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Air conditioning systems are one of the significant auxiliary loads on the vehicle powertrain. In an Electric Vehicle (EV) where the available energy is limited, it becomes crucial to optimize the overall energy consumption of the auxiliary loads. The major power consuming components in an automotive HVAC system (Heating, Ventilation and Air Conditioning) are: Compressor, Cabin blower, Condenser cooling fan and the Control devices. Significant progress is already made in enhancing the energy efficiency of the above-mentioned power consuming components part of vehicle HVAC system. Alternate energy sources are being explored recently, to reduce the energy demand from vehicle. One such proposal is to harness the abundant solar energy available, through solar panels and consume this energy to supplement the power required for HVAC system components. Solar panels convert solar energy to electrical energy by the principle of the photovoltaic effect.

The present work discusses the developed simulation model aimed to predict the heat rejection (HR) performance and external pressure drop characteristics of automotive charge air cooler (CAC). Heat rejection and airside pressure drop characteristics of CAC were predicted for the conditions of different charge air mass flow rates and different cooling air velocities. The lack of detailed research on CAC performance prediction has motivated the development of the proposed simulation model. The present 1-D simulation has been developed based on the signal library of AMESIM application tool. Input parameters for this simulation such as core size, tube pitch, tube height, number of tubes, fin density, louver angle, louver pitch, charge air mass flow rate, cooling air velocity, charge air inlet temperature, and ambient temperature. Heat rejection curve and airside pressure drop of CAC were the output of the present simulation.

This paper discusses the methodology setup for grill opening area prediction at the early development phase of the product development lifecycle, using a commercially available 1D simulation tool- AMESIM. Representative under hood has been modeled using Grill, Condenser, Radiator, intercooler, fan, and engine components. Vehicle velocity is used as an input to derive the airflow passing through the grill and other under-hood components based on ram air coefficient, pressure drop through different components (Grill, Heat exchanger, Fan & Engine). This airflow is used to predict the top tank temperature of the radiator. Derived airflow is correlated with airflow obtained from CFD simulation. A balance has been achieved between cooling drag & fan power consumption at different grill opening areas for target top tank temperature. Top tank temperature has been predicted at two different extreme engine heat rejection operating points.

Currently the Automotive industry demands highly competitive product to survive in the global tough competition. The engine cooling system plays a vital role in meeting the stringent emission norms and improving the vehicle fuel economy apart from maintaining the operating temperature of engine. The airflow through vehicle subsystems like the grille, bumper, the heat exchangers, the fan and shroud and engine bay are called as front-end flow. Front end flow is crucial factor in engine cooling system as well as in determining the aerodynamic drag of vehicle. The airflow through the engine compartment is determined by the front-end vehicle geometry, the CRFM and CAC package, the engine back restriction and the engine compartment geometry including the inlet and outlet sections. This paper discusses the 1D modelling method for front-end airflow rate prediction and thermal performance by 1D method. The underbody components are stacked using heat stack and simulated in pressure mode.

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.

The adoption of BSVI emission norms for Indian domestic market brought a very stringent window for pollutants. For CI engines, the major impact was in the reduction of NOx by 68% and PM by 82% from BSIV norms. Technologically advanced after treatment systems like SCR / DPF / LNT aid to meet the stringent emission norms. Implementation of high-end after treatment systems in vehicles, requires precise monitoring and fool proof feedback systems. On Board Diagnostics (OBD) makes this possible. OBD is used to monitor the performance of after treatment systems and warn the user in case of deterioration. The challenges in framing OBD strategy increases with more electronic hardware and complex algorithms taking control, to monitor precise information on system performance. For a fool proof OBD monitoring of the exhaust system, a complete understanding of the SCR system and its components in terms of hardware specifications and software functionality is critical.

