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Reducing vehicle CO2 emissions is an important measure to help address global warming. To reduce CO2 emissions on a global basis, Toyota Motor Corporation is taking a multi-pathway approach that involves the introduction of the optimal powertrains according to the circumstances of each region, including hybrid electric (HEVs) and plug-in hybrid electric vehicles (PHEVs), as well as battery electric vehicles (BEVs). This report describes the development of a new PHEV system for the Toyota Prius. This system features a traction battery pack structure, transaxle, and power control unit (PCU) with boost converter, which were newly developed based on the 2.0-liter HEV system. As a result, the battery capacity was increased by 1.5 times compared to the previous model with almost the same battery pack size. Transmission efficiency was also improved, extending the distance that the Prius can be driven as an EV by 70%.

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.

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 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.

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap.

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.

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.

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.

The world is currently facing environmental issues such as global warming, air pollution, and high energy demand. To mitigate these challenges, the electrification of vehicles is essential as it is effective for efficient fuel utilization and promotion of alternative fuels. The optimal approach for electrification varies across different markets, depending on local energy conditions and current circumstances. Consequently, Toyota has taken the initiative to offer a comprehensive lineup of battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and fuel cell electric vehicles (FCEV), aiming to provide sustainable solutions tailored to the unique situations and needs of each region. As part of this effort, Toyota has developed the 5th generation of hybrid electric vehicles. This paper describes the electric motor used in the new Toyota Camry which achieves high torque, high power, low losses, and compact design.

For further increase in thermal efficiency of heavy-duty diesel engines, flexible regulation of the heat release rate (HRR) profile combined with higher compression ratio could have more rooms to improve indicated thermal efficiency by overcoming various drawbacks relevant to higher compression ratio. A new ideal HRR profile, which starts as a kind of delta shape to fulfil the isobaric cycle from top-dead-center (TDC) and is followed by the significant increase in HRR to reach the maximum cylinder pressure in the retarded timing, was proposed. We call it as ‘High-heels’ HRR profile from its two-step-increase delta shape. To confirm the potential of the ideal HRR profile by utilizing a single- cylinder heavy-duty diesel engine, a variable fuel injection rate equipment, novel combustion chamber designs, and an offset orifices nozzle were investigated as the technologies for modifying HRR profile.

In this study, a new method to calculate the spray collapse in multiple-hole gasoline injectors was developed. The theoretical calculation method was proposed by applying the extended spray momentum theory. In this theoretical calculation method, the deflection of the spray direction toward the injector axis was calculated serially based on the imbalance of the momentum of backward gas-flow surrounding sprays. The calculation and spray observation for validation against the calculation result were performed under conditions with and without flash boiling.

DENSO started a pilot demonstration of on-site methanation as “CO2 circulation plant” as proactive initiative for CO2 capture and storage/utilization (CCUS) technologies toward achievement of carbon neutrality by 2035 in our own business. The CO2 circulation plant was designed to capture CO2 primarily generated by the plant and recycle it as an energy source of the facility. We also started work on the development of electric swing CO2 adsorption (ESA) technology to achieve low-energy CO2 capture.

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.

This research aimed to improve the PN filtration efficiency of a catalyst coated gasoline particulate filter (cGPF) to meet the next generation of emissions regulations for internal combustion engines. This paper proposes a concept that improves the PN filtration performance while maintaining low pressure drop by forming a thin PM trap layer on the surface of the cGPF substrate. The design guidelines for the coating particle size and coating amount of the PM trap layer were investigated, and actual manufacturing issues were also identified. The validity of this concept and guidelines was then verified on an actual vehicle.

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.

A hybrid powertrain offers the potential of a significant fuel saving for heavy-duty Diesel vehicles, which results in CO2 reduction of more than 20%, depending on the application. Using advanced future fuels, like HVO offers additional CO2 saving potential. In addition, the future Diesel engine needs to comply with the next generation of emission legislation, given by the European EUVII and the US EPA2027 regulatory frameworks. To achieve these limits, a combination of different technologies for the engine and the aftertreatment system are required. The proposed paper will present these technology solutions and their impact on CO2 and emissions by means of engine testing and simulation.

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.

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.