Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database. CAVs are capable of improving energy efficiency by incorporating these information. In particular, Eco-Cruise and Eco-Lane Selection on highways and/or motorways have immense potential to save energy, because there are generally fewer traffic controllers and the vehicles keep moving in general. In this paper, we present a cooperative and energy-efficient lane-selection strategy named MultiCruise, where each CAV selects one among multiple candidate lanes that allows the most energy-efficient travel. MultiCruise incorporates an Eco-Cruise component to select the most energy-efficient lane. The Eco-Cruise component calculates the driving parameters and prospective energy consumption of the ego vehicle for each candidate lane, and the Eco-Lane Selection component uses these values. As a result, MultiCruise can account for multiple data sources, such as the road curvature and the surrounding vehicles' velocities and accelerations. The eco-autonomous driving strategy, MultiCruise, is tested, designed and verified by using a co-simulation test platform that includes autonomous driving software and realistic road networks to study the performance under realistic driving conditions. Our experimental evaluations show that our eco-autonomous MultiCruise saves up to 8.5% fuel consumption.

Connected and automated vehicles (CAVs) have real-time knowledge of the immediate driving environment, actions to be taken in the near future and information from the cloud. This knowledge, referred to as preview information, enables CAVs to drive safely, but can also be used to minimize fuel consumption. Such fuel-efficient transportation has the potential to reduce aggregate fuel consumption by billions of gallons of gas every year in the U.S. alone. In this paper, we propose a planning framework for use in CAVs with the goal of generating fuel-efficient vehicle trajectories. By utilizing on-board sensor data and vehicle-to-infrastructure (V2I) communications, we leverage the computational power of CAVs to generate eco-friendly vehicle trajectories. The planner uses an eco-driver model and a predictive cost-based search to determine the optimal speed profile for use by a CAV. To evaluate the performance of the planner, we introduce a co-simulation environment consisting of a CAV simulator, Matlab/Simulink and a CAV software platform called the InfoRich Eco-Autonomous Driving (iREAD) system. The planner is evaluated in various urban traffic scenarios based on real-world road network models provided by the National Renewable Energy Laboratory (NREL). Simulations show an average savings of 14.5% in fuel consumption with a corresponding increase of 2% in travel time using our method.

With advances in sensing, computing, and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles. However, testing and verifying energy-efficient autonomous driving systems are difficult due to safety considerations and repeatability. In this paper, we present a co-simulation platform to develop and test novel vehicle eco-autonomous driving technologies named InfoRich, which incorporates the information from on-board sensors, V2X communications, and map database. The co-simulation platform includes eco-autonomous driving software, vehicle dynamics and powertrain (VD&PT) model, and a traffic environment simulator. Also, we utilize synthetic drive cycles derived from real-world driving data to test the strategies under realistic driving scenarios. To build road networks from the real-world driving data, we develop an Automated Parser and Calculator for Map/Scenario named AutoPASCAL. Overall, the simulation platform provides a realistic vehicle model, powertrain model, sensor model, traffic model, and road-network model to enable the evaluation of the energy efficiency of eco-autonomous driving.

Among all advanced energy improvement technologies sought after to reduce the energy footprint, connected and automated vehicle (CAV) technologies hold a special promise of simultaneously improving energy efficiency, safety, and mobility. This work explored the significant opportunities exist in reducing the overall vehicle energy consumption through optimizing the vehicle dynamics and powertrain controls in a coordinated fashion utilizing preview information. A practical solution of Eco-Approach, -Departure, and-Cruise were proposed in this paper. These eco-applications were then integrated with autonomous driving modules. The integrated driver model can perform those eco-applications while navigating through a comprehensive traffic scenario designed on a high-fidelity virtual simulator developed for this project. Overall performance was compared with that of the baseline autonomous driver model to show the fuel economy improvement.

