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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 concept of the vehicle has changed in accordance with the technological innovations on last decade. Today we can call these changes basically as "CASE" (Connected, Autonomous/Automated, Shared, and Electric). The ease of product access on the user side and the mass production related works have increased worldwide production volumes. This issue has resulted in a greater demand for manpower in the sector. In addition, management, productivity, and profitability related difficulties have occurred. In this project, improvements were made mainly around the productivity through the automation of "vehicle transfer operations in plant operations", which is one of a major problem and a manpower/hour consuming task. This system named as Remote-Control Auto Driving System (RCD). The advance technology used system enabling unmanned, secured operations, were implemented in mass production environment earlier than the rest of the world.

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 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.

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.

Reducing the carbon emissions associated with ICE- containing vehicles is a complimentary step towards carbon neutrality alongside the introduction of vehicles using newer energy vectors. In this study, the authors investigated emissions and efficiency impact of fully renewable E10-grade gasoline fuels blended with sustainable components at both 90 RON and 96 RON in comparison with reference regular E0 and premium certification gasolines across a range of ICE vehicle applications. Both renewable fuels were blended to the Japan JIS K2022 2012 E10 specification. The study shows very low carbon gasolines are technically feasible and potentially have an important role to play in decarbonizing both new advanced technology ICE vehicles and, critically, the existing ICE vehicle parc in the transition towards a zero emissions future.

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].

This paper describes the development of a new e-AWD hybrid system developed for SUVs. This hybrid system consists of a high-torque 2.4-liter turbocharged engine and a front unit that contains a 6-speed automatic transmission, an electric motor, and an inverter. It also includes a rear eAxle unit that contains a water-cooled high-power motor, an inverter, and a reduction gear, as well as a bipolar nickel-metal hydride battery. By combining a turbo engine that can output high torque across a wide range of engine rpm with two electric motors (front and rear), this system achieves both smooth acceleration with a torquey driving feeling and rapid response when the accelerator pedal is pressed. In addition, new AWD control using the water-cooled rear motor realized more stable cornering performance than the previous e-AWD system.

Global focus on CO2 reduction and environmental protection is increasing. To comply with stricter exhaust gas regulations and reduce real world emissions, it is becoming increasingly important to improve the performance of three-way catalysts. Therefore, highly efficient conversion of hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) is required. In general, the more active the precious metals used, the better the conversion performance. However, precious metals have supply risks, such as price fluctuation and the uneven distribution of production areas. Therefore, it is necessary to lower emissions while also lowering the amount of precious metals used. This paper focuses on how catalysts are used and describes the development of a new three-way catalyst for the purpose of strengthening cold conversion and decreasing the usage of precious metals.

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.

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations.

Uncertainties play a major role in vibroacoustics - especially in car body design in the preliminary development because of the overall spread in the production that should be covered with one simulation model. Therefore, we use uncertain input parameters to determine the stochastically distributed admittance of the car body before each part of the car is fully designed. To gain a stochastic result - the stochastically distributed admittance curve - we calculate a deterministic finite element simulation several times with sets of stochastically distributed input parameter values. To reduce simulation time and cost of the car model with many million degrees of freedom we focus on the uncertain parameters that show a significant influence on the admittance curve. It is therefore necessary to be able to accurately estimate for each parameter if its influence on the admittance of the car body plays a major role for the noise vibration harshness simulation.

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.

The reduction of CO2 emissions is one of the most important challenges for the automotive industry to contribute to address global warming. Reducing friction of internal combustion engines (ICEs) is one effective countermeasure to realize this objective. The improvement of engine oil can contribute to reduce fuel consumption by reducing friction between engine parts. Electrification of ICE powertrains increases the overall efficiency of powertrains and reduces the average engine oil temperature during vehicle operation, due to intermittent engine operation. An effective way of reducing engine friction is to lower the viscosity of the engine oil in the low to medium temperature range. This can be accomplished while maintaining viscosity at high temperatures by reducing the base oil viscosity and increasing the viscosity modifier (VM) content to raise the viscosity index (so-called “flat viscosity” concept).

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.

Raising demands towards lightweight design paired with a loss of originally predominant engine noise pose significant challenges for NVH engineers in the automotive industry. From an aeroacoustic point of view, low frequency buffeting ranks among the most frequently encountered issues. The phenomenon typically arises due to structural transmission of aerodynamic wall pressure fluctuations and/or, as indicated in this work, through rear vent excitation. A possible workflow to simulate structure-excited buffeting contains a strongly coupled vibro-acoustic model for structure and interior cavity excited by a spatial pressure distribution obtained from a CFD simulation. In the case of rear vent buffeting no validated workflow has been published yet. While approaches have been made to simulate the problem for a real-car geometry such attempts suffer from tremendous computation costs, meshing effort and lack of flexibility.