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As a viable alternative to the conventional hybrid electric vehicles, so called “mild” hybrid drivetrains are currently being implemented in production vehicles. These mild hybrid electric vehicles use an Integrated Starter Generator (ISG) to simply assist the internal combustion (IC) engine rather than drive the vehicle independently of the IC engine. Some of the production mild hybrid vehicles have been shown to achieve over a10 % increase in fuel efficiency with minimal additional costs. In this paper, we present a lookup table-based control scheme for the optimal control of the ISG and the IC engine on a mild hybrid vehicle. The developed control logic is implemented in Matlab/Simulink along with a mild hybrid vehicle model, which is based on an EPA light-duty vehicle model. The simulation results show that the optimally controlled mild hybrid vehicle has better fuel efficiency with comparable drivability when compared to a simple intuitive rule-based control strategy.

Engine and drivetrain simulation has become an integral part of the automotive industry. By creating a virtual representation of a physical system, engineers can design controllers and optimize components without producing a prototype, thus reducing design costs. Among the numerous simulation approaches, 1D physics-based models are frequently implemented due to balanced performance between accuracy and computational speed. Several 1D physics-based simulation software packages currently exist but cannot be directly implemented in MALAB/Simulink. To leverage MATLAB/Simulink's powerful controller design and simulation capabilities, a 1D physics-based engine simulation tool is currently being developed at The University of Alabama. Previously presented work allowed the user to connect engine components in a physically representative manner within the Simulink environment using a standard block connection scheme and embedded MATLAB functions.