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Parameter identification of a math-based spark-ignition engine model is studied in this paper. Differential-algebraic equations governing the dynamic behavior of the engine combustion model are derived using a quasi-dimensional modelling scheme. The model is developed based on the two-zone combustion theory with turbulent flame propagation through the combustion chamber [1]. The system of equations includes physics-based equations combined with the semi-empirical Wiebe function. The GT-Power engine simulator software [2], a powerful tool for design and development of engines, is used to extract the reference data for the engine parameter identification. The models is GT-Power are calibrated and validated with experimental results; thus, acquired data from the software can be a reliable reference for engine validation purposes.

This paper presents a math-based spark ignition (SI) engine model for fast simulation with enough fidelity to predict in-cylinder thermodynamic properties at each crank angle. The quasi-dimensional modelling approach is chosen to simulate four-stroke operation. The combustion model is formulated based on two-zone combustion theory with a turbulent flame propagation model [1]. Cylinder design parameters such as bore and stroke play an important role to achieve higher performance (e.g. power) and reduce undesirable in-cylinder phenomenon (e.g. knocking). A symbolic sensitivity analysis is used to study the effect of the design parameters on the SI engine performance. We used the symbolic Maple/MapleSim environment to obtain highly-optimized simulation code [3]. It also facilitates a sensitivity analysis that identifies the critical parameters for design and control purposes.

Mean value engine models (MVEMs) are intermediate-level internal combustion (IC) engine models which include more physical details than simplistic linear transfer function models, but significantly fewer details than large complex cylinder-by-cylinder models [1]. The MVEM is well-known as a suitable plant model for model-based control applications. The combinations of physics-based component models, which allow the physical parameter effects to be evaluated and controlled, and look-up table models, with fast response, make the MVEM suitable for control applications. A mean value engine model based on mathematical and parametric equations has recently been developed in the new MapleSim software. The model consists of three main components: the throttle body, the manifold, and the engine. The model is developed in the MapleSim environment which takes advantages from both Maple's powerful symbolic mathematical tool and Modelica's modern equation-based language.

Torque converters are used as coupling devices in automobile powertrains involving automatic transmissions. Efficient modeling of torque converters capturing various modes of operation is important for powertrain design and simulation, (Hroval and Tobler 1, Ishihara and Emori 2) optimization and control applications. Models of torque converters are available in various commercial simulation packages, Hadi et. al. 3. The information about the effect of model parameters on torque converter performance is valuable for any design operation. In this paper, a symbolic sensitivity analysis of a torque converter model will be presented. Direct differentiation (Serban and Freeman 4) is used to generate the sensitivity equations which results in equations in symbolic form. By solving the sensitivity equations, the effect of a perturbation of the model parameters on the behavior of the system is determined.

This paper presents math-based models of automotive torque converters in the MapleSim1 modeling environment, which has a multi-domain nature. The created models can be easily connected to models of other vehicle components from different domains (mechanical, electrical, and hydraulic) developed in the same package. The proposed model is a math-based dynamic model of a torque converter, based on the paper by Hrovat and Tobler, which includes both transient and steady state behavior of the torque converter. The dynamic model of a torque converter can be used as an essential element to investigate vehicle longitudinal dynamics, drive quality, emission, and fuel consumption during gear shifting and throttle stepping. A sensitivity analysis is used to evaluate the effects of the torque converter's parameters, such as radius, blade angles, and inertias, on the model's behavior.