Search Results

The vehicle Heating, Ventilation and Air conditioning (HVAC) system is designed to meet both the safety and thermal comfort requirements of the passengers inside the cabin. The thermal comfort requirement, however, is highly subjective and is usually met objectively by carrying out time dependent mapping of parameters like the velocity and temperature at various in-cabin locations. These target parameters are simulated for the vehicle interior for a case of hot soaking and its subsequent cool-down to test the efficacy of the AC system. Typically, AC performance is judged by air temperature at passenger locations, thermal comfort estimation along with time to reach comfortable condition for human. Simulating long transient vehicle cabin for thermal comfort evaluation is computationally expensive and involves complex cabin material modelling.

This paper addresses challenges in current Fuel Cell Stack Buses and presents a novel Fuel Cell Electric Vehicle Bus (FCEV-Bus) powertrain that combines fuel cells, ultra-capacitors, and batteries to enhance performance and reliability. Existing Fuel Cell Stack Buses struggle with responsiveness, power fluctuations, and cost-efficiency. The FCEV-Bus powertrain uses a Fuel Cell stack as the primary power source, ultra-capacitors for quick power response, and batteries for addressing power variations. Batteries also save costs in certain cases. This combination optimizes power management, improves system efficiency, and extends the FCEV-Bus's operational life. In conclusion, this paper offers an innovative solution to overcome traditional fuel cell system limitations, making FCEV-Buses more efficient and reliable for potential wider adoption.

DFSS is a disciplined problem prevention approach which helps in achieving the most optimum design solution and provides improved and cost effective quality products. This paper presents the implementation of DFSS method to design a distinctive cooling system where engine is mounted in the rear and radiator is mounted in the front of the car. In automobile design, a rear-engine design layout places the engine at the rear of the vehicle. This layout is mainly found in small, entry level cars and light commercial vehicles chosen for three reasons - packaging, traction, and ease of manufacturing. In conventional Passenger cars, a radiator is located close to the engine for simple packaging and efficient thermal management. This paper is about designing a distinctive cooling system of a car having rear mounted engine and front mounted radiator.

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.

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.

In developing nations, most passenger vehicles are equipped with mobile air conditioning (MAC) systems that work on Hydro Fluoro Carbons (HFC) based refrigerants. These refrigerants have a high global warming potential (GWP) and hence adversely affect the environment. According to the Kigali amendment to Montreal Protocol, Article-5 Group-2 countries including India must start phasing down HFCs from 2028 and replace them with low Global Warming Potential (GWP) refrigerants. One such class of low GWP refrigerant is Hydro Fluoro Olefins (HFO) In order to replace HFCs with HFOs in existing MAC systems, the various system performance parameters with the new refrigerant are required to be evaluated. Performance evaluation of MAC system is rendered quicker and cost-effective by deploying a digital simulation tool. There is good correlation and confidence established for MAC performance prediction with HFCs through 1D CAE.

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.

This paper aims at analysing the effect of regeneration braking on the amount of energy harnessed during vehicle braking, coasting and its effect on the drive train components like gear, crown wheel pinion, spider gear & bearing etc. Regenerative braking systems (RBS) is an effective method of recovering the kinetic energy of the vehicle during braking condition and using this to recharge the batteries. In Battery Electric Vehicles (BEV), this harnessed energy is used for controlled charging of the high voltage batteries which will help in increasing the vehicle range eventually. Depending on the type of the powertrain architecture, components between motor output to the wheels will vary, i.e., in an e-axle, motor is coupled with a gear box which will be connected with differential and the wheels. Whereas in case of a central drive architecture, motor is coupled with gearbox which is connected with a propeller shaft and then the differential and to the wheels.

Computer Aided Engineering (CAE) plays an important role in the product development. Now a days major decisions like concept selection and design sign off are taken based on CAE. All the Original Equipment Manufacturers (OEMs) are putting consistent efforts to improve accuracy of the CAE results. In recent years confidence on CAE prediction has been increased mainly because of good correlation of CAE predictions with the test results. Defining proper correlation criteria and using a systematic approach helps significantly in building the overall confidence level for predictions given by CAE simulations. Representation of manufacturing effects on material properties and material failure in the simulation is still a big challenge for achieving a good CAE correlation. This paper describes side impact test vs CAE correlation. The important parameters affecting the CAE correlation were discussed.

