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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.

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 assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

This paper and the associated lecture present an overview of technology trends and of market and business opportunities created by technology, as well as of the challenges posed by environmental and economic considerations. Commercial vehicles are one of the engines of our economy. Moving goods and people efficiently and economically is a key to continued industrial development and to strong employment. Trucks are responsible for nearly 70% of the movement of goods in the USA (by value) and represent approximately 300 billion of the 3.21 trillion annual vehicle miles travelled by all vehicles in the USA while public transit enables mobility and access to jobs for millions of people, with over 10 billion trips annually in the USA creating and sustaining employment opportunities.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

This paper describes the development and experimental validation of a Plug-in Hybrid Electric Vehicle (PHEV) dynamic simulator that enables development, testing, and calibration of a traction control strategy. EcoCAR 2 is a three-year competition between fifteen North American universities, sponsored by the Department of Energy and General Motors that challenges students to redesign a Chevrolet Malibu to have increased fuel economy and decreased emissions while maintaining safety, performance, and consumer acceptability. The dynamic model is developed specifically for the Ohio State University EcoCAR 2 Team vehicle with a series-parallel PHEV architecture. This architecture features, in the front of the vehicle, an ICE separated from an automated manual transmission with a clutch as well as an electric machine coupled via a belt directly to the input of the transmission. The rear powertrain features another electric machine coupled to a fixed ratio gearbox connected to the wheels.

For simulation and analysis of vehicles there is a need to have a means of generating drive cycles which have properties similar to real world driving. A method is presented which uses measured vehicle speed from a number of vehicles to generate a Markov chain model. This Markov chain model is capable of generating drive cycles which match the statistics of the original data set. This Markov model is then used in an iterative fashion to generate drive cycles which match constraints imposed by the user. These constraints could include factors such number of stops, total distance, average speed, or maximum speed. In this paper, systematic analysis was done for a PHEV fleet which consists of 9 PHEVs that were instrumented using data loggers for a period of approximately two years. Statistical analysis using principal component analysis and a clustering approach was carried out for the real world velocity profiles.

This paper discusses an improved design of a vehicle-based mobile off-road terrain profile measurement system. The proposed system includes an apparatus of sensors and on-board data acquisition hardware, equipped on a platform vehicle used to measure and record the relevant data while the vehicle travels through the off-road or terrain surface to be surveyed. A unique post-processing algorithm is then used to derive the elevation profile based on the collected data. The derived elevation profile data could be used to characterize the roughness of an off-road testing course or perform a general geographical survey or mapping. The major technical issue addressed in this system is to eliminate the effect of platform vehicle vibration on sensor measurement which if left unaddressed will result in large measurement error due to high amplitude pitch and roll movements of the platform vehicle.

Homogeneous Charge Compression Ignition (HCCI) is considered a very promising concept to achieve low NOx and Particulate Matter emissions in traditional spark ignition and Diesel engines. However, controlling the complex mechanisms which govern the combustion process and finding a proper method for the fuel introduction for Diesel HCCI engines have proven to still be a challenge. In addition, the well known IMEP limitations of HCCI combustion restrict the benefits on emissions to low engine load conditions. The current work attempts to extend the benefits of HCCI combustion to a broader range of engine operating conditions by blending the conventional Direct Injection (DI) with the external fuel atomization. A dual combustion system could potentially overcome the limits of low-load operations and allow for a gradual transition between the conventional DI mode at high load and the HCCI external mixture formation at idle and low load.

An appropriate fault diagnosis and Isolation (FDI) strategy is very useful to prevent system failure. In this paper, a model-based fault diagnosis strategy is developed for an internal combustion engine (ICE) under speed control. Engine throttle fault and the manifold pressure sensor fault are detected and isolated. A nonlinear observer based residual generation approach is proposed. Manifold pressure and throttle are observed. Fault codes are designed with redundancy to prevent bit error. Performance of fault diagnosis strategy has been evaluated with simulations.

Highway and roadway surface measurement is a practice that has been ongoing for decades now. This sort of measurement is intended to ensure a safe level of road perturbances. The measurement may be conducted by a slow moving apparatus directly measuring the elevation of the road, at varying distance intervals, to obtain a road profile, with varying degrees of resolution. An alternate means is to measure the surface roughness at highway speeds using accelerometers coupled with high speed distance measurements, such as laser sensors. Vehicles out rigged with such a system are termed inertial profilers. This type of inertial measurement provides a sort of filtered roadway profile. Much research has been conducted on the analysis of highway roughness, and the associated metrics involved. In many instances, it is desirable to maintain an off-road course such that the course will provide sufficient challenges to a vehicle during durability testing.

