Search Results

Gasoline Particulate Filters (GPF) are widely employed in exhaust aftertreatment systems of gasoline engines to meet the stringent particulate emissions requirements of Euro 6 and China 6 standard. Optimization of GPF performance requires a delicate trade-off between fuel economy, engine performance and drivability. This results in a complex lengthy and iterative calibration development process which uses a lot of hardware resources. To improve the calibration process and reduce hardware testing, physics-based modeling of the GPF system is used. A 1-D chemical model supplemented with 3D CFD solver is utilized to evaluate pressure drop and soot burning performance characteristics of the GPF under engine dynamometer test conditions. The chemical kinetics of soot burning for the 1D model is developed using test data obtained from well controlled laboratory environment.

In the current scenario of global emissions, growing demand for petroleum fuels and highly volatile crude prices, the current usage of petroleum fuel must be curbed to reduce dependence on fossil fuels and to reduce environmental pollution several alternative fuels are being explored. Butanol is one of the potential alternative fuels that can be used in IC engines in the same way of conventional fuels for reducing conventional fuels. An experimental study was conducted to establish the impact of n-butanol as a blending component for gasoline fuel in passenger car on chassis dynamometer. Commercial gasoline meeting Euro-IV fuel standards was used as the base fuel while n-butanol was used as the blending component in the ratio of 5, 10 & 20% by volume. The vehicle was tested on chassis dynamometer for fuel evaluation in respect of fuel economy, regulated and un-regulation emissions under standard driving cycle of NEDC.

Battery modeling is of major concern going forward for Hybrid Electric Vehicle (HEV) and Electric vehicle (EV) modeling. The major issue lies in characterizing the battery power, Charge acceptance and reaction to sudden load changes (transient behavior) in relation to battery’s State of Charge (SOC). In particular modeling the battery is challenging task as it requires a lot of test data to understand and validate modeled chemical and electrical characteristics in various operating conditions. Hence, the one of the ways of simulating Battery based Hybrid System is to use battery Hardware-in-the-Loop Simulation (HILS) or simply known as Battery-in-Loop (BIL). With this approach hybrid vehicle or more precisely battery management system (BMS) development time and cost can be significantly reduced by eliminating the detailed battery modeling. To understand the effectiveness of this approach, Battery Hardware-in-Loop test setup was developed.

In this paper, a mild hybrid system is studied for Indian drive conditions. The study is focused to first come up with detailed component sizing through simulation. Different features of mild hybrid system are studied for their individual and cumulative contribution in the fuel economy improvement over the base non-hybrid vehicle. Model based development approach has been employed to develop a supervisory control strategy for such a system. Model based design saves time and cost as it gives flexibility to the control engineer to validate the control design at an early stage of development. The supervisory control strategy is first tested in a simulated environment and then, on a vehicle. To prove the system function, a full hybrid vehicle is experimented as a mild hybrid configuration. Experiments are conducted on the test vehicle over MIDC (certification cycle) to understand the impact of mild hybridization on fuel economy and tail pipe emissions

In this work, a parallel full Hybrid Electric Vehicle (HEV) was optimized to further lower its carbon footprint without opting for any additional hardware change. The study was focused to first recognize the system efficiency of the HEV and identify its low efficiency points over the MIDC. Thereafter, different functions of the HEV were studied for their individual and cumulative contribution in the fuel economy improvement over the base non-hybrid vehicle. This, along with the low system efficiency points helped in identifying the potential areas for improvement in fuel economy. With changes in calibration and control strategies resulting in an optimal torque handling between the E-machine and the ICE, it was established through simulation and subsequent experiments conducted on chassis dynamometer, that the fuel economy of the HEV tested can be improved with the performance remaining unchanged and emissions meeting regulatory requirements.