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As an annual subscription, the Wiley Cyber Security Collection Add-On is available for purchase along with one or both of the following: Wiley Aerospace Collection Wiley Automotive Collection The titles from the Wiley Cyber Security Collection are included in the SAE MOBILUS® eBook Package. Titles: Network Forensics Penetration Testing Essentials Security in Fixed and Wireless Networks, 2nd Edition The Network Security Test Lab: A Step-by-Step Guide Risk Centric Threat Modeling: Process for Attack Simulation and Threat Analysis Applied Cryptography: Protocols, Algorithms and Source Code in C, 20th Anniversary Edition Computer Security Handbook, Set, 6th Edition Threat Modeling: Designing for Security Other available Wiley collections: Wiley SAE MOBILUS eBook Package Wiley Aerospace Collection Wiley Automotive Collection Wiley Computer Systems Collection Add-On (purchasable with the Wiley Aerospace Collection and/or the Wiley Automotive Collection)

Abstract A kinematic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towing vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point.

Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.

Abstract In this article, a unique design for Hydro-Pneumatic Energy Harvesting Suspension HPEHS system is introduced. The design includes a hydraulic rectifier to maintain one-way flow direction in order to obtain maximum power generation from the vertical oscillation of the suspension system and achieve handling and comfort car drive. A mathematical model is presented to study the system dynamics and non-linear effects for HPEHS system. A simulation model is created by using Advanced Modeling Environment Simulations software (AMEsim) to analyze system performance. Furthermore, a co-simulation platform model is developed using Matlab-Simulink and AMEsim to optimize the PID controller parameters of the external variable load resistor applied on the generator by using Particle Swarm Optimization (PSO).

Abstract This research developed methods for a virtual operator model (VOM) to learn the optimal control inputs for operation of a virtual excavator. Virtual design, used to model, simulate, and test new features, has often been limited by the fidelity of the virtual model of human operators. Human operator learns, over time, the capability, limits, and control characteristics of new vehicles to develop the best strategy to maximize the efficiency of operation. However, VOMs are developed with fixed strategies and for specific vehicle models (VMs) and require time-consuming re-tuning of the VOM for each new vehicle design. Thus, there typically is no capability to optimize strategies, taking account of variation in vehicle capabilities and limitations. A VOM learning capability was developed to optimize control inputs for the swing-to-pile task of a trenching operation. Different control strategies consisted of varied combinations of speed control, position control, and coast.

This document will provide recommendations to vehicle manufacturers and component suppliers in securing the SAE J1939-13 connector interface from the cybersecurity risks posed by the existence of this connector.

This paper presents a novel methodology to develop and validate fuel consumption models of Refuse Collecting Vehicles (RCVs). The model development is based on the improvement of the classic approach. The validation methodology is based on recording vehicle drive cycles by the use of a low cost data acquisition system and post processing them by the use of GPS and map data. The corrected data are used to feed the mathematical energy models and the fuel consumption is estimated. In order to validate the proposed system, the fuel consumption estimated from these models is compared with real filling station refueling records. This comparison shows that these models are accurate to within 5%.

Diesel Particulate Filters (DPF) are a key component in many on- and off-road aftertreatment systems to meet increasingly stringent particle emissions limits. Efficient thermal management and regeneration control is critical for reliable and cost-effective operation of the combined engine and aftertreatment system. Conventional DPF control systems predominantly rely on a combination of filter pressure drop measurements and predictive models to indirectly estimate the soot loading state of the filter. Over time, the build-up of incombustible ash, primarily derived from metal-containing lubricant additives, accumulates in the filter to levels far exceeding the DPF's soot storage limit. The combined effects of soot and ash build-up dynamically impact the filter's pressure drop response, service life, and fuel consumption, and must be accurately accounted for in order to optimize engine and aftertreatment system performance.

Cost reduction in the automotive industry becomes a widely-adopted operational strategy not only for Original Equipment Manufacturers (OEMs) that take cost leader generic corporation strategy, but also for many OEMs that take differentiation generic corporation strategy. Since differentiation generic strategy requires an organization to provide a product or service above the industry average level, a premium is typically included in the tag price for those products or services. Cost reduction measures could increase risks for the organizations that pursue differentiation strategy. Although manufacturers in the automotive industry dramatically improved production efficiency in past ten years, they are still facing the pressure of cost control. The big challenge in cost control for automakers and suppliers is increasing prices of raw materials, energy and labor costs. These costs create constraints for the traditional economic expansion model.

Implementation of EPA's heavy-duty engine NOx standard of 0.20 g/bhp-hr has resulted in the introduction of a new generation of emission control systems for on-highway heavy-duty diesel engines. These new control systems are predominantly based around aftertreatment systems utilizing urea-based selective catalytic reduction (SCR) techniques, with only one manufacturer relying solely on in-cylinder NOx emission reduction techniques. As with any new technology, EPA is interested in evaluating whether these systems are delivering the expected emissions reductions under real-world conditions and where areas for improvement may lie. To accomplish these goals, an in-situ gaseous emissions measurement study was conducted using portable emissions measurement devices. The first stage of this study, and subject of this paper, focused on engines typically used in line-haul trucking applications (12-15L displacement).

In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory (NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado were collected. Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle dynamics trends within the data and to explore vehicle operation variations between fleet locations.

