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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.

There has to be a good co-relation/ relationship between the pedal effort applied, pedal travel, deceleration level achieved and stopping distance for “good brake feel”. Brake feel also depend upon the time lag between the force applied on brake pedal and the response of braking system. Hence “brake feel” can be improved by reducing the response time of the brake system. Many vehicles are having “poor brake feel” complaints, pertaining to the above mentioned reasons. This paper relates to an improved brake system for automobile in which reduction in reaction time was done by artificially increasing differential pressure head across vacuum booster diaphragm. Brake booster is given an input of compressed air to the valve body during actuation, thereby increasing the differential pressure across the diaphragm. The compressed air is bled from turbocharger-intercooler of the vehicle which is stored in a reservoir, with one way valve, while cruising.

Designing a Passenger vehicles suspension system is a key challenge for all OEMs because balancing buzz, squeak, and rattle (BSR) acoustic performance at low-speed driving and improving ride quality at high-speed driving conditions are bet challenging. Suspension noise deteriorates in-cab acoustic quietness and overall vehicle performance. For this reason, optimizing these noises is becoming increasingly prioritized as a key design issue throughout the development process of suspension system. This paper studies the various components of suspension system and their noises in Passenger vehicles. Based on customer voice index and drive pattern, suspension anomalous Clunking noise was identified in Passenger vehicles. This noise phenomenon was cascaded from the vehicle level to BSR rig and eventually to the suspension rig for root cause analysis.

Authors were challenged with a task of developing a full vehicle simulation model, with a target to simulate the electrical system performance and perform digital tests like Battery Charge Balance, in addition to the fuel efficiency estimation. A vehicle is a complicated problem or domain to model, due to the complexities of subsystems. Even more difficult task is to have a control algorithm which controls the vehicle model with the required control signals to follow the test specification. Particularly, simulating the control of a vehicle with a manual transmission is complicated due to many associated control signals (Throttle, Brake and Clutch) and interruptions like gear changes. In this paper, the development of a full vehicle model aimed at the assessment of electrical system performance of the vehicle is discussed in brief.

Reduction of NOx (Oxides of Nitrogen) and particulates from engine exhaust is one of the prime considerations in current research and development in automotive industry. The present paper describes the combustion optimization done on a four cylinder, 4 liter DI diesel engine to meet stringent Euro-III emission norms. The engine FIE (Fuel Injection Equipment) and injector geometry was optimized for performance and emission. Smoke measurements were considered as indicative of soot, to predict particulate emissions. This was done to simplify the overall process and save development time. It was concluded that by combining the flexibility of electronically controlled fuel injection begin, with improved nozzle technologies, with higher spray velocities and spray penetration, a considerable reduction in NOx and particulate emissions can be achieved. This can serve as a low cost solution, without any exhaust after-treatment systems.

In developing nations, most passenger vehicles are equipped with mobile air conditioning (MAC) systems that work on Hydro Fluoro Carbons (HFC) based refrigerants. These refrigerants have a high global warming potential (GWP) and hence adversely affect the environment. According to the Kigali amendment to Montreal Protocol, Article-5 Group-2 countries including India must start phasing down HFCs from 2028 and replace them with low Global Warming Potential (GWP) refrigerants. One such class of low GWP refrigerant is Hydro Fluoro Olefins (HFO) In order to replace HFCs with HFOs in existing MAC systems, the various system performance parameters with the new refrigerant are required to be evaluated. Performance evaluation of MAC system is rendered quicker and cost-effective by deploying a digital simulation tool. There is good correlation and confidence established for MAC performance prediction with HFCs through 1D CAE.

Micro Hybrid Systems are a premier approach for improving fuel efficiency and reducing emissions, by improving the efficiency of electrical energy generation, storage, distribution and consumption, yet with lower costs associated with development and implementation. However, significant efforts are required while implementing micro hybrid systems, arising out of components like Intelligent Battery Sensor (IBS). IBS provides battery measurements and battery status, and in addition mission critical diagnostic data on a communication line to micro hybrid controller. However, this set of data from IBS is not available instantly after its initialization, as it enters into a lengthy learning phase, where it learns the battery parameters, before it gives the required data on the communication line. This learning period spans from 3 to 8 hours, until the IBS is fully functional and is capable of supporting the system functionalities.

