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Passive safety regulations specify minimum safety performance requirements of vehicle in terms of protecting its occupants and other road users in accident scenarios. Currently for majority cases, the compliance of vehicle design to passive safety regulations is assessed through physical testing. With increased number of products and more comprehensive passive safety requirements, the complexity of certification is getting challenged due to high cost involved in prototype parts and the market pressures for early product introduction through reduced product development timelines. One of the ways for addressing this challenge is to promote CAE based certification of vehicle designs for regulatory compliance. Since accuracy of CAE predictions have improved over a period of time, such an approach is accepted for few regulations like ECE-R 66/01, AIS069 etc which involves only loadings of the structures.

Air suspension systems had been introduced in automobiles since 1950s. These systems are being explored to improve the ride comfort, handling stability and also serve as a medium for better cargo protection. These system are well developed for buses and high end passenger sedans, also have feasibility for adapting for wide range of configurations of suspension system and axle. Passenger cars and Sports Utility Vehicle (SUV) pickup category of vehicle offers different challenges such as space availability, spring selection and characterization that need to be addressed for successful implementation of air suspension in these category vehicles. This work defines methodology to implement air suspension system in SUV Pickup category vehicle. Paper work includes concept study, mathematical co-relation, and prediction of air spring characteristics and integration of experimental and analytical tool for development of air suspension system.

This paper introduces xEV Simulator- A MATLAB based simulator platform capable of analyzing EV/HEV powertrain system in both backward and forward modelling. xEV Simulator employs Forward Simulation for drive-cycle performance evaluations and Backward simulation for powertrain component sizing and support xEV powertrain design. The powertrain subsystems are modelled in Simulink. This enables the model based system simulation and further controller prototyping and HiL testing. xEV Offline Simulator GUI enables user to simulate standard EV/HEV configurations with standard drive-cycles. The model parameters of different component subsystems can be configured. The Backward modelling and simulation support the estimation of subsystem values like Propulsion motor, Energy storage, etc., to perform as per the drive-cycle requirement.

Forging is a metal forming process involving shaping of metal by the application of compressive forces using hammer or press. Forging load of equipment is an important function of forging process and the prediction of the same is essential for selection of appropriate equipment. In this study a hot forging material i.e. 42CrMo4 steel is selected which is used in automotive components like axle, crank shaft. Hot forging experiments at 750°C are carried out on cylindrical specimens of aspect ratio 0.75 and 1.5 with true height strain (ln (ho/hf)) of 0.6. Forging load for the experiments is calculated using slab and upper bound deformation models as well as Metal forming simulation using commercially available FEA software. The upper bound models with 30% deviation from the simulation results are found to be more accurate compared to the slab models.

Occupant motion in a vehicle rollover accident is a function of many factors. Some important ones are vehicle kinematics, position of the occupant in the vehicle, occupant size, ground topology and restraint usage. The far side belted occupants are more vulnerable than the near side occupants in a rollover accident as they have more energy as a result of their trailing and higher side of the vehicle. This outcome is attributable to the inadequate safety performance of the conventional single loop; B-pillar mounted D-ring restraints. Roof crush tends to displace the vehicle's B-pillar, resulting in D-Ring displacement which causes slack in the lap portion of the restraint. This slack enables centrifugal loads to move the far side occupant further away from the vehicle's instantaneous point of rotation. In this scenario, the presence of any ejection portal can result in an occupant becoming partially or fully ejected.

Designers and analysts need to compare and conduct synthesis for selection of materials based on their properties involving simulation, optimization and correlation with test data. An example is that of acoustic material properties such as random and normal incidence sound absorption coefficient and sound transmission loss. The international test standards necessitate having standard operating procedures for characterization of these materials. This procedure is quite involved and addresses steps including test data acquisition, post processing, calculations, classification, report generation and most importantly, storage of such innumerable material properties in a structured manner to facilitate ease of retrieval and updating of properties. It is also highly desirable to have a synergy of the databank directly with simulation tools. Further, all of these steps need to be accurate, non-speculative and quick.

This paper presents a methodology for predicting thermal comfort inside Midibus cabin with an objective to modify the Heating, Ventilation and Air Conditioning (HVAC) duct design and parametric optimization in order to have improved thermal comfort of occupant. For this purpose the bus cavity is extracted from baseline CAD model including fully seated manikins with various seating positions. Solar Load has been considered in the computational model and passenger heat load is considered as per BSR/ASHRAE 55-1992R standard. CFD simulation predicted the air temperature and velocity distribution inside passenger cabin of the baseline model. The experimental measurements have been carried out as per the guidelines set in APTA-BT-RP-003-07 standard. The results obtained from CFD and Experimental test were analysed as per EVS EN ISO7730 standard and calculated occupant comfort in terms of thermal comfort parameters like Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD).

