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To realize a super-leanburn SI engine with a very-high compression ratio, it is required to design a new fuel which could have low ignitability at a low temperature for antiknocking, but high ignitability at a high temperature for stable combustion. Ethane shows a long ignition delay time at a low temperature close to that of methane, but a short ignition delay time at a high temperature close to that of gasoline. In the present study, the antiknocking effect of adding methane with the RON of 120, ethane with the RON of 108, or propane with the RON of 112 to a regular gasoline surrogate fuel with the RON of 90.8 has been investigated. Adding each gaseous fuel by less than 0.4 in heat fraction advances knocking limit in the descending order of SI timing advance of ethane, methane, and propane, and in the descending order of CA 50 advance of ethane, propane, and methane. Adding methane extends combustion duration slightly, but adding ethane or propane shortens it considerably.

The automotive industry is moving rapidly to electrification through development of Battery Electric Vehicles (BEV). Development and sizing of the battery and powertrain requires a detailed understanding of battery cell behaviour under different conditions. Achieving this is difficult due to the range of cells available and the large range of condition variables of each cell. Equivalent Standard Circuit (ESC) are used for BEV development. However, conventional battery cell characterization testing to parameterise these models are time and resource intensive. Characterization can be performed using well-known techniques such as Hybrid Pulse Power Characterisation (HPPC) or Galvanostatic Intermittent Titration Technique (GITT) which are used to optimise parameters of an ESC model pertinent to the dynamics of its voltage response.

Inadequately designed flow field layouts in bipolar plates within Proton Exchange Membrane fuel cells (PEMFCs) may lead to ineffective water removal and impede reactant transport. This work examines the typical flow channel designs found in bipolar plates of fuel cells and implements modifications to certain designs to alleviate pressure drops within the flow channels using computational fluid dynamics (CFD) analysis. These designs are optimized by changing different parameters such as size of the channel and rib width utilizing Taguchi L27 standard orthogonal array. The resultant reduction in pressure drop is anticipated to enhance the overall performance of the fuel cell. The optimal flow field design of bipolar plates (Graphite and Aluminum) are manufactured using CNC milling. Tests evaluating flexural strength, surface roughness, hardness, contact angle, and corrosion resistance are conducted to assess and compare the performance of these plates.

Hydrogen as a chemical energy carrier is considered as one of the most promising options to achieve effective decarbonization of the transportation sector, due to its carbon-free chemical composition. This is particularly true for applications that rely on internal combustion engines (ICEs), although much research is still needed to achieve stable, reliable, and safe operations of the engine. To this purpose, direct injection (DI) of gaseous hydrogen during the compression stroke offers great potential to avoid backfire and largely reduce preignition issues, as opposed to port-fuel injection. Recently, much research has been dedicated, both experimentally and numerically, to understanding the physics and chemistry connected with hydrogen’s mixing and combustion processes in ICEs. This work presents a computational fluid dynamics (CFD) study of the hydrogen DI process in an optical engine operating at relatively low tumble conditions.

In today’s competitive automotive market, customers are now looking for system efficiency as one of the important design parameters of system performance along with durability and reliability. It is essential to ensure products are designed to utilize maximum input power and have better system efficiency. In automotives, transmission and axle systems are power transmitting elements from prime mover to wheels and are one of the main contributors to overall vehicle efficiency. Hence, predicting and assessing overall system efficiency of these aggregates is of paramount importance. System efficiency is driven by component power losses for various speeds and torques, which are arising out of component design parameters, complex interaction within system, operating conditions, lubrication, temperatures etc. To capture multi-physics of speed and torque dependent losses of automotive axle, multidisciplinary and integrated approach is proposed in this paper.

In the quest for reduced development times and cost of fuel cell systems for industrial applications, we identified two major issues. First, the electrochemical behaviour of fuel cell systems is inherently difficult to predict. Second, testing fuel cell systems is resource intensive. These issues compound: Setting up an accurate model of a fuel cell system incurs long testing periods and does not guarantee acceptable results outside the tested parameters or for other membrane electrode assembly compositions. Our proposal to mitigate these two major issues is the use of an X in the Loop concept. Essentially, this is the direct integration of the test sample, here a single fuel cell, into the modelling environment of the whole system. In practice, we have defined two strategies with different levels of integration. Both assume a required power profile is given.