The intent of this research is to understand the effects worn dampers have on vehicle stability and safety through dynamic model simulation. Dampers, an integral component of a vehicle's suspension system, play an important role in isolating road disturbances from the driver by controlling the motions of the sprung and unsprung masses. This paper will show that a decrease in damping leads to excessive body motions and a potentially unstable vehicle. The concept of poor damping affecting vehicle stability is well established through linear models. The next step is to extend this concept for non-linear models. This is accomplished through creating a vehicle simulation model and executing several driving maneuvers with various damper characteristics. The damper models used in this study are based on splines representing peak force versus velocity relationships.

A methodology has been developed to generate military vehicle driving cycles for use in vehicle simulation models. This methodology is based upon the mission profile for a vehicle, which is typically given within a vehicle's specifications and lists the types of terrains that the vehicle is likely to encounter. A simplistic vehicle powertrain and road load model and the Bekker vehicle-soil interaction model are used to estimate the vehicle performance over each type of terrain. Two types of driving cycles are generated within a Graphical User Interface developed within MATLAB using the results of the vehicle models: Linear modes driving cycles, and Real-world driving cycles.

This paper presents the results of a design space exploration based on the simulations of the MTV (Medium Tactical Vehicle) 5.0 Ton Cargo Truck using MSC-ADAMS (Automatic Dynamic Analysis of Mechanical System). Design space study is conducted using ADAMS/Car and ADAMS/Insight to consider parametric design changes in suspension and the tires of the cargo truck. The methodology uses an industry acknowledged multibody dynamics simulation software (ADAMS) for the modeling of the cargo truck and a flexible optimization architecture to explore the design space. This research is a part of the work done for the U.S. Army TACOM (Tank Automotive and Armaments Command) at the Center for Automotive Research, The Ohio State University.

This paper reviews existing approaches to the estimation of the state of wear of an automotive damper, with the aim of developing a methodology for a quick and effective diagnostic procedure that could be carried out in any repair facility. It has always been desirable to leave the shock absorber in place at the time of such testing, and there are three general procedures that claim to be effective at determining damper wear. This research investigates a method of controlling a short drop of each corner of the vehicle while measuring the acceleration. The acceleration data is then analyzed with the aim of estimating the decay rate of the resulting oscillation, which is known to be related to the damping ratio of the suspension system. The rate of decay is then used to infer the condition of the vehicles damper. The paper reviews the state of the art, describes the methodology and presents experimental validation of a new concept.

Research has been conducted to develop a methodology for the generation of driving and duty cycles for refuse vehicles in conjunction with a larger effort in the design of a hybrid-electric refuse vehicle. This methodology includes the definition of real-world data that was collected, as well as a data analysis procedure based on sequencing of the collected data into micro-trips and hydraulic cycles. The methodology then applies multi-variate statistical analysis techniques to the sequences for classification. Finally, driving and duty cycles are generated based on matching the statistical metrics and distributions of the generated cycles to the collected database. Simulated vehicle fuel economy for these cycles is also compared to measured values.

In this paper, the detection and isolation of actuator faults (both measured and commanded) occurring in the engine breathing and the fueling systems of a spark-ignition direct-injection (SIDI) engine are described. The breathing system in an SIDI engine usually consists of a fresh air induction path via an electronically controlled throttle (ECT) and an exhaust gas recirculation (EGR) path via an EGR valve. They are dynamically coupled through the intake manifold to form a gas mixture, which eventually enters the engine cylinders for a subsequent combustion process. Meanwhile, the fueling system is equipped with a high-pressure common-rail injection for a precise control of the fuel quantity directly injected into the engine cylinders. Since the coupled system is highly nonlinear in nature, the fault diagnosis will be performed by generating residuals based on multiple nonlinear observers.

This paper shall present the design and development of a family of high power, high-speed transport and combat vehicles based on a common module. The system looks to maximize performance at both high-speed operation and low-speed, heavy/severe-duty operation. All-wheel drive/steer-by-wire autonomous traction modules provide the basis for the vehicle family. Each module can continuously develop 300-400 kW of power at the wheels and has nearly double peak capability, exploiting the flexibility of the electric traction system. The maximum starting tractive effort developed by one module can reach 10-15 tons, and the full rated power can be produced at speeds of 100 mph. This paper will present the design and layout of the autonomous modules. Details will be provided about the tandem electric axles, with electric differentials and independent steering.

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.