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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.

The ever-tightening regulation norms across the world emphasize the magnitude of the air pollution problem. The decision to leapfrog from BS4 to BS6 – with further reduction in emission limits -showed India’s commitment to clean up its atmosphere. The overall cycle emissions were reduced significantly to meet BS6 targets [1]. However, the introduction of RDE norms in BS6.2 [1] demanded further reduction in emissions under real time operating conditions – start-stop, hard acceleration, idling, cold start – which was possible only through strategies that demanded a cost effective yet robust solutions. The first few seconds of the engine operation after start contribute significantly to the cycle gaseous emissions. This is because the thermal inertia of the catalytic converter restricts the rate at which temperature of the catalyst increases and achieves the desired “light-off” temperature.

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.

Fuel cell electric vehicles generally have two power sources – the fuel cell power system and a high voltage battery pack - to power the vehicle operations. The fuel cell power system is the main source of power for the vehicle and its operations are supported by the battery pack. The battery pack helps to tackle the dynamic power demands from the vehicle such as during acceleration, to which the response of the fuel cell might be slower. The battery is also used to recover the energy from regeneration during braking and can also be used to extend the range of the vehicle in case the storage tanks runs out of hydrogen. In order to maximize the fuel efficiency of the fuel cell power system it is critical that these two power sources are used in conjunction with each other in an optimal manner.

Fuel cell electric vehicles (FCEVs) and battery electric vehicles are being touted worldwide by the automotive industry and policy makers as the answer to decarbonizing the transportation sector. FCEVs are especially suited for commercial vehicle applications as they offer very short re-fueling times that is comparable to conventional internal combustion engine vehicles. While this is entirely possible there are host of challenges that include safety, that need to be addressed to make short refilling times possible for commercial vehicles where the hydrogen storage requirement is higher (25 kg or more). This is due to the rise in temperature of the hydrogen in the cylinder due to compression and the negative Joule-Thompson coefficient. The SAE J2601 standard limits the safe temperature limit of hydrogen gas in the cylinder to 85 °C during filling.

With the revolutionary advancements in modern transportation, offering advanced connectivity, automation, and data-driven decision-making has put the intelligent transportation systems (ITS) to a high risk from being exposed to cyber threats. Development of modern transportation infrastructure, connected vehicle technology and its dependency over the cloud with an aim to enhance safety, efficiency, reliability and sustainability of ITS comes with a lot more opportunities to protect the system from black hats. This paper explores the landscape of cyber threats targeting ITS, focusing on their potential impacts, vulnerabilities, and mitigation strategies. The cyber-attacks in ITS are not just limited to Unauthorized Access, Malware and Ransomware Attacks, Data Breaches, Denial of Service but also to Physical Infrastructure Attacks.

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.

The auto industry is one of the major contributors for noise pollution in urban areas. Specifically, highly populated heavy commercial diesel vehicle such as buses, trucks are dominant because of its usage pattern, and capacity. This noise is contributed by various vehicle systems like engine, transmission, exhaust intake, tires etc. When the pass by noise levels exceeds regulatory limit, as per IS 3028, it is important for NVH automotive engineer to identify the sources & their ranking for contribution in pass by noise. The traditional methods of source identification such as windowing technique, sequential swapping of systems and subsystems which are time consuming.Also advanced method in which data acquisition with a synchronizing technology like telemetry or Wi-Fi for source ranking are effective for correctness.However they are time and resource consuming, which can adversely impact product development timeline.

Computed Tomography (CT) has become a potent instrument for non-invasive assessment of battery cell integrity, providing detailed insights into their internal structure. The present study explores the capabilities and advantages of employing CT for cell characterization through a systematic evaluation from various parameters. The evaluation results will be based on real-world experiments conducted on a standard battery cell, assessing the CT system’s ability to provide precise internal measurements, detect defects, and ensure the overall integrity of the cell. We outline a comprehensive framework that includes criteria such as system specifications, image quality, software capabilities, maintenance, service, and cost-effectiveness.

High strength aluminium alloys are an ideal material in the automotive sector leading to a significant weight reduction and enhancement in product safety. In recent past extensive development in the field of high strength steel and aluminium was undertaken. This development has been propelled due to demand for light weight automotive parts. The high strength to weight ratio possessed by Al alloy helps in reducing the total weight of the vehicle without effecting the overall performance, thereby increasing the fuel economy, and reducing the carbon emission level. Joining of high strength aluminium alloy is critical to develop durable automotive products. Joining of high strength aluminium alloy for mass production in automobile industry is a challenging task. Laser welding is recognized as an advanced process to join materials with a laser beam of high-power, high- energy density.

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.

A bus is integral part of public transportation in both rural and urban areas. It is also used for scheduled transport, tourism, and school transport. Buses are the common mode of transport all over the world. The growth in economy, the electrification of public transport, demand in shared transport, etc., is leading to a surge in the demand for buses and accelerating the overall growth of the bus industry. With the increased number of buses, the issue of safety of passengers and the crew assumes special importance. The comfort of driver and passenger in the vehicle involves the vibration performance and therefore, the structural integrity of buses is critically important. Bus safety act depicts the safety and comfort of bus operations, management of safety risks, continuous improvement in bus safety management, public confidence in the safety of bus transport, appropriate stakeholder involvement and the existence of a safety culture among bus service providers.

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.

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.

Since last decade automotive Industry is witnessing transition from ICE to EV due to stringent environmental laws by government bodies and technological breakthrough. EV technology is emerging day by day. Biggest challenge in front of OEM is the phase shift from ICE to EV. OEM need to decide on glide path for test rig development for this change to support ICE & EV powertrain validation to deliver reliable product to their customers. In EV development, major focus is on investment for battery development. Hence, for the Motor and Gearbox validation balanced approach is to upgrade existing ICE test bench for the EV with minimum effort and cost. This paper provides details on need and approach required to make the ICE test bench capable for EV powertrain validation. Proposed methodology helps to support both type of powertrain and have maximum utilization of the test bench.

This paper focuses on developing an application to extract insights from Android app reviews of Connected Car Applications and Twitter conversations related to OEM’ PV & EV Vehicles and features. Analyzing user sentiments and preferences in real-time can drive innovation and elevate OEMs' customer satisfaction. These insights have the potential to enhance vehicle performance and the manufacturing process. The application employs data collection and Natural Language Processing (NLP) techniques, including User-Driven Sentiment Classification and topic modeling, to analyze user sentiments and identify key discussion topics visually.

The automotive world is rapidly moving towards achieving shorter lead time using high-end technological solutions by keeping up with day-to-day advancements in virtual testing domain. With increasing fidelity requirements in test cases and shorter project lead time, the virtual testing is an inevitable solution. This paper illustrates method adopted to achieve best approximation to emulate driver behavior with 1-D (one dimensional) simulation based modeling approach. On one hand, the physical testing needs huge data collection of various parameters using sensors mounted on the vehicle. The vehicle running on road provides the real time data to derive durability test specifications. One such example includes developing duty cycle for powertrain durability testing using Road Torque Data Collection (RTDC) technique. This involves intense physical efforts, higher set-up cost, frequent iterations, vulnerability to manual errors and causing longer test lead-time.

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.

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.

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.