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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.

There has to be a good co-relation/ relationship between the pedal effort applied, pedal travel, deceleration level achieved and stopping distance for “good brake feel”. Brake feel also depend upon the time lag between the force applied on brake pedal and the response of braking system. Hence “brake feel” can be improved by reducing the response time of the brake system. Many vehicles are having “poor brake feel” complaints, pertaining to the above mentioned reasons. This paper relates to an improved brake system for automobile in which reduction in reaction time was done by artificially increasing differential pressure head across vacuum booster diaphragm. Brake booster is given an input of compressed air to the valve body during actuation, thereby increasing the differential pressure across the diaphragm. The compressed air is bled from turbocharger-intercooler of the vehicle which is stored in a reservoir, with one way valve, while cruising.

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.

Sound signature design is gaining more importance within global auto manufacturers. ‘Sportiness’ is one of the important point to consider while designing a sound character of a car for passionate drivers and those who love aggressive driving. Nowadays automobile manufacturers are more focused in developing a typical sound signature for their cars as a ‘unique design strategy’ to attract a niche segment of the market and to define their brand image. Exhaust system is one of the major aggregate determining the sound character of ICE vehicles which in turn has the direct influence on the customer perception of the vehicle and the Brand image and also the human comfort both inside and outside the cabin. This research work focuses on novel approaches to identify frequency range and order content by a detailed study of subjective feelings based on psycho-acoustics. Sound samples of various benchmark sporty vehicles have been studied and analyzed based on sound quality parameters.

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.

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.

On Board Diagnostics norms enforced by regulatory authorities of many countries require logging of distance traveled by the vehicle with MIL (malfunction indicator lamp) illuminated. This log needs to be maintained in non-volatile ECU memory. Conventional techniques maintain the log in a volatile memory during vehicle run-time and transfer the same to non-volatile memory when ignition is turned off. This requires use of a “power-hold” relay to keep an ECU power alive while the logged data in volatile memory is being transferred to non-volatile memory when ignition is switched-off. A novel strategy described in this paper avoids interface with power-hold relay, thereby reducing the system complexity. The design philosophy described makes use of an EEPROM to maintain the distance log. An innovative algorithm is employed to ensure that endurance specifications are not violated during the vehicle life-time.

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.

Adaption of EV powertrains in existing vehicle architecture has created many unique challenges in meeting performance, reliability, safety, ease of manufacturing & serviceability at optimum cost. Mounting of large size battery packs in existing vehicle architecture is one of them. Specific energy & the energy density of Lithium ion batteries are very lower compared to Diesel & Petrol, which requires high volume & weight for equivalent energy storage. For movement of many passengers and to ensure sufficient range EV buses typically needs large amount of energy and for storage of same bigger size battery packs are required. These large size batteries directly affect vehicle architecture, seating layout, ease of assembly & serviceability. Moreover the heavy mass of batteries directly influences vehicle dynamics & performance characteristics such as vehicle handling, roll & NVH. The most important consideration in design of EV vehicles in general and buses in specific is safety.

Customer preference towards quieter vehicles is ever-increasing. Exhaust tailpipe noise is one of the major contributors to in-cab noise and pass-by-noise of the vehicle. This research proposes a silencer with an integrated acoustic valve to reduce exhaust tailpipe noise. Incident exhaust wave coming from the engine strikes the acoustic valve and generates reflected waves. Incident waves and reflected waves cancel out each other which results in energy loss of the exhaust gas. This loss of energy results in reduced noise at the exhaust tailpipe end. To evaluate the effectiveness of the proposed silencer on the vehicle, NVH (Noise, vibration, and harshness) performance of the proposed silencer was compared with the existing silencer which is without an acoustic valve. A CNG (Compressed natural gas) Bus powered by a six-in-line cylinder engine was chosen for the NVH testing.

Now a day’s automated manual transmissions (AMT) are getting popular because of hassle-free gear shifting and improved fuel economy. OEMs are converting their existing manual gearbox to AMT gearbox with solution like hydraulic or electric AMT kit that replaces the manual shift mechanism to automated actuators. Generally, in manual gearbox, the operational principal of reverse gear is sliding mesh. Due to sliding mesh gear arrangement, it can create interruption for gearshift while controlling shift actuators. In this paper, reverse gear shift arrangement and its operational dynamics at different operating condition has been studied and analyzed in detail. Based on status of vehicle, to ease the gearshift, engagement flow process proposed. The control methods that increases probability of smooth and easier shifting in all operating condition discussed in detail. The developed control algorithm discussed along with its implementation on real vehicle and results.

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.

Steering pull during high speed braking of heavy commercial vehicles possesses a potential danger to the occupants. Even with negligible wheel-to-wheel brake torque variation, steering pull during the high speed braking has been observed. If the steering pull (i.e. steering rotation) is forcibly held at zero degree during high speed braking, the phenomena called axle twist, wheel turn and shock absorber deflection arise. In this work the data have been collected on the mentioned measures with an intention to develop a mathematical model which uses real time data, coming from feedback mechanism to predict the values of the measures in coming moments in order to aid steering system to ‘auto-correct’. Driven by the intention, ‘Time Series Analysis’, a well-known statistical methodology, has been explored to see how suitable it is in building the kind of model.

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.

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.

Better ride and comfort, enhanced safety, reliability and durability, lower running cost as well as cost of ownership continue to be challenges for automotive OEMs. Higher fuel efficiency is considered as USP not only for lower running cost but also is hygiene factor from sustainability point of view. This has necessitated the need for Augmenting Light weighting horizon in automotive OEMs. Augmenting this leads to invention of innovative materials and processes for emerging cost competitive market. This paper focuses on technology efforts towards augmenting light weighting Horizon in Automotive. Light weighting concepts being explored by OEMs with the help of automotive component manufacturers from Powertrain - Engines & Transmission, Chassis and Suspension are discussed.

Prime concern for electric vehicle where the application of the vehicle is public transport, is the charging of vehicle and operation of its infrastructure. Such an example of operating the EV buses is under the GCC (gross cost contract) model, with high operation time and comparatively lesser time for charging. It is challenging to meet these requirements. To counter this situation in fleet operated busses it is proposed to adapt an automated charging method which involves minimum man power intervention and automated mechanism to connect & disconnect the charging connectors. This paper proposes an automated pantograph mechanism based method of charging EV buses, meeting requirements as per SAE J3105 & ISO 15118 standards, which would be an ideal way to resolve the current situation.

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.