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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.

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.

The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

In military applications, diesel engines are required to achieve high power outputs and therefore must operate at high loads. This high load operation leads to high piston component temperatures and heat rejection rates limiting the packaged power density of the powertrain. To help predict and understand these constraints, as well as their effects on performance, a thermodynamic engine model coupled to a finite element heat conduction solver is proposed and validated in this work. The finite element solver is used to calculate crank angle resolved, spatially averaged piston temperatures from in-cylinder heat transfer calculations. The calculated piston temperatures refine the heat transfer predictions as well requiring iteration between the thermodynamic model and finite element solver.

In this work, a novel opposed piston architecture is proposed where one crankshaft rotates at twice the speed of the other. This results in one piston creating a 2-stroke profile and another with a 4-stroke profile. In this configuration, the slower piston operates in the 2-stroke CAD domain, while the faster piston completes 2 reciprocating cycles in the same amount of time (4-stroke). The key benefit of this cycle is that the 4-stroke piston increases the rate of compression and expansion (dV/dθ), which lowers the combustion-induced pressure rise rate after top dead center (crank angle location of minimum volume). Additionally, it lowers in-cylinder temperatures and pressures more rapidly, resulting in a lower residence time at high temperatures, which reduces residence time for thermal NOx formation and reduces the temperature differential between the gas and the wall, thereby reducing heat transfer.

Extending the operating range of the gasoline HCCI engine is essential for achieving desired fuel economy improvements at the vehicle level, and it requires deep understanding of the thermal conditions in the cylinder. Combustion chamber deposits (CCD) have been previously shown to have direct impact on near-wall phenomena and burn rates in the HCCI engine. Hence, the objectives of this work are to characterize thermal properties of deposits in a gasoline HCCI engine and provide foundation for understanding the nature of their impact on autoignition and combustion. The investigation was performed using a single-cylinder engine with re-induction of exhaust instrumented with fast-response thermocouples on the piston top and the cylinder head surface. The measured instantaneous temperature profiles changed as the deposits grew on top of the hot-junctions.

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.

Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track.

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 proposes a simulation based methodology to assess plug-in hybrid vehicle (PHEV) behavior over 24-hour periods. Several representative 24-hour missions comprise naturalistic cycle data and information about vehicle resting time. The data were acquired during Filed Operational Tests (FOT) of a fleet of passenger vehicles carried out by the University of Michigan Transportation Research Institute (UMTRI) for safety research. Then, PHEV behavior is investigated using a simulation with two different charging scenarios: (1) Charging overnight; (2) Charging whenever possible. Charging/discharging patterns of the battery as well as trends of charge depleting (CD) and charge sustaining (CS) modes at each scenario were assessed. Series PHEV simulation is generated using Powertrain System Analysis Toolkit (PSAT) developed by Argonne National Laboratory (ANL) and in-house Matlab codes.

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.

A quasi-dimensional computer simulation of the turbocharged spark-ignition engine has been developed in order to study system performance as various design parameters and operating conditions are varied. The simulation is of the “filling and emptying” type. Quasi-steady flow models of the compressor, intercooler, manifolds, turbine, wastegate, and ducting are coupled with a multi-cylinder engine model where each cylinder undergoes the same thermodynamic cycle. A turbulent entrainment model of the combustion process is used, thus allowing for studies of the effects of various combustion chamber shapes and turbulence parameters on cylinder pressure, temperature, NOx emissions and overall engine performance. Valve open areas are determined either based on user supplied valve lift data or using polydyne-generated cam profiles which allow for variable valve timing studies.

One of the major problems limiting the accuracy of piezoelectric transducers for cylinder pressure measurements in an internal-combustion (IC) engine is the thermal shock. Thermal shock is generated from the temperature variation during the cycle. This temperature variation results in contraction and expansion of the diaphragm and consequently changes the force acting on the quartz in the pressure transducer. An empirical equation for compensation of the thermal shock error was derived from consideration of the diaphragm thermal deformation and actual pressure data. The deformation and the resulting pressure difference due to thermal shock are mainly a function of the change in surface temperature and the equation includes two model constants. In order to calibrate these two constants, the pressure inside the cylinder of a diesel engine was measured simultaneously using two types of pressure transducers, in addition to instantaneous wall temperature measurement.

