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The Three Way Catalyst (TWC) is an effective pollutant conversion system widely used in current production vehicles to satisfy emissions regulations. A TWC’s conversion efficiency degrades over time due to chemical and/or thermal mechanisms causing the catalyst to age. This reduction in conversion efficiency must be accounted for to ensure full useful life emissions compliance. This paper presents an experimental study of the aging impact on TWC performance. Four TWCs differentiated by their age, given in terms of miles driven, were tested. It is shown that the dynamics of oxygen storage are substantially affected by aging of the TWC. A previously developed physics-based oxygen storage model [1] is subsequently used to incorporate the effect of aging on the total Oxygen Storage Capacity (OSC). Parameter identification results for the different age catalysts show that total oxygen storage capacity decreases substantially with aging and is insensitive to operating conditions.

Energy security and climate change challenges provide a strong impetus for investigating Electric Vehicle (EV) concepts. EVs link two major infrastructures, the transportation and the electric power grid. This provides a chance to bring other sources of energy into transportation, displace petroleum and, with the right mix of power generation sources, reduce CO₂ emissions. The main obstacles for introducing a large numbers of EVs are cost, battery weight, and vehicle range. Battery health is also a factor, both directly and indirectly, by introducing limits on depth of discharge. This paper considers a low-cost path for extending the range of a small urban EV by integrating a parallel hydraulic system for harvesting and reusing braking energy. The idea behind the concept is to avoid replacement of lead-acid or small Li-Ion batteries with a very expensive Li-Ion pack, and instead use a low-cost hydraulic system to achieve comparable range improvements.

The paper describes the approach, addresses integration challenges and discusses capabilities of the Hybrid Powertrain-in-the-Loop (H-PIL) facility for the series/hydrostatic hydraulic hybrid system. We describe the simulation of the open-loop and closed-loop hydraulic hybrid systems in H-PIL and its use for concurrent engineering and development of advanced supervisory strategies. The configuration of the hydraulic-hybrid system and details of the hydraulic circuit developed for the H-PIL integration are presented. Next, software and hardware interfaces between the real components and virtual systems are developed, and special attention is given to linking component-level controllers and system-level supervisory control. The H-PIL setup allows imposing realistic dynamic loads on hydraulic pump/motors and accumulator based on vehicle driving schedule.

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space.

Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles.

This paper is on the development process of a hydraulic hybrid passenger vehicle. A subcompact passenger vehicle is chosen for modification into a series hydraulic hybrid with the aim of achieving a fuel economy of 100 MPG (2.35 L/100km) on the Urban Dynamometer Driving Schedule (UDDS). This work develops a methodology for simultaneously designing a powertrain and power management strategy of a series hydraulic hybrid. The design process was initiated by developing a system level model validated using engine and hydraulic pump/motor testing by the US EPA at the National Vehicle and Fuel Efficiency Laboratory (NVFEL). Parametric studies were performed in order to determine the size of the pump/motors and accumulators. Several candidate engines were tested and the system models were used to determine which one could provide the best fuel economy while meeting performance constraints.

The focus of this paper is on developing, modeling and simulation framework for a bias free comparison of different engine concepts in a conventional and hybrid configuration. The first unique contribution of this paper is in the development of a shift logic algorithm that allows tailoring the shift schedule to unique engine characteristics in a consistent manner. The shift schedule is intentionally generated in a generic manner by using identical set of rules for all engines. Therefore, the methodology allows a fair comparison of different engine concepts, while taking into account the individual features of the engine i.e. speed range, efficiency and maximum performance. The latter establishes a baseline for the subsequent study of hybrid configurations. The second unique contribution is the hybrid strategy optimization algorithm, also tailored to a particular engine configuration.

