fuel cell stacks.

Knock in spark-ignition (SI) engines has been a subject of many research efforts and its relationship with high efficiency operating conditions keeps it a contemporary issue as engine technologies push classical limits. Despite this long history of research, literature is lacking coherent and generalized descriptions of how knock is affected by changes in the full cylinder temperature field, residence time (engine speed), and air/fuel ratio. In this work, two dimensionless numbers are applied to fully 3D SI conditions. First, the characteristic time of autoignition (ignition delay) is compared against the characteristic time of end-gas deflagration, which was used to predict knocking propensity. Second, the temperature gradient of the end-gas is compared against a critical detonation-based temperature gradient, which predicts the knock intensity.

The development of electric vehicles has been progressed, rapidly, to achieve Carbon neutrality by 2050. There have been increasing concerns about Electromagnetic Compatibility (EMC) performance due to increasing power for power trains of vehicles. Because same power train system expands to some vehicles, we have developed numerical simulations in order to predict the vehicle EMC performances. We modeled a vehicle which has inverter noises by numerical simulation to calculate electric fields based on GB/T18387. We simulated the common mode noise which flows through the shielding braid of the high voltage wire harnesses. As a result, it is confirmed a correlation between the electric fields calculated by numerical simulation and the measured one.

Combustion in a lean atmosphere diluted with a large amount of air can greatly improve fuel efficiency by reducing cooling loss [1, 2]. On the other hand, when air-fuel mixture in cylinder becomes lean, the turbulent combustion speed will decrease, resulting in problems such as the generation of unburned hydrocarbon (HC) and combustion instability [3, 4]. In order to solve these problems, it is important to increase the turbulence intensity and combustion speed [5, 6, 7, 8, 9, 10]. When designing combustion in cylinder by using Computational Fluid Dynamics (CFD), K-epsilon model is widely used for a turbulence model, and the calculated turbulence energy k or turbulence intensity u’ have been used as important indices of combustion velocity [11, 12].

Nitric Oxide (NO) can significantly influence the autoignition reactivity and this can affect knock limits in conventional stoichiometric SI engines. Previous studies also revealed that the role of NO changes with fuel type. Fuels with high RON (Research Octane Number) and high Octane Sensitivity (S = RON - MON (Motor Octane Number)) exhibited monotonically retarding knock-limited combustion phasing (KL-CA50) with increasing NO. In contrast, for a high-RON, low-S fuel, the addition of NO initially resulted in a strongly retarded KL-CA50 but beyond the certain amount of NO, KL-CA50 advanced again. The current study focuses on same high-RON, low-S Alkylate fuel to better understand the mechanisms responsible for the reversal in the effect of NO on KL-CA50 beyond a certain amount of NO.

Low frequency interior wind noise is typically dominated by underfloor flow noise. The source mechanisms are fluctuating surface pressure loading from both flow turbulence and acoustic field levels developed in the semi-reverberant cavity between floor and road. Previous studies have used computation fluid dynamics (CFD) to estimate the aero-acoustic loading applied to a vibro-acoustic model, which is then used to predict the transmitted interior wind noise. This paper reports a new perspective in two respects. First it uses novel surface pressure microphone arrays to directly measure the underfloor aero-acoustic loading in the wind tunnel. Second, it considers two different underfloor aerodynamic configurations - with and without lightweight aero cover panels, which are installed primarily to reduce aerodynamic drag.

Collisions between opened doors and approaching vehicles such as bicycles are common occurrences in urban areas around the world. For example, in Chicago, 20% of all bicycle accidents involve collisions with doors, which occur over 300 times a year. In addition, there are concerns about a further rise in accidents due to the recent increase in home delivery services and bicycle commuting during the COVID-19 pandemic. Some advanced driver assistance systems (ADAS) that are designed to help prevent this type of accident have already been introduced. These systems detect approaching vehicles with sensors and alert the person opening the door via LED lights or a buzzer when the door is opened. The occupant must understand the meaning of the alert and stop opening the door quickly to prevent an accident. However, if the occupant is an elderly person or a child, it is difficult to stop opening the door quickly.

This paper presents a design method for continuous fiber composites in three-dimensional space with locally varying orientation distribution and their fabrication method. The design method is formulated based on topology optimization by augmented tensor field design variables. The fabrication method is based on Tailored Fiber Placement technology, whereby a CNC embroidery machine prepares the preform. The fiber path is generated from an optimized orientation distribution field. The preform is formed with vacuum-assisted resin transfer molding. The fabricated prototype weighs 120 g, a 70% weight reduction, achieving 3.5× mass-specific stiffness improvement.

