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In this paper, we consider smart charging and vehicle-to-home (V2H) technologies for plugin electric vehicles coordinated with home energy management systems (HEMS) for automated demand response. In this system, plugin electric vehicles automatically react to demand response events with or without HEMS’s coordination, while vehicles are charged and discharged (i.e., V2H) in appropriate time slots by taking into account demand response events, time-ofuse rate information, and users’ vehicle usage plan. We introduce three approaches on home energy management: centralized energy control, distributed energy control, and coordinated energy control. We implemented smart charging and V2H systems by employing two sets of standardized communication protocols: one using OpenADR 2.0b, SEP 2.0, and SAE standards and the other using OpenADR 2.0b, ECHONET Lite, and ISO/IEC 15118.

This paper presents a modeling method for prediction of low-frequency road noise in a steady-state condition where rotating tires are excited by actual road profile undulation input. The proposed finite element (FE) tire model contains not only additional geometric stiffness related to inflation pressure and axle load but also Coriolis force and centrifugal force effects caused by tire rotation for precise road noise simulation. Road inputs act on the nodes of each rib in the contact patch of the stationary tire model and move along them at the driving velocity. The nodes are enforced to displace in frequency domain based on the measured road profile. Tire model accuracy was confirmed by the spindle forces on the rotating chassis drum up to 100Hz where Coriolis force effect should be considered. Full vehicle simulation results showed good agreement with the vibration measurement of front/rear suspension at two driving velocities.

This paper proposes a novel method of verifying comprehensive driver model used for the evaluation of driving safety systems, which is achieved by coupling the traffic simulation and the driving simulator (DS). The method consists of three-step procedure. In the first step, an actual driver operates a DS vehicle in the traffic flow controlled by the traffic simulation. Then in the next step, the actual driver is replaced by a driver model and the surrounding vehicle maneuvers are replayed using the recorded data from the first step. Then, the maneuver by the driver model is compared directly with the actual driver's maneuver along the simulation time steps.

A number of active safety systems are already developed to support drivers’ decision and action to help avoid accidents, but further enhancement of those active safety systems cannot be accomplished without increasing our understanding on driver behaviors and their interaction with vehicle systems. For this reason, a state-of-art driving simulator (DS) has been developed that creates very realistic scenarios as a means of realizing these requirements. The DS consists of a simulator cabin, turntable (inside the dome), a 6-DOF hexapod system, shakers (vehicle vertical vibration actuators), and a motion system capable of moving 35 meters longitudinally and 20 meters laterally. The system is also capable of projecting images of actual city streets and highways onto a 360° spherical screen inside of the dome. As a result, the DS is able to reproduce a driving environment that is very similar to real driving.

Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling. Furthermore, the evaluation of the model will be introduced at the end of the paper.

Automobile manufactures need to adopt new technologies to meet global CO2 (carbon dioxide) emission regulations and better fuel efficiency demands from customers. Also, the production cost should be as low as possible for an affordable vehicle. Therefore, it is advantageous for OEMs to develop fuel efficient technologies which can be controlled by software without additional hardware costs. The coasting control is a fuel efficiency improvement technology that can be implemented by the change of vehicle software only. The coasting control is a technology that reduces the driving resistance (Deceleration) when the driver releases the gas pedal. This technology leads to reducing the energy required for the vehicle to drive and results in improving the real-world fuel economy. In an internal combustion engine (ICE) vehicle, the coasting state is achieved by changing the gear to neutral, and the effect has been discussed and clarified by many previous studies.

With the support of Horiba and Horiba STEC, Toyota Motor Corporation has developed an exhaust emissions simulator to verify the accuracy of extremely low emissions measurement systems. It can reliably verify the accuracy (correlation) of each SULEV emission measurement system to within 5% under actual conditions. The simulator's method of simulating SULEV gasoline engine cold-start emissions is to inject bottled gases with known concentrations of each emission constituent to the base gas, which is clean exhaust gas from a SULEV vehicle with new fully warmed catalysts. First, the frequencies and dynamic ranges of the SULEV cold-start emissions were analyzed and the method of 2 injecting the bottled gases was considered based on the results of that analysis. A high level of repeatability and accuracy was attained for all injection flow ranges in the SULEV cold-start emission simulation by switching between high-response digital Mass Flow Controllers (MFCs) of different full scales.