Biodiesel is a promising renewable alternative fuel, which can be used in diesel-electric hybrid vehicles to potentially bring one kind of clean vehicle on the road. However, the fuel property variation can cause impacts to the powertrain and aftertreatment systems. In this paper, biodiesel's influence on post-injection effects is studied. Experimental results are obtained to demonstrate the differences between diesel and biodiesel, and to build up fuel-dependent maps of engine torque, temperature, and emissions. Utilizing those maps, an aftertreatment warm-up approach compatible with both fuels is developed by strategically enabling double post injections. Finally, a supervisory controller based on model predictive control method is designed to optimize and balance the tradeoff between hybrid vehicle fuel economy and tailpipe emissions. Simulations are conducted for both fuels with or without the proposed control strategy. The validation results show that for both fuels, the controller can significantly reduce tailpipe emissions by successfully regulating the catalyst temperature to the desired range sacrificing fuel economy. And for biodiesel, additional fuel cost is required in order to realize the tradeoff.

This paper proposes a dynamic traffic signal timing optimization strategy (DTSTOS) based on various vehicle fuel consumption and dynamic characteristics to minimize the combined total energy consumption and traffic delay for vehicles passing through an intersection. With increasing penetration of new vehicle types and configurations, vehicle fuel consumption characteristics have become rather diversified and dynamic and need to be explicitly incorporated in the traffic light timing control to reduce total energy consumption and traffic delay. Through vehicle-to-infrastructure (V2I) communications, information and states of individual vehicles around an intersection can be made available to the traffic light controller to produce optimal traffic light timing. Unified and control-oriented speed-type fuel consumption models for various types of vehicles in conjunction with a simplified traffic model are employed to conduct real-time traffic light timing control optimization using an iterative grid search (IGS) method. The effectiveness of the DTSTOS was evaluated and demonstrated with a traffic simulator in VISSIM with various traffic flows and vehicle types. The proposed timing plan was compared with Synchro, and consistent results were obtained.

For hybrid electric vehicles (HEVs), the well-studied fuel economy optimization approaches will usually implement frequent engine starts/stops, which can significantly impact the catalyst temperatures and performances of the aftertreatment systems. This paper aims to simultaneously optimize fuel economy and reduce tailpipe emissions for HEVs coupled with aftertreatment systems. First, a control-oriented model is developed by systematically incorporating HEV models with aftertreatment thermal dynamic models, both of which have been experimentally validated. The integrated model is capable of predicting engine-out gas temperature and NOx emissions and simulating the temperature dynamics of the aftertreatment systems. Additionally, post injections' characteristics are investigated and modeled. An aftertreatment warm-up approach is developed by strategically enabling double post injections. Eventually, a supervisory controller is designed to optimize the post-injection ratio and the torque split ratio of HEV powertrains. The controller designed is rooted in a model predictive control (MPC) scheme, and it has an intuitive interpretation in terms of operating costs. The validation results show that the controller can significantly reduce the warmup time and can successfully regulate the catalyst temperature to the desired range with a reasonable sacrifice of fuel economy for HEVs.

In this paper, a dynamic oxygen fraction model for a biodiesel-compatible engine with a dual-loop exhaust gas recirculation (EGR) system is developed. When oxygenated fuel is applied in an engine, the intake manifold oxygen fraction, which is an important factor for both combustion and emissions, can be chosen as a new reference for evaluating the equivalent EGR level instead of EGR ratio. Based on this model, an adaptive observer is designed, and it is able to simultaneously estimate the oxygen fraction states and unknown fuel blend level. The adaptive observer introduced here is advantageous for its simple convergence condition and its efficient implementation. The analysis of the observer's convergence and robustness is detailed. The performance of the observer is validated by the simulation and experimental results. The difference of intake oxygen fractions between pure diesel (B0) and pure soybean biodiesel (B100) are studied. The adaptive observer is expected to be valuable for adaptive control of the combustion and emissions of biodiesel-compatible engines.

In this paper, an adaptive observer, which can simultaneously estimate the oxygen fraction states and unknown intake mass air flow rate, is designed for a Diesel engine equipped with a dual-loop exhaust gas recirculation (EGR) system. A previously developed dynamic model and oxygen fraction observer for the air-path loop of the Diesel engine are introduced as the foundation of this work. As the measurement accuracy of the mass airflow (MAF) sensor may degrade due to aging phenomenon, the impact of MAF signal error on the performance of the previously developed Luenberger-like oxygen fraction observer is investigated through experimental results. To detect the measurement error and to correct the estimation error, the detailed procedure of developing the adaptive observer is presented. The observer's performance is validated against the experimental data.

