Govindaraj Kumaresan

To minimize the harmful emissions from engine exhaust and to enhance the vehicle performance, comfort and economy, the hybrid vehicle with multi-mode gear shift control system was modeled using AMESim simulation tool. The developed model includes gearbox controller, proportional Integral controllers for electric motor and internal combustion engine for smooth gear shift events, better acceleration with controlled fuel/power consumption, comfort and drivability. The main objective is to evaluate the influence of the dog clutch disturbances on vehicle comfort, vehicle acceleration performance and the fuel consumption. From the simulation results, the performance mode exhibits the highest and most frequent power steps with 30% higher average power demand than economy mode, and 15% higher than comfort mode. The performance mode consumes 8.8 seconds to reach the vehicle speed from 0-100 km/h, showing around 40% and 20% enhancements compared to economy mode and comfort mode respectively. The economy mode consumes 12 seconds of time to reach the vehicle speed range from 80-120 km/h, and then it was reduced to 5.7 and 6 seconds during comfort and performance modes. The fuel consumption was increased from 216.8 g/kWh to 245.6 g/kWh when moving from economy mode to comfort mode, then increased to 297.5 g/kWh during performance mode. Overall, the performance mode promotes enhanced performance acceleration metrics with increased fuel consumption and reduced comfort. The economy mode showed significant improvement in fuel consumption, acceleration peaks and comfort gear shifting events. The comfort mode strongly demonstrates the balanced efficiency and drivability with rapid gear shifting.

To meet the energy demand, passenger’s comfort and sustainability in the air conditioning system of vehicles, the Neural Network plays an intelligent role. In this study, the vehicle air conditioning loop was replaced with the reduced control model using a neural network including Proportional-Integral-Derivative temperature controller. The detailed model of vehicle air conditioning system with Proportional-Integral-Derivative controller and the reduced model with neural network were modelled and simulated using AMESim simulation tool. The five different driving cycles (NEDC, FTP75, US06, EPA-HWFET and SC03) were used to simulate the results of both detailed model and reduced model with neural network. The reduced control model with neural network was trained and validated through more history of data sets. Finally, the output simulation results such as vehicle cabin temperature, relative humidity at evaporator outlet, compressor shaft mechanical power, fidelity, CPU run time and compressor command were compared between detailed model and reduced control model with neural network among each driving cycle. The simulation results showed the greater reduction of CPU run time of a reduced control model with neural network than a detailed model of vehicle air-conditioning system. The same fidelity levels and speed up gains obtained in cabin temperature, relative humidity at evaporator outlet, compressor shaft mechanical power and compressor command. The drastically reduced CPU run time of a reduced model with neural network, results in lower computational complexity and its adaptability at dynamic operating situations make it suitable for real time embedded controlled air-conditioning control in vehicles. The validation of optimized parameters helps the manufacturers significantly break down the number of expensive and time consuming physical sub-models, resulting in adequate cost savings and a more sustainable development process.

With the increase of conventional fuels demand, increase in fuel price, battery charging restrictions in electric vehicles during long travel and costlier technologies to implement the current (or) future emission regulations, the hydrogen fuelled fuel cell system plays an important role. In recent fuel cell system, the platinum catalyst material is used for oxygen reduction reaction (ORR) which is costlier. The main aim of this research work is to find the low priced and better (or) highly performing oxygen reduction reaction catalyst materials in proton exchange membrane fuel cells. In this study, AMESim simulation tool was used to analyse the fuel cell system with different catalyst materials such as platinum, Fe-N2-C and Ag-alloy (Ag-Cu) for better oxygen oxidation reaction under dynamic conditions using adaptive Jacobian computation method. From the simulation results, the performance of catalyst materials used in the fuel cell system were compared with respect to vehicle performance, powertrain and stack output power, battery state of charge (SoC), compressor efficiency with air mass flow rate, hydrogen fuel efficiency and power/energy distribution. The fuel cell system with Fe-N2-C catalyst material shows nearly the same overall system performance when compare with platinum catalyst material, but the fuel cell system with Al-alloy shows comparatively less overall system performance than platinum and Fe-N2-C catalyst materials used in the fuel cell system. Also, the price of platinum is much expensive than Fe-N2-C material which makes the system costlier. From the simulation results comparison, the fuel cell system with Fe-N2-C catalyst proves better overall performance and cost benefited than Platinum and Ag-alloy catalyst materials.