Exhaust gas rebreathing is considered to be a practical enabler that could be used in HCCI production engines. Recent experimental work at the University of Michigan demonstrates that the combustion characteristics of an HCCI engine using large amounts of hot residual gas by rebreathing are very sensitive to engine thermal conditions. This computational study addresses HCCI engine operation with rebreathing, with emphasis on the effects of engine thermal conditions during transient periods. A 1-D cycle simulation with thermal networks is carried out under load and speed transitions. A knock integral auto-ignition model, a modified Woschni heat transfer model for HCCI engines and empirical correlations to define burn rate and combustion efficiency are incorporated into the engine cycle simulation model. The simulation results show very different engine behavior during the thermal transient periods compared with steady state.

Cam-phasing is increasingly considered as a feasible Variable Valve Timing (VVT) technology for production engines. Additional independent control variables in a dual-independent VVT engine increase the complexity of the system, and achieving its full benefit depends critically on devising an optimum control strategy. A traditional approach relying on hardware experiments to generate set-point maps for all independent control variables leads to an exponential increase in the number of required tests and prohibitive cost. Instead, this work formulates the task of defining actuator set-points as an optimization problem. In our previous study, an optimization framework was developed and demonstrated with the objective of maximizing torque at full load. This study extends the technique and uses the optimization framework to minimize fuel consumption of a VVT engine at part load.

This study investigates the impact of transient engine operation on the emissions formed during a tip-in procedure. A medium-duty production V-8 diesel engine is used to conduct experiments in which the rate of pedal position change is varied. Highly-dynamic emissions instrumentation is implemented to provide real-time measurement of NOx and particulate. Engine subsystems are analyzed to understand their role in emissions formation. As the rate of pedal position change increases, the emissions of NOx and particulates are affected dramatically. An instantaneous load increase was found to produce peak NOx values 1.8 times higher and peak particulate concentrations an order of magnitude above levels corresponding to a five-second ramp-up. The results provide insight into relationship between driver aggressiveness and diesel emissions applicable to development of drive-by-wire systems. In addition, they provide direct guidance for devising low-emission strategies for hybrid vehicles.

A comprehensive quasi-dimensional computer simulation of the spark-ignition (SI) engine was used to explore part-load, fuel economy benefits of the Variable Stroke Engine (VSE) compared to the conventional throttled engine. First it was shown that varying stroke can replace conventional throttling to control engine load, without changing the engine characteristics. Subsequently, the effects of varying stroke on turbulence, burn rate, heat transfer, and pumping and friction losses were revealed. Finally these relationships were used to explain the behavior of the VSE as stroke is reduced. Under part load operation, it was shown that the VSE concept can improve brake specific fuel consumption by 18% to 21% for speeds ranging from 1500 to 3000 rpm. Further, at part load, NOx was reduced by up to 33%. Overall, this study provides insight into changes in processes within and outside the combustion chamber that cause the benefits and limitations of the VSE concept.

This paper investigates the development of a deaeration device to remove nitrogen from the hydraulic fluid in hydraulic hybrid vehicles (HHVs). HHVs, which use accumulators to store and recycle energy, can significantly reduce vehicle emissions in urban delivery vehicles. In accumulators, nitrogen behind a piston cylinder or inside a bladder pressurizes an incompressible fluid. The permeation of the nitrogen through the rubber bladder into the hydraulic fluid limits the efficiency and reliability of the HHV system, since the pressure drop in the hydraulic fluid can in turn cause cavitation on pump components and excessive noise in the system. The nitrogen bubbles within the hydraulic fluid may be removed through the employment of commercial bubble eliminators if the bubbles are larger than a certain threshold. However, gas is also dissolved within the hydraulic fluid; therefore, novel design is necessary for effective deaeration in the fluid HHV circuit.

