bluuebunny/crossref_metadata_2025 huggingface dataset:
Powering Sustainable Mobility: Electric and Hybrid Vehicles Leading the Way
Authors: Thorat Jalindar Sukhadev , Kumar Dharmendra , Sandhu Jivraj Singh
Published: December 2024

Electric vehicles (EVs) have emerged as a pivotal solution for sustainable mobility amidst escalating global environmental challenges. According to the International Energy Agency (IEA) report, under current energy, climate, and industrial policies, EVs are projected to account for half of all global car sales by 2035. Furthermore, the report estimates that global electric car sales could reach approximately 17 million units by 2024, highlighting the rapid evolution and significance of EVs.

The adoption of EVs extends beyond passenger cars to encompass two-wheelers, three-wheelers, and commercial vehicles (light, medium, and heavy), illustrating a burgeoning trend in electrification across various modes and geographies. This report provides a detailed case study on automotive vehicle electrification, examining its impact across different transportation sectors.

Government policies, initiatives, standards, and regulations play a crucial role in accelerating this evolution. The report captures key governmental policies and initiatives and evaluates their influence on the current revolution. The robust growth of EVs is driving substantial investments in the supply chain and supporting industries such as charging stations and battery recycling, positioning them for future growth into the 2030s.The pace of EV adoption also hinges on affordability and Total Cost of Ownership (TCO) parity compared to conventional vehicles. Recent reductions in battery prices have significantly enhanced the competitiveness of EVs in the market. The industry is witnessing new trends and innovations driven by entrepreneurs, which are extensively covered in this report.

In the Indian automotive sector, multiple initiatives promoting alternate fuels such as EVs, hybrids, CNG, LNG, and hydrogen are underway, intensifying competition and market volatility. This paper examines crucial business strategies adopted by EV companies amidst increasing competition.

It also focuses on the rising demand for hybrid vehicles in India, exploring their viability and emerging trends such as smart charging solutions, renewable energy integration, and public-private partnerships within India's EV infrastructure ecosystem. Drawing insights from diverse geographical regions and urban contexts, this report provides valuable case studies and best practices for policymakers and industry stakeholders navigating the transition to electric and hybrid vehicles.
A Comprehensive Review of Advancement in Anode Material with Modified Architecture for Lithium-Ion Batteries
Authors: Borkar Shweta , Nahalde Sujay , Ruban J S Alwin , More Hemant
Published: December 2024

Anode material, responsible for the critical storage and release of lithium ions during charge and discharge cycles, holds paramount importance. By strategically altering the material design and composition of the current graphite, researchers aim to significantly improve fast charging capabilities, energy density, cycling stability and overall electrochemical kinetics within Lithium ion battery. Anode materials operate through three primary mechanisms: insertion/de-insertion that is allowing for reversible lithium ion accommodation within the host structure; alloying, where lithium ions form chemical bonds with the anode material; and conversion reactions, involving the creation of new phases during charge/discharge cycles. This review delves into a captivating array of advanced anode materials with the potential to surpass the limitations of traditional graphite. Carbon-based nanomaterials like graphene and its derivative, reduced graphene oxide, offer exceptional conductivity and structural integrity. Metal oxides and sulfides, exemplified by Fe₂O₃ (iron(III) oxide) and MoS₂ (molybdenum disulfide), boast high theoretical capacities, though challenges in volume change and conductivity persist. Metal oxide-carbon hybrids attempt to synergistically combine the strengths of both components. Mxenes, a new class of two-dimensional transition metal carbides, exhibit fascinating characteristics like high electrical conductivity and hydrophilicity, promoting fast lithium-ion transport. Perovskite structures, renowned for their diverse compositions and tunable properties, present exciting possibilities. High entropy alloys, with their unique multi-principal element compositions, offer exceptional structural stability during cycling. Finally, silicon-based anodes, while boasting the highest theoretical capacity among all potential anode materials, face significant volume change issues that necessitate innovative strategies for mitigation. By meticulously dissecting the diverse synthesis methods, unique electrochemical mechanisms, specific capacities, and long-term cycling capabilities of these advanced anode materials, researchers pave the way for the identification of a better candidate to replace the existing graphite anode, ultimately propelling Lithium ion battery technology towards a brighter future in powering the next generation of electric vehicles.
Thermal Modeling of Large Format Prismatic Lithium-Ion Cell – A Comparative Study
Authors: M Abhishek , Napa Nagaraju , Agrawal Manish Kumar , Tamma Bhaskar , Garg Akhil
Published: December 2024