Researches for automated construction machinery have been conducted for labor-saving, improved work efficiency and worker's safety, where a tracking control function was proposed as one of the key control system strategies for highly automated productive hydraulic excavators. An optimized digging trajectory that assures as much soils scooped as possible and less energy consumption is critical for an automated hydraulic excavator to improve work efficiency. Simulation models that we used to seek an optimized digging trajectory in this study consist of soil models and front linkage models of a hydraulic excavator. We developed two types of soil models. One is called wedge models used to calculate reaction forces from soils acting on a bucket during digging operation, based on the earth pressure theory. The other is called Distinct Element Method (DEM) model used to analyze soil behaviors and estimate amounts of soils scooped and reaction forces quantitatively.

The incorporation of hydrogen fuel cells into heavy duty tactical mobility vehicles can bring about great opportunities in reducing the pollutant emissions of this kind of platforms (GVW > 30,000 kg). Furthermore the transportation of fuel to operational areas has become a key aspect for any deployment therefore optimal use of this resource is of paramount importance. Finally, it is also quite common for such platforms to serve additional purposes, besides freight delivery, such as powering external equipment (i.e. field hospitals or mobile artillery pieces). This work will describe the intelligent energy management system for a PEM Fuel Cell-Battery-Ultracapacitor Hybrid 8×8 heavy truck of the aforementioned weight class which also contemplates an internal electric/traction power generation unit. It will describe how the system optimizes the use of battery and hydrogen fuel energy while keeping system efficiency and performance at a maximum.

The lightweight design of a vehicle can save manufacturing costs and reduce greenhouse gas emissions. For the off-road vehicle and truck, the chassis frame is the most important load-bearing assembly of the separate frame construction vehicle. The frame is one of the most assemblies with great potential to be lightweight optimized. However, most of the vehicle components are mounted on the frame, such as the engine, transmission, suspension, steering system, radiator, and vehicle body. Therefore, boundaries and constraints should be taken into consideration during the optimal process. The finite element (FE) model is widely used to simulate and assess the frame performance. The performance of the frame is determined by the design parameters. As one of the largest components of the vehicle, it has a lot of parameters. To improve the optimum efficiency, sensitivity analysis is used to narrow the range of the variables.

We investigate tradeoffs between the airflow strategies related to engine cooling and the aerodynamic-enabled fuel savings created by platooning. By analyzing air temperatures, engine temperatures and cooling air flow at different platoon distances, we show the thermal impact to the engine from truck platooning. Previously, we collected wind and thermal data for numerous heavy-duty truck platoon configurations (gaps ranging from 4 to 87 meters) and reported the significant fuel savings enabled by these configurations. The fuel consumption for all trucks in the platoon were measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate while travelling at 65 mph and loaded to a gross weight of 65,000 lb.

Lift axle is essentially provided in commercial vehicles to increase the vehicle’s load-carrying capacity. The axle is lowered in the case of a high payload and the load is evenly distributed among the wheels both on fixed axles and the lift axle. This ability to lift the axle implies better maneuverability in turns, better fuel consumption, and less wear and tear on the tires and brake shoes. Also, it will reduce the damage to the road surfaces. This lowering and lifting of the lift axle are controlled by a series of valves together called the Lift Axle Control System (LACS). This LACS must consider the vehicle load condition, the ignition state, and gear state to decide if the axle must be lifted or lowered. This paper deals with the modeling and simulation of the LACS system at the vehicle level and optimize the design for the respective desired design solution.

CONVENIENT is a project where prediction and integrated control are applied on several subsystems with electrified actuators. The technologies developed in this project are applied to a long-haul tractor and semi-trailer combination. A Volvo truck meeting the Eu6 emission standard is rebuilt with a number of controllable electrified actuators. An e-Horizon system collects information about future road topography and speed limits. Controllable aerodynamic wind deflectors reduce the wind drag. The tractor is also equipped with a full digital cluster for human machine interface development. A primary project goal is to develop a model-based optimal controller that uses predictive information from the e-Horizon system in order to minimize fuel consumption. Several energy buffers are controlled in an integrated and optimal way using model predictive control. Several buffers are considered, such as the cooling system, the battery, and the vehicle kinetic energy.

Displacement joystick controls are considered as most suitable for manual controls wherever proportional outputs are required for dynamic applications such as when variable speed sensitivity or position are required. These joysticks are being used widely in both open loop as well as in close loop controls. The operator applies force to either the joystick itself or to its proportional linear displacement thumb wheel switches. This movement is then detected by either resistive or Hall Effect sensors, placed right inside joystick, and converted into an electrical signal. These joysticks, along with proportional linear displacement thumb wheel switches, find a wide range of applications in off-road vehicles such construction and forestry vehicles, harvester machines, and etc. for applications like attachment speed controls, boom position control, rotation speed control, and etc.

Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

The electrification of off-highway machines are increasing significantly. Advanced functionalities, beneficial energy efficiency and effectiveness are only a few advantages of electric propulsion systems. To control these complex systems in varying environments intelligent algorithms at system level are needed. This paper addresses the topic of machine learning algorithms applied to off-highway machines and presents a methodology based on artificial neural networks to identify and recognize recurrent load cycles and work tasks. To gain efficiency and effectiveness benefits the recognized pattern settings are applied to the electric propulsion system to adjust relevant parameters online. A dynamic adaption of the DC-link voltage based on the operating points of the machine processes is identified as such a parameter.