M. Kumarasamy College of Engineering

With the widespread use of lithium-ion batteries in a wide range of consumer electronics products, the CE industry has undergone a dramatic shift. The Li-ion battery has emerged as the heart of electric cars, and the focus has now shifted to the automotive sector. Liquid crystal displays have evolved over time to meet the demands of automobiles. International research groups and the performance of production electric vehicles are used to discuss and inform vehicle-driven battery targets. There is still a lot of room for improvement in terms of energy, life expectancy, cost, safety, and fast-charging capabilities for LIBs suited for the automotive sector. In this study, a review of lithium-ion battery applications in electric vehicles is presented.

Abstract Groundwater plays a vital role in the sustainable development of agriculture, society and economy, and it's demand is increasing due to low rainfall, especially in arid and semiarid regions. In this context, delineation of groundwater potential zones is essential for meeting the demand of different sectors. In this research, the integrated approach consisting of analytical hierarchy process (AHP), multiple influence factors (MIF) and receiver operating characteristics (ROC) was applied. The demarcation of groundwater potential zones is based on thematic maps, namely Land Use/Land Cover (LULC), Digital Elevation Model (DEM), hillshade, soil texture, slope, groundwater depth, geomorphology, Normalized Difference Vegetation Index (NDVI), and flow direction and accumulation. The pairwise comparison matrix has been created, and weights are assigned to each thematic layer. The comparative score to every factor was calculated from the overall weight of two major and minor influences. Groundwater potential zones were classified into five classes, namely very poor, poor, moderate, good and very good, which cover an area as follows: 3.33 km 2 , 785.84 km 2 , 1147.47 km 2 , 595.82 km 2 and 302.65 km 2 , respectively, based on AHP method. However, the MIF groundwater potential zones map was classified into five classes: very poor, poor, moderate, good and very good areas covered 3.049 km 2 , 567.42 km 2 , 1124.50 km 2 868.86 km 2 and 266.67 km 2 , respectively. The results of MIF and AHP techniques were validated using receiver operating characteristics (ROC). The result of this research would be helpful to prepare the sustainable groundwater planning map and policy. The proposed framework has admitted to test and could be implemented in different in various regions around the world to maintain the sustainable practices.

Groundwater is widely recognized as an essential source of water for drinking and irrigation uses in the South India. It is essential to evaluate the characterization of groundwater for drinking and irrigation uses. A total of 67 samples were collected in different locations of study area from tube wells for physio-chemical analysis. Water quality index (WQI), nitrate pollution index (NPI), human health risk assessment (HHRA), and irrigation water quality index (IWQI) efficient tools have been used to evaluate the quality of groundwater in the present study. The Piper and Gibbs diagram revealed that weathering of parent rocks, evaporation, rock water interaction, and ion exchange process are influencing the nature of groundwater. The result of WQI showed that, 20.89% and 7.46% of the sample locations are moderate and poor quality of water for drinking purpose. In order to evaluate the nitrate contamination, NPI divulged that seven sample locations are significant very significant type of pollution. Based on HHRA, the children are at high risk compared to male and female in the study region. The higher percentage of the sample locations are suitable for irrigation uses. The study helps to identify the contaminated zones and also to follow emerging remedial measures to control the source of contamination in the region.

Nowadays ensure the performance of heat exchanger is one of the toughest roles in industries. In this work focused on improve the performance of shell and tube heat exchangers by reducing the pressure drop as well as raising the overall heat transfer. This work considered as a different nanoparticles such as Al2O3, SiO2, TiO2, and ZrO2 to form a nanofluids. This nanofluids possesses high thermal conductivity by using of this increase the heat transfer rate in shell and tube heat exchanger. The selected nanofluids are compared to base fluid based on the thermophysical properties as well as heat transfer characteristics. All the heat transfer characteristics are improved by applying of nanofluids particularly higher results are obtained with using of TiO2 and Al2O3 compared to SiO2 and ZrO2. Mixing of nanoparticles increased in terms of volume percentage it will be increases the all heat transfer characteristics as well as performance of the heat exchanger.