With the improvements in Diesel engine injection systems, the fueling-path, which is more accurate, flexible, and faster than the air-path, can be actively utilized in conventional and advanced combustion mode controls, especially for enhancing the combustion transient performance. In this paper, fuel injection split models are proposed to describe the relationship between fuel split ratio and two combustion outputs, i.e., the crank angle at 50% heat released (CA(50)) and the indicated mean effective pressure (IMEP). The model parameters are related to the engine in-cylinder thermal boundary conditions, referred to as the in-cylinder conditions (ICCs). The models were verified by engine experimental data with identical and different ICCs under different engine operating conditions. Such models can be potentially utilized in active fueling control for Diesel engine combustion control, and therefore benefit engine fuel efficiency and reduce engine-out emissions.

Biodiesel is an alternative fuel derived from vegetable oils, animal fats, or other sources, and it can be made into biodiesel blends by mixing with conventional diesel. To achieve optimal engine combustion as well as minimal emissions with biodiesel blends, on-board blend level estimation system is one of the prerequisites. The paper investigates a blend level estimation system by evaluating exhaust oxygen fraction differences between diesel and biodiesel. In the system, a wideband oxygen sensor is utilized to measure the oxygen concentration. Then, research on the oxygen-content based biodiesel blend level estimation system is extended by taking the exhaust gas recirculation (EGR) into account. Since estimation accuracy under the lean conditions for this kind of system has been especially problematic, the effect of EGR on system estimation uncertainty is discussed. The theoretical formulation shows that oxygen consumption factor is an intrinsic indicator of fuels, which is not affected by the EGR level. However, the “shifting effect” caused by introducing EGR can help moving the estimation points into a range where not only the oxygen sensor but also the whole estimation system exhibits lower measurement uncertainty. Validations are provided by both simulation results based on a high-fidelity GT-Power engine model and experimental results on a medium-duty turbo-charged diesel engine platform.

In this paper, the combustion characteristics of Diesel and biodiesel fuels are investigated and compared. Based on the experimental data obtained from a medium-duty Diesel engine, a multi-phase combustion model, which is applicable to both Diesel and biodiesel fuels, is developed and it shows satisfactory accuracy within the test range in this study. A preliminary ignition-delay correlation is established by considering not only the in-cylinder physical factors such as pressure and temperature, but also the fuel property variations. It is also capable of modeling the premixed and mixing-controlled combustion by using two cascaded Wiebe functions. Through a grey-box parameter identification approach, a set of Wiebe coefficients, which are partially physics-based, are found; then the model can have a reasonable accuracy for the range of experiments conducted. Based on the multi-phase model, prediction can be made for the CA50 (crank angle for 50% heat release), which is very important for combustion timing feedback control. (C) 2013 Elsevier Ltd. All rights reserved.

This paper investigates the influence of biodiesel on the effectiveness of exhaust gas recirculation (EGR) in modern Diesel engines equipped with dual-loop EGR systems. Intake manifold oxygen fraction, which is an important factor for both combustion and emissions, is selected as a new reference for evaluating the equivalent EGR level instead of EGR ratio. A Luenberger-like observer for the oxygen fraction is designed based on the dynamic model of the air-path loop with consideration of the existence of oxygen content in the fuel. The convergence of the observer is proved with the assistance of some physical insight into the engine system. The performance of the observer is validated on a high-fidelity engine model built in GT-Power. The results show that when the same amount of fuel is injected, there is an increase in the exhaust oxygen concentration for biodiesel as oxygen content in fuel increases. Then the higher exhaust oxygen concentration leads to an increase in the intake manifold oxygen fraction, since the engine control unit (ECU) commanded EGR valve angles are constant across different fuels. This real-time oxygen fraction estimation approach is potentially useful for mitigating the biodiesel NO x emission effect.

