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Suspension is an important chassis part which is vital to ride comfort [1]. However, it is difficult to achieve our targeted comfortability level in a short time. Therefore, improving efficiency of damper development is our primary challenge. We have launched a project which aims to reduce the workload on developing dampers by introducing analytical approaches to the improvement of ride comfort. To be more specific, we have been putting effort into developing the damping force prediction, the vehicle dynamics prediction and subjective rating prediction. This paper describes subjective rating prediction method which output a subjective rating corresponding to the physical value of the vehicle dynamics with deep learning. As a result of verification using objective data which was not used for learning process, DNN (Deep Neural Network) prediction method could fairly precisely predict subjective rating of the expert driver.

Many papers have published techniques for estimating SEA parameters using Finite Element Methods (FEM). We have also developed a technique for virtual prototyping of an automotive vehicle body using FEM. In our technique, FEM models are used to derive parameters for an SEA model. The technique does not require a FEM model of the whole system, but uses extracted FEM models that include subsystems sharing a single junction. The subsystem energies and power inputs are calculated by frequency response analysis in FEM. The determination of loss factors is based on the Power Injection Method (PIM). The objective of this work is the construction of an SEA model of a passenger car body-in-white. The model has only structural subsystems. In order to develop a model with high accuracy, the subdivision of the tested body-in-white is first evaluated using the Initial Slope Ratio (ISR) indicator that shows the coupling strength between subsystems.

This paper presents the “Virtual Failure Mode and Effects Analysis (vFMEA)” system, which is a high-fidelity electrical-failure-simulation platform, and applies it to the software verification of an electric power steering (EPS) system. The vFMEA system enables engineers to dynamically inject a drift fault into a circuit model of the electronic control unit (ECU) of an EPS system, to analyze system-level failure effects, and to verify software-implemented safety mechanisms, which consequently reduces both cost and time of development. The vFMEA system can verify test cases that cannot be verified using an actual ECU and can improve test coverage as well. It consists of a cycle-accurate microcontroller model with mass-production software implemented in binary format, analog and digital circuit models, mechanical models, and a state-triggered fault-injection mechanism.

In this paper the authors propose computerized support for Fault Tree Analysis (FTA) based on new knowledge management in product design. FTA is a method of analyzing and visualizing the causes of a fault event by Fault Tree diagram (FT diagram) that has a tree structure with logical step. When we develop the new product, we use FTA to analyze the fault events, to extract design parameters, and to plan test programs in Jatco's development process. Although FTA is an effective analytical tool for resolving problems, improving design quality and planning test programs, it is not easy to complete the FT diagram of a fault on Automatic Transmission and the component system without a mistake. To solve this problem, we propose the following approach using Computer-Aided knowledge management based on Quantity Dimension Indexing and block diagram. We classify FT diagrams into component failure, system failure, and control failure diagrams.

Highly automated driving systems have a responsibility to keep a vehicle safe even in abnormal conditions such as random or systematic failures. However, creating redundancy in a system to respond to failures increases the cost of the system, and simple redundancy cannot detect systematic failures because some systematic failures occur in each system at the same time. Systematic failures in automated driving systems cannot be verified sufficiently during the development phase due to numerous patterns of parameters input from outside the system. A safety concept based on a “safety sustainer” for highly automated driving systems is proposed. The safety sustainer is designed for keeping a vehicle in a safe state for several seconds if a failure occurs in the system and notifying the driver that the system is in failure mode and requesting the driver to take over control of the vehicle.

This paper describes the comparison of electromagnetic characteristics of two different superconducting-motor structures for electrified aircraft propulsion systems. Future electrified aircraft demand higher output (over 16 kW/kg) and higher efficiency (> 98%) for their motors in comparison with current ones. To satisfy the demands, two kinds of superconducting motors are dealt in this study: one is partially superconducting motors (PSCMs), made of superconducting field coils and copper armature windings; the other is the fully superconducting motors (FSCMs) made of superconducting field/armature windings. They are cooled at 20 K with liquid hydrogen. We designed these two motors with finite element method to obtain the output density of 16-20 kW/kg for future electrified propulsion systems. We selected 3.0- and 5.0 MW superconducting motors, considering the application to aircraft for almost 180 passengers and 44 MW rated power for take-off.

“Virtual Failure Mode and Effects Analysis” (vFMEA), a novel safety-verification method of control software for automotive electronic systems, was proposed to save prototyping cost at verification stage. The proposed vFMEA is system-level FMEA method, which uses virtualized electronic control units (ECUs) consisting of microcontroller models on a microcontroller simulator and a transistor-level circuit models on a circuit simulator. By using the structure, the control software in binary code formats can be verified when a circuit-level fault occurs in the ECU hardware. As an illustrative example, vFMEA was applied to an engine ECU. As a result of short-circuit fault into a driver IC, engine revolution and engine speed decreased. However, the engine continued to operate normally when an open-circuit fault occurred in a capacitor connected in parallel. Effects of the hardware faults in ECU on a vehicle are demonstrated; thereby software verification can be performed using vFMEA system.

The flight area of drones and other unmanned aerial vehicles (UAVs) had been highly restricted but has been relaxing, including flights beyond the scope of sight. Deregulation without aircraft-reliability improvement increases the risk of accidents. However, demanding high reliability for all aircraft leads to an increase in the price of the aircraft. Therefore, if airspace restrictions are relaxed for more reliable aircraft, the cost of higher reliability and its benefits can be balanced. This will improve efficiency and optimize cost-effectiveness. The purpose of this proposal is to balance the cost of aircraft-reliability improvement (which allows flight to continue in the event of a failure) and its advantages. Specifically, the author proposes rules that apply more relaxed airspace restrictions to UAVs with higher FCLs (Flight Continuity Possibility Levels) and stricter airspace restrictions to those with lower FCLs.