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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.

Even though hydraulic brakes are valuable safety elements for riders, they are necessary for braking in a good condition. Vibration signatures may be used to assess the condition of the brake components. In this research, the monitor and make status tracking and the dynamic data acquisition method with a piezo-electric transducer is suggested as a promising approach to these challenges by machine-learning. The Ford EcoSport rig was used to get the vibration signal for good and bad braking conditions. The vibration signals had analytical mathematical predictive characteristics, and the decision tree model, J48 was used in the selection of the signals. In order to define a certain concern, a structural decision does not state the number of features required. Therefore, to find the right number of features a rigorous analysis is required.

Car hydraulic brakes are important safety components for passengers and are thus the good condition of brakes are essential for braking. By using the vibrational signatures, the state of the brake components can be determined. In this proposed study, electronic condition monitoring is suggested as a possible solution to such issues by using a machine learning method with a piezo-electric transducer and a dynamic data acquisition system. Ford EcoSport setup was used to acquire the vibration signals for both good and bad braking conditions. The mathematical Descriptive statistical features from the vibration signals were obtained and the feature selection has been done with the C4.5 decision tree classifier. The appropriate number of features needed to classify a particular problem is not determined by a specific method. A thorough study is, therefore, necessary to find the right number of features.

The future of automobile industry is moving towards the electrification of vehicles due to the increase in pollution and Global-Warming. In this contest a suitable Battery Thermal Management system (BTMS) needs to be incorporated for efficient operation of batteries. The battery performance is greatly affected by operating temperature, and the successful control of its temperature improves performance which ensures the safe operation and extends lifespan. In the recent years many researches are going on to adopt phase change materials for BTMS. Since the Phase Change material is of the passive cooling system, it does not require any additional power source for its operation. The Current study is an attempt towards the optimization of the battery using PCM by varying the shape and cell spacing for paraffin based PCM Material. In the present work Computational analysis is carried out to analyse the thermal performance of various shapes of battery module.

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.