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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 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.

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.

In real-world automotive control, there are many constraints to be considered. In order to explicitly treat the constraints, we introduce a model-prediction-based algorithm called a reference governor (RG). The RG generates modified references so that predicted future variables in a closed-loop system satisfy their constraints. One merit of introducing the RG is that effort required in control development and calibration would be reduced. In the preceding research work by Nakada et al., only a single reference case was considered. However, it is difficult to extend the previous work to more complicated systems with multiple references such as the air path control of a diesel engine due to interference between the boosting and exhaust gas recirculation (EGR) systems. Moreover, in the air path control, multiple constraints need to be considered to ensure hardware limits. Hence, it is quite beneficial to cultivate RG methodologies to deal with multiple references and constraints.

Conventional CFD-based shape optimization technology that uses parametric shape modification and optimal solutions searching algorithms has the two problems: (1) outcome of optimized shapes depend on the selection of design parameters made by the designer, and (2) high computational costs. To resolve those problems, two innovative inverse analysis technologies based on the Adjoint Method were developed in previous study: surface geometry deformation sensitivity analysis to identify the locations to be modified, and topology optimization to generate an optimal shape for maximizing the cost function in the constrained design space. However, these technologies are only applicable to steady flows. Since most flows in a vehicle (such as engine in-cylinder flow) are transient, a practical technology for surface geometry sensitivity analysis has been developed based on the Transient Adjoint Method.

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions.

This paper describes the development of a fracture finite element (FE) model for laser screw welding (LSW) and validation of the model with experimental results. LSW was developed and introduced to production vehicles by Toyota Motor Corporation in 2013. LSW offers superb advantages such as increased productivity and short pitch welding. Although the authors had previously developed fracture FE models for conventional resistance spot welding (RSW), a fracture model for LSW has not been developed. To develop this fracture model, many comprehensive experiments were conducted. The results revealed that LSW had twice as many variations in fracture modes compared to RSW. Moreover, fracture mode bifurcations were also found to result from differences in clearance between welded plates. In order to analyze LSW fracture phenomena, detailed FE models using fine hexahedral elements were developed.

Test and verification procedures are a vital aspect of the development process for embedded control systems in the automotive domain. Formal requirements can be used in automated procedures to check whether simulation or experimental results adhere to design specifications and even to perform automatic test and formal verification of design models; however, developing formal requirements typically requires significant investment of time and effort for control software designers. We propose Signal Template Library (ST-Lib), a uniform modeling language to encapsulate a number of useful signal patterns in a formal requirement language with the goal of facilitating requirement formulation for automotive control applications. ST-Lib consists of basic modules known as signal templates. Informally, these specify a characteristic signal shape and provide numerical parameters to tune the shape.

In order to reduce the energy consumption of the automotive air conditioning system, adsorption heat pump (AHP) system is one of the key technologies. We have been developing compact AHP system utilizing the exhaust heat from the engine coolant system (80-100 °C), which can meet the requirements in the automotive application. However, AHP systems have not been practically used in automotive applications because of its low volumetric power density of the adsorber. The volumetric power density of the adsorber is proportional to sorption rate, packing density and latent heat. In general, the sorption rate is determined by mass transfer resistance in primary particle of an adsorbent and heat and mass transfer resistance in packed bed. In order to improve the volumetric power density of the adsorber, it is necessary to increase the production of the sorption rate and the packing density.

Toyota Safety Sense is a safety system package developed to help drivers avoid accident types with a high frequency of occurrence. This paper deals with pre-collision system which forms the core of Toyota Safety Sense, especially Toyota Safety Sense P which uses a combined sensor configuration consisting of a monocular camera paired with millimeter wave radar, in order to achieve both high recognition performance and reliability. The use of a wide-angle monocular camera, millimeter wave radar integrated in the front grill emblem, and a collision determination algorithm for pedestrian targets enabled the development of a pre-collision system comprising detection capability of crossing pedestrians. Toyota has developed warning and pre-collision brake assist for driver to assist in avoiding a collision effectively; In addition, Pre-collision brake has achieved high level of performance for the drivers who cannot avoid a collision.

