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Rotavator is an active tillage implement for breaking the Soil and for the preparation of seed bed for cultivation. The Farmers are currently facing problem due to usage of sub optimal speed of Rotavator which results in more fuel consumption, takes more time for completion of operation. Also, the Current Rental models work on Tractor + Implement as rental combination and customer not able to rent Rotavator as a standalone implement due to non-availability of Tracking information such as hours of utilization on Rotavator. Farmers not able to maintain the service periodicity, if oil change not done in prescribed duration then it may result in improper maintenance and breakdown of the Rotavator. To overcome these problems a smart Rotavator developed consists of an electronic unit fitted on the Rotavator shaft to measure the speed of the shaft rotation and in turn convert to Rotavator speed and also able to convert into Hours of usage based on the starting and stopping of the rotavator.

Connected vehicle services have come a long way from the early days of telematics, both in terms of breadth of the class of vehicles, and in terms of richness or complexity of the data being handled for Enhancing Customer Experience. The Connectivity Control unit (CCU) is a gateway device for the vehicle to the outside world. While it enables transmission of vehicle data along with the location information. CCU is currently validated in the vehicle to check functionality. It has cost, time drawbacks and prevents effective testing of many scenarios. Bench level validation will not be able to complete functionality validation. There is subset of validation tools or semi-automated solutions are available in the market, but they are not fully functional, and critically cannot perform end to end validation. Automated Test setup for CCU in lab simulating the entire field data of the vehicle with modifiable characteristics.

Being a safety critical aggregate, every aspect of brake system is considered significant in vehicles operations. Along with optimum performance of brake system in terms of deceleration generation, brake pedal feel or brake feel is considered as one of the key elements while evaluating brake system of vehicles. There are many factors such as liner and drum condition, road surface, friction between linkages which impress the pedal feel. Out of these, in this paper we will be discussing the factors which influence the brake pedal feel in relation to the driver comfort and confidence building. Under optimum braking condition, brake operation must be completed with pedal effort not very less or not very high, brake pedal feel must be firm throughout the operation, in such a way that it will not create fatigue and at the same time it will give enough confidence to the driver while operating with acceptable travel.

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.

The present scenario in automobile industry is formed on developing smart vehicles by introducing various feature towards fuel efficient, low emission, weight reduction, and advance safety feature with hybrid and micro-hybrid vehicles. One such feature gaining more popularity is the Belt Driven Starter Generator [1] for its contribution towards fuel efficiency, emission reduction [2], weight reduction and convenient packaging with engine/electrical interface. However this invention puts challenge of integration and increase in loading to various system like power steering pump and crank shaft pulley, as all these systems are interlinked with a common belt. In this interface links we observed the steering pump hub under risk of structural failure due to additional load to support Belt Driven Starter Generator. Failure to identify safe limits of hub load can affect safe vehicle operation [3].

Over the ages of automotive history, expectations of the customers increases vastly starting from driving comfort, better fuel economy and a safe vehicle. Requirement of good vehicle drivability from customers are increasing without any compromise of fuel economy and vehicle features. To enhance the product, it is a must for every OEM’s to have better drivability to fulfill the needs of the customer. This paper explains Objective Drivability Evaluation done on compact SUV vehicle and comparison with subjective drivability. Vehicle manufacturer usually evaluate drivability based on the subjective assessments of experienced test drivers with a sequence of certain maneuvers. In this study, we have used the objective drivability assessment tool AVL drive to obtain the vehicle drivability rating. The vehicle inputs from the accelerometer sensor which captures the longitudinal acceleration and CAN bus signals such as engine speed, vehicle speed, accelerator pedal, are fed into the software.

In any industry, early detection and mitigation of a failure in component is vital for feasible design changes or development iterations or saving money. So it becomes pivotal to capture the failure mode in an accelerated way. This theory poses many challenges in devising the methodology to validate the failure mode. In real world, vehicle head lamp is exposed to all possible kinds of harsh environments such as variable daily ambient, rain, dust and engine compartment temperature …etc. This brings rapid thermal stress onto headlamp resulting into warpage cracks. At vehicle level on particular model, this failure is typically observed after 20,000-25,000 kms in a span of 3-4 months of running. Any corrective action to revalidate the design change or improvement will need similar timelines in regular way to test, which is quite high in product development cycle.

By reducing overall noise emanating from Engine at design phase, permits to reduce both time-to-market and the cost for developing new engines. In order to reduce vibration and radiated noise in engine assembly, oil pan is one of the most critical components. This study explains the key-steps that are executed to optimize the oil pan design for 4-cylinder diesel engine by improving Normal Modes, modified Topology, reduced Forced Frequency Response and ATV analysis for reducing its noise radiation. Using Multi-body tool crankshaft forces were generated and the FE model of Base Design was analysed for its noise radiation and panel contribution was done for finding the most radiating panels using Boundary Element Method approach. A series of iterative optimization were carried out with commercial software. Parameters like Stiffness, material property, Ribbing patterns and Shape of the Oil pan was modified to shift the natural frequencies of the component and reduce the sound radiation.

