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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.

Till recently supercharging was the most accepted technique for boost solution in gasoline engines. Recent advents in turbochargers introduced turbocharging technology into gasoline engines. Turbocharging of gasoline engines has helped in powertrains with higher power density and less overall weight. Along with the advantages in performance, new challenges arise, both in terms of thermal management as well as overall acoustic performance of powertrains. The study focuses mainly on NVH aspects of turbocharging of gasoline engines. Compressor surge is a most common phenomenon in turbochargers. As the operating point on the compressor map moves closer to the surge line, the compressor starts to generate noise. The amplitude and frequency of the noise depends on the proximity of the operating point to the surge line. The severity of noise can be reduced by selecting a turbocharger with enough compressor surge margin.

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.

The ever-increasing customer expectations put a lot of pressure on car manufacturers to constantly reduce the noise, vibration, and harshness (NVH) levels. This paper presents the holistic approach used to achieve best-in-class NVH levels in a modern high-power density 1.5 lit 4-cylinder diesel engine. In order to define the NVH targets for the engine, global benchmark engines were analysed with similar cubic capacity, power density, number of cylinders and charging system. Moreover, a benchmark diesel engine (considered as best-in-class in NVH) was measured in a semi-anechoic chamber to define the engine-level NVH targets of the new engine. The architecture selection and design of all the critical components were done giving due consideration to NVH behaviour while keeping a check on the weight and cost.

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.

A typical powertrain mount design process starts with performing the system calculations to determine optimum mount parameters, viz. position, orientation and stiffness values to meet the desired NVH targets. Therefore, a 6 degrees of freedom lumped parameter system of powertrain and mounts is modelled in Matlab®. The approach is to decouple the torque roll axis mode from the remaining five rigid body modes so that the response to the torque pulses is predominantly ‘oscillations about Torque Roll Axis’. This is achieved by optimizing the above mount parameters within specified constraints so that ‘Rotation about the torque roll axis’ is one of the natural modes of vibration. The tool developed here uses ‘Particle Swarm Optimization(PSO) algorithm’ because of its ease of implementation and better convergence to the solution. The algorithm is programmed in TK solver®.

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.

In any engine cooling system, de-aeration capability of the system plays a very critical role to avoid over heating of an engine. In general, with recovery bottle engine cooling system there is one vent hose from radiator pressure cap to the recovery bottle and coolant in the bottle is exposed to atmospheric pressure. From this vent hose air bubbles will move to recovery bottle from the engine and radiator when pressure in the system exceeds pressure cap setting. With this arrangement, de-aeration from the engine will happen when thermostat opens only and till that time air bubbles will be in the engine only and in this time there will be chance of overheating at some critical conditions because of air pockets in to the engine water jacket and the entrained air in the cooling circuit. Also, secondly 100 % initial filling cannot be achieved.

The cylinder block for the power train has always been a classic example of concurrent engineering in which disciplines like NVH, Durability, thermal management and lubrication system layout contribute interactively for concept design. Since the concept design is based on engineering judgment and is an estimated design, the design iterations for optimization are inevitable. This paper aims at outlining a systematic approach for design of crankcase for fatigue which would eliminate design iterations for durability. This allows a larger scope for design improvement at the concept stage as the design specifications are not matured at this stage. A process of stress optimization is adopted which gives accurate dimensional input to design. The approach is illustrated with a case study where an existing crankcase was optimized for fatigue and significant weight reduction was achieved.

Dual mass flywheel (DMF) is an excellent solution to improve the noise, vibration and harshness (NVH) characteristic of any vehicle by isolating the driveline from the engine torsional vibrations. For the same reason, DMFs are widely used in high power-density diesel and gasoline engines. However, the real-world usage conditions pose a lot of challenges to the structural robustness of the DMF. In the present work, a new methodology is developed to evaluate the robustness of a DMF fitted in a compact sports utility vehicle (SUV) with rear-wheel drive architecture. The abuse conditions (mis-gear, sudden braking, etc) in the real-world usage could lead to a sudden engine stall leading to an abnormally high angular deceleration of the driveline components. The higher rate of deceleration coupled with the higher rotational moment of inertia of the systems might end up in introducing a significantly high impact torque on the DMF.

In automotive door engineering, fitting the side door glass surface from styling into the cylinder or torus is the basic requirement. Optimization is required to do this, which requires a solver which could be efficacious for best surface fitting. This paper propounds a methodology which could be used for fitting a side door glass surface from styling into the cylinder or torus. The method will significantly help in developing the required surface and can successfully eliminate the cumbersome manual calibrations. The mathematical model mentioned is a novel approach based on “Particle Swarm Optimization” (“PSO” will be used to represent in the paper) towards surface optimization technique. VB script is used to make it applicable in CATIA but could be easily applied in any other programming language like python, java etc. Usually the surface fitting problems deals with the initial guess of the required surface and then its further optimization.

