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Lithium-ion cell balancing using auxiliary battery and DC-DC CUK converter
Get PDFThe lithium-ion battery became more popular to use due to its prominent characteristics such as energy density, power density and high terminal voltage of a single cell. However, if there is power regulation issue during the charging and discharging processes, the performance of the battery will be affected. In this case, the life of the battery will considerably reduce, which may result in undesirable outcomes such as fire or explosion. In order to avoid these issues, Battery Management Systems (BMS) is used to provide proper power regulation. BMS includes substantial subsystems such as SOC estimation, thermal management and cell balancing. This research concentrates on the cell balancing mechanism, which is an essential part of the BMS for extending battery life. The two basic types of cell balancing are passive cell balancing and active cell balancing. The active balancing topology utilized in this research is a Single Switch Capacitor (SSC), capacitor base, in order to perform module balancing and cell balancing inside internal modules. The BMS is based on the pack modularization architecture, where a single capacitor is fitted to transfer the energy from module to module to achieve balancing. While, the internal module balancing is accomplished with the use of a Single Switch Capacitor (SSC), Auxiliary Battery (AB) and Unidirectional DC-DC Cuk Converter (UCC) for boost charging. Finally, the BMS simulation is modelled using MATLAB/SIMULINK to validate the implementation system's results
Improving thermal and tribological properties of enhanced biolubricant with graphene and maghemite nano-additives
No full textFailure of biolubricants at elevated temperature hinder their efficient performance. These challenges are ameliorated using nano-additives to enhance the thermal and anti-wear properties of lubricants. In this study, coconut oil as base fluid was dispersed with 0.1% volume concentration of maghemite (γFe2O3) and exfoliated graphene (XGnP) nano-additives. Thermogravimetric analysis (TGA) was performed using thermal analyser to evaluate thermal degradation of nanolubricants and the base oil. In addition, anti-wear properties and viscosity of base coconut oil and the enhanced nanolubricants were evaluated. The TGA results indicates that oxidation onset temperature was retarded by 9 ℃ and 31.82 ℃ for maghemite (MGCO) and graphene (XGCO) enhanced nanolubricants respectively in comparison with base coconut oil (CCO). Friction reduction and anti-wear property of the nanolubricants showed better performance over the base oil. For graphene enhanced nanolubricant, a reduction of 10.4% and 5.6% in terms of COF and WSD respectively was observed while 3.3% and 4.3% reduction in COF and WSD respectively for maghemite enhanced nanolubricant when compared with the base oil (CCO). The excellent property improvement of thermal stability and tribological properties makes the enhanced lubricants a suitable candidate for consideration as machining lubricants
Multi-core 16-bit CPUs for PLC processor
Get PDFPLCs (Programmable Logic Controllers) are in great demand across a wide range of industries. A PLC can be used to model a controlled processing plant using a Ladder Logic Diagram (LLD). The PLC will read all the sensors, process the logic network of input and output, and provides the corresponding output signals to all actuators. LLD is widely being used to model most of the PLCs on the market today since it is user-friendly and simple to grasp by users from different levels of background. The PLC in this research will be sped up by employing a Ladder Rung Processor (LRP) architecture in a Field Programmable Gate Array (FPGA). However, LRP is only good for binary inputs. For more than a single bit input or data processing, a general purpose CPU is needed to save resources. The trend toward concurrent processing, resulting in multicore CPUs, will make it easier for a PLC processor to complete numerous tasks at the same time, enhancing performance under the demands of powerful applications and programmes. As an example of controlling a 5 axes robotic arm, a single core CPU of PLC can only control a maximum of 2 axes at the same time. Hence, a combination of LRP and multicore CPU approach can increase the throughput of a PLC processor to do concurrent processing for automation, control and robotic applications. Several architectures of multicore CPU will be investigated before determining the best solution for a PLC processor. Therefore, several performances of multicore CPU for a PLC processor will be evaluated. A RISC based CPU will be used in this research due to its simplicity while maintaining the possibility for future expansion. A 16 bits RISC CPU will be the baseline reference in this research. Several modifications will be done in terms of memory allocation and task scheduling for each of the cores will be tackled to make sure they are fully utilized in order to boost the performance to maximum. Verilog HDL language will be the preference language in the designation of multicore PLC processor. The cyclic scans frequency of PLC will be the performance benchmark that will be compared with the existing PLC in the market. To verify the architecture of multicore PLC processor, simulation on a PC and interfaced with a PLC on an FPGA will be implemented. A multicore approach in designing a complete PLC processor will increase the overall performance compared to a single core PLC processor by handling multiple processes in a manufacturing line
Effect of low molarity alkaline solution on the compressive strength of fly ash based geopolymer concrete
