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In an automotive braking system, Vacuum pump is used to generate vacuum in the vacuum servo or brake booster in order to enhance the safety and comfort to the driver. The vacuum pump operation in the braking system varies from conventional to electric vehicles. The vacuum pump is connected to the alternator shaft or CAM shaft in a conventional vehicle, operates continuously at engine speed and supplies continuous vacuum to the brake servo irrespective of vacuum requirement. To sustain continuous operation, these vacuum pumps are generally oil cooled. Whereas in electric vehicles, the use of a motor-driven vacuum pump is very much needed for vacuum generation as there is no engine present. Thus, with the assistance of an electronic control unit (ECU), the vacuum pump can be operated only when needed saving a significant amount of energy contributing to fuel economy and range improvement and emission reduction.

With the introduction of Connected Vehicles, it is possible to extend the limited horizon of vehicles on the road by collective perceptions, where vehicles periodically share their information with other vehicles and servers using cloud. Nevertheless, by the time the connected vehicle spread expands, it is critical to understand the validation techniques which can be used to ensure a flawless transfer of data and connectivity. Connected vehicles are mainly characterized by the smartphone application which is provided to the end customers to access the connectivity features in the vehicle. The end result which is delivered to the customer is through the integrated telematics unit in the vehicle which communicates through a communication layer with the cloud platform. The cloud server in turn interacts with the final application layer of the mobile application given to the customer.

The automotive application places very special demands on the air conditioning system. As is the case with any other process, system efficiency is very important and the automotive air-conditioning application is no exception. While the characteristics of all the major components in the air conditioning system like compressor, condenser, evaporator and blower contribute to overall system efficiency, localized inefficiencies do play a part and so must be kept to a minimum, especially in this day and age when extra emphasis is being laid on sustainability. One such phenomenon that contributes to the system inefficiency is heat pick-up in suction line. Since the temperature at the evaporator-outlet is quite lower than ambient and also its surroundings (steering system pipes and hoses, engine, air intake pipes and so on), the refrigerant picks up heat as it moves along the suction line up to the compressor inlet. This heat pick-up is detrimental to the overall system performance.

With the advent of BS VI regulations, automotive manufacturers are required to innovate the powertrains, fuel systems, exhaust and its after treatment systems to meet the regulatory requirements. The exhaust regulations can be met either by reducing the exhaust gases being generated by the engine (attacking the source) or by treating the exhaust gases in after treatment devices. The choice of the opted system varies with the manufacturer. The after-treatment devices such as catalytic converters are generally mounted in the engine compartment to take advantage of high temperature of exhaust gases to yield the reactions. Such an arrangement imposes a lot of thermal load on the peripheral components such as gearshift cables, bearings, oil seals, driveshafts etc. Thermal shields or thermal sleeve are used to address thermal issue and to protect transmission components.

Various vehicle manufacturers are launching electric vehicles, which are more sustainable and environmentally friendly. The major component in electric vehicles is the battery, and its performance plays a vital role. Usually, the end of life of a battery in the automobile sector is when the battery capacity reaches 80% of its maximum rated capacity. The capacity of a lithium-ion cell declines with the number of cycles. So, a semi-empirical model is developed for estimating the maximum stored capacity at the end of each cycle. The parameters considered in the model explain the changes in battery internal structure, like capacity losses at different conditions. The capacity estimated using the semi-empirical model is further taken as the inputs for estimating capacity using the Artificial Neural Network (ANN) and Machine Learning (ML) techniques i.e., Linear Regression (LR), Gaussian Process Regression (GPR), Support Vector Machine methods (SVM).

The automotive industry has achieved remarkable advances in passenger car safety systems to mitigate the risk of injuries and fatalities caused by road accidents. However, to further improve vehicle safety, it is essential to have a deeper understanding of real-world accidents and the true safety benefits of various safety systems in the field. This requires a framework to evaluate the effectiveness of safety systems in reducing occupant injury and fatalities. This study aims to use machine-learning techniques to predict occupant injury severity by considering accident parameters and safety systems, using the Road Accident Sampling System - India (RASSI) real-world accident data. The RASSI database contains comprehensive accident data, including various factors that contribute to occupant injury. The study focused on fifteen accident parameters that represent key aspects of crash scenarios such as vehicle type, accident type, vehicle speed, and occupant details.