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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.

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.

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.

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.

Side Door closing velocity is one of the key customer touch points which depicts the build quality of the vehicle. Side door closing velocity results from the interaction of different parts like door and body seals, door check arm, door hinge, latch, and alignment of door hinge axis. In this paper, a high door closing velocity issue in a sports utility vehicle is discussed. Physical studies are carried out to understand each parameter in door closing velocity and its contribution is defined in terms of velocity. Many physical trials are conducted to conclude the contribution of each parameter. Studies revealed that the body and door seal are contributing around 70% of door closing velocity. Check arm and hinge axis deviation are contributing around 10% of the door closing velocity. Physical trials are conducted by reducing the compression distance of the body seal.

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.

The side door closing effort is one of the main evaluating parameters which demonstrates the build quality of the vehicle. The side door hinge axis inclination is one of the key attributes that affect the side door closing effort. Commonly, the hinge axis is inclined in two directions of a vehicle to have necessary door rise during the door opening event. Due to the process and assembly variations in the door assembly, the upper and lower hinge axis of the side door deviates from the design axis. In this paper, the deviations in the side door hinge axis and its effects on the side door closing velocity is discussed. The deviations of the side door hinge axis are studied with a coordinate measuring machine. The side door closing velocity of the vehicle is measured with the velocity meter. The study revealed that side door closing velocity is increasing with an increase in the deviation of the top and bottom door hinge axis from the design hinge axis.

Today’s automotive world has moved towards an age where safety of a vehicle is given the topmost priority. Many stringent crash norms and testing methodology has been defined in order to evaluate the safety of a vehicle prior to its launch in a particular market. If the vehicle fails to meet any of these criteria then it is debarred from that particular market. With such stringent norms and regulations in place it becomes quite important on the engineer’s part to define the structural requirements and protect the space to meet the same. If the concept level platform definition is done properly it becomes very easy to achieve the crash targets with less cost and weight impact.

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.

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].

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.

Tractor weight transfer is the most common farm-related cause of fatalities nowadays. As in India it is getting mandatory for all safety devices across all HP ranges. Considering any changes in the weight from an attachment such as Rops, PTO device, tow hook and draw bar etc. can shift the center of gravity towards the weight. center of gravity is higher on a tractor because the tractor needs to be higher in order to complete operations over crops and rough terrain. Terrains, attachments, weights, and speeds can change the tractor’s resistance to turning over. This center of gravity placement disperses the weight so that 30 percent of the tractor’s weight is on the front axle and 70 percent is on the rear axle for two-wheel drive propelled tractors and it must remain within the tractor’s stability baseline for the tractor to remain in an upright position.

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.