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An engine cooling system is required to maintain stable operating temperature for the engine and prevent it from overheating. Thermal distortion of engine parts can take place if proper cooling is not maintained and engine may loss efficiency. One of the major problem in this domain is to incorporate separate cooling systems for the different variants of engines (different power rating). A single optimized cooling unit is desired to manage the entire range of engine rated power. The factors that affect the cooling system are front end grill opening area, air recirculation, location of snorkel inlet, radiator core size, which need to be tuned to get appropriate results. The above parameters are tuned to obtain appropriate results using the Computational Fluid Dynamics (CFD) simulations. In the next stage, on road cooling trials are performed and real time data is collected.

The Advancement in Connected vehicles Technology in recent years has propelled the use of concepts like the Internet of Things (IoT) and big data in the automotive industry. The progressive electrification of the powertrain has led to the integration of various sensors in the vehicle. The data generated by these sensors are continuously streamed through a telematics device on the vehicle. Data analytics of this data can lead to a variety of applications. Predictive maintenance is one such area where machine learning algorithms are applied to relevant data to predict failure. Field vehicle malfunction or breakdown is costly for manufacturers’ aftermarket services. In the case of commercial vehicles, downtime is the biggest concern for the customer. The use of predictive maintenance techniques can prevent many critical failures by tending to the root cause in the early stages of failure. Engine overheating is one such problem that transpires in diesel engines.