The new Toyota Mirai hydrogen fuel cell electric vehicle (FCEV) was launched in December 2020. Achieving a low-cost, high-performance FC stack is an important objective in FCEV development. At the same time, it is also necessary to ensure vehicle safety. This paper presents an overview of the safety requirements for onboard FC stacks. It also describes the simulation and evaluation methods for the following matters related to the FC stack. i) Impact force resistance: The FC stack was designed to prevent cell layer slippage due to impact. Constraint force between the cell layers is provided by the frictional force between the cells and an external constraint. A simulation of the behavior of the cell layers under impact force was developed. The impact force resistance was confirmed by an impact loading test. ii) Hydrogen safety: The FC stack was designed so that permeated hydrogen is ventilated and the hydrogen concentration is kept below the standard.

Due to new CO2 regulations and increasing demand for improved fuel economy, reducing aerodynamic drag has become more critical. Aerodynamic drag at the rear of the vehicle accounts for approximately 40% of overall aerodynamic drag due to low base pressure in the wake region. Many studies have focused on the wake region structure and shown that drag reduction modifications such as boattailing the rear end and sharpening the rear edges of the vehicle are effective. Despite optimization using such modifications, recent improvements in the aerodynamic drag coefficient (Cd) seem to have plateaued. One reason for this is the fact that vehicle design is oriented toward style and practicality. Hence, maintaining flexibility of design is crucial to the development of further drag reduction modifications. The purpose of this study was to devise a modification to reduce rear drag without imposing additional design restrictions on the upper body.

In recent years, GPFs (Gasoline particulate filters) have been installed in gasoline engines to comply with stricter environmental regulations in China and Europe. In particular, coated-GPFs having a catalytic purification function are required to have high conversion performances, high filter efficiencies in the sense of a high collection efficiency, and low pressure loss. It is not easy to design a filter that satisfies all these parameters. Experimental studies are being conducted, but it is costly to study in trial productions. In this technical paper, a GPF design optimization method will be proposed that combines multi-scale simulation, surrogate models by machine learning, and an optimization algorithm. By using this method, a GPF design that minimizes pressure loss while providing high conversion performance and particle collection rates that satisfy current regulations can be created.

Combustion of a lean air-fuel mixture diluted with a large amount of air or Exhaust Gas Recirculation (EGR) gas is one of the important technologies that can reduce thermal NOx and improve gasoline engine fuel economy by reducing cooling loss. On the other hand, lean combustion increases unburned Hydro Carbon (HC) and unburned loss compared to stoichiometric combustion. This is because lean combustion reduces the burning rate of the air-fuel mixture and forms a thick quenching layer near the wall surface. In this study, the relationship between the thickness of the unburned HC and the excess air ratio is analyzed using Laser Induced Fluorescence (LIF) method and Computational Fluid Dynamic (CFD) of combustion. The HC distribution near the engine liner when the excess air ratio is increased is investigated by LIF. As a result, it is found that the quenching distance of the flame in the cylinder is larger for lean conditions than the general single-wall quenching relationship.

Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling. Furthermore, the evaluation of the model will be introduced at the end of the paper.

Silver metallic colors with thin and smooth aluminum flake pigments have been introduced for luxury brand OEMs. Regarding the paint formulation for these types of colors, low non-volatile(NV) and high aluminum flake pigment contents are known as technology for high metallic appearance designs. However, there are two technical concerns. First is mottling which is caused by uneven distribution of the aluminum flake pigments in paint film and second is poor film property due to high aluminum pigment concentration in paint film. Therefore, current paint systems have limitation of paint design. As a countermeasure for those two concerns, we had investigated cellulose nanofiber (CNF) dispersion liquid as both the coating binder and rheology control agent in a new type of waterborne paint system. CNF is an effective rheology control agent because it has strong hydrogen bonds with other fiber surfaces in waterborne paint.

Given a forecast of speed and load demands during a trip, a hybrid powertrain power-split Trajectory Optimization Problem (TOP) can be solved to optimize fuel consumption. This can be done on desktop to set performance benchmarks; however, it has been believed that the TOP could not be solved in real-time and is not a realizable controller. As such, several approximations of the TOP have been made in the interest of obtaining a real-time near-optimal controller, for example, Equivalent Consumption Minimization Strategies (ECMS) and their adaptive counterparts. These strategies decide on the power-split by, at each sampled time instant, minimizing a Horizon-0 (without predicting forward in time) composite function of fuel consumption and equivalent battery energy. The fuel economy that results from these strategies is highly sensitive to the calibration of the associated equivalence factor, and furthermore, must be chosen differently for different drive cycles.

Numerical modeling of engine combustion products requires detailed chemistry and therefore large computational resources and time. This becomes a constraint when engine optimization at system level is desired. In this paper, a 1D modeling approach is proposed for fast computation to predict emission level at various engine running conditions. A new chemical solver within 1D framework is developed along with suitable reduced chemical mechanism. The reduced reaction scheme is validated against detailed chemistry on 0D and 1D reactors. It is confirmed that the new fast 1D solver achieves more than 100X speed improvement. Now larger mechanisms can be used for better accuracy with reasonable turnaround time. This methodology allows virtual engine emission map generation, with a refined emission map completed within 12 hours of computational time.