During the vehicle launch (i.e. moving the vehicle from “0” speed), the clutch would be slowly engaged by the Driver or Transmission Control Unit (in Automatic Transmission/Automatic Manual Transmission vehicle) for smooth torque transfer between engine and transmission. The clutch is designed to transfer max engine torque with min heat generation. During the clutch engagement, the difference in flywheel and gearbox input shaft speed is called the clutch slipping phase which then leads to a huge amount of energy being dissipated in terms heat due to friction. As a result, clutch surface temperature increases consistently, when the surface temperature crosses the threshold limit, the clutch wears out quickly or burns spontaneously. Hence it is crucial to predict the energy dissipation and temperature variation in various components of clutch assembly through virtual simulation.

This paper deals with the study of the phenomenon of crevice corrosion of aluminium by using an example of a corrosion failure of a joint in the automobile coolant circuit. A number of joint failures were studied to understand the corrosion pattern and for various metallurgical aspects like chemistry, hardness and microstructure. The corrosion products were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDS). This analysis indicated that the corrosion products mostly contained Aluminium Oxides with other contaminants like chlorides. The studies revealed that the clamped joint of the aluminium part and rubber hose led to the formation of a crevice with the engine coolant acting as the corrosive medium. The corrosion behavior at the location was affected by environmental factors like temperature, pH and chloride contamination.

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.

Lean approaches are being implemented in various manufacturing facilities across the globe. The application of lean approaches are extended to Body proto build shop to maximize the efficiency of the shop with lesser floor space and optimized equipment. Weld fixture, Weld equipment and assembly tools are the major tools required essentially for proto BIW assembly. This paper explains how the Weld equipment planning was carried out with lean approaches and implemented effectively in proto body assembly shop. The implemented lean concepts are compared with Italy and Japanese proto body build makers to validate the frugal planning of the facility for the said intent. The implemented facility is capable of producing more than a model at a time. Weld parameter selection for weld gun, gun movement to the fixture with minimized change over time and movable weld gun gantry are the lean approaches implemented.

The cabin cool down performance is influenced by heat load, AC system components and Air handling components. The air handling components are AC duct, vane and vent. Design of AC duct vane plays a crucial role in the airflow directivity in cabin which enhances the cabin cool down performance. Simulations are carried out by rotating the vanes manually and requires post process for every iteration. It leads to more time consuming and more number of simulations to achieve the target value. Research articles focusing on automation and optimization of vane articulation studies are scanty. Thus, the objective of this work is to execute the vane articulation study with less manual intervention. A parametric approach is developed by integrating ANSA and ANSYS FLUENT tools. With Direct Fit Morphing and DoE study approach from ANSA delivers the surface mesh model for the different vane angle configurations.

Air conditioning systems are one of the significant auxiliary loads on the vehicle powertrain. In an Electric Vehicle (EV) where the available energy is limited, it becomes crucial to optimize the overall energy consumption of the auxiliary loads. The major power consuming components in an automotive HVAC system (Heating, Ventilation and Air Conditioning) are: Compressor, Cabin blower, Condenser cooling fan and the Control devices. Significant progress is already made in enhancing the energy efficiency of the above-mentioned power consuming components part of vehicle HVAC system. Alternate energy sources are being explored recently, to reduce the energy demand from vehicle. One such proposal is to harness the abundant solar energy available, through solar panels and consume this energy to supplement the power required for HVAC system components. Solar panels convert solar energy to electrical energy by the principle of the photovoltaic effect.

This paper makes an attempt to focus on a study to evaluate angle of vision and obstruction in a vehicle, it is an objective assessment through different percentiles of population. In a view of Safety and comfort of a driver, a good perception of environment in which his vehicle is operating will be a determining component. Driver visibility and hidden corner in vehicle is a major safety area for passengers and pedestrian. Driver eye vision is an important key factor to design vehicle windshield, rear window and A-Pillar/ B-Pillar, positioning of side view mirror and IRVM based on anthropometry data. This study focuses on method of capturing and measuring the i) Driver's Direct field of vision that the driver sees directly by moving his/her eyes ii) Driver's Indirect field of vision in which driver views indirectly by using imaging devices Rear View mirror, Display cameras. iii) Driver's Angle of obstruction - by A pillar, B pillar.