The development and use of a simulation of an Integrated Starter Alternator (ISA) for a High Mobility Multi-purpose Wheeled Vehicle (HMMWV) is presented here. While the primary purpose of an ISA is to provide electric power for additional accessories, it can also be utilized for mild hybridization of the powertrain. In order to explore ISA's potential for improving HMMWV's fuel economy, an ISA model capable of both producing and absorbing mechanical power has been developed in Simulink. Based on the driver's power request and the State of Charge of the battery (SOC), the power management algorithm determines whether the ISA should contribute power to, or absorb power from the crankshaft. The system is also capable of capturing some of the braking energy and using it to charge the battery. The ISA model and the power management algorithm have been integrated in the Vehicle-Engine SIMulation (VESIM), a SIMULINK-based vehicle model previously developed at the University of Michigan.

This paper describes a test cell setup for concurrent running of a real engine and a vehicle system simulation, and its use for evaluating engine performance when integrated with a conventional and a hybrid electric driveline/vehicle. This engine-in-the-loop (EIL) system uses fast instruments and emission analyzers to investigate how critical in-vehicle transients affect engine system response and transient emissions. Main enablers of the work include the highly dynamic AC electric dynamometer with the accompanying computerized control system and the computationally efficient simulation of the driveline/vehicle system. The latter is developed through systematic energy-based proper modeling that tailors the virtual model to capture critical powertrain transients while running in real time. Coupling the real engine with the virtual driveline/vehicle offers a chance to easily modify vehicle parameters, and even study two different powertrain configurations.

Variable Valve Actuation (VVA) technology provides high potential in achieving high performance, low fuel consumption and pollutant reduction. However, more degrees of freedom impose a big challenge for engine characterization and calibration. In this study, a simulation based approach and optimization framework is proposed to optimize the setpoints of multiple independent control variables. Since solving an optimization problem typically requires hundreds of function evaluations, a direct use of the high-fidelity simulation tool leads to the unbearably long computational time. Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time. To demonstrate the proposed methodology, the cam-phasing strategy at Wide Open Throttle (WOT) is optimized for a dual-independent Variable Valve Timing (VVT) engine.

Medium trucks constitute a large market segment of the commercial transportation sector, and are also used widely for military tactical operations. Recent technological advances in hybrid powertrains and fuel cell auxiliary power units have enabled design alternatives that can improve fuel economy and reduce emissions dramatically. However, deterministic design optimization of these configurations may yield designs that are optimal with respect to performance but raise concerns regarding the reliability of achieving that performance over lifetime. In this article we identify and quantify uncertainties due to modeling approximations or incomplete information. We then model their propagation using Monte Carlo simulation and perform sensitivity analysis to isolate statistically significant uncertainties. Finally, we formulate and solve a series of reliability-based optimization problems and quantify tradeoffs between optimality and reliability.

Modern diesel engines manufactured for commercial vehicles are calibrated to meet EPA emissions regulations. Many of the technologies and strategies typically incorporated to meet emissions targets compromise engine performance and efficiency. When used in military applications, however, engine performance and efficiency are of utmost importance in combat conditions or in remote locations where fuel supplies are scarce. This motivates the study of the potential to utilize the flexibility of emissions-reduction technologies toward optimizing engine performance while still keeping the emissions within tolerable limits. The study was conducted on a modern medium-duty International V-8 diesel engine with variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR). The performance-emissions tradeoffs were explored using design of experiments and response surface methodology.

An experimental study has been carried out to provide qualitative and quantitative insight into gas to wall heat transfer in a gasoline fueled Homogeneous Charge Compression Ignition (HCCI) engine. Fast response thermocouples are embedded in the piston top and cylinder head surface to measure instantaneous wall temperature and heat flux. Heat flux measurements obtained at multiple locations show small spatial variations, thus confirming relative uniformity of in-cylinder conditions in a HCCI engine operating with premixed charge. Consequently, the spatially-averaged heat flux represents well the global heat transfer from the gas to the combustion chamber walls in the premixed HCCI engine, as confirmed through the gross heat release analysis. Heat flux measurements were used for assessing several existing heat transfer correlations. One of the most popular models, the Woschni expression, was shown to be inadequate for the HCCI engine.

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.