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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 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.

In comparison to traditional gasoline-powered vehicles, Electric vehicles (EVs) development and adoption is driven by several factors such as zero emissions, higher performance, cost effective in maintenance, smoother and quieter ride. Global OEMs are competing to provide a reduced in-cab noise for ensuring a smooth and quiet driving experience. Short project timelines for EV demands quick design and development. In initial stages of project, input data availability of EV is limited and a simplified approach is necessary to accelerate the development of vehicle. This paper focuses on simulation methodology for predicting structure borne noise from powertrain deploying Transfer Path Analysis approach. Current simulation methodology involves full vehicle model with multiple flexible bodies and full BIW flexible model which leads to complex modelling and longer simulation times.

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.

Groan is a low frequency noise generated when moderate brake pressure is applied between the surfaces of the brake disc and the brake pad at a low-speed condition. Brake groan is often very intense and can cause large numbers of customer complaints. During a groan noise event, vehicle structure and suspension components are excited by the brake system and result in a violent event that can be heard and felt during brake application. The cause of noise is friction variation of stick-slip phenomenon between friction material and disc. Creep groan is the structure-borne noise that is related to dynamic characteristic of the vehicle. However, it has been mainly improved through friction material modifications in the past. In this paper, transfer path of creep groan noise was analyzed by means TPA and structural countermeasure to creep groan noise was suggested. This paper discusses the approach for prediction and mitigation of brake groan noise for passenger vehicles having disc brakes.

Durability is an important indicator to measure the automobile quality and reliability. Automotive industry is striving to develop products having excellent performance to weight ratios and along with high safety standards. A successful product should have adequate robustness during normal customer operation and the ability to withstand high impact events without impairment of function or safety relevant damage. Road Load Data Acquisition (RLDA) along with efficient design and validation processes are, among others, critical factors for success in the automotive industry. Physical RLDA is expensive and time consuming, the prototype vehicles being costly and only available at a later stage in the vehicle development cycle. Component failures occurring on the proto test vehicles can prove to be a major setback, delaying the product launch by months. In order to overcome above challenge, this paper presents an innovative methodology to carry out Digital RLDA (dRLDA).

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.

Tire inflation pressure has a significant impact over vehicle driving dynamics, fuel consumption as well as tire life. Therefore, continuous monitoring of tire pressure becomes imperative for ride comfort, safety and optimum vehicle handling performance. Two types of tire pressure monitoring systems (TPMS) used by vehicles are - direct and indirect TPMS. Direct systems deploy pressure sensors at each wheel and directly send pressure value to the vehicle Controller Area Network (CAN). Indirect sensors on the other hand use the information from already existing sensors and some physics-based equations to predict the value of tire pressure. Direct TPMS tend to be more accurate but have higher cost of installation while indirect TPMS comes with a minimum cost but compromised accuracy. A digital proof-of-concept study for indirect TPMS development of a non-ESP vehicle based on machine learning (ML) technique is elaborated in this paper.

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.

Handling performance of a vehicle is a key characteristic determining the response of vehicle under different operating scenarios. An insight into these vehicle-handling characteristics at early stage can be extremely useful in the design and development process. Tire characterization and tuning is important and mandatory to scrutinize each functional and individual parameter of tire. Tire force and moment data is having a significant effect in vehicle handling. Segregation of tire parameter, which is contributing vehicle-handling performance, helps to identify and perform optimization for improvisation. The main objective of this study is development and integration optimized 1D tire model into multibody dynamics model of the vehicle to observe various vehicle compliances towards its handling performance target.

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.

While designing the exhaust system of passenger car on one hand there is stringent emission regulations, packaging constraints, high NVH performance requirement. On the other hand with lightweight vehicle design there is tremendous pressure on weight reduction of exhaust system while keep the same NVH performance levels. Exhaust system consist of muffler, bellows, pipes and hangers. For muffler design both acoustic (transmission loss, pass-by noise, tail pipe noise etc.) and non-acoustic (backpressure) parameters needs to be considered. In the current paper, methodology for muffler design optimization using 1D acoustic simulation software is presented. The baseline exhaust design consist of two mufflers; main muffler and post muffler. Simulation methodology is developed to optimize main muffler design in order to eliminate post muffler requirement while achieving the same performance of baseline exhaust design.

The objective of the present study is to experimentally characterize the drag torque in open multi-disks wet clutches. For this purpose, two groups of experiments are performed using an advanced SAE#2 test setup. The experiments are designed under real conditions of variable automatic transmission fluid (ATF) flow rate and higher clutch speed range as experienced by wet clutches in real operation. The first test group examines the influence of parameters such as ATF flow rate, ATF temperature, and disks size whereas, the second test group investigates the effect of disks rotational conditions on the drag torque. By analyzing, the obtained experimental results, the relationship between the amount by which the parameters are varied and the corresponding change in the drag torque are established. In addition, a general qualitative behavior of the drag torque as function of clutch speed is proposed and the associated physical phenomena are explained.