Lithium-ion batteries are one of the prominent energy storage devices that can store electrical energy in the form of chemical energy and release it as required. During their operation, Lithium-ion batteries generate heat that needs to be removed to ensure their optimum performance and safety. A prior knowledge of heat generation rates can help design efficient thermal management systems. Numerical models are quite successful in estimating the heat generation inside Lithium-ion cells. In the present study, a computational fluid dynamics (CFD) model is developed to estimate the heat generation and temperature distribution within a 30 Ah Prismatic Lithium-ion cell using two modelling approaches namely, the Newman, Tiedemann, Gu, and Kim (NTGK) model and the Pseudo 2-Dimensional (P2D) model. The NTGK model, known for its robust representation of electrochemical and thermal aspects is evaluated against the P2D model while providing a detailed explanation of the battery's internal states and interactions. Both models are implemented to simulate the thermal behavior under varying operating conditions such as different discharge rates (2C, 5C) at Tamb = 25°C, and coolant flow (natural and forced convection). The results indicate that the computationally less intensive NTGK model provides a reasonable approximation of temperature distribution and thermal gradients, making it suitable for real-time applications and large-scale simulations. Conversely, the computationally expensive P2D model offers more precise insights into the internal thermal and potential hotspots, necessary for advanced design and optimization. This comparative analysis highlights the tradeoff between model complexity and accuracy associated with these modelling approaches and guides towards the selection of appropriate thermal management strategies for battery cooling applications.
Cost Optimization Techniques for EV Charging Stations Based on Renewable Energy Sources and Grid Power
Authors: Shukla Ankit , Kushwah Yogendra Singh , Suman Saurabh
Published: December 2024

Adoption of Electric Vehicles (EVs) reduces air pollution by reducing harmful gas emissions. Such adoption, however, needs a reliable and convenient charging infrastructure, including smart EV charging. Renewable energy sources such as solar photovoltaic cells, battery and wind energy systems can address these infrastructural gaps which work in conjunction with main grid power supply thereby providing low-cost electricity. This paper introduces an energy management algorithm for integrated renewable and grid power sources available at charging stations across India that considers techno-economic and environmental factors.

The current work proposes a supervisory controller model that manages the load power demand of the charging station. The controller effectively deploys low cost energy sources based on the status of all available power supplies and reduces the overall charging costs in real time. The energy management algorithm ensures adequate stand-alone energy generation and storage at charging station for charging electric cars, even when renewable energy generation is low for variable charging load.
A Predictive Controller for Battery Cooling and Air-Conditioning Systems to Tackle the Comfort-Safety-Efficiency Dilemma
Authors: Palacio Torralba Javier , Kulkarni Shridhar Diliprao , Shah Geet , Jaybhay Sambhaji , Kapoor Sangeet , Locks Olaf
Published: December 2024

The advent of electric vehicles has increased the complexity of air conditioning systems in vehicles which now must maintain the safety and comfort of occupants while ensuring that the high voltage battery temperature is kept within safe limits. This new task is critical due to the influence of the cell and battery pack temperature on the efficiency. Moreover, high temperatures within the battery pack can lead to undesirable effects such as degradation and thermal runaway.

Classical solutions to this problem include larger air conditioning components to support worst case scenario conditions where the cooling request from the battery and the cabin happen at the same time. In such conditions, for the safety of the battery, the cooling request is assigned to battery system which may cause discomfort to the passengers due the significant temperature increase in the cabin during such events. The probability of such events happening is certainly dependent on the weather conditions but in areas of the world where high average temperatures are common, it is not unlikely to find such situations.

This paper shows a methodology that limits the occurrence of these events by predicting and shifting in time the cooling requests on the air conditioning system. As a result, the maximum load on the cooling system is reduced which may lead to a reduction in the size and weight of the components, with the result of a more efficient vehicle overall.
Development of New Generation Battery Cooling Hoses with TPV Material
Authors: Murugesan Annarajan
Published: December 2024

The market for battery-fitted electric cars continues to experience robust growth globally as well as in Indian market. During the charging process heat generation happen because of internal resistance of the battery cells and electrical connectors. Making an efficient battery cooling system is vital for all electric vehicles. One common cause of battery overheating is due to low cooling efficiency. So this research highlights the importance of scientifically designing coolant circuits and selecting appropriate coolant hose materials. Currently, EPDM (ethylene propylene diene monomer) material is widely used for battery cooling hoses due to its design Flexibility, Compatibility with a 50:50 glycol-water mixture and Resistance to thermal and ozone cracking [1]. This study benchmarks EPDM hose technical properties with leading EV battery cooling plastic hose materials, such as mono layer polyamide, mono layer TPVs (thermoplastic vulcanizates) and PA PP two layer hose. Comparative experiments, including fluid compatibility, burst pressure, vacuum collapse, permeability, Aging, ozone resistance and mechanical property tests, were conducted on specimens made from these different materials. The results indicated that while EPDM performs adequately, it tends to degrade under the combined influence of air and coolant liquid stresses, particularly under thermal and mechanical conditions. Conversely, plastic materials TPV (Thermoplastic Vulcanizate) significantly improve all required functional properties like low internal surface friction, low outer surface condensation, enhancing recyclability, thus supporting end-of-vehicle-life sustainability, however excluding flexibility. The advanced manufacturing process of plastic hose also demonstrate a lower Co2 footprint compared to EPDM, because of low density, weight reduction by wall thickness reduction and with higher productivity rate. This research highlights the potential benefits of transitioning from EPDM to plastic materials for battery cooling hoses in electric vehicles, resulting in improved performance, environmental benefits, and increased manufacturing efficiency.
Crash Simulation of Steel-Wood Hybrid Road Safety Barrier with Perforated Post Design: TB 11 Evaluation
Authors: Bendre Sagar , Das Alakendu , Jaiswal Manish
Published: December 2024