This paper proposes a cost effective method, with simple techniques, to evaluate Bump Steer and Brake Steer on a rigid axle vehicle under dynamic conditions. A relationship between calculated values, measured values and a subjective assessment of the vehicle lateral deviation is established. An array, of inter-relationship of the parameters such as offset of steering arm, draglink length, front spring stiffness, height of spring hanger bracket is done. Percentage of influence of the parameter change on the performance of the vehicle is evaluated and standard statistical analysis is used to arrive at inter-relationship of various parameters and ranking of their influence on lateral deviation of the vehicle under braking is established, there by resulting in reduction in iterative process. The results obtained display a good correlation with ADAMS Analysis to the tune of 90% and are in agreement with subjective assessment.

This paper aims at analysing the effect of regeneration braking on the amount of energy harnessed during vehicle braking, coasting and its effect on the drive train components like gear, crown wheel pinion, spider gear & bearing etc. Regenerative braking systems (RBS) is an effective method of recovering the kinetic energy of the vehicle during braking condition and using this to recharge the batteries. In Battery Electric Vehicles (BEV), this harnessed energy is used for controlled charging of the high voltage batteries which will help in increasing the vehicle range eventually. Depending on the type of the powertrain architecture, components between motor output to the wheels will vary, i.e., in an e-axle, motor is coupled with a gear box which will be connected with differential and the wheels. Whereas in case of a central drive architecture, motor is coupled with gearbox which is connected with a propeller shaft and then the differential and to the wheels.

Computer Aided Engineering (CAE) plays an important role in the product development. Now a days major decisions like concept selection and design sign off are taken based on CAE. All the Original Equipment Manufacturers (OEMs) are putting consistent efforts to improve accuracy of the CAE results. In recent years confidence on CAE prediction has been increased mainly because of good correlation of CAE predictions with the test results. Defining proper correlation criteria and using a systematic approach helps significantly in building the overall confidence level for predictions given by CAE simulations. Representation of manufacturing effects on material properties and material failure in the simulation is still a big challenge for achieving a good CAE correlation. This paper describes side impact test vs CAE correlation. The important parameters affecting the CAE correlation were discussed.

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.

Steering pull during high speed braking of heavy commercial vehicles possesses a potential danger to the occupants. Even with negligible wheel-to-wheel brake torque variation, steering pull during the high speed braking has been observed. If the steering pull (i.e. steering rotation) is forcibly held at zero degree during high speed braking, the phenomena called axle twist, wheel turn and shock absorber deflection arise. In this work the data have been collected on the mentioned measures with an intention to develop a mathematical model which uses real time data, coming from feedback mechanism to predict the values of the measures in coming moments in order to aid steering system to ‘auto-correct’. Driven by the intention, ‘Time Series Analysis’, a well-known statistical methodology, has been explored to see how suitable it is in building the kind of model.

Engine Stop/Start System (ESS) promises to reduce greenhouse emissions and improve fuel economy of vehicles. Previous work of the Authors was concentrated on bridging the gap of improvement in fuel economy promised by ESS under standard laboratory conditions and actual driving conditions. Findings from the practical studies lead to a conclusion that ESS is not so popular among the customers, due to the complexities of the system operation and poor integration of the system design with the driver behavior. In addition, due to various functional safety requirements, and traffic conditions, actual benefits of ESS are reduced. A modified control algorithm was proposed and proven for the local driving conditions in India. The ways in which a given driver behaves on the controls of the vehicles like Clutch and Brake Pedals, Gear Shift Lever were not uniform across the demography of study and varied significantly.

