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Recent advancements towards autonomous heavy-duty vehicles are directly associated with increased interconnectivity and software driven features. Consequently, rise of this technological trend is bringing forth safety and cybersecurity challenges in form of new threats, hazards and vulnerabilities. As per the recent UN vehicle regulation 155, several risk-based security models and assessment frameworks have been proposed to counter the growing cybersecurity issues, however, the high budgetary cost to develop the tool and train personnel along with high risk of leakage of trade secrets, hinders the automotive manufacturers from adapting these third party solutions. This paper proposes an automated Threat Assessment & Risk Analysis (TARA) framework aligned with the standard requirements, offering an easy to use and fully customizable framework. The proposed framework is tailored specifically for heavy-duty vehicular networks and it demonstrates its effectiveness on a case study.

Common aerodynamic research models have been used in aerodynamic research throughout the years to assist with the development and correlation of new testing and numerical techniques, in addition to being excellent tools for gathering fundamental knowledge about the physics around the vehicle. The generic truck utility (GTU) was introduced by Woodiga et al. [1] in 2020 following successful adoption of the DrivAer (Heft et al. [2]) by the automotive aerodynamics community with the goal to capture the unique flow fields created by pickups and large SUVs. To date, several studies have been presented on the GTU (Howard et. al 2021 [3], Gleason, Eugen 2022 [4]), however, with the increasing prevalence of electric vehicles (EVs), the authors have created additional GTU configurations to emulate an EV-style underbody for the GTU.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

This is an extension of simple fuel consumption modeling toward HEV. Previous work showed that in urban driving the overhead of running an ICEV engine can use as much fuel as the traction work. The bidirectional character and high efficiency of electric motors enables HEVs to run as a BEV at negative and low traction powers, with no net input from the small battery. The ICE provides the net work at higher traction powers where it is most efficient. Whereas the network reduction is the total negative work times the system round-trip efficiency, the reduction in engine running time requires knowledge of the distribution of traction power levels. The traction power histogram, and the work histogram derived from it, provide the required drive cycle description. The traction power is normalized by vehicle mass, so that the drive trace component becomes invariant, and the road load component nearly invariant to vehicle mass.

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.

This study focused on occupant responses in very large pickup trucks in rollovers and was conducted in three phases. Phase 1 - Field data analysis: In a prior study [9], 1998 to 2020 FARS data were analyzed; Pickup truck drivers with fatality were 7.4 kg heavier and 4.6 cm taller than passenger car drivers. Most pickup truck drivers were males. Phase 1 extended the study by focusing on the drivers of very large pickup trucks. The size of 1999-2016 Ford F-250 and F-350 drivers involved in fatal crashes was analyzed by age and sex. More than 90% of drivers were males. The average male driver was 179.5 ± 7.5 cm tall and weighed 89.6 ± 18.4 kg. Phase 2 – Surrogate study: Twenty-nine male surrogates were selected to represent the average size of male drivers of F-250 and F-350s involved in fatal crashes. On average, the volunteers weighed 88.6 ± 5.2 kg and were 180.0 ± 3.2 cm tall with a 95.2 ± 2.2 cm seated height.

Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle.

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output.

Fuel power consumption for light duty vehicles has previously been shown to be proportional to vehicle traction power, with an offset for overhead and accessory losses. This allows the fuel consumption for an individual powertrain to be projected across different vehicles, missions, and drive cycles. This work applies the power-based model to commercial vehicles and demonstrates its usefulness for projecting fuel consumption on both regulatory and customer use cycles. The ability to project fuel consumption to different missions is particularly useful for commercial vehicles, as they are used in a wide range of applications and with customized designs. Specific cases are investigated for Light and Medium Heavy- Duty work trucks. The average power required by a vehicle to drive the regulatory cycles varies by nearly a factor 10 between the Class 4 vehicle on the ARB Transient cycle and the loaded Class 7 vehicle at 65 mph on grade.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain.

Traditionally, ground vehicle aerodynamics has been researched with highly simplified models such as the Ahmed body and the SAE model. These models established and advanced the fundamental understanding of bluff body aerodynamics and have generated a large body of published data, however, their application to the development of passenger vehicles is limited by the highly idealized nature of their geometries. To date, limited data has been openly published on aerodynamic investigations of production vehicles, most likely due to the proprietary nature of production vehicle geometry. In 2012, Heft et al. introduced the realistic generic car model ‘DrivAer’ that better represents the flow physics associated with a typical production vehicle.

