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Toyota has developed a new 2.4L L4 turbo (2.4L-T) engine with 8AT and 1-motor hybrid electric powertrains for midsize pickup trucks. The aim of these powertrains is to fulfill both strict fuel economy and emission regulations toward “Carbon Neutrality”, while exceeding customer expectations. The new 2.4L L4 turbocharged gasoline engine complies with severe Tier3 Bin30/LEVIII SULEV30 emission regulations for body-on-frame midsize pickup trucks improving both thermal efficiency and maximum torque. This engine is matched with a newly developed 8-speed automatic transmission with wide range and close step gear ratios and extended lock-up range to fulfill three trade-off performances: powerful driving, NVH and fuel economy. In addition, a 1-motor hybrid electric version is developed with a motor generator and disconnect clutch between the engine and transmission.

The kinematic response of vehicle occupants involved in tractor-to-passenger vehicle sideswipes was examined through a series of 13 crash tests. Each test vehicle and its occupants were instrumented with accelerometer arrays to measure and quantify the impact severity at various inter-vehicular angles and impact velocities. The passenger vehicle was occupied by a volunteer test subject in the driver and right-front passenger positions. The impact angle was varied between 3° and 11° to produce a sideswipe collision between the front bumper, steered wheel, and side components of the tractor and the side panels of the struck vehicle. The passenger vehicles were struck at different locations along their longitudinal axis at impact velocities between 3 mph and 11.5 mph. Accelerations were measured at the lumbar, cervicothoracic, and head regions of the driver and right-front passenger of the struck vehicle and the tractor driver.

Modern heavy vehicles may be equipped with an Advanced Driver Assistance System (ADAS) designed to increase highway safety. Depending on the vehicle or manufacturer, these systems may detect objects in a driver’s blind spot, provide an alert when the ADAS determines that the vehicle is leaving its lane of travel without the use of a turn signal, or notify the driver when certain road signs are detected. ADASs also include adaptive cruise control, which adjusts the vehicle’s set cruise speed to maintain a safe following distance when a slower vehicle is detected ahead of the truck. In addition, the ADAS may have a Collision Mitigation System (CMS) component that is designed to help drivers respond to roadway situations and reduce the severity of crashes. CMSs typically use radar or a combination of radar and optical technologies to detect objects such as vehicles or pedestrians in the vehicle’s path.

The hydrogen (H2) internal combustion engine (ICE) is emerging as an attractive low life-cycle carbon powertrain configuration for applications that require high power, high duty cycle operation. Owing to the relative ease of conversion of heavy duty (HD) diesel ICEs to H2 and the potential for low exhaust emissions, H2 ICEs are expected to play a strong role in rapidly decarbonizing hard-to-electrify markets such as off-road, rail, and marine. The conversion of HD diesel ICEs to spark ignited H2 with port fuel injection is typically accompanied by a de-rating of engine power and torque. This is due to several fuel- and system-related challenges, including the high risk of abnormal combustion resulting from the low auto-ignition energy threshold of H2, and boost system requirements for highly dilute operation that is used to partially mitigate this abnormal combustion risk.

Emission regulations are becoming more stringent, as global temperature continues to rise due to the increasing greenhouse gases in the atmosphere. Battery electric vehicles (BEV), which have zero tailpipe emissions, are expected to become widespread to solve this problem. As the powertrain of BEV is more efficient than conventional powered vehicles, the proportion of energy loss during driving due to aerodynamic drag becomes greater. Therefore, reducing aerodynamic drag for improved energy efficiency is important to extend the pure electric range. At Honda, Computational Fluid Dynamics (CFD) and wind tunnel testing are used to optimize vehicle shape and reduce aerodynamic drag. Highly accurate CFD is essential to efficiently guide the development process towards reducing aerodynamic drag. Specifically, the prediction accuracy for the exterior shape, underfloor devices, tires, and wheels must meet development requirements.

Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

The global push towards reducing green-house gas and criteria pollutant emissions is leading to tighter emission standards for heavy-duty engines. Among the most stringent of these standards are the California Air Resource Board (CARB) 2024+ HD Omnibus regulations adopted by the agency in August 2020. The CARB 2024+ HD Omnibus regulations require up to 90% reduction in NOx emissions along with updated compliance testing methods for on-road heavy-duty engines. Subsequently, the agency announced development of new Tier 5 standards for off-road engines in November 2021. The Tier 5 standards aim to reduce NOx/PM emissions by 90%/75% respectively from Tier 4 final levels, along with introduction of greenhouse gas emission standards for CO2/CH4/N2O/NH3. Furthermore, CARB is also considering similar updates on compliance testing as those implemented in 2024+ HD Omnibus regulations including, low-load cycle, idle emissions and 3-bin moving average in-use testing.