Research areas in Road furniture have become critical due to the rising incidence of road accidents and fatalities. Enhancing road attributes such as crash barriers, crash cushions, crash poles, and emergency communication systems can significantly reduce these fatalities. Among these, crash barriers promise particular attention as they serve as immediate safety mechanisms. When a vehicle loses control, crash barriers can effectively mitigate the severity of accidents by restraining the vehicle and preventing more severe outcomes. This paper focuses on the performance of a novel steel-wood hybrid crash barrier with perforated post parallel to vehicles direction, designed to enhance road safety in hilly areas. Utilizing finite element analysis (FEA) in LS-DYNA software, renowned for simulating structural deformation under loading, we evaluated the structural response and crashworthiness of the hybrid barrier under various impact scenarios. Our simulations assessed the barrier's effectiveness in dissipating kinetic energy and reducing vehicle deceleration during collisions, considering material properties, geometry, and impact conditions. The results provide valuable insights into the mechanical behavior and performance characteristics of the steel-wood hybrid crash barrier, informing the optimization of design parameters and the development of advanced safety features for transportation infrastructure. By combining steel and wood, this hybrid barrier aims to reduce the required working width while maintaining high safety standards, particularly in challenging terrains, thereby contributing to safer roads for all users.
ADAS Simulation on Cloud
Authors: Ramapuram Vinay Goud , Dhar Jayshri , Munaiahgari Mallikarjuna Reddy
Published: December 2024

The rapid evolution of new technologies in the automotive sector is driving the demand for advanced simulation solutions, enabling faster software development cycles. Developers often encounter challenges in managing the vast amounts of data generated during testing. For example, a single Advanced Driver Assistance System (ADAS) test vehicle can produce several terabytes of data daily. Efficiently handling and distributing this data across multiple locations can introduce delays in the development process. Moreover, the large volume of test cases required for simulation and validation further exacerbates these delays. On-premises simulation setups, especially those dependent on High-Performance Computing (HPC) systems, pose several challenges, including limited computational resources, scalability issues, high capital and maintenance costs, resource management inefficiencies, and compatibility problems between GPU drivers and servers, all of which can impact both performance and costs.

To address these issues, transitioning to cloud-based simulations using AWS services—such as S3, Batch, Auto Scaling Group (ASG), and Elastic Container Registry (ECR)—allows for parallel execution of Software-In-the-Loop (SIL) tests. Multiple compute instances can be deployed simultaneously, significantly reducing testing times. Additionally, GPU-based simulations can be executed on cost-effective instances, potentially reducing simulation costs by up to threefold. The Docker container used in these simulations packages the necessary software algorithms, simulation tools, and test recordings. Key driving functions, including Blind Spot Detection (BSD), Lane Change Assist (LCA), and Occupant Safety Exit (OSE), are tested using real-world driving scenario data to ensure their effectiveness. The workbench offers an innovative environment for developing software solutions using virtual ECUs, even before the physical hardware becomes available. Furthermore, the automotive operating system utilized for software development provides robust middleware, ensuring secure and standardized communication between computers and the cloud.
Analysis on Test Case Assortment and Priortization Strategy
Authors: Choudhary Mukund Kumar
Published: December 2024

Agile software development aims to create high-quality products while minimizing waste, reducing project costs. Nevertheless, costs are not decreasing despite shorter project cycles and more compact, flexible teams. One area where consideration is being given to reevaluating the stages in software product development is RT. Regression testing is a form of testing done to assure that changes done in Model do not adversely affect the software's functionality. As software develops, test suites typically expand and may become too expensive to run through in their entirety The initial application might have good set of test cases, and running the entire test suite could make testing more expensive. Three ways for reducing the cost of RT include test instance prioritization, test suite minimizing, and regression test selection. In order to verify that software satisfies customer requirements, identify faults or bugs in the code, and determine how to fix these issues to make the software more beneficial, regression testing is essential to software development. In addition to being prepared for testing, test cases and test suites should be completed as quickly as possible when using Test Case Assortment and Prioritization (TCAAP) techniques. TCAAP's primary goal is to test software as quickly and expertly as possible. This paper presents an empirical investigation of TCAAP methodologies in software real-time analysis. This research focuses on studies that are beginning and intermediate.
An Experimental Approach to Understand Various Factors Influencing Low-Temperature Gear Shifting Performance
Authors: Mishra Subodh , Siddharth Kumar , Singh Manoj
Published: December 2024