This paper presents a new way to evaluate vehicle speed profile aggressiveness, quantify it from the perspective of the rapid speed fluctuations, and assess its impact on vehicle fuel economy. The speed fluctuation can be divided into two portions: the large-scale low frequency speed trace which follows the ongoing traffic and road characteristics, and the small-scale rapid speed fluctuations normally related to the driver's experience, style and ability to anticipate future events. The latter represent to some extent the driver aggressiveness and it is well known to affect the vehicle energy consumption and component duty cycles. Therefore, the rapid speed fluctuations are the focus of this paper. Driving data collected with the GPS devices are widely adopted for study of real-world fuel economy, or the impact on electrified vehicle range and component duty cycles.

This paper presents an Organic Rankine Cycle (ORC) system model for heavy-duty diesel (HDD) applications. The dynamic, physics-based model includes: heat exchangers for parallel exhaust and EGR circuits, compressible vapor working fluid, distribution and flow control valves, a high pressure pump, and a reservoir. A finite volume method is used to model the evaporator, and a pressure drop model is included to improve the accuracy of predictions. Experimental results obtained on a prototype ORC system are used for model calibration and validation. Comparison of predicted and measured values under steady-state conditions is pursued first, followed by the analysis of selected transient events. Validation reveals the model’s ability to track real-world temperature and pressure dynamics of the ORC system. Therefore, this modeling framework is suitable for future system design studies, optimization of ORC power generation, and as a basis for development of control-oriented ORC models.

This paper investigates the impact of battery cooling ancillary losses on fuel economy, and optimal control strategy for a series hybrid electric truck with consideration of cooling losses. Battery thermal model and its refrigeration-based cooling system are integrated into vehicle model, and the parasitic power consumption from cooling auxiliaries is considered in power management problem. Two supervisory control strategies are compared. First, a rule-based control strategy is coupled with a thermal management strategy; it controls power system and cooling system separately. The second is optimal control strategy developed using Dynamic Programming; it optimizes power flow with consideration of both propulsion and cooling requirement. The result shows that battery cooling consumption could cause fuel economy loss as high as 5%.

This paper presents a low-cost path for extending the range of small urban pure electric vehicles by hydraulic hybridization. Energy management strategies are investigated to improve the electric range, component efficiencies, as well as battery usable capacity. As a starting point, a rule-based control strategy is derived by analysis of synergistic effects of lead-acid batteries, high efficient operating region of DC motor and the hydraulic pump/motor. Then, Dynamic Programming (DP) is used as a benchmark to find the optimal control trajectories for DC motor and Hydraulic Pump/Motor. Implementable rules are derived by studying the optimal control trajectories from DP. With new improved rules implemented, simulation results show electric range improvement due to increased battery usable capacity and higher average DC motor operating efficiency.

This paper develops a methodology to optimize the supervisory controller for a heavy-duty series hybrid electric vehicle, with consideration of battery aging and cooling loss. Electrochemistrybased battery aging model is integrated into vehicle model. The side reaction, reductive electrolyte decomposition, is modeled to determine battery aging rate, and the thermal effect on this reaction rate is considered by Arrhenius Law. The resulting capacity and power fading is included in the system-level study. Sensitivity analysis shows that battery aging could cause fuel economy loss by 5.9%, and increasing temperature could improve fuel economy at any given state-of-health, while accelerating battery aging. Stochastic dynamic programming algorithm is applied to a modeled system to handle the tradeoff between two objectives: maximizing fuel economy and minimizing battery aging.

This paper proposes a methodology to develop a nonlinear model predictive control (NMPC) of a dual-independent variable valve timing (di-VVT) engine using discretized nonlinear engine models. In multiple-input-multiple-output (MIMO) systems, model based control methodologies are critical for realizing the full potential of complex hardware. Fast and accurate control oriented models (COM) that capture combustion physics, actuator and system dynamics are prerequisites for developing NMPC. We propose a multi-scale simulation approach to generate the non-linear combustion model, where the high-fidelity engine cycle simulation is utilized to characterize effects of turbulence, air-to-fuel ratio, residual fraction, and nitrogen oxide (NOx) emissions. The input-to-output relations are subsequently captured with artificial neural networks (ANNs). Manifold and actuator dynamics are discretized to reduce computation efforts.

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.