Get PDFThe escalating of cement production had significant effect on greenhouse gases emission, thus innovation of geopolymer concrete is crucial to reduce the environmental impacts. Besides, high concentration of alkaline solution not only exhibits corrosive nature, but also increases the cost of construction. Therefore, this research studies the engineering properties and effect of elevated temperature on fly ash-based geopolymer concrete using low molarity alkaline solution. The alkaline solution used in this research is sodium hydroxide (NaOH) combines with sodium silicate (Na2SiO3) with different molarity of 2M, 4M and 10M. The binder to solution ratio of 0.45, NaOH mixed with Na2SiO3 at mass ratio of 2.5 and binder to aggregates with proportion of 1:3 was fixed. Test specimens was prepared and tested at the age of 3, 7 and 28 days. Results show that with reduced alkaline solution molarity, the compressive strength reduced by 45 and 55% for geopolymer with 4M and 2M, respectively. However, the compressive strength exhibits are within the normal strength geopolymer concrete with comparable density and workability. Therefore, it can be concluded that low molarity geopolymer concrete can be applied in construction due to more environmentally friendly and lower cost
Shape oriented object recognition on grasp using features from enclosure based exploratory procedure
No full textThe potential of humans to recognize known objects while grasping, without the help of vision, is an exciting supposition to the robotics community. With a focus on reproducing such a natural aptitude in prosthetic hands, this paper reports a kinematic approach to exploring the human hand’s object recognition functionality during a grasp. Finger kinematics vary while grasping objects of different shapes and sizes. The authors emphasized learning the variations while grasping different objects through a forward kinematics model of the human hand. Finger joint kinematics for objects of two specific shape categories: spherical and cylindrical, were recorded during grasping experiments using a customized data glove to deduce the fingertip coordinates. An algorithm has been developed to derive novel three-dimensional grasp polyhedrons from fingertip coordinates. Areas of these polyhedrons and finger kinematics have been used as features to train classification algorithms. Comparing the recognition results using only finger kinematics as features revealed that the inclusion of the shape primitives increases the accuracies of the classifiers by 2–6% while recognizing the objects. This work analytically confirms that finger kinematics and the object’s shape primitives are vital information for visionless object recognition
One shot learning for acoustics classification of Malaysia bird species
Get PDFMalaysia is famed for its beautiful bio-diverse forest and its bird species, some of it is still understudied. Using acoustic detection, we can study these bird as current advanced in machine learning application have resulted in cutting edge performance for acoustic classification application. However, most of these applications need large amount of data for prediction to have acceptable accuracy. We, however, do not have this kind of resources. This situation is experienced by large demographic of this country, which provide importance to our studies. Adding to that problem, one common issue that come with studying of species is that we can only know the status of species in a habitat up to certain point. Such problem needs to be solve using methodologies that can cope with the fluidity of information. As such, we propose neural network framework that able to notice any changes in the class categories and learn new classes on the fly. To solve our issue, we seek to design a Siamese Style convolutional Neural Network for one-shot-learning architecture. Additionally, we would train it using base convolutional neural network with low complexity so that it can be realistically implement in hardware of low computing power. We evaluated and benchmarked our framework, showing promising results as the Network is able to classify trained bird species with accuracy of 90% or higher with only around 100 sound clips per bird species. Additionally, it is able to detect new bird species on the fly and add it to its class successfully, however, it still needs some work as the accuracy is around 50% on this part. All of these is achieved using base Convolutional Neural Networks of low complexity with only 4 layers of conv layers and 2 layers of fully connected layers. From this thesis, It is shown that this neural network can work and I am optimistic that this work can be further improved, which can be done by using a higher variety of dataset and transfer learning and a further tweaking of the base neural network architecture
Characterization of nanosheet transistor logic and its performance
Get PDFConsidering Moore's law requires transistor scaling, we have now entered the nanoscale era, which brings with it new challenges. Fin-shaped Field-Effect Transistors (FinFETs), the current transistor technology, is not up to the challenge when we descend below the 7 nm scale. The short channel effect downgrades the system performance and reliability as the MOSFET is scaled down further. For 5 nm technology node and beyond, nanosheet FET (NSFET) is an alternative architecture that compensates for this limitation due to superior short channel control at a smaller footprint. NSFET can give more effective width, and therefore current, by stacking nano sheet atop one another. In this project, the research gap and past efforts on showing the superiority of NSFET over FinFET were discussed. Using the Sentaurus tool from Synopsys, a three-stacked NSFET 3D structure with sheet thickness of 7nm was created and characterised. The NSFET is being build based on the parameters as per suggested from the reference. The simulation is being validated to the reference. This work is focus on the characteristic of a p-channel NSFET and the analog parameters of the NSFET. Simulation of the electrical characteristics for the NSFET includes current voltage characteristics and extract the electrical parameters such as threshold voltage (Vt), ON-current (Ion), OFF-current (Ioff) ON-OFF current ratio (Ion/Ioff), subthreshold slope (SS), transconductance (gm) and output resistance (Ro). The data collected to be utilised to develop NSFET circuits A p-type and n-type NSFET is being combined to build an inverter. Under the same footprint, NSFETs are expected to have superior current drivability and gate-to-channel controllability than FinFETs, resulting in higher intrinsic gain for circuit applications