Gear shifting performance in vehicles is critical for smooth operation, especially under cold environment conditions or sub-zero conditions. In this comprehensive study, we delve into the multifaceted aspects that influence gear shifting behaviour during cold conditions especially after overnight vehicle soaking at low temperature below -8°C to simulate real world scenarios. Notably, our efforts on these bench trials focuses on isolating the synchronization load from the maximum block release force, a phenomenon arising from the interaction between synchronizer rings and gear cones in case of high drag of input and counter shafts. Our experimental trials involved multiple bench level testing for lower gear shifting case especially 2nd to 1st gear and 1st to 2nd gear shifting. Factors under study are focusing on changing the Oil (viscosity and quantity), different combination of synchronizer ring material, change of inertia etc.

Shifting load in cold condition mainly includes two loads, firstly synchronization load which can be checked with matching target RPM value of Input shaft and second load is block release of Synchronizer ring. This phenomenon of block release is very important in case of high drags of rotating components which is generally occurs during cold environment when transmission oil viscosity increases significantly. Stability of transmission oil viscosity to wide temperature range, especially in cold condition plays important role here to reduce block release load and same is also investigated here. These experimental findings provide valuable insights for optimizing gear shifting performance in cold environments. By understanding these factors, manufacturers and engineers can enhance vehicle drivability and reliability, ensuring seamless gear change even in challenging conditions.
A Study on Powertrain Modes for Optimizing the NVH Performance
Authors: Jha Niraj Kumar , Yeezaku Antony Neomin , Victor Priyanka Esther , Krishnamurthy Govindasamy
Published: December 2024

The stiffness and positioning of engine mounts are crucial in determining the powertrain rigid body modes and kinetic energy distribution. Therefore, optimizing these mounts is essential in the automotive industry to separate the torque roll axis (TRA) and minimize vibration. This study aims to enhance mount locations by isolating the engine rigid body modes and predicting the inter-component force (ICF) and transfer function of the vehicle.

The individual ICFs for engine mountings are calculated by applying a unit force at the bearing location. Critical frequencies are identified where the amplification exceeds the unit force at the mounting interface between the engine and the frame. The transfer function approach is utilized to assess the vibration at the handlebar. Both ICF and transfer functions analyze the source and path characteristics linked to critical response frequencies. This understanding aids in enhancing mounting positions to minimize vibration levels, thereby enhancing NVH performance.
Enhancing Vehicle Emission Monitoring Through Tree-Based Machine Learning: Optimizing IUPR Under BSVI Stage 2 Regulation
Authors: Kumar Kosalaraman , Venkat Harish , Avanashilingam Jayanth Balaji
Published: December 2024

The new Bharat Stage (BS) VI Stage 2 regulation for automotive vehicles in India requires monitoring the performance of emission control components, such as Selective Catalytic Reduction (SCR) systems, Diesel Oxidation Catalysts (DOCs), Diesel Particulate Filter (DPF), Nitrous Oxides (NOx) Sensors, and Exhaust Gas Recirculation (EGR). The regulation also mandates that a minimum In-Use Performance Ratio (IUPR) must be met, which is the ratio of the number of times a component's performance is monitored to the number of drive cycles the engine has undergone. The IUPR must be tracked throughout the vehicle's lifetime after an initial run-in period. In an effort comply with the minimum IUPR requirement, the engine and after-treatment system calibrations must ensure that the conditions and threshold ranges for enabling performance monitoring of emission-critical components are met across all vehicles operating duty cycles and varying geographic conditions. This study explores the novel method of using Tree Based Machine Learning classifier coupled with tree traversing technique to improve the IUPR for different vehicle duty cycles and emission components. The findings of the proposed techniques are presented that determines the ideal threshold ranges, therefore optimizing the performance monitoring window for a certain duty cycle and lowering the manual efforts by many times.
Anomaly Detection in Fleet Vehicle Data and Statistical Approach to Develop Engine System OBD Thresholds
Authors: Kumar Amit , Hegde Karthik , Challa Krishna , H YASHWANTH
Published: December 2024

On-Board-Diagnostics (OBD) are crucial for ensuring the proper functioning of Engine’s emission control system by continuously monitoring various sensors and components. When the failure is detected, the Check Engine Light is triggered on Vehicle’s dashboard, alerting the driver to seek professional service to address the issue. However, the task of developing the monitoring strategies and performing robust calibration is challenging and time consuming. Model in loop (MIL) Simulation and testing is a technique used to understand and estimate the behavior of a system or sub system. The diagnostics model can be tested and refined within the model-based environment allowing a complex system to be efficiently regulated.

MIL framework could be explored at various stages of development from early in the design phase to later stages of series developments through vehicle fleet data. This framework allows early identification and correction of errors and bugs in a standalone dependent environment. In this paper, the data visualization dashboard is used to detect anomalies in Fleet Vehicle data and understand the relativity with different systems. A data driven MIL Simulation set up is established to validate and iterate for robust calibration. The drift monitor controller model is discussed as a case study to replicate the behavior and resolve the real time calibration issue. This approach on OBD systems to assess the reliability of models and make robust calibration through Monitor Performance Index (MPI) is carried out in Simulink environment. The key emphasis of this paper is to discuss a framework to identify anomaly from fleet data and resolve through MIL simulation to optimize monitor performance. Different combinations of calibration input data and Fleet Vehicle data are tested for validation to ensure repeatability of performance and decisions related to fault detection criteria.
Phenomenological Modeling of Radar Sensor and Realization of Noise Effects
Authors: Hanumanthaiah Manjunath , S Girish , Durairaj Priya
Published: December 2024

The rapid advancement in the autonomous vehicle industry has underscored the critical role of sensors in identifying and tracking traffic participants. Among these sensors, radar plays a pivotal role due to its ability to function reliably in various weather and lighting conditions. This paper presents a phenomenological radar sensor model designed to simulate the behavior of real radar systems under diverse scenarios, including noisy environments and accidental situations. As the complexity of autonomous systems increases, relying solely on on-road and bench testing becomes insufficient for meeting stringent safety and performance standards. These traditional testing methods may not encompass the wide range of potential scenarios that autonomous vehicles might encounter. As a result, virtual environment modeling has emerged as a crucial tool for validating driving functions, assistance systems, and the strategic placement of multiple sensors. In contrast to high-fidelity radar models, which require detailed environmental inputs and extensive computational resources, phenomenological sensor models offer a balance between performance and accuracy. These medium-fidelity models reduce the need for exhaustive environmental details and complex computations, such as Fourier tracing, thereby enhancing simulation efficiency. Despite the trade-off in accuracy, phenomenological models provide a more realistic and reliable alternative to basic ideal sensor models. This paper details the development of the radar model, which incorporates fundamental radar theory and geometric principles. The model simulates radar detection images by integrating synthetic ground truth data with an open simulation interface standard. It also accounts for various degrading effects and environmental noise that impact radar performance. By doing so, the model can replicate the nuanced behaviors of radar sensors in real-world conditions. The validity of the proposed radar model is assessed through extensive comparisons with actual radar recordings. Various output parameters of the detection data are analyzed to evaluate the model's realism and reliability. The results demonstrate that the phenomenological radar sensor model closely mirrors the performance of real radar systems, thereby offering a robust tool for advancing the development and testing of autonomous vehicle technologies.
“Smart Testing Methodology for Air Suspension System of Buses”
Authors: Tangade Atul Bandu , Babar Sunil , Bankar Milind Achyutrao , Mehendale Ravindra , Dhumal Kailas , Bhusari Deepak , Sonawane Ravindra , Shinde Saurabh
Published: December 2024

In the fast growing automotive sector, reliability & durability are two terms of utmost importance along with weight and cost optimization. Therefore it is important to explore new technology which has less weight, low manufacturing cost and better strength. It also seek for a quick, cost effective and reliable methodology for its design validation so that any modification can be made by identifying the failures.

This paper presents the rig level real world usage pattern simulation methodology to validate and correlate the vehicle level targets for micro strain, wheel forces and displacement on suspension components like optimized Z spring, torque rods, pan hard rod & mounting brackets of newly developed air suspension for buses.
An Approach to Reduce Noise Generated by Thermal Management Systems in Electric Vehicles
Authors: Shedge Atharva Vikas , Shalgar Sandeep , Srivastava Sarvesh , Nagarhalli Prasanna V
Published: December 2024

Electric vehicles are transforming the future of transportation for the world while achieving the goal of sustainable development. While the concept and use of battery-powered vehicles has also penetrated the logistics and supply chain industry, it has also brought upon new challenges during its development. As OEMs strive to develop BEVs with extended range, the components within the propulsion and drivetrain system tend to demand a higher power output from the energy storage systems. This results in BEVs generating higher levels of thermal heat energy, which must be dissipated / rejected for optimal performance and safety. Consequently, to reject this heat, BEVs must utilize thermal management systems with higher capacities. This increase in size is accompanied by the inclusion of many parts that generate noise and contribute to increased noise levels in BEVS. In today’s market, optimization of noise levels is a significant factor when ensuring passenger comfort, while also showing consideration for the environment and pedestrians. This study introduces multiple noise optimization techniques employing diverse approaches for commercial and passenger BEV’s. Additionally, this article highlights several efforts to validate these methods across several system and sub-system levels. Overall, a significant reduction is observed in the NVH levels without a compromise in the heat rejection capacity of the thermal management system. In conclusion, the study also mentions the future scope for improvements for in-cab noise while improving the efficiency of the thermal management system.
Improving Crash Box Energy Absorption: Impact of Single Screw Integration - A Finite Element Analysis Study
Authors: R Teddy Samuel , Mayakrishnan Jaikumar
Published: December 2024