With the current state of ever rising fuel prices and unavailability of affordable alternate technologies, significant research and development efforts have been invested in recent times towards improving fuel efficiency of vehicles powered with conventional internal combustion engines. To achieve this, a varied approach has been adopted by researchers to cover the entire energy chain including fuel quality, combustion quality, power generation efficiency, down-sizing, power consumption efficiency, etc. Apart from energy generation, distribution and consumption, another domain that has been subjected to significant scrutiny is energy recuperation or recovery. A moving vehicle and a running engine provide a number of opportunities for useful back-recovery and storage of energy. The most significant sources for recuperation are the kinetic energy of the moving vehicle or running engine and to a lesser extent the thermal energy from medium such as exhaust gas.

This paper addresses the "Grunt Noise" anomaly in Hydraulic Power Assisted Steering (HPAS) systems, detailing an extensive effort to resolve this disruptive issue. HPAS, while cost-efficient, faces challenges as it adapts to customer demands for reduced steering effort and enhanced handling. Intensified HPAS intervention requires components to withstand higher pressures and tighter tolerances, leading to occasional anomalies. "Grunt Noise" arises from Torsion bar (T-bar) resonance with fluid pressure pulsations. A comprehensive study identifies load conditions, transfer paths, and frequency bands, extending from vehicle to Pinion Valve assembly levels. Root cause analysis traces the issue from Steering Wheel to T-bar, validating the approach. The T-bar's twisting operation renders torsional stiffness crucial for Grunt Noise. Lower stiffness T-bar, when overpowered by liquid force, causes microsecond imprecise valve openings, leading to cavitation-induced Rack & Pinion vibrations.

In high-end commercial vehicles, technologies like Electronic Braking Systems (EBS) help pull away the vehicle from a standstill on steep gradients with no risk of rolling back. Tata Motors has developed an indigenous Anti-Roll Back (ARB) system that effectively minimizes this risk but without the use of EBS/HSA. The ARB delivers identical functionality to the HSA feature in the EBS but autonomously, and by purely electric means. In the proposed system, the electric traction motor develops a high positive torque when the vehicle tries to roll back upon minimal accelerator pedal press. The system is autonomous in the sense that the driver does not need to press any HSA switch on the dashboard and the system works on relatively flatter road also which otherwise is not the case with HSA as it negatively affects the operation on flatter road by locking wheels and vehicle launches with a very high torque when brakes are automatically released by EBS upon threshold torque build-up.

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 automotive industry is heading in the direction of signing off the exhaust system durability based on computer simulation rather than rig simulation and physical vehicle testing. This is due to the cost, time and availability of prototype vehicles and test track. Use of Finite Element Method (FEM) enables to assure the structural integrity of the exhaust system and also contribute to better understanding of the system behavior in the various operating conditions and evaluation of structural strength. This paper deals with dynamic analysis of a modular automotive exhaust system where it is directly mounted on power train pack. Selection of dynamic loads, processing of the test data, and effect of assembly loads along with material property variation due to temperature are explained. It also includes validation of the CAE model, prediction of probable failure locations and improving the design based on analysis outcome.

Better ride and comfort, enhanced safety, reliability and durability, lower running cost as well as cost of ownership continue to be challenges for automotive OEMs. Higher fuel efficiency is considered as USP not only for lower running cost but also is hygiene factor from sustainability point of view. This has necessitated the need for Augmenting Light weighting horizon in automotive OEMs. Augmenting this leads to invention of innovative materials and processes for emerging cost competitive market. This paper focuses on technology efforts towards augmenting light weighting Horizon in Automotive. Light weighting concepts being explored by OEMs with the help of automotive component manufacturers from Powertrain - Engines & Transmission, Chassis and Suspension are discussed.

Global concerns over availability and environmental impact of conventional fuels in recent years have resulted in evolution of Electric Vehicles. Research and development focus has shifted towards one of its main components, Lithium-ion battery. Development of high performing, long lasting batteries within challenging timelines is the need of the industry. Lithium-ion batteries undergo “battery ageing”, limiting its energy storage and power output, affecting the EV performance, cost & life span. It is critical to be able to predict the rate of battery ageing & the impact of different environmental conditions on battery lifetime/capacity. Conventionally, extensive physical vehicle level testing is carried out on batteries to map the battery capacity in various conditions. This is a lengthy & expensive process affecting the product development cycle, paving the way for an alternative process.