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The given paper presents the main elements of frictional power loss distribution in an automotive axle for passenger car. For reference two different axles were compared of two different sizes to understand the impact of size and ratio of gear and bearings on power loss characteristics. It was observed that ~50% of total axle power loss is because of pinion head-tail bearing and its seals, which is very significant. Roughly 30% of total power loss is contributed by pinion-ring gear pair and differential bearings and remaining ~20% by wheel end bearing and seals. With this study the automotive companies can take note of the area where they need to focus more to reduce their CO2 emissions to meet the stringent BS6, CAFÉ and RDE emission norms.

Batteries have become increasingly important in automotives with increase in vehicle electrical loads. Therefore the reliability of battery is a critical issue in automotive applications. It has been noticed that most batteries have limited cycle durability, that is, the total capacity drops when a battery is charged and discharged for a number of cycles. If a battery is too weak to offer sufficient energy, it should be replaced at the right time. But current problem is that there is no reliable method to quantify the capacity loss and to estimate the remaining capacity of battery. Complete discharge, which is the only way of capacity estimation, which will effect the battery plates therefore it cannot be used too frequently. This paper summarizes the experimental work in the development of the battery status estimation algorithm.

Suspension plays a crucial role in stabilizing, comfort and performance of a vehicle. During vehicle braking operation, load transfer happens from rear axle to front axle resulting in shifting of vehicle’s center of gravity towards vehicle front for a momentarily duration which is called diving. This phenomenon leads to dropping of traction at rear wheel end resulting in lifting of rear axle with front wheel as pivot. This causes increase in front to rear weight ratio of vehicle system and compromising driver safety due to skidding and locking of rear wheel-end. To minimize this phenomenon’s affect, optimum anti-dive suspension geometry is used to have better rear wheel end traction resulting in improved braking stability.

Development of Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs) is gaining traction across all geographies to help meet increasing fuel economy regulations and as a pathway to offset concerns due to climate change. But HEVs and EVs have so far been a nascent market for India. These technologies have primarily shifted towards Lithium-ion batteries (LIB) for energy storage due to its high energy and power densities. In order to make actual business sense of these technologies, of which, battery is a major cost driver, it is necessary for these batteries to provide similar performance and life expectancy across the operating boundary of the vehicles, as well as provide the requirements at a competitive cost. In other words, the LIBs have to sustain the normal life cycle requirements and withstand wide range of storage temperatures that the conventional gasoline/diesel vehicles have been good at and still ensure good life.

Range anxiety is one of the major factors to be dealt with for increasing penetration of EVs in current Automotive market. The major reasons for range anxiety for customers are sparse charging infrastructure availability, limited range of Electric vehicles and range uncertainty due to diverse real-world usage conditions. The uncertainty in real world range can be reduced by increasing the correlation between the testing condition during vehicle development and real-world customer usage condition. This paper illustrates a more accurate test methodology development to derive the real-world range in electric vehicles with experimental validation and system level analysis. A test matrix is developed considering several variables influencing vehicle range like different routes, drive modes, Regeneration levels, customer drive behavior, time of drive, locations, ambient conditions etc.