In this research aimed to estimate the overall heat transfer coefficient of counter flow shell and tube heat exchanger. Heat transfer is the phenomenon to analysis of heat transfer from one medium of fluid to another medium of fluid, it is considered as a major role in industrial applications. Numerous heat exchangers are available, in this research considered as shell and tube heat exchanger. Overall heat transfer coefficient informed that three major factors are influenced as passing of fluid (film) media coefficient inside the tubes, circulating of fluid (film) media coefficient over in the shell, and the resistance of wall made on metal. In this study Taguchi L9 orthogonal array is executed to found the overall heat transfer coefficient with effective process parameters. Three major parameters are considered for this work are coil diameter (25 mm, 30 mm, and 35 mm), baffle thickness (15 mm, 20 mm, and 25 mm) and baffle gap (200 mm, 300 mm, and 400 mm. Baffle plate thickness is highly significant factor for this experiment.

The main objective of this study was to evaluate the groundwater quality for domestic, agriculture use and to describe fluoride contamination in groundwater and their impacts on human health. 67 groundwater samples were collected and analyzed for major ions. Water Quality Index (WQI), Piper diagram and Gibbs diagrams were calculated to measure the suitability of groundwater for drinking purpose. The hazards index value was calculated to estimate the noncarcinogenic risk to adult (male, female) and children suggested by the United States Environmental Protection Agency (USEPA). The irrigation indices were calculated to evaluate the quality of water for irrigation purpose. Statistical methods such as principal component and hierarchical cluster analysis were used to analyses the inter-relationship of data. Hydrochemistry of the samples shows, the major ions in the order of Ca 2+ >Mg 2+ >Na + and Cl ->SO4 -in the study area. WQI value of groundwater, 74.62% of sample locations are good and 25.38 % of sample locations need primary treatment for drinking purpose. The results of the hazards index show that 65.67% of the sample locations exceeds the tolerable limit for non-carcinogenic risk (greater than one) for children higher than the risk level for Male and female. Statistical report of PCA and HCA reveals that Ca-Na-HCO3-F has positive loading and TDS-EC has negative loading. The study results show that rock-water interaction and anthropogenic activities are the major factors that influence the quality of groundwater. The continuous intake of excess concentration fluoride causes bone diseases and teeth problems.

Aluminum is among the most preferred materials based on the desired properties. This investigation focused on to evaluate the wear rate of the AA6066 aluminium alloy composite by using pin-on-disc apparatus. The composites were created with three materials such as AA6066 alloy, high-speed steel, and copper which have a volume percentage variation of 92%, 5%, and 3%, respectively. These three parameters were considered for the experimental results of the wear rate such as load applied, sliding speed, and sliding distance. Experimental results of the composites were compared using an applied load of 20 N, a sliding velocity of 3.0 m/s and 1800 m of sliding distance with AAHSSCu reinforced composites offering a minimum wear rate. Similarly, using a 40 N applied load, the minimum wear rate is obtained. Further increasing the applied load to 60 N with 600 m of sliding distance provided a lower wear rate. The various graphical representations such as three-dimensional surface plots, contour plots, and bar charts were used for the experimental results. Wear rate consequences were expressed individually compared based on the considered parameters. Experimental results were having the reliability of nearly ninety-one percentage with only wear rate being focused. Finally, an optimized wear rate is obtained at the sliding distance of 1200 m with an applied load of 40 N and a spindle speed of 3 m/s.

Automobile industries were ready to recycle the waste old parts as well as the damaged parts of the old vehicles as much as possible. This study mainly focused on the recycling of the waste and damaged aluminium frames of the automobile bodies. These aluminium‐based frames only collected the metal matrix composite created by reinforcement of 3% silicon carbide (SiC) and 3% high carbon steel. The stir casting method is chosen to make the composites. Optimization is done by Taguchi ANOVA technique. Three input parameters such as stir speed, time of squeeze, and the temperature of the preheating were considered. The outputs such as compressive strength and porosity were experimentally measured with the combination of nine (L9) experimental trails. The measured experimental results were analyzed and optimized with the help of Taguchi technique with different plots for clear identification. The optimized parameters based on low porosity and high compressive strength were recommended for conclusion.