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).

Manual transmission (MT) is still the most preferred solution for emerging markets due to the lower cost of ownership and maintenance coupled with a higher transmission efficiency. In this regard, continuous improvement of the transmission shift quality is quite essential to meet the growing customer expectations. In the present work, a detailed evaluation of the gear-shift impulse (experienced at the gear-shift knob) is conducted between two different architectures of a manual, high-torque (450 Nm input torque) inline transmission meant for a sports utility vehicle (SUV). The conventional manual inline transmission architecture comprises a common gear pair at the input of the transmission. While this input reduction architecture is the most widely used architecture, having the common gear pair at the output of the transmission is also another option. The synchronizers of the manual transmission need to match the speed of the rotating components just before the gear-shifting event.

It is imperative that all automobile manufacturers conduct vehicle level benchmarking at the initial stage of any new project. From the benchmark information, the manufacturers can set relevant targets for their own vehicles under development. In this regard, an accurate prediction of the engine operating points can improve the correlation of the measured fuel economy of the benchmark vehicle. The present work describes a novel method that can be used for the accurate prediction of the engine operating points of any benchmark vehicle. Since the idea of instrumenting the crankshaft/driveshaft with torque transducers is a costlier and time-consuming process, the proposed method can be effective in reducing the benchmarking. Hence, the objective of this work is to develop a mathematical model to calculate the real-time engine operating points (engine speed and torque) using parameters like vehicle speed, accelerator pedal map, driveline inertia, vehicle coastdown force and gradient.

NVH is becoming one of the major factor for customer selection of vehicle along with parameters like fuel economy and drivability. One of the major NVH challenges is to have a vehicle with aggressive drivability and at the same time with acceptable noise and vibration levels. This paper focuses on the compact utility vehicle where the howling noise is occurring at higher rpm of the engine. The vehicle is powered by three cylinder turbocharged diesel engine. The noise levels were higher above 2500 rpm due to the presence of structural resonance. Operational deflection shapes (ODS) and Transfer path analysis (TPA) analysis was done on entire vehicle and powertrain to find out the major reason for howling noise at higher engine rpm. It is observed that the major contribution for noise at higher rpm is due to modal coupling between powertrain, half shaft and vehicle sub frame.

Vibrational fatigue is a metal fatigue caused by the forced vibrations which are purely random in nature. The phenomenon is predominantly important for the components/systems which are subjected to extreme vibration during its operation. In a vehicle, an engine is the main source of vibration. The vibrational fatigue, therefore, plays a key role in the deterioration of engine mounted components. Multiple test standards and methodologies are available for validating engine mounted parts of an automobile. These might not be appropriate in the case of an off- road vehicle as the vibrational exposure of engine mounted components of an off-road vehicle is entirely different. In the case of an off-road vehicle, the engine mounted components are subjected to a comparatively higher level of vibration for a longer duration of time as compared to the passenger cars.

Development of Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs) is gaining traction across all geographies to help meet increasing fuel economy regulations and as a pathway to offset concerns due to climate change. But HEVs and EVs have so far been a nascent market for India. These technologies have primarily shifted towards Lithium-ion batteries (LIB) for energy storage due to its high energy and power densities. In order to make actual business sense of these technologies, of which, battery is a major cost driver, it is necessary for these batteries to provide similar performance and life expectancy across the operating boundary of the vehicles, as well as provide the requirements at a competitive cost. In other words, the LIBs have to sustain the normal life cycle requirements and withstand wide range of storage temperatures that the conventional gasoline/diesel vehicles have been good at and still ensure good life.

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.

Canopy design is governed by CPCB regulations. The regulations explicitly tells about noise levels. It’s very important to have the proper ventilation of canopy to ensure the proper working at all climatic conditions. Mostly it is installed at commercial locations & hence the ownership cost matters. Reducing the footprint without affecting the power output is challenging. It implies the need of the CFD simulation to predict the cooling performance of the canopy. The baseline canopy is tested to estimate the performance parameters. It is modelled in CFD with all the minute details. All the parts including engine, alternator, fan, fuel tank are modelled. MRF(Moving Reference Frame) model used to simulate fan performance. The cooling systems like radiator & oil cooler is modelled as porous region. The total flow across canopy & the air velocity across critical regions is used to define the performance.

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.