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The piston and piston ring are used in a severe contact environment in engine durability tests, which causes severe wear to the piston ring groove, leading to significant development costs for countermeasures. Conventionally, in order to ensure functional feasibility through wear on the piston top ring groove (hereinafter “ring groove”), only functional evaluations through actual engine durability testing were performed, and there was an issue in determining the limit value for the actual amount of wear itself. Because of this, the mechanism that may cause wear on the ring groove was clarified through past research, but this resulted in judgment criteria with some leeway from the perspective of functional assurance. To establish judgment criteria, it was necessary to understand both functional effect from ring groove wear and the mechanism behind it.

To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

In recent years, an increase in vehicle weight due to the electrification of automobiles, specifically EVs, has increased the input loads on anti-vibration rubber parts. Moreover, the characteristics of these loads have also changed due to the rotational drive of electric motors, regenerative braking, and other factors. When designing a vehicle, in advance it is necessary to set specifications that take into account the spring characteristics and durability of the anti-vibration rubber parts in order to meet functional requirements. In this study, the hyperelastic and fatigue characteristics (S-N diagram and Haigh diagram) of Rubbers which is widely used for anti-vibration rubber parts, were experimentally obtained, and structural and fatigue analyses using FEM (Finite Element Method) were conducted in conjunction with spring and fatigue tests of anti-vibration rubber parts to determine the correlation between their spring and fatigue characteristics.

Fretting is a phenomenon in which a fatigue crack is initiated by a small relative slip between two objects, resulting in crack propagation and fracture at stresses far below the fatigue limit [1, 2]. Since the mechanism behind fretting is complex and covers multiple disciplines, it is not easy to develop a consistent evaluation method. In the field of engine development, fretting events can also pose an issue due to the complexity of the mechanism [3]. In particular, it has been a challenge to help predict changes in the presence and severity of fretting events, as the engine temperature fluctuates with operating conditions. As one method for evaluating fretting, Sato, et al. have made predictions using analytical models based on the finite element method (FEM) [4, 5]. However, their predictions did not take into account temperature fluctuations in the system, and they were unable to predict events in which the occurrence of fretting fatigue changed with temperature fluctuations.

Model-based Development (MBD) has been employed for engine development to reconcile the contradictory relationship between numerous functions and systems at a high level and in a short span of time. However, in actuality, as engines have become more advanced, it has become challenging to even satisfy the requirements of individual components. Moreover, reconciling multiple contradictory functions like engine power and strength and durability performance, as well as coordinating many related systems, requires an even higher level of skill. Such harmonization techniques require total optimization studies that cover a wide range of designs, and which requires several years of examination with current development processes. Multi-objective Design Exploration (MODE) methods [1] using parametric models [2] and surrogate models [3] are being used to shorten the development period and achieve more balanced designs.

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.

Recent automobile engines are equipped with many devices that are driven by oil pressure. Generally, engine oil is used for oil pressure, and in addition to its conventional functions of lubrication and cooling, etc., it also plays an important role in accurately driving such devices. One of the factors that can interfere with the characteristics of engine oil is air contamination. Excessive air contamination can cause issues with driving devices. Although there are various factors that contribute to air contamination, this paper focuses on, and attempts to help predict, the air generated by engine oil falling and colliding with the surface of the oil in the oil pan as it returns from the top to the bottom of the engine. Using the particle method as the prediction method, the coupled Moving Particle Simulation (MPS) and Discrete Element Method (DEM) calculations were used to represent the generation of air.

Ethanol blending is one method that can be used to reduce knock in spark ignition engines by decreasing the autoignition reactivity of the fuel and modifying its laminar flame speed. In this paper, the effects of ethanol blending on knock propensity and flame speed of petroleum and low-carbon gasoline fuels is analyzed. To do so, surrogate fuels were formulated for methanol-to-gasoline (MTG) and ethanol-to-gasoline (ETG) based on the fuels’ composition, octane number, and select physical properties; and 0-D and 1-D chemical kinetics simulations were performed to investigate reactivity and laminar flame speed, respectively. Results of MTG and ETG were compared against those of PACE-20, a well-characterized surrogate for regular E10 gasoline. Similarly to PACE-20, blending MTG and ETG with ethanol increases the fuel’s research octane number (RON) and sensitivity.