Hardware In Loop (HIL) testing is an important step in software development lifecycle. HiL setup incurs high development costs, extended deployment time and elevated commissioning efforts. These highly complex HiL systems also consume considerable maintenance costs in the long run and due to these factors only limited number of HiL systems are generally deployed for validations. So, increase in number of users can cause a crunch in HiL availability leading to delay in testing and impacting the software release timeline in general. To meet the forementioned shortcomings, a custom-made Test box with sufficient IO’s along with a dedicated and independent processor to run the plant model will serve as a compact HiL. Compact HiL comprises of a rapid prototyping ECU, plant model and a customized test box (with control modules to serve specific purposes – analog, digital, resistance, CAN etc.).

Electrification is driving the use of batteries for a range of automotive applications, including propulsion systems. Effective management of thermal energy in lithium-ion battery pack is essential for both performance and safety. In automotive applications especially, understanding and managing thermal energy becomes a critical factor. Cells in the propulsion battery pack dissipate heat at high discharge rates. Cooling performance of battery can be realized by optimizing the various parameters. Computational Fluid Dynamics (CFD) model build and simulations are resource intensive and demands high performance computing. Traditionally, evaluating thermal performance involves time-consuming CFD simulations. To address this challenge, the proposed novel approach using Generalized Neural Network Regression (GNNR) eliminates complex CFD model building and significantly reduce simulation time. GNNR achieves up to 85% accuracy in predicting heat transfer coefficient.

Additive Manufacturing (AM) techniques, particularly Fusion Deposition Modeling (FDM), have received considerable interest due to their capacity to create complex structures using a diverse array of materials. The objective of this study is to improve the process control and efficiency of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material by creating a predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The study investigates the impact of FDM process parameters, including layer height, nozzle temperature, and printing speed, on key printing attributes such as tensile strength, flexibility, and surface quality. Several experimental trials are performed to gather data on these parameters and their corresponding printing attributes. The ANFIS predictive model is built using the collected dataset to forecast printing characteristics by analyzing input process parameters.

Urban areas around the world are facing an increasing number of issues, such asair pollution, parking shortages, traffic congestion, and inadequate transit options, all of which necessitate innovative solutions. Lot of people are becoming interested in micromobility in urban areas as a replacement for quick excursions and round trips to get to or from transportation services (e.g., Offices, Institutions, Hospitals, Tourist spots, etc.). This research examines the critical role that micromobility plays, concentrating on the effectiveness of micromobility smart electric scooters in resolving urgent urban problems. Micromobility which includes both human and electric-powered vehicles presents a viable substitute for normal and short-distance urban commuting. This study presents a micromobility smart electric scooter that is portable and easy to operate, with the goal of transforming urban transportation. 3D model was designed using SOLIDWORKS and analysed using ANSYS.

The advancements towards autonomous driving have propelled the need for reference/ground truth data for development & validation of various functionalities. Traditional data labelling methodologies are time consuming, skills intensive & have many drawbacks. These challenges are addressed through ALiVA (automatic lidar, image & video annotator), a semi-automated framework assisting for event detection & reference data generation through annotation/labelling of video & point-cloud data. ALiVA is capable of processing large volumes of camera & lidar sensor data. Main pillars of framework are object detection-classification models, object tracking algorithms, cognitive algorithms & annotation results review functionality. Automatic object detection functionality generates precise bounding box around the area of interest & assigns class labels to annotated objects.

Advanced Driver Assistance Systems is a growing technology in automotive industry, intended to provide safety and comfort to the passengers with the help of variety of sensors like radar, camera, LIDAR etc. The camera sensors in ADAS used extensively for the purpose of object detection and classification which are used in functions like Traffic sign recognitions, Lane detections, Object detections and many more. The development and testing of camera-based sensor involves the greater technologies in automotive industry, especially the validation of camera hardware and software. The testing can be done by various process and methods like real environment test, model-based testing, Hardware and Software in loop testing. A fully matured ADAS camera system in the market comes after crossing all these verification process, yet there are lot of new failures popping up in the field with this ADAS system.

In the context of Battery Electric Vehicles (BEVs), airborne noise from Heating, Ventilation and Air Conditioning (HVAC) ducts becomes a prominent concern in the view of passenger comfort. The automotive industry traditionally leverages Computational Fluid Dynamic (CFD) simulation to refine HVAC duct design and physical testing to validate acoustic performance. Optimization of the duct geometry using CFD simulation is a time-consuming process as various design configurations of the duct have to be studied for best acoustic performance. To address this issue effectively, the proposed a novel methodology uses Gaussian Process Regression (GPR) to minimize duct noise. Present solution demonstrates the power of machine learning (ML) algorithms in selecting the optimal duct configuration to minimize noise. Utilizing both real test data and CFD results, GPR achieves remarkable accuracy in design validation, especially for HVAC air ducts.