We have developed a novel engine cycle simulation program (UniDES: universal diesel engine simulator) to reproduce the diesel combustion process over a wide range of engine operating parameters, such as the amount of injected fuel, the injection timing, and the EGR ratio. The approach described in this paper employs a zoning model, where the in-cylinder region is divided into up to five zones. We also applied a probability density function (PDF) concept to each zone to consider the effect of spatial non-homogeneities, such as local equivalence ratios and temperature, on the combustion characteristics. We linked this program to the commonly used commercial GT-Power® software (UniDES+GT). As a result, we were able to reproduce transient engine behavior very accurately.

We developed a progressive system, “virtual and real simulator (V&R-S)” for engine. To innovate the process of engine development, the test system creates dynamic load of drivetrain, wheel, body and road with the virtual vehicle model. We set the phenomena such as drivetrain vibration for reproducing object of this system. The load is transmitted to the engine crankshaft end as torque with the connecting shaft made of fiberglass. The mainly developed technologies are the dynamometer with rotational inertia as low as engine, correction method of transmitted torque error of connecting shaft by H-infinity control. Thanks to these, we achieved the capability of optimization for most of dynamic characteristics (emission, fuel consumption, drivability) on engine test bench. And we now be able to limit real vehicle test to the final tuning. As a result, we have realized new engine evaluation and optimization process.

Toyota Motor Corporation (TMC) began a wireless charging field test in February 2014. A wireless charging system was installed at the residences of test subjects with the aim of identifying issues related to convenience and installation in daily usage. The test vehicle was fabricated by installing a wireless charging system into a Prius PHV (Plug-in Hybrid Vehicle). The installed system had the same charging power as the cable charging system used on the base vehicle, and had a charging time of 1.5 hours. A high-frequency 85 kHz power supply and primary coil were produced for the charging infrastructure. To identify differences in charging behavior, the test subjects were asked to use the cable charging system for the first month before changing to the wireless charging system for two months. Data acquisition was performed by an on-board data logger and through interviews with the test subjects.

A hybrid navigation system [1] that performs route calculations and highly flexible natural speech location searches in the cloud using dynamic databases that combine probe data collected from the vehicle and external data, and transmits to on-board devices has been developed. The system automatically switches to the on-board device when the vehicle is out of mobile network communication range or when faster processing is required for tasks such as re-routing. The transition between the on-board devices and the cloud provide a seamless user experience adapted to use conditions and other factors. In addition, representing the route downloaded from the cloud by the on-board device requires synchronizing the map with the cloud, and a map caching function has been used to reduce the volume of data that needs to be synchronized. The cloud-based route calculation is based not only on average travel time, but on dispersion as well.

Future Automotive Systems Technology Simulator (FASTSim) is a free and open-source tool developed by National Renewable Energy Lab (NREL). Among the attractive capabilities of the FASTSim is that it can perform computationally efficient fuel economy simulations of automotive vehicles with reasonable accuracy for standard or arbitrary drive cycles. The modeling capability includes vehicles with various types of powertrains such as: conventional vehicles (CVs), hybrid-electric vehicles (HEVs), plugin hybrid electric vehicles (PHEVs) and battery-only electric vehicles (BEVs). The public version of FASTSim available from NREL is implemented in Excel, which achieves the goal of good accessibility to a broad audience, but has some limitations, including: i) bottleneck in computations when importing arbitrary drive cycles, ii) slower computations in general than other scripting or programming languages, and iii) less portable to integration with other applications and/or other platforms.

