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The advent of digital computers and the availability of ever cheaper and faster micro processors have brought a tremendous amount of control system applications to the automotive industry in the last two decades. From engine and transmission systems, to virtually all chassis subsystems (brakes, suspensions, and steering), some level of computer control is present. Control systems theory is also being applied to comfort systems such as climate control and safety systems such as cruise control or collision mitigation systems.

HVAC system design plays an important role in the acoustic comfort of passenger vehicles and becomes prominent in electric vehicles. In the case of buses, cabin volume is larger than in cars, thus HVAC system required inside buses are bigger in size, and to meet comfort requirement numerous blowers are used for airflow delivery. Due to multiple blowers rotating inside the mixing unit and large delivery of air inside of the large HVAC system airborne noise is produced during its operations. One way to evaluate this airborne noise with CFD methods would be using the traditional sliding mesh approach around all the blowers to resolve the flow and turbulence, which is computationally very expensive. However, ability to predict noise inside the bus cabin with lesser turnaround time is important to accommodate quick design changes at early product development stage.

NVH (Noise, Vibration and Harshness) of the electric drive axle (EDA) is a key attribute in electric-vehicle development. The NVH level of the EDA directly determines the driving comfort and customer feeling of the vehicle. Especially in pure electric models, the EDA noise is more prominent without the engine noise masking. The paper aimed at the problem of the 380±50Hz resonance band on the commercial EDA and caused abnormal noise inside the vehicle. Adopted modal analysis, MASTA simulation, modulation noise analysis and exchange parts DOE, gradually refined the source of the problem to a single part, and finally locked to the source of gear parameters Rs and Fr. By adjusting the production process of gear and the second shaft, the assembly process error was avoided, and the gear parameter targets are formulated.

In your profession, an educated understanding of internal combustion engines is required, not optional. This two-day technology survey seminar covers the most relevant topics - ranging from the chemistry of combustion to the kinematics of internal components of the modern internal combustion engine - for maximum comprehension. Attendees will gain a practical, hands-on approach to the basics of the most common designs of internal combustion engines, as they apply to the gaseous cycles, thermodynamics and heat transfer to the major components, and the design theories that embody these concepts.

Use of Model-Based Systems Engineering (MBSE) has been growing across industry, extending beyond defense and aerospace to include various commercial enterprises such as automotive and healthcare. Tool vendors are quick to point out benefits of this model-based approach and practices but are not always clear how MBSE benefits can be realized on a project. When deployed successfully, several key considerations should be addressed that maximize the value for a use-case. This four-hour class will discuss the nature and purpose of the MBSE approach and how key information is used for successful MBSE deployment as it relates to Systems Management.

Lithium-ion batteries (LIBs) have been used as the main power source for Electric vehicles (EVs) in recent years. The mechanical behavior of LIBs subject to crush loading is crucial in assessing and improving the impact safety of battery systems and EVs. In this work, a detailed 3D finite element model for a commercial vehicle battery was built, in order to better understand battery failure behavior under various loading conditions. The model included the major components of a prismatic battery jellyroll, i.e., cathodes, anodes, and separators. The models for these components were validated against the corresponding material coupon tests (e.g., tension and compression). Then the components were integrated into the cell level model for simulation of jellyroll loading and damage behavior under three types of compressive indenter loading: (1) Flat-end punch, (2) Hemispherical punch and (3) Round-edge wedge. The comparisons showed reasonable agreement between modeling and experiments.

Practical direct injection hydrogen combustion applications typically require operating the engine in the lean regime. Lean hydrogen flames feature strong thermo-diffusive instability effects making 3D CFD simulations challenging. In particular where the calibrated model is required to operate across a range of equivalence ratios without adjustment and provide accurate results on coarse grids necessitated by the run-times of 3D CFD. In this paper we present a 3D CFD study of a Euro VI HD diesel engine converted to operate on hydrogen gas using direct injection. A scaling methodology recently proposed for conversion from constrained to freely propagating flame based on DNS data is implemented. A laminar flame speed tabulation is developed based on the conversion of 1D results obtained from direct kinetics simulations to freely propagating flame expression considering the behaviour of the thermo-diffusive instability for a wide range of pressures, temperatures and equivalence ratios.

Mixture formation in a hydrogen-fueled heavy-duty engine with direct injection and a nearly-quiescent top-hat combustion chamber was investigated using laser-induced fluorescence imaging, with 1,4-difluorobenzene serving as a fluorescent tracer seeded into hydrogen. The engine was motored at 1200 rpm, 1.0 bar intake pressure, and 335 K intake temperature. An outward opening medium-pressure hollow-cone injector was operated at two different injection pressures and five different injection timings from early injection during the intake stroke to late injection towards the end of compression stroke. Fuel fumigation upstream of the intake provided a well-mixed reference case for image calibration. This paper presents the evolution of in-cylinder equivalence ratio distribution evaluated during the injection event itself for the cylinder-axis plane and during the compression stroke at different positions of the light sheet within the swirl plane.