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 farm tractors, front end loaders are becoming popular attachments for primarily material handling such as loading, moving and unloading of woodchips, sand, gravels etc. It is also used for some severe load application such as tree uprooting and ripping operation which requires validation of loader frame and tractor as well. To validate the design, a standard pull-push test is carried out on tractor with loader in a laboratory. In this test front loader bucket is pushed against a rigidly clamped fixture with full engine throttle and maximum hydraulic cylinder pressure of loader. To avoid surprise failures during the test, a virtual simulation method needs to be developed and validated. In this paper, a method has been proposed by authors for the above objective. A multi-body dynamics model of tractor with loader is created in MSC ADAMS and actual event is simulated using test loads & boundary conditions.

Connected vehicles technology is experiencing a boom across the globe. Vehicle manufacturers have started using telematics devices which leverage mobile connectivity to pool the data. Though the primary purpose of the telematics devices is location tracking, the additional vehicle information gathered through the devices can bring in much more insights about the vehicles and its working condition. Cloud computing is one of the major enabled for connected vehicles and its data-driven solutions. On the other hand, machine learning and data analytics enable a rich customer experience understanding different inferences from the available data. From a fleet owner perspective, the revenue and the maintenance costs are directly related to the usage conditions of the vehicle. Usage information like load condition could help in efficient vehicle planning, drive mode selection and proactive maintenance [1].

Access to Knowledge resources around the world demands special skills and calls for judicious investments. Networking is an effective tool and building networks through consortia approach is dire need of the hour. Although this approach has been used in academia, its application in industry, especially among corporate entities, is rare. This paper describes in brief an optimised way to access databases, subscriptions & memberships of different technical societies from global platforms for research & progress of Indian automotive industry. It is imperative for Indian automotive industry to enhance and strengthen its knowledge resource base, particularly in product development and manufacturing domains. This will enable the industry to achieve its mission -“AMP-2016” of becoming a hub for global auto industry. Instead of “compete and grow,” an approach of “collaborate and grow” is thought of.

Crush box in an automotive passenger car has become an integral part of structural design performing various functions like optimizing energy absorption in high speed impacts, replaceable part during low speed impacts etc. Design of crush box for high speed impacts is very important as it is the first major energy absorbing component in the load path and its deformation significantly affects the overall vehicle crash behavior. The present paper explains development of a hydro-formed crush box in the front end of a sports utility vehicle. Hydro-formed components have residual plastic strains and non - uniform thickness variation throughout their length which is difficult to measure from a physical test coupon. It is critical to add hydro-forming effects onto crash FE models as it significantly affects the deformation under high speed impact. But detailed forming simulations need mature design and material data which is not available during early phases of product development.

The complexity of Urban driving conditions and the human behavior introduces undesired variabilities while establishing Fuel economy for a vehicle. These variabilities pose a great challenge while trying to determine that single figure for assessment of vehicle’s fuel efficiency on an urban driving cycle. This becomes even more challenging when two or more vehicles are simultaneously evaluated with respect to a reference vehicle. The attempt to fit a generalized linear model, between Fuel Economy as predicted variable and components of a driving cycle as predictor variables produced oxymoronic and counter-institutive results. This is primarily due to existence of multi-collinearity among the predictor variables. The context of the study is to consider the event of driving on a cycle as a random sampling experiment. The outcome of a driving cycle is summarized into a list of predictor variables or components.

The automotive industries around the world is undergoing massive transformation towards identifying technological capabilities to improve vehicle performance. In this regard, the engine dynamic torque plays a crucial role in defining the transient performance and drivability of a vehicle. Moreover, the dynamic torque is used as a visualization parameter in performance prediction of a vehicle to set the right engineering targets and to assess the engine potential. Hence, an accurate measurement and prediction of the engine dynamic torque is required. However, there are very few methodologies available to measure the engine dynamic torque with reasonable accuracy and minimum efforts. The measurement of engine brake torque using a torque transducer is one of the potential methods. However, it requires a lot of effort and time to instrument the vehicle. It is also possible to back-calculate the engine torque based on fuel injection quantity and other known engine parameters.