This study proposes a novel energy absorber design by incorporating a screw into the groove of crash boxes and evaluates its performance using finite element analysis (FEA). The methodology integrates CAD modelling, material characterization, and detailed analysis of simulation data to assess the influence of single screw integration on crash box performance. LS-Dyna simulation was employed to investigate the behaviour of Aluminium Alloy 5052 (Al5052) crash boxes with screws in the groove. Results from FEA reveal key performance indicators such as displacement, stress, strain, internal energy, and acceleration across various crash box thicknesses. Analysis of the data demonstrates that as the thickness of the crash box increases, there is a decreasing trend where displacement, stress levels, and acceleration are concerned. The presence of the screw leads to a reduction in stress levels and acceleration across different thicknesses of crash boxes, indicating superior performance. These findings suggest that the proposed design holds promise for reinforcing vehicle safety systems, particularly in low-speed crash scenarios. Overall, this study contributes to advancements in automotive safety and crashworthiness methodologies by optimizing energy absorption mechanisms, acceleration control strategies, and strain management techniques in crash box design. Future research directions include exploring advanced materials and structural designs to further enhance crash box performance and adaptability to different collision scenarios.
Thermal Performance of Encapsulated LFP Battery Packs Encapsulants
Authors: Somarajupalli Shanmukhadev , Vedantam Srikanth , Gupta Shubham , Jha Kaushal Kumar
Published: December 2024

Electric vehicles (EVs) represent a pivotal shift in the automotive industry, offering a sustainable alternative to traditional gasoline-powered vehicles. Central to their operation are lithium-ion batteries, which are favoured for their high energy density and long lifespan. Ensuring thermal stability during battery pack operation is crucial for both safety and efficiency. To enhance heat transfer within the battery pack, various encapsulants are employed. This study utilizes simulation to investigate the thermal performance of a 3.072kWh, 51.2V, 60Ah battery pack composed of 6Ah 32700 LFP cells, encapsulated with commercially available materials such as polyurethane (PU) foam, silicone, and silicone-modified epoxy under 1C and 2C discharge conditions. The findings show that, at 1C and 2C discharge rates, respectively, the battery pack potted with silicone attains a maximum temperature that is 2.57°C and 3.84°C lower than the pack simulated with air. Additionally, silicone-modified epoxy facilitates 1.92 times greater heat transfer in the battery pack compared to the pack without encapsulant at 1C, at 2C the heat transfer is 2.07 times higher. While encapsulants with higher thermal conductivities result in lower peak temperatures, they also exhibit a higher temperature gradient across the battery. The findings indicate that as the thermal conductivity of the encapsulant increases, the rate of improvement in the battery pack's heat transfer capabilities tends to decrease.
Optimization of Wire Electrical Discharge Machining Parameters for SAE 1010 Material Using Taguchi-Based Grey Relational Approach
Authors: Natarajan Manikandan , Pasupuleti Thejasree , Kiruthika Jothi , Krishnamachary PC , Silambarasan R
Published: December 2024

Wire Electrical Discharge Machining (WEDM) has attracted considerable attention in contemporary manufacturing because of its capacity to accurately form conductive materials. This study aims to optimize the parameters of Wire Electrical Discharge Machining (WEDM) for SAE 1010 material, which is a commonly used low-carbon steel. The Taguchi-based Grey Relational Approach (GRA) is employed for this purpose. The goal is to optimize machining efficiency and quality while minimizing production costs. The research methodology combines the Taguchi method for experimental design with the GRA for multi-response optimization. The Taguchi L27 orthogonal array is utilized to carry out experiments, taking into account three controllable factors: pulse-on time, pulse-off time, and discharge current. In addition, the performance characteristics to be optimized include surface roughness (Ra) and material removal rate (MRR). The experimental results are analyzed using the GRA (Grey Relational Analysis) to determine the correlation between process parameters and performance characteristics. The grey relational grades are computed to identify the most favorable parameter settings that result in the maximum machining performance. The analysis offers valuable information on the sensitivity of each parameter and their interactions, making it easier to identify the best process conditions for WEDM of SAE 1010 material. The proposed Taguchi-based GRA approach provides a systematic and efficient methodology for optimizing WEDM parameters, allowing manufacturers to improve productivity and product quality while reducing production costs. The results of this study enhance the development of machining techniques for SAE 1010 material and provide a valuable resource for improving similar manufacturing processes in different industries.
Application of TOPSIS Approach for Optimization on Additive Manufacturing (Fusion Deposition Modeling) of TPU Material: A Systematic Approach for Process Enhancement
Authors: Pasupuleti Thejasree , Natarajan Manikandan , Kiruthika Jothi , Ramesh Naik Mude , Silambarasan R
Published: December 2024