Toyota Motor Corporation has developed the new compact-class hybrid vehicle (HV). This vehicle incorporates a new hybrid system for the improvement of fuel efficiency. For this system, a new Power Control Unit (PCU) is developed. The feature of the PCU is downsizing, lightweight, and high efficiency. In expectation of rapid popularization of HV, the aptitude for mass production is also improved. The PCU, which plays an important role in the new system, is our main focus in this paper. Its development is described.

A vehicle parking manoeuvre is characterized by low or zero speed, small turning radius and large yaw velocity of the steered wheels. To predict the forces and moments generated by a wheel under these conditions, the Pacejka Magic Formula model has been extended to incorporate the effect of spin (turn slip model) in the past years. The extensions have been further developed and incorporated in the MFTyre/MF-Swift 6.2 model. This paper describes the development of a method for the identification of the turn slip parameters. Based on the operating conditions of a typical parking manoeuvre, the dominant parameters of the turn slip model are firstly defined. At an indoor test facility, the response of a tyre under the identified operating conditions is measured. An algorithm is developed to identify the dominant turn slip parameters from the measured responses.

This study focuses on fluctuations in the aerodynamic load acting on a hatchback car model under steady-state conditions, which can lead to degeneration of vehicle motion performance due to excitation of vehicle vibrations. Large eddy simulations were first conducted on a vehicle model based on a production hatchback car with and without additional aerodynamic devices that had received good subjective assessments by drivers. The numerical results showed that the magnitudes of the lateral load fluctuations were larger without the devices at Strouhal numbers less than approximately 0.1, where surface pressure fluctuations indicated a negative correlation between the two sides of the rear end, which could give rise to yawing and rolling vibrations. Based on the numerical results, wind-tunnel tests were performed with a 28%-scale hatchback car model.

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers.

The application of Model-Based Development (MBD) techniques for automotive control system and software development have become standard processes due to the potential for reduced development time and improved specification quality. In order to improve development productivity even further, it is imperative to introduce a systematic Verification and Validation (V&V) process to further minimize development time and human resources while ensuring control specification quality when developing large complex systems. Traditional methods for validating control specifications have been limited by control specification scale, structure and complexity as well as computational limitations restricting their application within a systematic model-based V&V process. In order to address these issues, Toyota developed Hierarchical Accumulative Validation (HAV) for systematically validating functionally structured executable control specifications.

For over a decade and a half, Toyota Motor Corporation has been developing fuel cell vehicles (FCVs) and is continuing various approaches to enable mass production. This study used new methods to quantitatively observe some of the mass transfer phenomena in the reaction field, such as oxygen transport, water drainage, and electronic conductivity. The obtained results are applicable to the design requirements of ideal reaction fields, and have the potential to assist to reduce the size of the fuel cell.

Automotive control systems such as powertrain control interact with the open physical environment, and from this nature, expensive prototyping is indispensable to capture a deep understanding of the system requirements and to develop the corresponding control software. Model-based development (MBD) has been promoted to improve productivity by virtual prototyping. Even with MBD, systematic validation of the software specification remains as a major challenge and it still depends heavily on individual engineers' skill and knowledge. Though the introduction of graphical software modeling improved the situation, it requires much time to identify the primal functions, so-called “design interests”, from a large complex model where irrelevant components are mixed with, and to validate it properly.

Merging a CAE shape optimization system and a concept Taguchi method SN-ratio index, a robustness-focused automated shape optimization method has been developed. Applying this method to diesel intake ports, with mold position tolerance set as the error factor, SN-ratio was defined for swirl stability. As a result of the optimization provided by a multi-objective genetic algorithm, simultaneous improvement of flux, swirl rotation and SN ratio was achieved.

A hardening depth evaluation technique using eddy current has been developed, which can be applied to a mass production line for destructive (cutting) inspections. Using this technique, changes in the hardness of the induction-hardened structure can be detected based on the changes in magnetic permeability. This technique reduces the thermal effect and improves measurement accuracy through a multi-frequency exciting method and a difference method algorithm.