Optimization of heavy materials like steel, in order to create a lighter vehicle, it is a major goal among most automakers, since heavy vehicles simply cannot compete with a lightweight model's fuel economy. Thinking this way, this paper shows a case study where the Size Optimization technique is applied to a front floor reinforcement. The reinforcement is used by two different vehicles, a subcompact and a crossover Sport Utility Vehicle (SUV), increasing the problem complexity. The Size Optimization technique is supported by Finite Element Method (FEM) tools. FEM in Computer Aided Engineering (CAE) is a numerical method for solving engineering problems, and its use can help to optimize prototype utilization and physical testing.

Analysis of road accidents has shown that an important portion of fatal crashes involving Commercial Vehicles are caused by rollovers. ESC systems in Commercial Vehicles can reduce rollovers, severe understeer or oversteer conditions and minimize occurrences of jackknifing events. Several studies have estimated that this positive effect of ESC on road safety is substantial. In Europe, Electronic Stability Control (ESC) is expected to prevent by far the most fatalities and injuries: about 3,000 fatalities (-14%), and about 50,000 injuries (-6%) per year. In Europe, Electronic Stability Control Systems is mandatory for all vehicles (since Nov. 1st, 2011 for new types of vehicle and Nov. 1st, 2014 for all new vehicles), including Commercial Vehicles, Buses, Trucks and Trailers.

Flexibility and backlash of vehicle drivelines typically cause unwanted oscillations and noise, known as shuffle and clunk, during tip-in and tip-out events. Computationally efficient and accurate driveline models are necessary for the design and evaluation of torque shaping strategies to mitigate this shuffle and clunk. To accomplish these goals, this paper develops a full-order physics-based model and uses this model to develop a reduced-order model (ROM), which captures the main dynamics that influence the shuffle and clunk phenomena. The full-order model (FOM) comprises several components, including the engine as a torque generator, backlash elements as discontinuities, and propeller and axle shafts as compliant elements. This model is experimentally validated using the data collected from a Ford vehicle. The validation results indicate less than 1% error between the model and measured shuffle oscillation frequencies.

The development costs that new design requires are subject to everyday discussions and saving opportunities are mandatory. Using CAE to predict design changes can avoid excessive costs with prototypes parts, considering the high reliability those current mathematical models can provide. This paper presents the methodology used during the development of a parabolic leaf spring for the rear suspension of a commercial truck, considering mainly the parabolic profiles and stress distribution on the leaves, calculated using CAE software (ANSYS) and experimental tests to measure the actual stress on each leaf, certifying the correlation between computational calculations and real stress on the parts during bench and vehicle evaluations.

Characterizing or reconstructing incidents ranging from light to heavy crashes is one of the enablers for mobility solutions for fleet management, car-sharing, ride-hailing, insurance etc. While crashes involving airbag deployment are noticeable, light crashes without airbag deployment can be hidden and most drivers do not report these incidents. In this paper, we are using vehicle responses together with a dynamics model to trace back if abnormal forces have been applied to a vehicle so as to detect light crashes. The crash location around the perimeter of the vehicle, the direction of the crash force, and the severity of the crashes are all determined in real-time based on on-board sensor measurements which has further application in accident reconstruction. All of this information will be integrated to a feature called “Incident Report”, which enable reporting of minor accidents to the relevant entities such as insurance agencies, fleet managements, etc.

The air suspension development and its applications have becoming increasingly relevant for commercial vehicles to provide dynamic ride comfort to driver and reduce the load impact onto driver and or cargo. This paper shows the analysis and application of an air suspension system for commercial tractor vehicles and its dynamic influence. A special focus was given to pneumatic actuation system, responsible for leveling and control of suspension´s stiffness under different conditions of usage, laden and unladed. The project was conducted starting with the vehicle dynamic performance analysis, evaluating the pneumatic suspension circuit modifications in order to obtain vehicle dynamic behavior improvement, ensuring directional stability under different maneuvering conditions.

The increased availability of natural gas (NG) in the United States (US) and its relatively low cost versus diesel fuel has increased interest in the conversion of medium duty (MD) and heavy duty (HD) engines to NG fueled combustion systems. The aim for development for these NG engines is to realize fuel cost savings and increase operating range while reduce harmful emissions and maintaining durability. Traditionally, port-fuel injection (PFI) or premixed NG spark-ignited (SI) combustion systems have been used for light duty LD, and MD engines with widespread use in the US and Europe [1]. However, this technology exhibits poor thermal efficiency and is load limited due to knock phenomenon that has prohibited its use for HD engines. Spark Ignited Direct Injection (SIDI) can be used to create a partially stratified combustion (PSC) mixture of NG and air during the compression stroke.

Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved.