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With the current state of ever rising fuel prices and unavailability of affordable alternate technologies, significant research and development efforts have been invested in recent times towards improving fuel efficiency of vehicles powered with conventional internal combustion engines. To achieve this, a varied approach has been adopted by researchers to cover the entire energy chain including fuel quality, combustion quality, power generation efficiency, down-sizing, power consumption efficiency, etc. Apart from energy generation, distribution and consumption, another domain that has been subjected to significant scrutiny is energy recuperation or recovery. A moving vehicle and a running engine provide a number of opportunities for useful back-recovery and storage of energy. The most significant sources for recuperation are the kinetic energy of the moving vehicle or running engine and to a lesser extent the thermal energy from medium such as exhaust gas.

Reduction of NOx (Oxides of Nitrogen) and particulates from engine exhaust is one of the prime considerations in current research and development in automotive industry. The present paper describes the combustion optimization done on a four cylinder, 4 liter DI diesel engine to meet stringent Euro-III emission norms. The engine FIE (Fuel Injection Equipment) and injector geometry was optimized for performance and emission. Smoke measurements were considered as indicative of soot, to predict particulate emissions. This was done to simplify the overall process and save development time. It was concluded that by combining the flexibility of electronically controlled fuel injection begin, with improved nozzle technologies, with higher spray velocities and spray penetration, a considerable reduction in NOx and particulate emissions can be achieved. This can serve as a low cost solution, without any exhaust after-treatment systems.

The use of Wheel Force Transducers (WFTs) to acquire data for laboratory simulation is becoming standard industry practice. However, in test rigs where we have only the suspension module and not the complete vehicle, does the reproduction of the orthogonal forces and moments at the wheel centre guarantee an accurate replication of the fatigue damage in the suspension components? The objective of this paper is to review the simulation methodology for a highly non-linear suspension in a 3 DOF (degree-of-freedom) suspension test rig in which the simulation was carried out using only the three orthogonal loads and vertical displacement. The damage at critical locations in the suspension is compared with that on the road and an assessment of the simulation using the WFT is made based on a comparison of the damage on the road vs. the rig.

Micro Hybrid Systems are a premier approach for improving fuel efficiency and reducing emissions, by improving the efficiency of electrical energy generation, storage, distribution and consumption, yet with lower costs associated with development and implementation. However, significant efforts are required while implementing micro hybrid systems, arising out of components like Intelligent Battery Sensor (IBS). IBS provides battery measurements and battery status, and in addition mission critical diagnostic data on a communication line to micro hybrid controller. However, this set of data from IBS is not available instantly after its initialization, as it enters into a lengthy learning phase, where it learns the battery parameters, before it gives the required data on the communication line. This learning period spans from 3 to 8 hours, until the IBS is fully functional and is capable of supporting the system functionalities.

Engine Stop/Start System (ESS) promises to reduce greenhouse emissions and improve fuel economy of vehicles. Previous work of the Authors was concentrated on bridging the gap of improvement in fuel economy promised by ESS under standard laboratory conditions and actual driving conditions. Findings from the practical studies lead to a conclusion that ESS is not so popular among the customers, due to the complexities of the system operation and poor integration of the system design with the driver behavior. In addition, due to various functional safety requirements, and traffic conditions, actual benefits of ESS are reduced. A modified control algorithm was proposed and proven for the local driving conditions in India. The ways in which a given driver behaves on the controls of the vehicles like Clutch and Brake Pedals, Gear Shift Lever were not uniform across the demography of study and varied significantly.

Spot welding is the primary joining method used in automobiles. Spot-weld plays a major role to maintain vehicle structural integrity during impact tests. Robust spot weld failure definitions is critical for accurate predictions of structural performance in safety simulations. Spot welds have a complex metallurgical structure, mainly consisting of fusion and heat affected zones. For accurate material property definitions in simulation models, huge number of inputs from test data is required. Multiple tests, using different spot weld joinery configurations, have to be conducted. In order to accurately represent the spot-weld behavior in CAE, detailed modeling is required using fine mesh. The current challenge in spot-weld failure assessment is developing a methodology having a better trade-off between prediction accuracy, testing efforts and computation time. In view of the above, cohesive zone models have been found to be very effective and accurate.

Authors were challenged with a task of developing a full vehicle simulation model, with a target to simulate the electrical system performance and perform digital tests like Battery Charge Balance, in addition to the fuel efficiency estimation. A vehicle is a complicated problem or domain to model, due to the complexities of subsystems. Even more difficult task is to have a control algorithm which controls the vehicle model with the required control signals to follow the test specification. Particularly, simulating the control of a vehicle with a manual transmission is complicated due to many associated control signals (Throttle, Brake and Clutch) and interruptions like gear changes. In this paper, the development of a full vehicle model aimed at the assessment of electrical system performance of the vehicle is discussed in brief.

Shock tube is used to simulate blast loading conditions on materials for studying the failure behavior of different materials under blast pressures on smaller scale. This paper describes CAE method developed for simulating shock tube experiment in LS-DYNA3D environment. The objective of shock tube simulation is to characterize material failure parameters so as to predict risk of material failure in full vehicle blast simulations while developing vehicle for blast protection applications. The paper describes modeling of shock wave and its interaction with test specimen in shock tube environment. Arbitrary Lagrangian-Eulerian (ALE) techniques are applied to simulate shock tube experiment in LS-DYNA3D and simulation predictions are compared with experimental test data. CAE correlation studies were carried out with respect to incident and reflected pressures in shock tube, deformation and plastic strains on test specimen, shock wave velocity etc.

Tire inflation pressure has a significant impact over vehicle driving dynamics, fuel consumption as well as tire life. Therefore, continuous monitoring of tire pressure becomes imperative for ride comfort, safety and optimum vehicle handling performance. Two types of tire pressure monitoring systems (TPMS) used by vehicles are - direct and indirect TPMS. Direct systems deploy pressure sensors at each wheel and directly send pressure value to the vehicle Controller Area Network (CAN). Indirect sensors on the other hand use the information from already existing sensors and some physics-based equations to predict the value of tire pressure. Direct TPMS tend to be more accurate but have higher cost of installation while indirect TPMS comes with a minimum cost but compromised accuracy. A digital proof-of-concept study for indirect TPMS development of a non-ESP vehicle based on machine learning (ML) technique is elaborated in this paper.

Global concerns over availability and environmental impact of conventional fuels in recent years have resulted in evolution of Electric Vehicles. Research and development focus has shifted towards one of its main components, Lithium-ion battery. Development of high performing, long lasting batteries within challenging timelines is the need of the industry. Lithium-ion batteries undergo “battery ageing”, limiting its energy storage and power output, affecting the EV performance, cost & life span. It is critical to be able to predict the rate of battery ageing & the impact of different environmental conditions on battery lifetime/capacity. Conventionally, extensive physical vehicle level testing is carried out on batteries to map the battery capacity in various conditions. This is a lengthy & expensive process affecting the product development cycle, paving the way for an alternative process.

The basic function of steering system is to control the direction of the vehicle. The driver applies effort on the steering wheel and receives feedback through the steering system as a result of tire to road interaction. This feedback consists of a haptic (force) feedback which is directly felt by the driver and it is termed as steering feel. Precise steering feel gives better driving experience and is decisive factor for customer to buy a vehicle as well as for OEMs in building brand image. Along with steering parameters, suspension and tire parameters also has significant impact on steering feel. In past, modelling of the steering system was done at component level or with simplified vehicle system. Such approaches had not given accurate results of steering feel metric and resulted in incorrect steering design parameter selection. In order to replicate actual vehicle characteristics, complex and detailed modelling of steering, tire and suspension subsystems is necessary.

One of the very first customer touchpoint in a vehicle is quality of gear shifting. Gearshift quality is perceived as a symbol of refinement of a vehicle. Globally, lot of efforts are taken to refine the gearshift quality. Design improvements in internal components of transmission, cable and shifter assembly, knob design iterations are carried out to arrive at optimum gearshift quality at the vehicle level. Current practice for this activity includes processes such as design modification, manufacturing of proto components, assembly of components and fitment in the vehicle. This vehicle is then instrumented with sensors and data acquisition units to capture the parameters which determine the gearshift quality. This is an iterative process which goes on until necessary refinement/improvement is achieved. This process requires investment of lot of time, efforts and the budget. This paper describes a virtual approach to arrive at optimum design of components.

Climate change due to global warming calls for more fuel-efficient technologies. Parallel Full hybrids are one of the promising technologies to curb the climate change by reducing CO2 emissions significantly. Different parallel hybrid electric vehicle (HEV) architectures such as P0, P1, P2, P3 and P4 are adopted based on different parameters like fuel economy, drivability, performance, packaging, comfort and total cost of ownership of the vehicle. It is a great challenge to select right hybrid architecture for different vehicle segments. This paper compares P2 and P3 HEV with AMT transmission to evaluate most optimized architecture based on vehicle segment. Vehicles selected for study are from popular vehicle segments in India with AMT transmission i.e. Entry segment hatch and Compact SUV. HEV P2 and P3 architectures are simulated and studied with different vehicle segments for fuel economy, performance, drivability and TCO.