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In recent years, the push for reduced product development timelines has been more than ever with significant changes in the automotive market. High electrification, intelligent vehicle systems and increased number for car manufacturers are a few key drivers to the same. The front loading of development activities is now a key focus area for achieving faster product development. From vehicle dynamics point of view availability of subjective evaluation feedback plays a key role in optimization various system specifications. This paper discusses an approach for front loading through parallel development of the tire and vehicle chassis system, using advanced simulation and driving simulator technology. The proposed methodology uses virtual tire models which in combination with real-time vehicle model enables subjective evaluation of vehicle performance in driver-in-loop simulators.

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.

As customers are inching towards adoption of electric vehicles as an alternative to internal combustion engines, automotive OEM’s will have to embrace this change and equip with new product development process. When it comes to Electric Vehicle (EV) in comparison with Internal Combustion Engine (ICE), NVH plays a major differentiator for vehicle refinement. Squeak and rattles will account for 20-25% of overall in-cabin noise source in an electric vehicle, most of which is observed from interior trims. Trims are mounted using small plastic clips which function as attachments and play a significant role in part retention and part integrity during normal operation and in case of any transient events. The engineering specifications for selecting a clip is force in newtons and it is mostly driven by ease of assembly, serviceability, and durability. A single DOF system with a specimen mass is developed and stiffness and damping are calculated based on transmissibility.

This paper examines one of the approaches used to identify the root causes of sound quality issues in vehicles, including the direct impact of psychoacoustics on the human experience. Specifically, the absence of masking effects provided by traditional combustion engines has made noise and vibration from electric drives significant factors in decision-making processes, with high-pitched tonal noise from electric motors causing annoyance and sound quality concerns for electrified propulsion systems. During vehicle testing at different speeds, a whining noise was observed, leading to an NVH test to locate the noise source. The noise is traced to the transmission by the dominating order of input reduction along with the contribution from the casing resonance. A multi-physics-based e-NVH analysis was performed, and the test data were correlated.

The brass synchronizers are not resistant to abusive conditions of gearbox operations, but they are very durable and cheap when used on their favorable material property working limit. The main failure which can occur in the gearbox due to the synchronizer is crash noise. During gear shifting the gear crash will create high discomfort for the driver and must apply high force to change the gears. The main factors which contribute to the crash phenomenon are the insufficient coefficient of friction, high drag in the system, and high wear rate of the synchronizer rings before the intended design life of the synchronizer. The brass synchronizers were tested on the SSP-180, ZF synchronizer test rig to know the effect of the synchronizer performance parameters like the coefficient of friction, sleeve force, slipping time as well as durability parameters like wear rate when the operating temperature of the oil is changed.

Gears are one of the vital components to transmit torque efficiently. Helical gears are chosen as they transmit higher torque with lesser noise compared to spur gears of same size. All new age gearboxes require to transmit maximum torque with minimum packaging space available to improve torque density. Ways of reducing weight are using lesser density material, decreasing centre distance, and thereby reducing pitch circle diameter of all gears, etc. However, they will also affect torque carrying capacity of gearbox which can lead to gear failure in conventional transmission architecture gearboxes with input reduction method. In input reduction method, torque gets multiplied from input shaft to countershaft. Countershaft torque is multiplied to output shaft gears requiring higher torque capacity gears on output shaft. In this research, output shaft reduction architecture is proposed to avoid torque multiplication from input shaft to countershaft gears.

Electric Vehicle is the need of an hour, as due to excessive usage of IC Engine vehicles has resulted in the depletion of the ozone layer to a significant level and fuel cost is increasing. With new technologies coming into the market, challenges come hand in hand because of Electric Vehicle. In comparison to IC Vehicle, areas of thermal management or the number of components for which thermal management needs to be done is higher and rather complex. As the thermal management system is the second highest energy consuming source after the powertrain of the electric vehicle, an efficient and reliable design is mandatory to ensure better range in an Electric Vehicle. Thermal Management of the Electric Vehicle has been identified as one of the critical parameters for balancing both cabin comfort as well as Battery temperature. One of the major concerns is meeting the Cabin comfort during colder weather with minimum energy consumption.

Original Equipment Manufacturers (OEMs) should innovate ways to delight customers by creating affordable products with improved drive experience and occupant comfort. Vehicle refinement is an important initiative that is often take-up by the project teams to ensure that the product meets the customer’s expectations. A few important aspects of vehicle refinement include improving the Noise Vibration Harshness (NVH), ride and handling performance pertaining to the Functional Image (FI) of the product. Optimizing the suspension design variables to meet both ride and handling performance is often challenging as improving the ride will have a deteriorating effect on handling and vice-versa. The present work involves Multi-Objective Design Optimization (MDO) of the suspension system of an automotive Sports Utility Vehicle (SUV) platform considering both ride and handling requirements, simultaneously.

Modern day automotive market demands shorter time to market. Traditional product development involves design, virtual simulation, testing and launch. Considerable amount of time being spent on virtual validation phase of product development cycle can be saved by implementing machine learning based predictive models for key performance predictions instead of traditional CAE. Durability oil canning loadcase for vehicle hood which impacts outer styling and involves time consuming CAE workflow takes around 11 days to complete analysis at all locations. Historical oil canning CAE results can be used to build ML model and predict key oil canning performances. This enables faster decision making and first-time right design. In this paper, prediction of buckling behaviour and maximum displacement of vehicle hood using ML based predictive model are presented. Key results from past CAE analysis are used for training and validating the predictive model.

The bolted joints in suspension systems are subjected to severe external service loads during vehicle operation. To prevent the loaded joint from loosening and allowing it to retain its potential energy stored during assembly, a holistic design approach is needed. This paper explains the methodology to design and optimize bolted joints for the suspension systems of a modern 7-seater sports utility vehicle.