In the European FULMEN program, a collection together with an analysis of available data on lightning have been collected in a public database produced by Aerospatiale. This database contains data on In-flight and ground measurements, on In-flight incidents and manufacturer transfer functions. In this paper, the data of the in-flight measurements are presented. The In-flight data have been extracted from the Convair and Transall campaigns performed during summer 1985 and 1988 respectively. The measurements have shown that a lightning strike to an aircraft can be decomposed into four main phases: (1) the pre-breakdown phase associated with the general electrostatic condition just before the lightning, (2) the leaders development phase, (3) the recoil streamers phase and (4) the continuous current phase. For each phase, the main physical parameters (current, number of impulse current, impulse period, impulse duration, electric field, …) have been collected.

Collision reconstruction often involves calculations and computer simulations, which require an estimation of the weights of the involved vehicles. Although weight data is readily available for automobiles and light trucks, there is limited data for heavy vehicles, such as tractor-semitrailers, straight trucks, and the wide variety of trailers and combinations that may be encountered on North American roads. Although manufacturers always provide the gross vehicle weight ratings (GVWR) for these vehicles, tare weights are often more difficult to find, and in-service loading levels are often unknown. The resulting large uncertainty in the weight of a given truck can often affect reconstruction results. In Canada, the Ministry of Transportation of Ontario conducted a Commercial Vehicle Survey in 2012 that consisted of weight sampling over 45,000 heavy vehicles of various configurations.

For model year 2018, Freightliner introduced the New Cascadia model to their lineup of Class 8 trucks. Testing of the Freightliner New Cascadia with Detroit Diesel engines was conducted to evaluate the accuracy of the reported event data contained in the engine Electronic Control Units (ECUs) for these trucks. The testing showed that there are occurrences in DDEC Reports, specifically in the Last Stop Record and Hard Braking event data, when the time between successive event data points was two seconds rather than the reported one second interval. The occurrence of the two-second anomaly was not always present in a Last Stop Record or Hard Braking event. When the two-second anomaly was present in the event data, it occurred randomly and no pattern to when this anomaly occurs was determined. No method was found to be able to detect the presence of this anomaly from the review of a Last Stop Record or Hard Braking event.

Airbag deployment thresholds can be a useful metric of collision severity in accident reconstruction applications. The National Automotive Sampling System (NASS) has provided a publicly-available database of real-world motor vehicle collisions, including more than 10,000 event data recorder (EDR) reports retrieved from airbag control modules. These reports typically indicate the airbag deployment status and the corresponding Delta-V of each recorded event. A prior study analyzing crash data in the NASS database demonstrated the airbag deployment threshold varies between vehicle manufacturers and over time. However, the analysis was limited to Ford and GM vehicles due to insufficient data. This paper expands on the prior study of frontal airbag deployment thresholds by analyzing newer years of NASS EDR data (4,000 additional reports). We found that the Delta-V threshold for a 50% probability of deployment event is higher for Toyota than for GM and Ford vehicles.

MOSA (Modular Open System Approach) provides a framework for efficient and sustainable design of complex integrated systems. In domain of embedded technology, the MOSA as-is does a good job in identifying modular software and hardware frameworks required to establish a common baseline for generic open architecture. On the other hand, it does not cover physical aircraft integration, integration methodology and other constituent elements essential for design of robust interfaces and integrated embedded systems, which are owned by OEMs and their suppliers. The definition of open interfaces is a key constituent in definition of MOSA-compliant architectures. An efficient system integration lifecycle requires unambiguous interfacing among hosted functions. Open interfaces and Ethernet are core system integration technologies and should be integrated and configured with other software/hardware framework elements, to enable hard RT, real-time and soft-time application hosting.

Battery electric transit buses sold in Canada generally include a fuel-fired diesel auxiliary heater for cabin heating in cold weather. This report details a test project, performed in collaboration with OC Transpo, to capture and quantify the emissions from such a fuel-fired heater (FFH) installed on a New Flyer XE40 battery electric transit bus from OC Transpo’s fleet in Ottawa, Canada. The FFH was tested while the bus was both stationary and being driven on-road in cold conditions. The results include the emissions rates of carbon dioxide, carbon monoxide, nitrogen oxides, hydrocarbons and methane, and soot. Additionally, total particulate matter results were obtained during stationary testing. The results of stationary testing were compared to the California Air Resources Board and European Union standards for FFH emissions, even though these standards do not apply directly to buses operated outside of these jurisdictions.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.