This paper investigates the impact of fuel property variations on the common rail pressure fluctuation in high-pressure common rail (HPCR) system and explores the possibility of identifying the fuel types based on the measurement of rail pressure for internal combustion engines. Fluid transients, particularly the water hammer effect in a HPCR system, are discussed and the 1D governing equations are given. A typical HPCR system model is developed in GT-Suite with the injectors, three-plunger high-pressure pump, and pressure control valve being modeled in a relatively high level of detail. Four different fuels including gasoline, ethanol, diesel, and biodiesel are modeled and their properties including density, bulk modulus, and acoustic wave speed are validated against data in the literature. Simulation results are obtained under different conditions with variable rail pressures and engine speeds. To reduce the excessive rail pressure oscillation caused by multiple injections, only four main-injections are enabled in each engine revolution. The results show that the natural frequency of a common rail varies with the type of fuel filled in it. By applying the fast Fourier transform (FFT) to the pressure signal, the differences of fuel properties can be revealed in the frequency domain. The experiment validation is conducted on a medium-duty diesel engine, which is equipped with a typical HPCR system and piezo-electric injectors. Tests results are given for both pure No. 2 diesel and pure soybean biodiesel at different rail pressure levels and different engine speeds. This approach is proved to be potentially useful for fuel property identification of gasoline-ethanol or diesel-biodiesel blends on internal combustion engines.

Biodiesel is an alternative fuel derived from vegetable oils, animal fats, or other sources, and it can be made into biodiesel blends by mixing with conventional diesel. To achieve optimal engine combustion as well as minimal emissions with biodiesel blends, on-board blend level estimation system is one of the prerequisites. The paper explores two possible approaches of estimation: 1) oxygen-based method, which differentiates diesel and biodiesel by evaluating oxygen fraction in exhaust; 2) energy-based method, which estimates the blend level by taking advantages of the fact that biodiesel has lower calorific content than diesel. By measuring injected fuel mass and calculating net heat release based on measurement of in-cylinder pressure, energy content of the fuel was estimated, thus was the biodiesel blend level. In oxygen-based method, wideband oxygen sensor can be utilized to measure exhaust oxygen concentration. The influence of exhaust gas recirculation (EGR) on this method and its estimation accuracy were also analyzed and demonstrated. A high-fidelity, direct injection, diesel engine model was developed in GT-Power for validation purpose. Through simulations, the effectiveness of these two estimators was evaluated and their accuracies were compared.

The automotive industry has been under continued pressure to improve fuel efficiency because of air pollution, global warming, and rising gasoline prices. One technology to address this need is electronic valve timing. It promises to achieve fuel savings of 10%-15% by reducing pumping losses, introducing cylinder deactivation, and enabling new combustion strategies, like homogeneous charge compression ignition. To date, valve actuators for this application primarily rely on resonant spring arrangements to achieve the necessary dynamics. This leads to a fixed amplitude of the valve trajectory and only allows for variable valve timing. In this paper, a fully flexible valve actuation system for intake valves is introduced that provides variable lift in addition to variable timing, without reducing valve dynamics or energy efficiency. Optimization procedures for the mechanical system, the servo motor selection, and the valve trajectory are presented. The combined effect of these optimizations leads to valve accelerations that are an order of magnitude higher than conventional electric servo systems. Simulations and an experimental test bed are used to validate the system performance. A comparison with other electronic valve actuation systems confirms the excellent performance of this approach.

Electromagnetic actuators show considerable promise in replacing camshafts in automotive combustion engines. Most research published to date focuses on reducing valve seating velocity. This is usually achieved using a current feed-forward controller during most of the valve travel and a trajectory controller with a fixed trajectory close to the end of travel. Such systems face considerable challenges in the presence of varying combustion forces, since the feed-forward strategy cannot ensure constant starting conditions for the tracking controller. The work shown here replaces the feed-forward controller with an energy based feedback controller. For the tracking controller with feedback linearization at the end of the valve travel, real-time trajectories that adapt to different starting conditions are derived. The algorithms can adapt to large changes in combustion pressure without relying on a priori combustion information. The algorithms are validated using simulations and on an experimental test bed.