In order to reduce CO₂, Electric Vehicles (EV) and Hybrid Vehicles (HV) are effective. Those types of vehicles have powertrains from conventional vehicles. Those new powertrains drastically improve their efficiency from conventional vehicles keeping the same or superior power performance. On the other hand, those vehicles have an issue for thermal energy shortage during warming up process. The thermal energy is very large, and seriously affects the fuel economy for HV and the mileage for EV. In this paper, we propose VHDL-AMS multi-domain simulation technique for the estimation of the vehicle performance at the concept planning stage. The VHDL-AMS is IEEE and IEC standardized language, which supports not only multi-domain (physics) but also encryption. The common modeling language and encryption standard is indispensable for full-vehicle simulation.

This paper describes a new method to predict the rupture of spot welds, suitable for vehicle crash simulation. In a crash simulation used for vehicle development process, the calculation is performed assuming that the spot welds in the vehicle do not rupture. However, if some spot welds rupture in test of a prototype vehicle, the simulated deformation and test deformation may not match, resulting in inaccurate estimation of deformation from simulation. Therefore accuracy of predicting the rupture of spot welds is crucial in accurately estimating the deformation and improving reliability of vehicle crash simulation results. The new method to predict the rupture of spot welds which relates axial and shear forces and bending moment of spot weld to stress around nugget has been developed by authors. Based on developed method, the rupture risk of spot welds has been estimated. The new method was applied to estimate the spot weld rupture using three types of specimens.

Teammate Advanced Drive is a driving support system with state-of-the-art automated driving technology that has been developed for customers’ safe and secure driving on highways based on the Toyota’s Mobility Teammate Concept. This SAE Level 2 (L2) system assists overtaking, lane changes, and branching to the destination, in addition to providing hands-free lane centering and car following. The automated driving technology includes self-localization onto a High Definition Map, multi-modal sensing to cover 360 degrees of the surrounding environment using fusion of LiDARs, cameras, and radars, and a redundant architecture to realize fail-safe operation when a malfunction or system limitation occurs. High-performance computing is provided to implement deep learning for predicting and responding to various situations that may be encountered while driving.

This paper describes a method for assisting pedestrians to cross a road. As motorization develops, pedestrian protection techniques are becoming more and more important. Advanced driving assistance systems (ADAS) are improving rapidly to provide even greater safety. However, since the accident risk of pedestrians remains high, the development of an advanced walking assistance system for pedestrian protection may be an effective means of reducing pedestrian accidents. Crossing a road is one of the highest risk events, and is a complex phenomenon that consists of many dynamically changing elements such as vehicles, traffic signals, bicycles, and the like. A road crossing assistance system requires three items: real-time situational recognition, a robust decision-making function, and reliable information transmission. Edge devices equipped with autonomous systems are one means of achieving these requirements.

Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles.

Knowledge of experts is necessary for judging motor current waveforms. Here, we develop an automatic judgement system for motor current waveform by establishing an AI model trained by knowledge of experts and CAE technology.

This paper describes a design tool and a software platform for FlexRay systems that are investigated in Nagoya University and are proposed to JasPar. The design tool reads the specification of a system as a task graph that consists of a set of tasks and messages among them. The design tool, then, allocates the tasks to ECUs and schedules the messages on a FlexRay network. The software platform consists of a middleware called time-trigger module (TTM) which dispatches time-triggered tasks, a communication middleware for a time-triggered network (TT-COM), a network management middleware for FlexRay (FlexRay-NM), and a device driver for FlexRay controller.

Woven Core, Inc. has developed Teammate Advanced Drive, a driving support system with state-of-the-art automated driving technology based on the Mobility Teammate Concept by Toyota Motor Corporation. Teammate Advanced Drive enables intelligent Ramp to Ramp hands-off driving on highways. The system features a self-localization estimation system that uses an HD-Map (High Definition Map) and high-level redundancy across sensors, computing, actuators, power supplies, and data communication. The system also includes digital data uploading and downloading capabilities wirelessly OTA (Over the Air) in order to provide customers the latest map updates as well as new software features and upgraded performance. A number of characteristically unique sensors have been combined to monitor the entire perimeter of the vehicle with high reliability.