Nowadays, a huge amount of digital data is frequently changed among different embedded devices over wireless communication technologies. Data security is considered an important parameter for avoiding information loss and preventing cyber-crimes. This research article details the low power high-speed hardware architectures for the efficient field programmable gate array (FPGA) implementation of the advanced encryption standard (AES) algorithm to provide data security. This work does not depend on the Look-Up Table (LUTs) for the implementation the SubBytes and InvSubBytes stages of transformations of the AES encryption and decryption; this new architecture uses combinational logical circuits for implementing SubBytes and InvSubBytes transformation. Due to the elimination of LUTs, unwanted delays are eliminated in this architecture and a subpipelining structure is introduced for improving the speed of the AES algorithm. Here, modified positive polarity reed muller (MPPRM) architecture is inserted to reduce the total hardware requirements, and comparisons are made with different implementations. With MPPRM architecture introduced in SubBytes stages, an efficient mixcolumn and invmixcolumn architecture that is suited to subpipelined round units is added. The performances of the proposed AES-MPPRM architecture is analyzed in terms of number of slice registers, flip flops, number of slice LUTs, number of logical elements, slices, bonded IOB, operating frequency and delay. There are five different AES architectures including LAES, AES-CTR, AES-CFA, AES-BSRD, and AES-EMCBE. The LUT of the AES-MPPRM architecture designed in the Spartan 6 is reduced up to 15.45% when compared to the AES-BSRD.

The present datasets reveal that to assess the suitability of groundwater quality for drinking and irrigation uses in both Pre and Post Monsoon Season in Sarabanga River region, Tamilnadu, India based on various water quality indices. A total of 50 groundwater samples were collected in different location in a research area. Water Quality Index (WQI) is a number which indicates the suitability of water for drinking purpose. Sodium Absorption Ratio (SAR), Permeability Index (PI), Residual Sodium Carbonate (RSC), Percentage Sodium (%Na), Kelly Ratio (KR) and Magnesium Hazards (MH) are index value which elaborates the fitness of groundwater for agriculture uses. The WQI value for groundwater in both seasons reveals that 74.5 sq.km and 37.24 sq.km of the area were unfit for domestic purposes. Based on irrigation indices, almost all sample locations are suitable for irrigation purposes. The dataset demonstrates how water quality indices would be applied to policymakers to manage, handle and sustainably improve society at large.

A new Symmetric Solar Fed Inverter (SSFI) proposed with a reduced number of components compared to the classical, modified, conventional type of Multilevel Inverter (MLI). The objective of this architecture is to design fifteen-level SSFI, this circuit uses a single switch with minimizing harmonics, and Modulation Index (MI) values. Power Quality (PQ) is developed by using the optimization algorithms like as Particle Swarm Optimization (PSO), Genetic algorithm (GA), Modified Firefly Algorithm (MFA). It’s determined to generate the gating pulse and finding optimum firing angle values calculate as per the input of MPP intelligent controller schemes. The proposed circuit is solar fed inverter used for optimization techniques governed by switching controller approach delivers a major task. The comparison is made for different optimization algorithm has significantly reduced the harmonic content by varying the modulation index and switching angle values. SSFI generates low distortion output uses through without any additional filter component through utilizing MATLAB Simulink software (2020a). The SSFI circuit assist Xilinx Spartan 3-AN Filed Program Gate Array (FPGA) tuned by optimization techniques are presented for the effectiveness of the proposed model.

This study investigates the heat transfer and the pressure drop of cone helically coiled tube heat exchanger using (Multi wall carbon nano tube) MWCNT/water nanofluids. The MWCNT/water nanofluids at 0.1%, 0.3%, and 0.5% particle volume concentrations were prepared with the addition of surfactant by using the two-step method. The tests were conducted under the turbulent flow in the Dean number range of 2200 < De < 4200. The experiments were conducted with experimental Nusselt number is 28%, 52% and 68% higher than water for the nanofluids volume concentration of 0.1%, 0.3% and 0.5% respectively. It is found that the pressure drop of 0.1%, 0.3% and 0.5% nanofluids are found to be 16%, 30% and 42% respectively higher than water. It is studied that the prepared MWCNT/water nanofluids show good stability even after 45 days of preparation and there is no considerable deposit of nanotubes on the tube inner wall. It is also studied that there is no immediate risk of handling MWCNT and studied that there is no significant erosion of coiled tube inner wall surface even after several test runs. Therefore the MWCNT/water nanofluids are the alternate heat transfer fluids for traditional fluids in the cone helically coiled tube heat exchanger to improve the heat transfer with considerable pressure drop.

