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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.

The rise of Software-Defined Vehicles (SDV) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS), Autonomous Vehicle (AV), and Battery Electric Vehicle (BEV) technology. While AVs need power to compute data from perception to controls, BEVs need the efficiency to optimize their electric driving range and stand out compared to traditional Internal Combustion Engine (ICE) vehicles. AVs possess certain shortcomings in the current world, but SAE Level 2+ (L2+) Automated Vehicles are the focus of all major Original Equipment Manufacturers (OEMs). The most common form of an SDV today is the amalgamation of AV and BEV technology on the same platform which is prominently available in most OEM’s lineups. As the compute and sensing architectures for L2+ automated vehicles lean towards a computationally expensive centralized design, it may hamper the most important purchasing factor of a BEV, the electric driving range.

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.

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.

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 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.

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%.

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.

Rapid adoption of battery electric vehicles means improving the energy consumption and energy efficiency of these new vehicles is a top priority. One method of accomplishing this is regenerative braking, which converts kinetic energy to electrical energy stored in the battery pack while the vehicle is decelerating. Coasting is an alternative strategy that minimizes energy consumption by decelerating the vehicle using only road load. A battery electric vehicle model is refined to assess regenerative braking, coasting, and other deceleration strategies. A road load model based on public test data calculates tractive effort requirements based on speed and acceleration. Bidirectional Willans lines are the basis of a powertrain model simulating battery energy consumption. Vehicle tractive and powertrain power are modeled backward from prescribed linear velocity curves, and the coasting trajectory is forward modeled given zero tractive power.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing.

In response to the steadily worsening impact of global warming, greater efforts are being made to achieve carbon neutrality. Toyota Motor Corporation developed an in-vehicle solar charging system that utilizes generated solar energy to drive the vehicle. While the ignition is off, energy generated from a solar panel is used to charge the main battery. Then, while the ignition is on, this energy is supplied to the 12 V system to reduce consumption of the main battery energy, thereby helping to improve the electric driving range. This 1st-generation solar charging system adopted in the Prius PHV in 2017 was the first mass-produced in-vehicle solar charging system in the world. In 2022, the 2nd-generation solar charging system was developed and adopted in the bZ4X, including performance improvements such as a newly designed solar roof and lightweight charging system.

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.

This paper describes a new control technology that coordinates the operation of multiple actuators in a new hybrid electric vehicle (HEV) system consisting of a turbocharged engine, front and rear electric motors, two clutches, and a 6-speed automatic transmission. The development concept for this control technology is to achieve the driver’s desired acceleration G with a natural feeling engine speed. First, to realize linear acceleration G even while the engine is starting from EV mode, clutch hydraulic pressure reduction control is implemented. Furthermore, the engine start timing is optimized to prevent delayed drive force response by predicting the required maximum power during cranking. Second, to realize linear acceleration, this control selects the proper gear position based on the available battery power, considering noise and vibration (NV) restrictions and turbocharging response delays.

In 2022, Toyota launched new battery electric vehicle (BEV), the Toyota bZ4X. Unlike gasoline-powered vehicles, BEVs require charging. Users want increased range and shorter charging times. bZ4X's charging system increased range and shortened AC/DC charging time compared to the Lexus UX300e launched in 2020. A new unit called Electricity Supply Unit (ESU) was developed that integrated a DCDC converter, on-board charger, DC relays, and a branch box for power distribution function into a single unit. The design moved the branch box out of the battery pack to make room for the battery capacity, and it integrated the power conversion function into a single unit, making it more compact than if each unit were mounted separately. A 7 kW or 11 kW on-board charger is included with the vehicle. The 7 kW on-board charger is inside ESU; the 11 kW charger is external to the ESU.

Toyota has launched a new BEV which incorporates our newest evolutions in BEV powertrain systems and vehicle platform innovations. The new BEV uses newly developed large format battery cells, which, in addition to achieving our key performance and safety targets, also incorporates new technologies resulting in improved battery energy density and a reduction in battery deterioration. For the BEV battery cooling, to enhance safety, the cooling plate and the battery cells are separated by a chamber structure. The battery pack also incorporates a newly developed high resistance coolant with low conductivity. The new BEV improves system efficiency by leveraging some technologies that were originally developed for HEV and developing new systems. For example, radiant heating and a newly developed heat pump system improve EV driving range. This presentation will introduce our new battery technologies and discuss our new BEV system.

It is necessary for us to reduce CO2 emissions in order to hold down global warming which is advancing year by year. Toyota Motor Corporation believes that not only the introduction of BEVs but also the sale of the hybrid vehicles must spread in order to achieve the necessary CO2 reduction. Therefore, we planned to improve the attractiveness of future hybrid vehicles. Prius has always made full use of hybrid technologies and leading to significant CO2 reduction. Toyota Motor Corporation has developed a 2.0L hybrid system for the new Prius. We built the system which could achieve a comfortable drive along following the customer’s intention while improving the fuel economy more than a conventional system. The engine improves on both output and thermal efficiency. The transaxle decreases mechanical loss by downsizing the differential, and adoption of low viscosity oil.

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.