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The steering system of an automobile serves as the initial point of contact for the driver and is a crucial determinant in the purchasing choice of the vehicle. The present steering system is equipped with a singular Electric Power Assisted Steering (EPAS) map, resulting in a consistent steering sensation during maneuvers conducted at both low and high velocities. Certain vehicles are equipped with a steering system that includes fixed driving modes that require manual intervention. This paper presents a proposed Machine Learning based Adaptive Steering System that aims to address the requirements and limitations of fixed mode steering systems. The system is designed to automatically transition between comfort and sports modes, providing users with the desired soft or hard steering feel. The system utilizes vehicle response to driver input in order to identify driving patterns, subsequently adjusting steering assist and torque automatically.

In electric power assisted steering system (EPAS), the steering assistance torque is provided by the electric motor. The motor rating is decided based on rack force requirement which depends on the vehicle weight, steering gear ratio, wheel angles etc. The load on the EPAS motor varies with respect to the steered angles of the road wheels. The motor experiences higher load towards the road wheel lock position. Most of the steering systems used on passenger cars has rack and pinion gear with constant gear ratio (C-factor). The constant gear ratio is decided to create right balance between vehicle handling behavior and steering effort. The constant gear ratio exerts higher steering load which the EPAS motor is required to support up to road wheel lock angles and hence EPAS motor size increases. This paper presents variable gear ratio (VGR) steering system in which gear ratio varies from center towards end lock stroke of rack & pinion.

Error in suspension asymmetry or tire parameters may lead to vehicle drifting laterally from its intended straight-line path, which is called vehicle pull. Driver then needs to apply constant steering correction to maintain the vehicle in straight line which will lead to high driver fatigue and deteriorate driving experience. Manufacturing a perfectly symmetric suspension system is impractical, however an insight into the manufacturing tolerances of suspension system at the early design stage can be extremely useful. Also tire force and moment parameters at straight line operation and its maximum allowable variations will help in defining the tire parameter specifications and tolerances. The objective of this study was to develop a 1D model of suspension and tire system which can predict the torque experienced in steering and drift of the vehicle from straight line due to the tire force and moment and asymmetric suspension geometry.

The basic function of steering system is to control the direction of the vehicle. The driver applies effort on the steering wheel and receives feedback through the steering system as a result of tire to road interaction. This feedback consists of a haptic (force) feedback which is directly felt by the driver and it is termed as steering feel. Precise steering feel gives better driving experience and is decisive factor for customer to buy a vehicle as well as for OEMs in building brand image. Along with steering parameters, suspension and tire parameters also has significant impact on steering feel. In past, modelling of the steering system was done at component level or with simplified vehicle system. Such approaches had not given accurate results of steering feel metric and resulted in incorrect steering design parameter selection. In order to replicate actual vehicle characteristics, complex and detailed modelling of steering, tire and suspension subsystems is necessary.

In the recent years steering feel characteristics have emerged as one of the important brand image attributes of automotive OEMs. Since past few decades, the hydraulic assisted steering system (HPAS) on which lot of research was done to tune the steering feel has been taken over by electric power assisted steering (EPAS) system. The EPAS primarily uses an electric motor controlled by an electronic control unit to assist the driver in maneuvering the vehicle. The next big leap in the steering system advancement is steer-by-wire (SbW) technology where the mechanical linkage between the steering wheel and the road wheels is eliminated. The advantages of this system are ease to use, elimination of noise-vibration-harshness of steering system caused by road forces, modularly of steering system for packaging, improved visibility to front-end displays and road ahead and a fun to drive concept.

In hydraulic power assisted steering (HPAS) system higher steering oil temperature can cause deterioration of oil reservoir, thermal failure of pump/valves and can diminish system performance. Thermal analysis is performed for HPAS system architecture development in order to maintain steering oil temperature within design limits for optimal performance & increased life of HPAS steering system. In present study mathematical model of HPAS system consisting of steering pump, flow and pressure control mechanism, rotary valve, steering circuit pipes and hoses, thermal interaction with ambient is developed. The model is able to predict steering torque-hydraulic pressure dynamics of HPAS system as per design. Developed HPAS system model is integrated with steering kinematic and uniquely developed tire-road contact patch sliding model for estimating non-linear rack force behavior at higher steering angle.

Vehicles have a wide range of resonance band due to design nature & characteristics of its aggregates. First order, vehicle speed dependent, wheel disturbance due to wheel imbalances can result in excitation of different vehicle aggregates. Steering wobble refers specifically to first order road wheel excitation effects, in frequency range of 10-16 Hz, that manifest themselves as significant steering wheel torsional vibrations at highway speeds i.e. at the range of 80 km/h to 120 km/h on smooth roads. The tire, being an elastic body analogous to an array of radial springs, may exhibit variations in stiffness about its circumference; hence, it may vibrate at different frequencies due to wheel imbalance. This paper introduces dynamic steering wobble analysis methodology either using vehicle speed at Discrete (individual speeds) or by Sweep (low to high speed) method to investigate steering wobble in the virtual environment using the full vehicle MBD model.