There is more demand for natural fiber‐reinforced composites in the energy sector, and their impact on the environment is almost zero. Natural fiber has plenty of advantages, such as easy recycling and degrading property, low density, and low price. Natural fiber’s thermal properties and flexural properties are less than conventional fiber. This work deals with the changes in the thermal properties and mechanical properties of S‐glass reinforced with a sodium hydroxide‐treated pineapple leaf (PALF) and banana stem fibers. Banana stem and pineapple leaf fibers (PALF) were used at various volume fractions, i.e., 30%, 40%, and 50%, and various fiber lengths of 20 cm, 30 cm, and 40 cm with S‐glass, and their effects on the thermal and mechanical properties were studied, and their optimum values were found. It was evidenced that increasing the fiber volume and fiber length enhanced the flexural and thermal properties up to 40% of the fiber volume, and started to decrease at 50% of the fiber volume. The fiber length provides an affirmative effect on the flexural properties and a pessimistic effect on the thermal properties. The PALF S‐glass combination of 40% fiber load and 40 cm fiber length provides maximum flexural strength, flexural modulus, storage modulus, and lowest loss modulus based on hybrid Taguchi grey relational optimization techniques. PALF S‐glass hybrid composite has been found to have 7.80%, 3.44%, 1.17% higher flexural strength, flexural modulus, and loss modulus, respectively, and 15.74% lower storage modulus compared to banana S‐glass hybrid composite.

A HalF Century Ago, a young engineer named Gordon E. Moore took a look at his fledgling industry and predicted big things to come in the decade ahead. In a four-page article in the trade magazine Electronics, he foresaw a future with home computers, mobile phones, and automatic control systems for cars. All these wonders, he wrote, would be driven by a steady doubling, year after year, in the number of circuit components that could be economically packed on an integrated chip. A decade later, the exponential progress of the integrated circuit-later dubbed "Moore's Law" - showed no signs of stopping. And today it describes a remarkable, 50-year-long winning streak that has given us countless forms of computers, personal electronics, and sensors. The impact of Moore's Law on modern life can't be overstated. We can't take a plane ride, make a call, or even turn on our dishwashers without encountering its effects. Without it, we would not have found the Higgs boson or created the Internet. But what exactly is Moore's Law, and why has it been so successful? Is it evidence of technology's inevitable and unstoppable march? Or does it simply reflect a unique time in engineering history, when the special properties of silicon and a steady series of engineering innovations conspired to give us a few decades of staggering computational progress?

The recent advancement in wireless body area networks (WBAN) plays an important role in remote health care systems. However, these networks are suffering from data security and privacy threats. Lack of anonymous authentication and secure data communication leads to operation failure in WBAN. Computational cost and privacy preservation are the two major hindrances for anonymous authentication in many existing schemes. Therefore, a secure and efficient privacy-preserving anonymous authentication scheme is proposed to provide data security and privacy to the users with low computational and communication cost. The performance analysis and experimental simulation results ensure that our proposed anonymous authentication method outperforms the existing systems in terms of providing data security and privacy with less computational overhead.

The recent developments in the wireless body area networks (WBAN) play a vital role in the modern remote health care monitoring system. In WBAN, the deployed intelligent, resource-limited body sensors will collect the patient's biological information (BI) and communicate the same to the doctor for further action through the Internet. But during the communication among the WBAN entities, the privacy, security of the BI, and user's personal data need to be protected against various security threats. To address these security flaws, in this article, a computationally efficient privacy-preserving anonymous authentication scheme is proposed for resource-limited WBAN. Also, it preserves the physical security of sensors and the confidentiality of BI and provides conditional privacy to the WBAN users. The comprehensive security and performance evaluation phase ensures that the proposed scheme is efficient and secure in terms of computational and communication complexity when compared with the other conventional schemes.

