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With the passage of the Kigali Amendment to the Montreal Protocol, HFC-134a refrigerant will be phased down in all markets worldwide, including those where automotive companies have been slow to embrace HFO-1234yf. Engineers are currently being challenged to design MAC systems using alternate low GWP refrigerants that are allowed by regulations, and are simultaneously cost-effective to manufacture, energy efficient, safe, reliable, affordable for consumers, and also suitable in electrified vehicles.

In today’s manual transmissions of car, gearshift system requires high performance with particular emphasis on low effort, minimal travel and positive feel. To meet these targets, a high capacity multi cone synchronizers along with higher co-efficient of friction material used for lower gears. The design of synchronizer with these specifications is influenced by torsional fluctuations from engine. Excessive torsional vibrations leads to wobbling of synchro rings within the peripheral clearances with surrounding parts. Wobbling leads to abrasion wear of frictional area of synchro ring causing grating or crashing noise of gears during shifting. This paper presents the optimization of the multiple cone synchronizer design exposed to excessive torsional vibrations and validation of the same on test bench during development stage instead vehicle level validation.

The primary function of an engine mounting bracket is to support the powertrain system in all road conditions without any failure. The mount has to withstand different road conditions and driving maneuvers which exert loads on it. Also, it is challenging to change the mounting locations and types after the engine is built; hence it is paramount to verify the mounting brackets against all abuse loads in the design stage. The Car manufacturers ensure engine mount bracket design meets CAE's (Computer-aided engineering) static and fatigue load cases. The CAE is performed using digital RLD (Road load data) loads. The design checks cumulative strain or stress against specified service life requirements during break and fatigue FOS (Factor of safety) calculations. However, it is difficult to simulate the material's fracture toughness to estimate the effect of the impact load on the mounting bracket.

In the context of vehicular safety and performance, brake pads represent a critical component, ensuring controlled driving and accident prevention. These pads consist of friction materials that naturally degrade with usage, potentially leading to safety issues like delayed braking response and NVH disturbances. Unfortunately, assessing brake pad wear remains challenging for vehicle owners, as these components are typically inaccessible from the outside. Moreover, Indian OEMs have not yet integrated brake pad life estimation features. This research introduces a hybrid machine learning approach for predicting brake pad remaining useful life, comprising three modules: a weight module, utilizing mathematical formulations based on longitudinal vehicle dynamics to estimate vehicle weight necessary for calculating braking kinetic energy dissipation; and temperature and wear modules, employing deep neural networks for predictive modeling.