Computer Aided Engineering (CAE) simulations are an integral part of the product development process in an automotive industry. The conventional approach involving pre-processing, solving and post-processing is highly time-consuming. Emerging digital technologies such as Machine Learning (ML) can be implemented in early stage of product development cycle to predict key performances without need of traditional CAE. Oil Canning loadcase simulates the displacement and buckling behavior of vehicle outer styling panels. A ML model trained using historical oil canning simulation results can be used to predict the maximum displacement and classify buckling locations. This enables product development team in faster decision making and reduces overall turnaround time. Oil canning FE model features such as stiffness, distance from constraints, etc., are extracted for training database of the ML model. Initially, 32 model features were extracted from the FE model.

E-mobility is creating more challenges and great opportunities for automotive textile industries to bring out new textiles for light weight, more aesthetic, better feel, sustainable and biomaterial to meet the customer perception. Textiles allows a more design freedom to in terms of construction, weaving and wrapping solutions. A hard rough plastic surface could be transferred into a more pleasant soft touch surface by a simple wrapping with textiles. The introduction of electric vehicle will convert the car as more silent as it replaces the engine by motor and battery mechanism. The more silent is the car, the more silent is the BSR behavior of the material. This work discloses of a polyester textile developed to meet automotive lightweight to strength requirements with its new nonwoven construction for seat insert and bolster application which demands for high breaking strength, abrasion resistance, stretch and set and soiling resistance.

In an off-road vehicle, Vehicle Structure plays a major role in passenger safety, Aesthetics, Durability, through a validated construction of canopy structure. This structure is to maintain the shape of the vehicle and to support various loads acting on the vehicle. In present market a safe, Durable, Robust, Waterproof, Noise less, Light weight and cost-effective off-road vehicle will always be a delight for any customer. However, the current conventional way of Soft top vehicle structure use metal brackets and formed sheet parts to create a structure to retain the canopy shape in place. These conventional structures are often heavier and would have many demerits such as heavy weight, Corrosion, Risk of canopy tear due to metallic structure edges and inappropriate draining, water management. Considering this we replaced the heavy metal brackets in to blow molded plastic parts.

Ground clearance plays a vital role in an off-road vehicle during off roading. Higher the ground clearance, higher is the difficulty during ingress & egress of the vehicle. This brings in the necessity to provide entry-assist grab-handles for vehicle with more ground clearance (>200mm). Entry-assist grab handles alleviates the pain of the occupants during ingress and egress. For entry-assist grab handles’ purpose to be served, it should provide comfortable ergonomic grip & have to take the load of passengers while ingress or egress through-out the complete life cycle of the vehicle. Entry Assist grab handles can be fitted on A-Pillar zone to assist first row passengers & on B-pillar zone to assist second row passenger. Providing entry-assist grab handles on pillar trims make the grab-handles exposed to head-impact zone and hence, in most of the cases, it should pass the head impact regulations framed for respective countries.

Accurate ride and handling prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of damper model. Conventional methods used for modelling complex vehicle components (like bushings and dampers) are often inadequate to represent behaviour over wide frequency ranges and/or different amplitudes. This is difficult in the part of OEMs to model the physics-based model as the damper’s geometry, material and characteristics property is proprietary to part manufacturer. This is also usually difficult to obtain as a typical data acquisition exercise takes lots of time, cost, and effort. This paper aims to address this problem by predicting the damper force accurately at different velocity/ frequency and amplitude of measured data using Artificial Neural Networks (ANN).

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.

In today’s scenario, internal combustion engines have conflicting requirements of high power density and best in class weight. High power density leads to higher loads on engine components and calls for a material addition to meet the durability targets. Lightweight design not only helps to improve fuel economy but also reduces the overall cost of the engine. Material change from cast iron to aluminium has a huge potential for weight reduction as aluminium has 62% lesser mass density. But this light-weighting impacts the stiffness of the parts as elastic modulus drops by around 50%. Hence, this calls for revisiting the design and usage of optimization tools for load-bearing members on the engine to arrive at optimized sections and ribbing profiles. This paper discusses the optimization approach for one of the engine components i.e., the FEAD (front end accessory drive) bracket.

Aerodynamics of on-road vehicles has come to the limelight in the recent years. Better aerodynamic design of vehicle would improve vehicle fuel efficiency with increased acceleration performance. To obtain best aerodynamic body, the series of design modifications and different testing methodologies must be involved in vehicle design and validation phase. Wind tunnel aerodynamic force measurement, road load determination and computational fluid dynamics were the common methods used to evaluate the aerodynamic behavior of the vehicle body. As a novel approach, the present work discusses about the on-road (Real time) testing methodology that is aimed to evaluate the aerodynamic performance of vehicle body using surface pressure mapping. A 64-Channel digital pressure scanner has been utilized in this work for mapping the pressure at different locations of the typical vehicle body.

This paper discusses the development of an all speed governed diesel-natural gas dual fuel engine for agricultural farm tractor. A 45 hp, 2.9 liters diesel-natural gas dual fuel engine with a novel closed loop secondary fuel injection system was developed. A frugal approach without any modification of the base mechanical diesel fuel injection system was followed. This approach helped to minimize the cost impact, while meeting performance and emissions at par with neat diesel operation. Additional cost on gas injection system is redeemed by cost savings on diesel fuel. The dual fuel technology developed by Mahindra & Mahindra Ltd., substitutes on an average approximately 40% of diesel with compressed natural gas, meeting the TREM III A emission norms for dual fuel while meeting all application requirements. The governing performance of the tractor was found to be superior than base diesel tractor.