The paper presents an approach used to generate a customer-oriented drive cycle using the MATLAB-based drive cycle generation tool for EVs developed by Isuzu Technical Center of America. The drive cycle generation tool extracts important features from the customer vehicle data and compares it with the globally used pre-existing candidate cycle to generate a representative drive cycle. The tool can read multiple file formats of preprocessed data or raw data from the vehicle telematics systems. This data is then processed using a unique and efficient algorithm developed by the Isuzu engineers, calculating seven important vehicle dynamic parameters. These selected parameters are compared with the pre-existing candidate cycles used across the globe in multiple iterations to generate a custom representative drive cycle that best fits real-world customer driving behavior.

When commercial vehicles have less than ideal lateral dynamics traveling at high speeds, those dynamics can sometimes be a significant factor in serious and fatal accidents that occur. The primary goal of this study was to create a validated vehicle dynamics model to aid in handling evaluations and a validated model that can play an important role in accurately predicting the movement of the vehicle in limit conditions. The model is used to simulate the effect on roll gradient by altering spring stiffnesses and adding a rear stabilizer for a class 6 or class 7 commercial vehicle (CV). Outputs from 4-post Kinematics & Compliance (K&C) tests of a prototype vehicle were used to model the suspension system. The tire model was developed in collaboration with Calspan [1] using physical tire testing. The vehicle masses, inertias and stiffnesses were modelled using IPG TruckMaker for Simulink (TM4SL) [2].

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles.

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to accommodate an ever-increasing number of vehicles every day. However, some roadways, during specific hours of the day, had already been on the brink of reaching their capacity to withstand the number of vehicles travelling on them. Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the speed of a vehicle to prevent it from being affected by bottlenecks has been presented. First, co-simulations were run between MATLAB Simulink and CarSim to test different deceleration profiles.

Electrification of the transportation sector requires an energy-efficient electric powertrain supported by renewable sources of energy to limit the use of fossil fuels. However, the integration of battery electric powertrains in heavy-duty trucks seems more challenging than other types due to the high battery demand and negative impacts on the truck’s cargo capacity. In this paper, the battery sizing of a 41-tons Mercedes Actros truck is performed based on battery safety zone operating conditions. A parametric study is conducted to assess the impacts of sizing on a truck’s total cargo capacity as well as the body dynamic parameters. The numerical model of the Mercedes Actros electric powertrain is developed in AVL CRUISETM M software. The hybrid pulsed power characterization tests are performed on 3Ah lithium-ion NMC cells in the lab for fitting the second-order equivalent circuit model’s parameters used in the analysis.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located.

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving.

Regenerative braking is an effective way to increase the cruising range of vehicles. In commercial vehicles with large vehicle mass, regenerative braking can be maintained in a high-power working state for a long time theoretically because of the large braking torque and long braking time. But in fact, it is often impossible to run at full power because of battery safety problems. In this paper, a control strategy is designed to maintain the maximum power operation of regenerative braking as much as possible. The maximum charging power of the battery is obtained through the battery model, and it is set as the battery limiting parameter. The regenerative braking torque and power are obtained by using the motor model. The eddy current retarder is used to absorb the excess power that the battery can't bear, and the braking torque of the eddy current retarder is calculated. Finally, mechanical braking is used to make up the insufficient braking torque.

There have been many studies regarding the stability of vehicles following a sudden air loss event in a tire. Previous works have included literature reviews, full-scale vehicle testing, and computer modeling analyses. Some works have validated physics-based computer vehicle simulation models for passenger vehicles and other works have validated models for heavy commercial vehicles. This work describes a study wherein a validated vehicle dynamics computer model has been applied to extrapolate results to higher event speeds that are consistent with travel speeds on contemporary North American highways. This work applies previously validated vehicle dynamics models to study the stability of a five-axle commercial tractor-semitrailer vehicle following a sudden air loss event for a steer axle tire. Further, the work endeavors to understand the analytical tire model for tires that experience a sudden air loss.

This article presents an autonomous steering control scheme for articulated heavy vehicles (AHVs). Despite economic and environmental benefits in freight transportation, lateral stability is always a concern for AHVs in high-speed highway operations due to their multi-unit vehicle structures, and high centers of gravity (CGs). In addition, North American harsh winter weather makes the lateral stability even more challenging. AHVs often experience amplified lateral motions of trailing vehicle units in high-speed evasive maneuvers. AHVs represent a 7.5 times higher risk than passenger cars in highway operation. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous steering control of AHVs.

For car-following models, the car-following characteristics differ depending on the vehicle type, such as passenger cars, motorcycles, and trucks. Therefore, constructing a model for each category is essential. To that end, various modeling methods have been proposed; however, herein, we particularly focused on the long short-term memory (LSTM), which is the best method for forecasting long-term time-series data.[1, 2] The objective of this study was to construct a car-following model for each vehicle category using the LSTM and to evaluate the model accuracy for each vehicle category. In this study, US-101 and I-80 data provided by the next-generation simulation (NGSIM), which is based on natural traffic flow data, were used. In the NGSIM, only car-following situations were selected as car-following data, and these were classified into the vehicle type: motorcycles, passenger cars, and trucks.

Image segmentation has historically been a technique for analyzing terrain for military autonomous vehicles. One of the weaknesses of image segmentation from camera data is that it lacks depth information, and it can be affected by environment lighting. Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. The volume of points generated by LiDAR provides the vehicle with distance and spatial data surrounding the vehicle.