Active control mainly comprises of three parts; sensor-detects the input disturbance, actuator -provide counter measures and control logic -processing of input disturbances and converting it into logical output. Lot of methods for active vibration control are available but this paper deals with active control of steering wheel vibrations of an LCV. A steering wheel is, one such component that directly transfers vibration to the driver. Active technique described here is implemented using accelerometer sensor, IMA (Inertial Mass Actuator) and feed forward Fx-LMS (Filtered reference Least Mean Square) control algorithm. IMA is a single-degree-of-freedom oscillator. To enable a control, IMA needs to be coupled to the structure at a single point, acting as an add-on to the passive system. Fx-LMS is a type of adaptive algorithm which is computationally simple and it also includes compensation for secondary path effects by using an estimate of the secondary path.

Energy dispersive X-ray fluorescence (EDXRF) analysis have made it possible to conduct elemental analysis on a variety of fields, including those with environmental, automotive, geological, chemical, pharmaceutical, archaeology, and biological origins. The ability of EDXRF to deliver quick, non-destructive, and multi-elemental analytical findings with increased sensitivity is of great importance. It is a vital tool for quality control and quality assurance applications. Thus, EDXRF plays an important role to compare batch-to-batch products for meeting quality standards. This paper presents application of EDXRF as an effective tool for quick qualitative and quantitative evaluation of given samples.

The paper talks about Quantification of Alertness for vision based Driver Drowsiness and Alertness Warning System (DDAWS). The quantification of alertness, as per Karolinska Sleepiness Scale (KSS), reads the basic input of facial features & behaviour recognition of driver in a standard manner. Although quantification of alertness is inconclusive with respect to the true value, the paper emphasised on systematic validation process of the system covering various scenarios in order to evaluate the system’s functionality very close to the reality. The methodology depends on definition of threshold values of blink and head pose. The facial features are defined by number of blinks with classification of heavy blink and light blink and head pose in (x, y, z) directions. The Human Machine Interface (HMI) warnings are selected in the form of visual and acoustic signals. Frequency, Amplitude and Illumination of HMI alerts are specified.

One of the primary reasons for road accidents is driving while distracted or drowsy. Often, long and monotonous road journeys lead to distracted or drowsy driving. Therefore, there is a need for a system which alerts a distracted or drowsy driver. Moreover, as the levels of autonomy move beyond SAE Level 2, the system assumes a larger share of the dynamic driving task. Under challenging circumstances, the system might ask the driver to take back vehicle control. To guarantee safety, it’s crucial to monitor the driver’s condition in order to assess their readiness to regain control of the vehicle. An advanced safety feature known as a driver monitoring system (DMS), sometimes referred to as a driver state sensing (DSS) system, is designed to monitor a driver’s attentiveness and alertness, providing warnings or alerts to refocus their attention on driving when drowsiness or distraction is detected.

With the recent development in virtual modelling and vehicle simulation technology, many OEM’s worldwide are using digital road profiles in virtual environment for vehicle durability load prediction and virtual design evaluation. For precise simulation results, it is important to have the tire digital twin which is the realistic representation of tire in the virtual environment. The study comprises of discussion about different types of tire models such as empirical, solid model, rigid ring model and flexural ring models such as Pacejka, MF Swift, CD tire, F tire etc. and also the complexity involved in development of these tire models. Generation of virtual tire model requires highly sophisticated test rigs as well as vehicle level testing with Wheel Force transducers and other vehicle dynamics sensors. The large number of data points generated with testing are converted in standard TYDEX format to be further processed in various software tool for virtual model generation.

Design of vehicle for targeted customer usage is one of the key steps during vehicle development process. Due to globalization, most of vehicles, aggregates, components are being designed for global market considering worldwide load spectrum. Generally for doing this the vehicle response is being measured for different markets but this process is very time consuming. Also for getting these vehicle dependent parameters, exercises need to be repeated on each type/class of vehicle. So there is a need to have a robust procedure, tools which will helps OEM’s to predict the loads, vehicle response for different market segments at an early stage of vehicle development program using the inputs which are vehicle independent. The solution for this could be to use vehicle independent input such as digitized road profiles (2D or 3D) of target customer markets in combination with proper MBD simulation tools.