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Diesel-powered engines are used worldwide for efficient transportation and stationary power generation. The significant drawback of a diesel engine is its harmful emissions. The stringent emission norms enforced by the different organization demands effective catalyst system to control the gaseous emissions. Diesel oxidation catalysts are the extensively used technique for diesel engines to control HC and CO emissions. Currently the catalyst in the diesel oxidation system employs precious metals such as Pt/Pd/Rh to reduce the emissions and makes the DOC system expensive. This paper presents a cost-effective catalyst prepared to employ non-noble mixed oxides of copper and nickel supported on non-conventional support (i.e.) ceria doped calcium borophosphates (Ce-SCaPB). Initially, ceramic beads (5mm X 5mm) were coated with (Ce-SCaPB) support material. Secondly, the copper and nickel salts were deposited on the Ce-SCaPB coated ceramic beads and subsequently reduced and calcined.

Exhaust after treatment devices in diesel engines play a crucial role in control of harmful emissions. The noxious emission released from diesel engines causes a variety of problems to both human beings and the environment. The currently used devices are implemented with new catalyst technologies like DOC, SCR and catalytic converter are all designed to meet stringent emission regulations. Although these devices have considerable conversion efficiency, they are not without drawbacks. The catalysts used in these devices are rarely available and are also very expensive. Diesel Particulate Filter (DPF) is the device currently employed to collect particulate matter. It also has drawbacks like high back pressure, thermal durability restrictions, regeneration issues and poor collection of smaller size particles. In the case of biodiesel these fine sized particles are emitted in larger quantity.

Misfire represents a crucial problem for vehicles, adding to the energy depletion in the midst of air pollution such as CO and NOx caused by exhaust gases. Because of a special cylinder, misfire produces a particular vibration pattern. These patterns can isolate and interpret valuable properties to detect misfires. In this paper, a machine learning approach is used as a predictive model for the identification of misfires. In the current research, vibratory signals were taken as a kind of misfire that is unique to each cylinder (acquired with the help of a piezoelectric accelerometer). Statistical characteristics are then extracted and feature selection is applied using the J48 decision tree algorithm from the features obtained. In the classification of misfires in the cylinders, the K* classification was used. The experiment was conducted in Maruti Suzuki Baleno. Every single cylinder was tested on a separate basis.

Parametric thermal analysis of permanent magnet synchronous motor by considering real time driving cycle is presented. As driving motors of electric vehicle, permanent magnet motors exhibit high efficiency and high power density. However, they are susceptible to suffer irreversible demagnetization and insulation failure of coils under severe thermal condition. Therefore, it is essential to precisely predict heat losses and temperature distribution in driving motors under real time driving cycle. The thermal behavior of the driving motor is analyzed by means of thermal parametric methods. Here we are using simulation software to measure the temperature distribution and heat losses in the Driving motor, when an electric motor is operated for a certain amount of time the motor produces heat because of the Heat produced by the motor the performance, efficiency and reliability of the motor is affected.