To reduce the emissions from an air-conditioning system, the air-cooled chiller unit is equipped with duplex compressors-condensers parallel circuit where it used SiO2/HFC134a nano-refrigerant. Also to minimize the power consumption of centralized air-conditioning system, only the required space (inside rooms excluding the corridor areas) was installed with evaporator unit, and the in/out pipe lines were effectively insulated. The experimental scientific data were noted for base refrigerant HFC134a and also for SiO2/HFC134a nano-refrigerant at two dissimilar concentrations of 0.20% and 0.30% SiO2 . From the evaluated results, it was concluded that the cooling capacity and COP (coefficient of performance) of the nano-refrigerant with 0.20% and 0.30% SiO2 concentrations are increased by 4.68% and 2.54%; and 17.39% and 13% respectively when compare with base refrigerant HFC134a. The annual power consumption (and/or) annual CO2 emissions of nano-refrigerant with 0.20% and 0.30% SiO2 concentrations of are decreased by the same amounts of 10.83% and 9.29% respectively when compare with base refrigerant HFC134a. The SiO2/HFC134a nano-refrigerant with 0.20% of SiO2 concentration shown better results in terms of cooling capacity, COP, energy consumption and CO2 emissions.

The complex geometry with sliding contact surfaces of hypoid gear pair with differential unit in automotive vehicles results in increased frictional losses and reduced mechanical efficiency. This study focuses on the influence of shaft offset, spiral angle of hypoid gears, shaft rotational speed of shaft and torque transmitted on the efficiency and power losses of hypoid gears with differential units used in automotive vehicles. AMESim simulation tool was used to optimize the key parameters towards maximum efficiency, and lower power losses through bearings as well as gear pair. The geometry of hypoid gear train with differential unit including forces (ra-dial, axial and tangential) consideration was modeled and tested for gear efficiency and power losses at various values of shaft offset, spiral angle, shaft rotational speed and torque. From the simulation results, it is observed that the maximum increment in total power loss achieved from 10 mm to 25mm shaft offset was 81%. The global efficiency of the gear system showed maximum when the shaft offset was small and the spiral angle was small. The maximum efficiency drop from 40° to 50° spiral angle was about 51% and the total efficiency drop from 30° to 50° spiral angle was 81%. The global efficiency of the gear system was increased gradually with the increase of rotational speed. The maximum percentage increase in total power losses from 1000 rpm to 2000 rpm speed was 102.6%. The significant power losses and maximum increase in ef-ficiency happen between 200 Nm to 300 Nm and its total power loss increase was 51%. This study serves as a valuable resource for gear manufacturers to improve the efficiency of the hypoid gear train with differential units through optimizing the input key parameters with reduced power losses.

Featherweight material to sustain mountainous loads is an extreme contrast. Still, in reality, the automobile and aerospace industries seek very light materials with high specific strength to survive in global competition. This study offers a timely answer to the problems faced by those sectors. Through the use of a stir-assisted squeeze casting method, the current investigation aims to generate a lightweight magnesium composite that is reinforced with porous low-density pumice particles at weight percentages of 5%, 10%, and 15%. An examination of the density indicates that there is a downward tendency in conjunction with the growing reinforcement. In comparison to the magnesium alloy in its natural state, the Pumice 15 coupon achieves a tensile strength increment of 47%, flexural strength increment of 35%, and a 10-time increment in service cycle at fatigue increase. The micromechanics model is implemented to justify the strengthening process in terms of the adhesion between the filler matrix and the interface shear strength. The property plots that were drafted provide evidence that the suggested material is lightweight while exhibiting a significant amount of strength in tensile, flexural, and fatigue. Post-fracture surface morphology analysis exhibits distinct tensile, flexural, and fatigue patterns.