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As the FTP-75 drive cycle does not have a prescribed gear shift pattern, automotive OEMs have the flexibility to design. Conventionally, gear shift pattern was formulated based on trial and error method, typically with 10 to 12 iterations on chassis dynamometer. It was a time consuming (i.e. ~ 3 to 4 months) and expensive process. This approach led to declaring poor fuel economy (FE). A simulation procedure was required to generate a gear shift pattern that gives optimal trade-off amongst conflicting objectives (FE, performance and emissions). As a result, a simulation tool was developed in MATLAB to generate an optimum gear shift pattern. Three different SUV/UV models were used as test vehicles in this study. Chassis dyno testing was conducted, and data was collected using the base and optimized gear shift patterns. Dyno test results with optimized gear shift pattern showed FE improvement of ~ 4 to 5% while retaining the NOx margin well above engineering targets.

With an intense competitive automotive environment, it becomes imperative for any OEM to launch their products into the market in a short span of time & with a ‘First Time Right’ approach. Within the current scenario in the Automotive Industry, the selection of optimum set of hard points and wheel geometry often becomes an iterative or a trial-and-error process which is both time consuming and involves higher development cost as there may be instances where 2 to 3 sets of iterations are needed before specification is finalized for production. Through this paper, an attempt has been made to develop a methodology for deciding wheel geometry parameters (covered in the later section of this paper like Caster, Camber, Mechanical trail, etc.) [1, 2, 3, 4] for a three wheeled vehicle as a First Time Right (FTR) approach to cut down on conventional, expensive & time-consuming iterative approach.

Automotive steel wheel is a critical component for human safety. For validating steel wheel various tests will be performed at component and vehicle level. Cornering test performed at vehicle level is one of the tests, where wheel will be validated for high cornering loads. Cornering test performed at vehicle level consists of three different events i.e., rotations of vehicle in track1, rotations of vehicle track 2 and rotations of vehicle in track3. As wheel will experience different loading in each of the events of cornering test, correlating the virtual Finite Element Analysis (FEA) with physical test is quite challenging. If in FEA we can predict the damage and life very near to the physical validation, we can create a safe wheel for high cornering loads without any test concerns. Vent hole shape and Hat depth are two important aspects in wheel disc design. Vent hole shape and size will influence the heat dissipation of braking.

Vehicle pull during braking can be defined as the deviation of vehicle travel from intended path of the vehicle by a margin of half a wheel track or more. It is a dynamic phenomenon with very complex inter-dependencies among the combined functioning of various aggregates such as steering system, suspension system, axles, and brakes. The problem is aggravated with shorter wheelbase & higher CG (Centre of Gravity) height, where the instantaneous load transfers are sudden and of relatively high magnitude which can lead to a combination of forces that are responsible for vehicle drifting or pulling to anyone side of centre-line travel. Vehicle with shorter wheelbases, high GVW and high CG heights are more prone to this unstable behaviour due to sudden change in dynamic forces acting on the tires while turning and braking.

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.

Toe setting is one of the major wheel alignment parameters which directly effects handling of a vehicle. Correct toe setting ensures desired dynamic behavior of an automobile like straight line stability, cornering behavior, handling and tire durability. Incorrect setting of toe during design stage significantly deteriorates tire durability and leads to uneven tire wear. In the present scenario of automotive industry, toe setting is majorly an iterative or a trial and error process which is both time consuming and involves higher development cost as there may be instances where 2 to 3 sets of iterations are needed before specification is finalized for production. Therefore, determining optimum toe setting at an early stage of a product development will not only save significant development time but it will also benefit in reducing product validation time and cost.

Structural elastomer components like bushes, engine mounts are required to meet stringent and contrasting requirements of being soft for better NVH and also be durable at different loading conditions and different road conditions. Silent block bushes are such components where the loading in radial direction of bushes are high to ensure the durability of bushes at high loads, but has to be soft on torsion to ensure good NVH. These requirements present with unique challenge to optimize the leaf spring bush design, stiffness and material characteristics of the rubber. Traditionally, bushes with varying degree of stiffness are selected, manufactured and tested on vehicle and the best one is chosen depending on the requirements. However, this approach is costly, time consuming and iterative. In this study, the stiffness targets required for the bush were analysed using static and dynamic load cases using virtual simulation (MSC.ADAMS).

In driving, Steering is the input motion to the vehicle. The driver uses steering input to change the direction of the vehicle. During Parking or U turn bends the Steering is locked and later released to follow the desired path. Steering return ability is defined as the ratio of difference between steering wheel position at lock condition and steering wheel angle after 3 seconds of release to the steering wheel angle at lock condition. Having proper steering return ability characteristics has an important effect on vehicle steering characteristics. In this study, a full vehicle ADAMS model is prepared, and virtual steering return ability have been simulated in ADAMS/CAR for a Pickup truck vehicle. Simulated responses in the steering wheel angle have been validated by comparison with measurements. A Design of Experiment study is setup and Iterations are carried out to find the effect of Hard points and friction parameters.