Powertrain development requires an efficient development process with no rework and model-based development (MBD). In addition, to performance design that achieves low CO2 emissions is also required. Furthermore, it also demands fuel economy performance considering real-world usage conditions, and in North America, the EPA (U.S. Environmental Protection Agency) 5-cycle, which evaluates performance in a combination of various environments, is applied. This evaluation mode necessitates predicting performance while considering engine heat flow. Particularly, simulation technology that considers behavior based on engine temperature for Hybrid Electric Vehicle (HEV) is necessary. Additionally, in the development trend of vehicle aerodynamic improvement, variable devices like Active Grille Shutter (AGS) are utilized to contribute to reducing CO2 emissions.

Emission regulations are becoming more stringent, as global temperature continues to rise due to the increasing greenhouse gases in the atmosphere. Battery electric vehicles (BEV), which have zero tailpipe emissions, are expected to become widespread to solve this problem. As the powertrain of BEV is more efficient than conventional powered vehicles, the proportion of energy loss during driving due to aerodynamic drag becomes greater. Therefore, reducing aerodynamic drag for improved energy efficiency is important to extend the pure electric range. At Honda, Computational Fluid Dynamics (CFD) and wind tunnel testing are used to optimize vehicle shape and reduce aerodynamic drag. Highly accurate CFD is essential to efficiently guide the development process towards reducing aerodynamic drag. Specifically, the prediction accuracy for the exterior shape, underfloor devices, tires, and wheels must meet development requirements.

Toyota has developed a new 2.4L L4 turbo (2.4L-T) engine with 8AT and 1-motor hybrid electric powertrains for midsize pickup trucks. The aim of these powertrains is to fulfill both strict fuel economy and emission regulations toward “Carbon Neutrality”, while exceeding customer expectations. The new 2.4L L4 turbocharged gasoline engine complies with severe Tier3 Bin30/LEVIII SULEV30 emission regulations for body-on-frame midsize pickup trucks improving both thermal efficiency and maximum torque. This engine is matched with a newly developed 8-speed automatic transmission with wide range and close step gear ratios and extended lock-up range to fulfill three trade-off performances: powerful driving, NVH and fuel economy. In addition, a 1-motor hybrid electric version is developed with a motor generator and disconnect clutch between the engine and transmission.

The challenges concerning noise, vibration, and harshness (NVH) performance in the vehicle cabin have been significantly changed by the powertrain shift from a conventional drive unit with an internal-combustion engine (ICE) to electric drive units (eAxles). However, there is few research regarding the impact of electrification on NVH considering the influence of the context such as multi-stimuli and traffic rules during a real-life driving. In this study, the authors conducted test drives using EVs and ICEVs on public roads in Europe and conducted a statistical analysis of the difference in driver impression of NVH performance based on interviews during actual driving. The impression data were categorized into clusters corresponding to related phenomena or features based on driver comments. Furthermore, the vehicles data (vehicle speed, acceleration, GPS information, etc.) were recorded to associate the driver impressions with the vehicle’s conditions when the comments were made.

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.

Internal combustion engines will play an important role in the coming decades, even considering targets of carbon neutrality for a sustainable future. This will be especially true in regions where pure electrified vehicle implementation is not yet practical, or for long-range heavy load transportation purposes, even in regions where BEV infrastructure is well established. HEV/PHEV’s importance and contribution to CO2 emission reduction together with carbon neutral fuels such as hydrogen, e-fuel and biomass fuel etc. will remain crucial regardless of region/transport sectors. In this respect, brake thermal efficiency improvements by friction reduction needs further investigation. This is especially so with the crankshaft bearings’ lubrication system, which can provide as much as 40% of the total mechanical losses in some cases. It is a well-established fact, that plain bearings require a minimum oil flow volume to maintain their real function rather than oil pressure.

With the promotion of electrification of powertrains, development using the simulation model is being promoted to achieve highly efficient development of the powertrains. Since the hybrid powertrain includes many subsystems, the complexity of the development process bloats the development scale. To proceed with large-scale development efficiently, it is necessary to utilize simulation. Furthermore, to maximize efficiency, improvement in development speed is required. In this study, we selected the real-time technology for 1D engine model that is suitable for the concurrent development aiming at increasing the development speed. The real-time technology achieved a high prediction accuracy, a lower modeling workload, and reliable real-time computation speed. And as a practical application of this technology, we conducted collaborative development of engine and transmission using the real-time engine model.

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.