N-gram feature extraction and Naïve Bayes classifier for malware detection using FPGA implementation
Get PDFNowadays malicious software, or commonly known as malwares, play a very critical role in almost every network intrusion attack that attempts to harm the connected devices. Thus, installing malware detection systems to protect the network environment has become even more imperative. Naïve Bayes classifier is a probabilistic supervised machine learning algorithm that can be launched on most general-purpose devices to solve a wide range of classification problems, including malware detection. Apart from the classifier, a good feature extractor is important to improve the performance and reliability of the classifier model. However, when it comes to real time applications, the general-purpose devices are limited in terms of their computational throughput. Therefore, the aim of this project is to implement n-gram feature extractor and Naïve Bayes classifier on hardware environments. To improve the throughput and latency of the malware detection, parallel processing capability of field-programmable gate array (FPGA) has been exploited whereby multiple processing units have been designed for the inference module to be implemented on the hardware. Besides, the inference module is designed to be pipelined with six stages. Other than that, hardware-friendly algorithms which have implemented base 2 logarithm transformation and floating-point to fixed-point conversion are used in this study. From the result, both software and hardware designs have obtained similar accuracy of 99.18% on the test dataset. Besides, it is found out that the higher number of parallel processing units, n in this design leads to higher throughput, resource utilization, power consumption, and energy efficiency for malware detection. Hardware design with n = 62 is the optimal design in this project, as it has achieved the highest value of throughput and energy efficiency at the same time
Overcurrent relay coordination with different penetration in distributed generation
Get PDFPower distribution network is suspected to performance degrading factors alike load fluctuation which trigger the power losses. In order to substitute the losses in distribution network; known amount of power is being injected directly into load through using of distributed generators (DGs). This alternative can be used to substitute power in range of 25 to 40 percent of the respective busbar load. The integrating of DG into power system influences the protection relays stability which leads to relays miscoordination. Prolonged fault clearance time is one of the negative correspondence of relays miscoordination. The more time required to clear faults is biggest threat to distribution network safety which allows more damages of power equipments. Traditionally, heuristic algorithm such as genetic algorithm are used to optimize the relay parameters d such as dialling time TSD. In this project, we modified the standard fitness function of the genetic algorithm by integrating a new parameter into it for best optimization. The proposed algorithm is outperformed while it is applied on 6 bus distribution system
Electrical power generation of hybrid PV/Wind/Diesel battery system for Gubio Village of Nigeria Gubio Local Government, Borno State, Nigeria
Get PDFWorking toward photovoltaic energy source have attracted the attention of scientists’ recent time due it renewability. However, it come with several challenges especially getting accurate design such accurate photovoltaic size. To increase the efficiency and dependability of an off-grid PV system, precise sizing of the photovoltaic (PV) cell and battery storage parameters is a vital requirement. This project is intended to Gubio Village in northern Nigeria where the model configuration of wind, photovoltaic and diesel stand-alone system is proposed. This project's major objective is to size and manage by figuring out the ideal amount of PV modules, battery sizes, and bank needed for the case study. The proposed systems were put to the test using MATLAB simulations run under ideal test conditions to pinpoint changes in irradiance pattern and other system uncertainties in advance. For PV, Battery, Wind Turbine, and Diesel, the outline of various search ranges defined and their objective function are also described. In this study, GA, PSO, and DE algorithms were contrasted to determine the best size for an off-grid PV, wind turbine, and diesel system taking Gubio Village into consideration. The optimization result shows that PSO offered the best optimal solution in terms of cost and convergence time where it shows lowest LPSP and highest LCOE 0.012, 0.3564 respectively. For the purpose of this project, PSO algorithm is more efficient as compared to DE and GA. PSO algorithm and requires less computational time and memory for implementation as it converges faster. Thus, the project successfully accomplished all it objectives by designing electrical power generation of hybrid pv/wind/diesel battery system for Gubio village of Nigeria Gubio local government, Borno state, Nigeria