Additive Manufacturing (AM), specifically Fusion Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of complex structures using a wide range of materials. The objective of this study is to enhance the FDM process for Thermoplastic Polyurethane (TPU) material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study examines the influence of FDM parameters, such as layer height, nozzle temperature, and infill density, on important characteristics of the printing process, such as tensile strength, flexibility, and surface finish. The collection of experimental data is achieved by conducting systematic FDM printing trials that cover a variety of parameter combinations. The TOPSIS optimization method is utilized to determine the optimal parameter settings by evaluating each parameter combination against the ideal and anti-ideal solutions. This method determines the optimal parameter configuration that maximizes the overall printing quality by considering multiple objectives simultaneously. The effectiveness of the TOPSIS-optimized FDM process is assessed using statistical analysis and compared to the baseline outcomes. The proposed TOPSIS optimization method offers a valuable tool for optimizing the AM process. It allows manufacturers to enhance productivity and product quality while minimizing production costs. This study enhances the comprehension of Fused Deposition Modeling (FDM) techniques using Thermoplastic Polyurethane (TPU) material and provides valuable knowledge for improving Additive Manufacturing (AM) operations in different industrial sectors.
Optimization and Regression Modeling of Additive Manufacturing (Fused Deposition Modeling) of PETG Material for Automobile Applications
Authors: Natarajan Manikandan , Pasupuleti Thejasree , Kiruthika Jothi , D Palanisamy , Silambarasan R
Published: December 2024

Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a highly promising method for creating intricate shapes using different materials. Polyethylene Terephthalate Glycol (PETG) is a highly utilized thermoplastic that is recognized for its exceptional strength, resistance to chemicals, and effortless processing. This study aims to optimize the process parameters of the FDM technique for PETG material using Taguchi Grey Relational Analysis (GRA). An empirical study was carried out to examine the impact of various FDM process parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on important outcome variables like dimensional accuracy, surface quality, and mechanical properties. The Taguchi method was used to systematically design a series of experiments, while GRA was used to optimize the process parameters and performance characteristics. The results unveiled the most effective parameter combinations for attaining exceptional printing quality and mechanical properties of PETG parts. Furthermore, Grey Relational Grades helped to identify the key factors that affect the performance of the AM process. This study enhances the progress of additive manufacturing methods, particularly Fused Deposition Modeling (FDM), for PETG material. It focuses on meeting the increasing need for efficient and affordable production in diverse industries such as aerospace, automotive, and medical sectors. This study utilizes Taguchi Grey Relational Analysis to offer valuable insights into parameter optimization strategies that can be applied to a wide variety of additive manufacturing applications.
Optimization of Wire Electrical Discharge Machining Parameters for Cupronickel Using Taguchi-Based Grey Relational Analysis
Authors: Pasupuleti Thejasree , Natarajan Manikandan , Kiruthika Jothi , Ramesh Naik Mude , Silambarasan R
Published: December 2024

Wire Electrical Discharge Machining (WEDM) is an important method engaged to make intricate shapes in conductive materials like Cupronickel, which is well-known for its ability to resist corrosion and conduct heat. The intention of this exploration is to enhance the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for Cupronickel material by utilizing a Taguchi-based Grey Relational Analysis (GRA). The study examines the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on key machining outcomes such as surface roughness (Ra), material removal rate (MRR). A comprehensive dataset is generated for analysis through a systematic series of experiments designed using the Taguchi method. Grey relational grades are assessed to measure the connections between the input parameters and machining responses, making it easier to determine the best parameter settings. The Taguchi-based GRA approach provides a systematic approach for optimizing multiple responses, taking into account the conflicting nature of objectives. The results of this study help improve the efficiency of WEDM processes for Cupronickel material by providing information on the sensitivity and interaction of parameters. Manufacturers can improve machining efficiency and quality while reducing production costs by identifying the most effective parameter combinations. The Taguchi-based Grey Relational Analysis is a reliable method for optimizing parameters in Wire Electrical Discharge Machining (WEDM). It provides manufacturers with a valuable tool to enhance productivity and quality in machining operations involving Cupronickel material. This study improves the comprehension of WEDM procedures and offers practical recommendations for attaining exceptional machining results in different engineering fields.
Optimization of Wire Electrical Discharge Machining Parameters for SAE 1010 Material Using TOPSIS Method
Authors: Natarajan Manikandan , Pasupuleti Thejasree , Kiruthika Jothi , Katta Lakshmi Narasimhamu , Silambarasan R
Published: December 2024