The steering system of an automobile serves as the initial point of contact for the driver and is a crucial determinant in the purchasing choice of the vehicle. The present steering system is equipped with a singular Electric Power Assisted Steering (EPAS) map, resulting in a consistent steering sensation during maneuvers conducted at both low and high velocities. Certain vehicles are equipped with a steering system that includes fixed driving modes that require manual intervention. This paper presents a proposed Machine Learning based Adaptive Steering System that aims to address the requirements and limitations of fixed mode steering systems. The system is designed to automatically transition between comfort and sports modes, providing users with the desired soft or hard steering feel. The system utilizes vehicle response to driver input in order to identify driving patterns, subsequently adjusting steering assist and torque automatically.

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.

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.

This paper introduces a novel approach to automate PID calibration for closed-loop control systems and the creep control function in an electric vehicle. Through a comprehensive literature survey, it is found that this method is the first of its kind to be applied in the field of automated electric vehicle calibration for Creep function. The proposed approach utilizes a systematic methodology that automatically tunes the PID parameters based on predefined performance criteria, including energy consumption and jerk. To implement this methodology, the ETAS INCA FLOW software, which provides guided calibration methods for in-vehicle testing & calibration, is employed. The calibration process is performed on a real-time electric vehicle platform to validate the effectiveness of the proposed approach. The results of this study showcases the advantages of automated PID calibration for closed-loop control systems and creep control function in small commercial electric vehicle.

Induction motor is very much used in mild hybrid vehicles because of its low cost, rugged structure and reliability. To test the induction motor controller in hardware-in-the-loop (HIL) simulation environment efficiently in both motoring and generating modes, generally, an instantaneous dynamic model of induction motor drive is used which requires the instantaneous values of PWM signals of inverter switches and hence a very high sampling frequency of about twenty times the switching frequency is required to effectively capture all the switching information of MOSFETS. This requires a HIL system with very powerful processor which increases the overall cost of system. In this paper, a dynamic average-value model of induction motor drive is developed in MATLAB/Simulink which requires only the duty cycle information instead of instantaneous switching information of PWM signals. Its performance is compared with the instantaneous model which is also developed in MATLAB/Simulink.

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.

Vehicle incab booming perception, a low frequency response of the structure to the various excitations presents a challenging task for the NVH engineers. The excitation to the structure causing boom can either be power train induced, depending upon the number of cylinders or the road inputs, while transfer paths for the excitation is mainly through the power train mounts or the suspension attachments to the body. The body responds to those input excitations by virtue of the dynamic behavior mainly governed by its modal characteristics. This paper explains in detail an integrated approach, of both experimental and numerical techniques devised to investigate the mechanism for boom noise generation. It is therefore important, to understand the modal behavior of the structure. The modal characteristics from the structural modal test enable to locate the natural frequencies and mode shapes of the body, which are likely to get excited due to the operating excitations.

Vehicle transmission gear rattle is one of the most critical NVH irritants for refined vehicles. It is perceived more dominantly in lower gears of vehicle running. It depends on various design parameters like engine input torque amplitude & fluctuations, driveline torsional vibrations, gear micro & macro geometry, shaft flexibility, etc. Establishing exact contribution of each of these parameters to transmission rattle, thru experimental or simulation technique, is very challenging. Current paper explains the NVH CAE benchmark approach deployed to understand difference in rattle behavior of two transmission designs. Paper focuses on simulation of gear impact power and its sensitivity to transmission shaft deflections.

New noise regulations, with reduced noise limits, have been proposed by UN-ECE. A new method which aims at representing urban driving of the vehicles more closely on roads is proposed and is considerably different from the existing one (IS 3028:1998). It is more complex; we also found that some of the low powered vehicles can not be tested as per this method. The paper proposes ways of improvement in the test method. The new noise reduction policy options will have a considerable impact on compliance of many categories of vehicles. Technological challenges, before the manufacturers, to meet all performance needs of the vehicle along with the cost of development will be critical to meet the new noise limits in the proposed time frame.

The acceptable noise and vibration performance is one of the most important requirements in a passenger bus as it is intended for widest spectrum of passengers covering all age groups. Gear rattle, being one of the critical factors for NVH and durability, plays a vital role in passenger comfort inside vehicle. The phenomenon of gear rattle happens due to irregularity in engine torque, causing impacts between the teeth of unloaded gear pairs of a gearbox which produce vibrations giving rise to this unacceptable acoustic response. In depth assessment of the dynamic behavior of systems and related components required to eliminate gear rattle. During normal running conditions, abnormal in-cab noise was perceived in a bus. Initial subjective evaluation revealed that the intensity was high during acceleration and deceleration. Objective measurements and analysis of the in-cab noise and vibration measurements had indicated that the noise is mainly due to gear rattling.

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.