In today's competitive scenario, understanding mental modal map of individual customer perception plays a major role to create the brand image of vehicle. Among them “comfortable sound” is one of the important criteria for customer satisfaction, especially in case of diesel vehicle, where in-cab sound quality plays a crucial factor. Often customer perception concerning comfort in automotive industry relies on subjective comfort evaluation method. Converting the customer perception into objective measurements and to correlate them is often tough task for NVH engineers. It is because of human sensation behavior differs from persons to person, mental map, geographical location and domain knowledge. In addition acoustic & comfort relevant aspects are often subjectively evaluated based on jury trials conducted on the prototype vehicle and class competitive benchmark vehicles to get the feel & confidence of product for different gateways.

Batteries have become increasingly important in automotives with increase in vehicle electrical loads. Therefore the reliability of battery is a critical issue in automotive applications. It has been noticed that most batteries have limited cycle durability, that is, the total capacity drops when a battery is charged and discharged for a number of cycles. If a battery is too weak to offer sufficient energy, it should be replaced at the right time. But current problem is that there is no reliable method to quantify the capacity loss and to estimate the remaining capacity of battery. Complete discharge, which is the only way of capacity estimation, which will effect the battery plates therefore it cannot be used too frequently. This paper summarizes the experimental work in the development of the battery status estimation algorithm.

The SAE J1100 based standard cargo volume index methods and predefined luggage objects are very specific to United States population. The European luggage volume calculation and standard luggage calculations are primarily based on DIN and ISO standards. Luggage volume declaration by manufacturers are based on any of these methods. The calculations are complicated and there is a possibility of declaring different values for similar luggage compartments. The major purchase decision of vehicle is based on its luggage capacity and current methods are very limited to make an intelligent decision by a customer. Market specific customer usage patterns for luggage requirements and protecting them in vehicle architecture upfront in concept stage is important to retain the market position and buying preference of customers. The usage patterns is collected from customer clinics and marketing inputs.

Increasing complexity in E/E architecture poses several challenges in developing comfortable, clean and safe cars. This mandates robust processes to mitigate potential hazards due to malfunction of electronic systems throughout the product life cycle. With the advent of ISO 26262 [1] which provides guidelines for developing safe cars, the process is getting standardized towards safer mobility. In this paper, the functional safety process is briefly covered and a case study of Hazard Analysis and Risk Assessment for specific E/E system is presented. An in-house tool developed for functional safety process and management is covered.

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.

Highly competitive automotive market demands shorter product development cycle while maintaining higher standards of performance in terms of durability and Noise Vibration & Harness (NVH). Engine cranktrain system is one of the major vibration sources in engine and first torsional mode frequency is a key parameter which influences vibration characteristics. Current CAE (Computer Aided Engineering) workflow for evaluating cranktrain system performance is time-consuming and takes around 55 Hrs. It involves crankshaft geometry cleanup, stiffness calculation, 1D model building and post processing. Over the time, significant historical data has been created while performing this virtual simulation during the product development cycle. Having a trained Machine Learning (ML) model based on this historical data, which can predict first torsional mode frequency accelerates the virtual validation. In this paper, prediction of first torsional frequency of cranktrain system using ML is presented.

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.

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.

In the past few decades, improvement on fuel efficient technologies have progressed rapidly, whereas little emphasis is being made on how the vehicle should be driven. Driving habits significantly influences fuel consumption and poor driving habits leads to increased fuel consumption. In this paper a new system called “Green Drive” is being presented wherein driving habits are closely monitored, evaluated and details are systematically presented to the user. Green Drive system monitors key driving parameters like speed, gear selection, acceleration, unwanted engine idling periods, aggressive braking and clutch override and presents an ecoscore on the infotainment system which is reflection of users driving behavior. The system also offers guidance on the scope for improving driving habits to achieve better ecoscore and hence reduced fuel consumption.

Agriculture tractor industry is highly competitive in the current market scenario with global majors competing in various markets. A tractor having an optimum design is of prime importance to keep the cost low while providing higher value to the customers. Technology advances in instrumentation methodology and data acquisition helps not only in providing the right inputs for the design of a component/system but also very much useful for design/system optimization. Front Axle Support of an agricultural tractor is one of the structural member which is connected to the Chassis which is called skid of the tractor to which the Front Axle is mounted through Pillow block (Plummer block) arrangement to facilitate axle oscillation about the tractor longitudinal center line. Front ballast weights are mounted on a bracket which is intern mounted to the front axle support to maintain the required front reaction in various agricultural operations.

One of the primary excitation sources in a passenger car comes from the powertrain [1]. Refinement of powertrain induced noise is the most critical tasks during a vehicle refinement. Due to ever increasing demand for reduced design and development cycle, most critical decisions have to be made at the concept stage. Powertrain radiated noise is one of the most important performance factor that must be evaluated during the concept stage. Solution time for calculating the radiated noise using the existing acoustic solvers is very high and requires very expensive resources like software and hardware. Arriving the optimal design with conventional method is very tedious job. A new method has been adopted for identifying the critical areas and coming up with the optimal design modifications within a short span of time. Powertrain radiated noise has been calculated with the help of acoustic solver.