Wire Electrical Discharge Machining (WEDM) is a highly adaptable machining process that is extensively employed across various engineering industries to achieve precise machining of conductive materials. SAE 1010, a steel with low carbon content, is widely used in automotive, aerospace, and machinery components because it can be welded easily and shaped effectively. The aspiration of this study is to optimize the parameters of WEDM for SAE 1010 material by employing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. An empirical study was carried out to examine the impact of crucial machining variables, such as pulse-on time, pulse-off time, applied current on performance metrics of machining, such as material removal rate (MRR), surface roughness (Ra) and Overcut. The utilization of the design of experiments (DOE) methodology enabled the methodical investigation of the parameter space. Taguchi based TOPSIS provides a comprehensive approach to parameter optimization in WEDM by taking into account both the closeness to the ideal solution and the distance from the negative ideal solution. The results showcased the efficacy of the TOPSIS technique in identifying the optimal parameter combinations for improving the efficiency of machining and the quality of the surface of SAE 1010 components. The implemented optimization methodology offers a methodical structure for enhancing the efficiency of WEDM procedures, specifically in demanding materials such as SAE 1010 steel. This study enhances the comprehension of WEDM of SAE 1010 material and provides valuable guidance on optimizing parameters for various machining processes. Manufacturers can enhance efficiency and quality in the machining of intricate components made from materials like SAE 1010 by utilizing the TOPSIS method.
Diesel Exhaust Fluid (DEF) Associated Corrosion Risk Prediction for Aftertreatment System
Authors: Udhane Tushar Sudam , Nanduru Enoch , Warwick Michael , Willey Donald , Giri Nikhil , Parikh Tanishq
Published: December 2024

With emission regulations becoming increasingly stringent, the integration of Diesel Exhaust Fluid (DEF) in aftertreatment systems has become essential for reducing nitrogen oxide (NOx) emissions in compliance with these evolving standards. DEF dosing is a very critical component in Selective Catalytic Reduction (SCR) systems, where it chemically reacts with NOx in the exhaust stream to form harmless nitrogen and water vapor, thus significantly reducing the environmental impact of diesel engines. However, the introduction of DEF presents a challenge of corrosion risk within the aftertreatment system components.

This study aims to predict the location of corrosion, and its risk associated with DEF usage in Diesel aftertreatment system, by employing a multi-faceted approach that includes physical testing and computational modelling. Specifically, the focus of this paper is on predicting corrosion locations from unsteady DEF spray analysis without modelling detailed corrosion chemistry mechanism using computational fluid dynamics (CFD). This unsteady DEF spray analysis focuses on characterizing the spray behavior, its interactions with the reactor walls, and identifying key factors in DEF spray dynamics that can predict potential corrosion locations within the decomposition reactor tube (DRT). The DRT is a critical component in the aftertreatment system, serving as the conduit through which DEF spray is injected and transported to the SCR section. The key factors for prediction of corrosion locations include temperature of solids, film velocity, DEF concentration and direct DEF spray impingement zones. The criteria of these factors are studied, and relationships are established among these factors to provide insight into potential corrosion risk locations in DRT. This paper also discusses the approaches to speed the simulation and reduce the turnaround time to help make the design-development decision faster.

The validation of simulation methodology through physical testing has shown a good correlation. This good agreement between simulation and experimental results underscores the effectiveness of simulation methodology in accurately identifying the potential corrosion locations.
Validation Methodology of Different Driver Monitoring Systems Using a Modular Motion Drive Simulator
Authors: Raj Prem raj Anand , Selvam Dinesh Kumar , Thanikachalam Ganesh , Sivakumar Vishnu
Published: December 2024

In India, Driver Drowsiness and Attention Warning (DDAW) system-based technologies are rising due to anticipation on mandatory regulation for DDAW. However, readiness of the system to introduce to Indian market requires validations to meet standard (Automotive Industry Standard 184) for the system are complex and sometimes subjective in nature. Furthermore, the evaluation procedure to map the system accuracy with the Karolinska sleepiness scale (KSS) requirement involves manual interpretation which can lead to false reading. In certain scenarios, KSS validation may entail to fatal risks also. Currently, there is no effective mechanism so far available to compare the performance of different DDAW systems which are coming up in Indian market. This lack of comparative investigation channel can be a concerning factor for the automotive manufactures as well as for the end-customers. In this paper, a robust validation setup using motion drive simulator with 3 degree of freedom (DOF) is proposed to overcome the KSS validation and performance of multiple DDAW systems. Moreover, designing and testing methodology are explained in stage-by-stage approach. In addition, this study details on integration of multiple DDAW technologies and real time testing of those solutions under simulated road scenario. Thereby, data of multiple driving behavior are fetched to analyze drowsiness pattern and performance comparison study.