In this paper, an improved fuel consumption and emissions control strategy based on a mathematical and heuristic approach is presented to optimize Parallel Hybrid Electric Vehicles (HEVs). The well-known Sequential Quadratic Programming mathematical method (SQP-Hessian approach) presents some limitations to achieve fuel consumption and emissions control optimization, as it is not able to find the global minimum, and it generally shows efficient results in local exploitation searches. The usage of a combined Modified Artificial Bee Colony algorithm (MABC) with the SQP approach is proposed in this work to obtain better optimal solutions and overcome these limitations. The optimization is performed with boundary conditions, considering that the optimized vehicle performance has to satisfy Partnership for a New Generation of Vehicles (PNGV) constraints. The weighting factor of the vehicle’s performance parameters in the objective function is varied, and optimization is carried out for two different driving cycles, namely Federal Test Procedure (FTP) and Economic commission Europe—Extra Urban Driving Cycle (ECE-EUDC), using the MABC and MABC with SQP approaches. The MABC with SQP approach shows better performance in terms of fuel consumption and emissions than the pure heuristic approach for the considered vehicle with similar boundary conditions. Moreover, it does not present significant penalties for final battery charging and it offers an optimized size of the key vehicle’s components for different driving cycles.

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures.

Planning the path an autonomous robotic vehicle will take is an essential part of developing and utilizing such a system. The task is deciding on the best route and navigating technique to get the vehicle to its destination quickly and safely. The purpose of route planning is to select a route for the vehicle that will result in the greatest fuel savings. The planner aids the vehicle in accomplishing its goals in the least amount of time and using the least amount of fuel by determining the optimal route by considering variables including traffic, road conditions, and distance. The result is higher production and lower operating expenses. An autonomous robotic vehicle (ARVs) is a self-driving vehicle that uses advanced technologies to navigate through the environment without human intervention. These vehicles can be used for various applications, including transportation, logistics, surveillance, and exploration. Route planning (RP) is the process of determining the most efficient and safe route for a vehicle, pedestrian, or any other mode of transportation to reach a destination. Route management is the process of selecting a collision-free path through an environment, which in practice is frequently crowded. Therefore, offering a RP solution for robotic systems is essential. The particle swarm optimization (PSO) method incorporates inertia weights and imitates the cooperative behavior of the flock’s population as well as its predatory nature to address route modeling issues. The Dijkstra algorithm (DA) works by determining the shortest path among the closest vertices between the source and destination. To choose the best path, inertia weight is also taken into account. By analyzing algorithms, we presented the combination technique for RP. In order to give a reliable route planning method, we suggested the weight-controlled particle swarm-optimized Dijkstra algorithm (WCPSODA). MATLAB was used to run the simulation, and conventional tools were used to evaluate the results. The findings of the study show that the suggested systems are capable of performing well.

Consumer expectations and demands for quality of service (QoS) from network service providers have risen as a result of the proliferation of devices, applications, and services. An exceptional study is being conducted by network design and optimization experts. But despite this, the constantly changing network environment continues to provide new issues that today’s networks must be dealt with effectively. Increased capacity and coverage are achieved by joining existing networks. Mobility management, according to the researchers, is now being investigated in order to make the previous paradigm more flexible, user‐centered, and service‐centric. Additionally, 5G networks provide higher availability, extremely high capacity, increased stability, and improved connection, in addition to quicker speeds and less latency. In addition to being able to fulfil stringent application requirements, the network infrastructure must be more dynamic and adaptive than ever before. Network slicing may be able to meet the present stringent application requirements for network design, if done correctly. The current study makes use of sophisticated fuzzy logic to create algorithms for mobility and traffic management that are as flexible as possible while yet maintaining high performance. Ultimately, the purpose of this research is to improve the quality of service provided by current mobility management systems while also optimizing the use of available network resources. Building SDN (Software‐Defined Networking) and NFV (Network Function Virtualization) technologies is essential. Network slicing is an architectural framework for 5G networks that is intended to accommodate a variety of different networks. In order to fully meet the needs of various use cases on the network, network slicing is becoming more important due to the increasing demand for data rates, bandwidth capacity, and low latency.