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Traditional User/Maintenance Manuals provide useful information when dealing with simple machines. However, when dealing with complex systems of systems and highly miniaturized technologies, like UAVs, or with machines with millions of parts, a commercial aircraft is a case in point, new technologies taking advantage of Augmented Reality can rapidly and effectively support the maintenance operations. This paper presents a User/Maintenance Manual based on Augmented Reality to help the operator in the detection of parts and in the sequence to be followed to assemble/disassemble systems and subsystems. The proposed system includes a handheld device and/or an head mounted display or special goggles, to be used by on-site operators, with software management providing data fusion and overlaying traditional 2D user/maintenance manual information with an augmented reality software and appropriate interface.

Added masses computation is a crucial aspect to be considered when the density of a body moving in a fluid is comparable to the density of the fluid displaced: added mass can be defined as the inertia added to a system because an accelerating or decelerating body displaces some volume of neighboring fluid as it moves through it. The motion of vehicles like airships and ships can be addressed only by keeping into account the effect of added masses, while in case of aircrafts and helicopters this contribution is usually neglected. Lighter Than Air flight simulation, unmanned airships flight control system, airships flight dynamics are typical applications in which added masses are fundamental to achieve an effective and realistic modeling. A panel based method using the mesh of an airship external shape is developed to account for the added massed.

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.

The aim of this work is to propose modifications to the managing of the 1st generation Common Rail injectors in order to reduce actuation time towards multiple injection strategies. The current Common Rail injector driven by 1st ECU generation is capable of operating under stable conditions with a minimum dwell between two consecutive injections of 1.8 ms. This limits the possibility in using proper and efficient injection strategies for emission control purposes. A previous numerical study, performed by the electro-fluid-mechanical model built up by Matlab-Simulink environment, highlighted different area where injector may be improved with particular emphasis on electronic driving circuit and components design. Experiments carried out at injector Bosch test-bench showed that a proper control of the solenoid valve allowed reducing drastically the standard deviation during the pilot pulses.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

The widely used Extended Kalman Filter (EKF) is applied to a planar model of an articulated vehicle to predict jackknifing events. The states of hitch angle and hitch angle rate are estimated using a vehicle model and the available or “measured” states of lateral acceleration and yaw rate from the prime mover. Tuning, performance, and compromises for the EKF in this application are discussed. This application of the EKF is effective in predicting the onset of instability for an articulated vehicle under low-μ and low-load conditions. These conditions have been shown to be most likely to render heavy articulated vehicles vulnerable to jackknife instability. Options for model refinements are also presented.

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

This paper presents the details of the model for the physical steering system used on the National Advanced Driving Simulator. The system is basically a hardware-in-the-loop (steering feedback motor and controls) steering system coupled with the core vehicle dynamics of the simulator. The system's torque control uses cascaded position and velocity feedback and is controlled to provide steering feedback with variable stiffness and dynamic properties. The reference model, which calculates the desired value of the torque, is made of power steering torque, damping function torque, torque from tires, locking limit torque, and driver input torque. The model also provides a unique steering dead-band function that is important for on-center feel. A Simulink model of the hardware/software is presented and analysis of the simulator steering system is provided.

This paper presents an optimization algorithm, based on discrete dynamic programming, that aims to find the optimal control inputs both for energy and thermal management control strategies of a Plug-in Hybrid Electric Vehicle, in order to minimize the energy consumption over a given driving mission. The chosen vehicle has a complex P1-P4 architecture, with two electrical machines on the front axle and an additional one directly coupled with the engine, on the rear axle. In the first section, the algorithm structure is presented, including the cost-function definition, the disturbances, the state variables and the control variables chosen for the optimal control problem formulation. The second section reports the simplified quasi-static analytical model of the powertrain, which has been used for backward optimization. For this purpose, only the vehicle longitudinal dynamics have been considered.

Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function.

As we continue to create ever-lighter road vehicles, the challenge of balancing weight reduction and structural performance also continues. One of the key parts this occurs on is the hood, where lighter materials (e.g. aluminum) have been used. However, the aerodynamic loads, such as hood lift, are essentially unchanged and are driven by the front fascia and front grille size and styling shape. This paper outlines a combination CFD/FEA prediction method for hood deflection performance at high speeds, by using the surface pressures as boundary conditions for a FEA linear static deflection analysis. Additionally, custom post-processing methods were developed to enhance flow analysis and understanding. This enabled the modification of existing test methods to further improve accuracy to real world conditions. The application of these analytical methods and their correlation with experimental results are discussed in this paper.

Deployment of autonomous vehicles requires an extensive evaluation of developed control, perception, and localization algorithms. Therefore, increasing the implemented SAE level of autonomy in road vehicles requires extensive simulations and verifications in a realistic simulation environment before proving ground and public road testing. The level of detail in the simulation environment helps ensure the safety of a real-world implementation and reduces algorithm development cost by allowing developers to complete most of the validation in the simulation environment. Considering sensors like camera, LiDAR, radar, and V2X used in autonomous vehicles, it is essential to create a simulation environment that can provide these sensor simulations as realistically as possible.

