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Controllability is defined in ISO 26262 as a driver’s ability to avoid a specified harm caused by a malfunction of electrical and electronic systems installed in road vehicles. According to Annex C of Part 12 of ISO 26262, simulation is one of the techniques that the Controllability Classification Panel (CCP) can use to evaluate comprehensively the controllability class (C class) of motorcycles. With outputs of (i) an index for the success of harm avoidance and (ii) the magnitude of the rider’s compensatory control action required to avoid harm, the simulation is useful for evaluating the C class of the degrees of malfunction that cannot be implemented in practice for the sake of the test rider’s safety. To aim at supplying data that the CCP can use to judge the C class, we try to estimate the vehicle behavior and a rider’s compensatory control actions following a malfunction using vehicle dynamics simulations.

The Composite Lightweight Automotive Suspension System is a composite rear suspension knuckle/tieblade consisting of UD prepreg (epoxy resin), SMC (vinylester resin) carbon fibre and a steel insert to reduce the weight of the component by 35% and reduce Co2. The compression moulding manufacturing process and CAE optimisation are unique and ground-breaking for this product and are designed to allow high volume manufacture of approx. 30,000 vehicles per year. The manufacturing techniques employed allow for multi-material construction within a five minute cycle time to make the process viable for volume manufacture. The complexities of the design lie in the areas of manufacturing, CAE prediction and highly specialised design methods. It is a well-known fact that the performance of a composite part is primarily determined by the way it is manufactured.

The ISO 26262 standard specifies the requirement for functional safety of electrical and electronic systems within road vehicles. We have accumulated case studies based on actual riding tests by subjective judgment of expert riders to define a method for determining the controllability class (C class). However, the wide variety of practical traffic environments and vehicle behaviors in case of malfunction make it difficult to evaluate all C classes in actual running tests. Furthermore, under some conditions, actual riding tests may cause unacceptable risks to test riders. In Part 12 Annex C of ISO/DIS 26262, simulation is cited as an example of a technique for comprehensive evaluations by the Controllability Classification Panel. This study investigated the usefulness of mathematical simulations for evaluating the C class of a motorcycle reproducing a malfunction in either the front or rear brakes.

In applying the ISO 26262 controllability classification for motorcycles in actual riding tests, a subjective evaluation by expert riders is considered to be the appropriate approach from the viewpoint of safety. We studied the construction of an expert-rider-based C class evaluation method for motorcycles and developed some evaluation test cases reproducing various hazardous events. We determined that it was necessary to accumulate more evaluation cases for further representative scenarios and that, to avoid variations in such evaluations, a method in which different expert riders can carry out testing following a common understanding had to be devised. Considering these problems for practical application, this study aimed at establishing an actual riding test method for C class evaluation by expert riders and to develop a deeper understanding of test procedures and management.

ISO 26262, an international functional safety standard of electrical and/or electronic systems (E/E systems) for motor vehicles, was published in November 2011 and it is expected that the scope will be extended to motorcycles in a second edition of ISO 26262 going to be published in 2018. ISO/DIS 26262 second edition published in 2016 has Part 12 as a new part in order to apply ISO 26262 to motorcycle. Proper estimation of Exposure, Controllability, and Severity in accordance with ISO/DIS 26262 Part 12, are key factors to determine Motorcycle Safety Integrity Level. To estimate precise these factors, there would be a case that it might not be appropriate to apply studies done for passenger car to motorcycle, and it would be necessary to apply motorcycle specific knowledge and estimation methods. In our previous studies we clarified these motorcycle specific issues and studied the method for the adaptation.

For applying ISO 26262 to motorcycles, controllability classification (C class evaluation) by expert riders is considered an appropriate technique. Expert riders have evaluated commercial product development for years and can appropriately conduct vehicle tests while observing safety restrictions (such as avoiding the risk of falling). Moreover, expert riders can ride safely and can stably evaluate motorcycle performance even if the test conditions are close to the limits of vehicle performance. This study aims to construct a motorcycle C class evaluation method based on an expert rider’s subjective evaluation. On the premise that expert riders can rate the C class, we improved a test procedure that used a subjective evaluation sheet as the concrete C class evaluation method for an actual hazardous event.

ISO 26262 was established in 2011 as a functional safety standard for road vehicles. This standard provides safety requirements according to ASIL (Automotive Safety Integrity Level) in order to avoid unreasonable residual risk caused by malfunctioning behavior of electrical and/or electronic systems. The ASIL is determined by considering the estimate of three factors including injury severity. While applicable only to passenger cars at present, motorcycles will be included in the scope of application of ISO 26262 in the next revision. Therefore, our previous study focused on severity class evaluation for motorcycles. A method of classifying injury severity according to vehicle speed was developed on the basis of accident data. In addition, a severity table for motorcycles was created using accident data in representative collision configurations involved with motorcycles in Japan.

ISO 26262, an international functional safety standard of electrical and/or electronic systems (E/E systems) for motor vehicles, was published in November 2011 and it is expected that the scope will be extended to motorcycles in a second edition of ISO 26262 going to be published in 2018. In order to apply ISO 26262 to motorcycle, proper estimation of Exposure, Controllability, and Severity are key factors to determine Motorcycle Safety Integrity Level (MSIL). Exposure is a factor to indicate the probability of the state of an operational situation that can be hazardous with the E/E system malfunction. And it is not easy to estimate the motorcycle Exposure due to less availability of back ground data in actual operational situation compared to motor vehicle. Therefore real traffic situation should be investigated in order to provide rationales for MSIL determination.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.

ISO 26262 was established in 2011 as a functional safety standard for passenger cars. In this standard, ASILs (Automotive Safety Integrity Levels) representing safety levels for passenger cars are determined by evaluating the hazardous events associated with each item constituting an electrical and/or electronic safety-related system according to three evaluation criteria including injury severity. On the other hand, motorcycles will be included in the scope of application of ISO 26262 in the next revision. It is expected that a severity evaluation for motorcycles will be needed because motorcycles are clearly different from passenger cars in vehicle mass and structure. Therefore, this study focused on severity class evaluation for motorcycles. A method of classifying injury severity according to vehicle speed was developed on the basis of accident data.

ISO 26262, a functional safety standard for motor vehicles, was published in November 2011. Although motorcycles are not included in the scope of application of the current edition of ISO 26262, it is expected that motorcycles will be included in the next revision. However, it is not appropriate to directly apply automotive safety integrity levels (ASILs) to motorcycles because the situation of usage in practice presumably differs between motorcycles and motor vehicles. In our previous study, we newly defined safety integrity levels for motorcycles (MSILs) and proposed that the levels of MSILs should correspond to levels one step lower than those of ASILs; however, we did not investigate the validity of their connections. Accordingly, in this research, we validated the connections. We defined the difference of levels of SILs between motorcycles and motor vehicles as the difference of target values of random hardware failure rates specified in ISO 26262-5.

Controllability (C class) represents the level of the ability to avoid harm and is one of the parameters that determine the Automotive Safety Integrity Level in the ISO 26262 functional safety standard, which applies to the electrical and/or electronic systems. This study aimed to consider an appropriate C class evaluation technique for expert riders in applying ISO 26262 to motorcycles. This study attempted to show a C class evaluation method without deviation by the riders and presented examples of the evaluation of three hazardous events in actual vehicle tests. In addition, riders' comments regarding their understanding of the circumstances that resulted in the evaluation were collected, and the correspondence of these comments was examined. We selected “unintended acceleration” or “unintended deceleration” due to the malfunction of the electronic throttle control system as hazard examples and conducted tests to reproduce hazardous events.

A new turbocharged 60° 2.7L 4V-V6 gasoline engine has been developed by Ford Motor Company for both pickup trucks and car applications. This engine was code named “Nano” due to its compact size; it features a 4-valves DOHC valvetrain, a CGI cylinder block, an Aluminum ladder, an integrated exhaust manifold and twin turbochargers. The goal of this engine is to deliver 120HP/L, ULEV70 emission, fuel efficiency improvements and leadership level NVH. This paper describes the upfront design and optimization process used for the NVH development of this engine. It showcases the use of analytical tools used to define the critical design features and discusses the NVH performance relative to competitive benchmarks.

Drivelines used in modern pickup trucks commonly employ universal joints. This type of joint is responsible for second driveshaft order vibrations in the vehicle. Large displacements of the joint connecting the driveline and the rear axle have a detrimental effect on vehicle NVH. As leaf springs are critical energy absorbing elements that connect to the powertrain, they are used to restrain large axle windup angles. One of the most common types of leaf springs in use today is the multi-stage parabolic leaf spring. A simple SAE 3-link approximation is adequate for preliminary studies but it has been found to be inadequate to study axle windup. A vast body of literature exists on modeling leaf springs using nonlinear FEA and multibody simulations. However, these methods require significant amount of component level detail and measured data. As such, these techniques are not applicable for quick sensitivity studies at design conception stage.

OEMs are racing to develop lightweight vehicles as government regulations now mandate automakers to nearly double the average fuel economy of new cars and trucks by 2025. Lightweight materials such as aluminum, magnesium and carbon fiber composites are being used as structural members in vehicle body and suspension components. The reduction in weight in structural panels increases noise transmission into the passenger compartment. This poses a great challenge in vehicle sound package development since simply increasing weight in sound package components to reduce interior noise is no longer an option [1]. This paper discusses weight saving approaches to reduce noise level at the sources, noise transmission paths, and transmitted noise into the passenger compartment. Lightweight sound package materials are introduced to treat and reduce airborne noise transmission into multi-material lightweight body structure.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

This paper proposes an approach to determine driver's driving behavior, style or habit during vehicle handling maneuvers and heavy traction and braking events in real-time. It utilizes intelligence inferred from driver's control inputs, vehicle dynamics states, measured signals, and variables processed inside existing control modules such as those of anti-lock braking, traction control, and electronic stability control systems. The algorithm developed for the proposed approach has been experimentally validated and shows the effectiveness in characterizing driver's handling behavior. Such driver behavior can be used for personalizing vehicle electronic controls, driver assistant and active safety systems, and the other vehicle control features.

A deeper understanding of the complex phenomenology associated with the multiphase flow-induced noise and vibration in a dynamic valve is of critical importance to the automotive industry. To this purpose, a two-dimensional axisymmetric numerical model has been developed to simulate the complex processes that are responsible for the noise and vibration in a poppet valve. More specifically, an Eulerian multiphase flow model, a dynamic mesh and a user-defined function are utilized to facilitate the modeling of this complicated two-phase fluid-structure interaction problem. For a two-phase flow through the valve, our simulations showed that the deformation and breakup of gas bubbles in the gap between the poppet and the valve seat generates a vibration that arises primarily from the force imbalance between the spring and the two-phase fluid flow induced forces on the poppet.