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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.

Due to rising fuel prices and environmental concerns, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have been gaining market share as fuel-efficient, environmentally friendly alternatives. Lithium-ion batteries are commonly used in EV and HEV applications because of their high power and energy densities. During controls development of HEVs and EVs, hardware-in-the-loop simulations involving real-time battery models are commonly used to simulate a battery response in place of a real battery. One physics-based model which solves in real-time is the reduced-order battery model developed by Dao et al. [1], which is based on the isothermal model by Newman [2] incorporating concentrated solution theory and porous electrode theory [3]. The battery models must be accurate for effective control; however, if the battery parameters are unknown or change due to degradation, a method for estimating the battery parameters to update the model is required.