This paper discusses the derivation and validation of planar models of articulated vehicles that were developed to analyze jackknife stability on low-μ surfaces. The equations of motion are rigorously derived using Lagrange's method, then linearized for use in state-space models. The models are verified using TruckSim™, a popular nonlinear solid body vehicle dynamics modeling package. The TruckSim™ models were previously verified using extensive on-vehicle experimental data [1, 2]. A three-axle articulated model is expanded to contain five axles to avoid lumping the parameters for the drive and semitrailer tandems. Compromises inherent in using the linearized models are discussed and evaluated. Finally, a nonlinear tire cornering force model is coupled with the 5-axle model, and its ability to simulate a jackknife event is demonstrated. The model is shown to be valid over a wide range of inputs, up to and including loss of control, on low-and-medium-μ surfaces.

The (Vehicle Inertia Parameter Evaluation Rig) VIPER II is a full vehicle mass and inertia parameter measurement machine. The VIPER II expands upon the capabilities of its predecessor and is capable of measuring vehicles with a mass of up to 45,360 kg (100,000 lb), an increase in capacity of 18,100 kg (40,000 lb). The VIPER II also exceeds its predecessor in both the length and width of vehicles it can measure. The VIPER II's maximum vehicle width is 381 cm (150 in) an increase of 76 cm (30 in) and maximum distance from the vehicle CG to the outer most axle is 648 cm (255 in) an additional 152 cm (60 in) The VIPER II is capable of performing measurements including vehicle CG height, pitch, roll, and yaw moments of inertia and the roll/yaw cross product of inertia. While being able to measure both heavier and larger vehicles, the VIPER II is designed to maintain a maximum error of 3% for all inertia measurements and 1% for CG height.

Heavy trucks are involved in many accidents every year and Electronic Stability Control (ESC) is viewed as a means to help mitigate this problem. ESC systems are designed to reduce the incidence of single vehicle loss of control, which might lead to rollover or jackknife. As the working details and control strategies of commercially available ESC systems are proprietary, a generic model of an ESC system that mimics the basic logical functionality of commercial systems was developed. This paper deals with the study of the working of a commercial ESC system equipped on an actual tractor trailer vehicle. The particular ESC system found on the test vehicle contained both roll stability control (RSC) and yaw stability control (YSC) features. This work focused on the development of a reliable RSC software model, and the integration of it into a full vehicle simulation (TruckSim) of a heavy truck.

In all Ducati L-twins the crankshaft supports are rolling bearings. Due to the higher performance, compactness and lightness, the service life of the crankcase is becoming shorter and in future can become critical in the Ducati 999, the most powerful engine of the Borgo Panigale company. The engine block sidewalls must be strengthened in order to improve the reliability of the component. This can be done by reducing the size of the main bearing housings by the adoption of the plain bearings which have smaller radial dimensions. The stress field of the Ducati 999 crankcase in the two different configurations is calculated by means of the finite element method, applying the engine load in the critical conditions.

This paper covers research conducted at the National Highway Traffic Safety Administration's Vehicle Research and Test Center (VRTC) examining the performance of semitrailer anti-lock braking systems (ABS). For this study, a vehicle dynamics model was constructed for the combination of a 4×2 tractor and a 48-foot trailer, using TruckSim. ABS models for the tractor and trailer, as well as brake dynamics and surface friction models, were created in Simulink so that the effect of varying ABS controller parameters and configurations on semitrailer braking performance could be studied under extreme braking maneuvers. The longitudinal and lateral performances of this tractor-trailer model were examined for a variety of different trailer ABS controller models, including the 2s1m, 4s2m, and 4s4m configurations. Also, alternative controllers of the same configuration were studied by varying the parameters of the 2s1m controller.

This paper explores the effects of changes in vehicle loading on vehicle inertial properties (center-of-gravity location and moments of inertia values) and handling responses. The motivation for the work is to gain better understanding of the importance vehicle loading has in regard to vehicle safety. A computer simulation is used to predict the understeer changes for three different vehicles under three loading conditions. An extension of this loading study includes the effects of moving occupants, which are modeled for inclusion in the simulation. A two-mass model for occupants/cargo, with lateral translational and rotational degrees of freedom, has been developed and is included in the full vehicle model. Using the simulation, the effects that moving occupants have on vehicle dynamics are studied.

The study emphasizes transitioning school buses from diesel to electric to mitigate their environmental impact, addressing challenges like limited driving range through predictive models. This research introduces a comprehensive control-oriented model for estimating auxiliary loads in electric school buses. It begins by developing a transient thermal model capturing cabin behavior, divided into passenger and driver zones. Integrated with a control-oriented HVAC model, it estimates heating and cooling loads for desired cabin temperatures under various conditions. Real-world operational data from school bus specifications enhance the model’s practicality. The models are calibrated using experimental cabin-HVAC data, resulting in a remarkable overall Root Mean Square Error (RMSE) of 2.35°C and 1.88°C between experimental and simulated cabin temperatures.

A determination of the vehicle states and tire forces is critical to the stability of vehicle dynamic behavior and to designing automotive control systems. Researchers have studied estimation methods for the vehicle state vectors and tire forces. However, the accuracy of the estimation methods is closely related to the employed model. In this paper, tire lag dynamics is introduced in the model. Also application of estimation methods in order to improve the model accuracy is presented. The model is developed by using the global searching algorithm, a Genetic Algorithm, so that the model can be used in the nonlinear range. The extended Kalman filter and sliding mode observer theory are applied to estimate the vehicle state vectors and tire forces. The obtained results are compared with measurements and the outputs from the ADAMS full vehicle model. [15]