Evolution engine technology in exhaust gas recirculation for heavy-duty diesel engine

In this present year, engineers have been researching and inventing to get the optimum of less emission in every vehicle for a better environmental friendly. Diesel engines are known reusing of the exhaust gas in order to reduce the exhaust emissions such as NOx that contribute high factors in the pollution. In this paper, we have conducted a study that EGR instalment in the vehicle can be good as it helps to prevent highly amount of toxic gas formation, which NOx level can be lowered. But applying the EGR it can lead to more cooling and more space which will affect in terms of the costing. Throughout the research, fuelling in the engine affects the EGR producing less emission. Other than that, it contributes to the less of performance efficiency when vehicle load is less

Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.The authors were supported by the Vitoria-Gasteiz Mobility Lab Foundation, an organization of the government of the Provincial Council of Araba and the City Council of Vitoria-Gasteiz through the following project grant (“Generación de mapas mediante drones e Inteligencia Computacional”)

Sustav za efikasno upravljanje solarnom energijom

Solar power is the major renewable energy source opted by developing countries as stand-alone / Grid enabled system. Industries and educational institutions are opting for solar energy to combat power crisis. This paper proposes knowledge based, self configurable, smart controller to efficiently use solar energy according to load, under frequent grid failure environment. It is enabled with fault identification and isolation. Extension to higher power capacity is easily achieved with plug and play mechanism. Proposed control architecture is implemented using Field Programmable Gate Array (FPGA), that supports modular level implementation with well defined interfaces for each sub-system. It can be used with low power as well as high power photo-voltaic system. Efficiency of the proposed architecture is demonstrated for the photo-voltaic system installed in educational institution.Solarna energija spada među glavne obnovljive izvore energije odabrana od strane zemalja u razvoju kao samostalnih izvora ili umrežene s ostalim izvorima. Industrija i edukacijske institucije predlažu solarnu energiju u borbi protiv energetske krize. U ovome radu predstavljen je samokonfigurabilan regulator za efikasno korištenje solarne energije s obzirom na opterećenje i česte promjene u mreži. To je omogućeno uz identifikaciju kvara. Ekstenzija na visoke snage jednostavno se postiže sa uređajem koji se može odmah koristiti. Regulator je implementiran koristeći programirljive logičke sklopove (FPGA) koji podržavaju modularnu implementaciju svake razine sa sučeljem prema svakom podsustavu. Predloženi sustav može biti korišten za niske snage kao i za visoke snage kod fotonaponskih sustava. Efikasnost predložene arhitekture testirana je na fotonaponskom sustavu postavljenom na edukacijskoj instituciji

A Comparison Between CCCV and VC Strategy for the Control of Battery Storage System in PV installation

To meet demand with unpredictable daily and seasonal variations, the power grid faces significant hurdles in transmission and distribution. Electrical Energy Storage (EES), in which energy is stored in a specific state, depending on the technology utilized, and is converted to electrical energy, is acknowledged as a technology involved with significant potential for solving these difficulties. This paper deals with the modeling and control of a renewable energy production system based on solar panel. To improve the performance of the investigated power generation system, a lithium-ion battery storage system and bidirectional converter are associated to a solar panel that is unable to compensate for rapid variations in load power demand. In this situation, to meet load power demand, a rule-based energy management algorithm is used to share energy between the grid and the energy production system. Furthermore, two solutions are developed and compared: VC (Variable Current) and CC-CV (Constant Current Constant Voltage). The VC approach is used in conjunction with an energy management and protection system, whereas the CC-CV method is used in conjunction with an artificial neural network (ANN). The simulation results show that the VC control strategy give greater energy performance and installation stability compared to the CC-CV strategy, but not improved safety and protection of lithium-ion batteries

Switched-battery boost-multilevel inverter with GA optimized SHEPWM for standalone application

This paper presents a boost-multilevel inverter design with integrated battery energy storage system for standalone application. The inverter consists of modular switched-battery cells and a full-bridge. It is multifunctional and has two modes of operation: the charging mode which charges the battery bank and the inverter mode which supplies AC power to the load. This inverter topology requires significantly less power switches compared to conventional topology such as cascaded H-bridge multilevel inverter, leading to reduced size/cost and improved reliability. To selectively eliminate low-order harmonics and control the desired fundamental component, nonlinear system equations are represented in fitness function through the manipulation of modulation index and the Genetic Algorithm is employed to find the optimum switching angles. A 7-level inverter prototype is implemented and experimental results are provided to verify the feasibility of the proposed inverter design

Modeling, analysis and simulation of a high-efficiency battery control system

This paper explains step-by-step modeling and simulation of the full circuits of a battery control system and connected together starting from the AC input source to the battery control and storage system. The three-phase half-controlled rectifier has been designed to control and convert the AC power into DC power. In addition, two types of direct current converters have been used in this paper which are a buck and bidirectional DC/DC converters. These systems adjust the output voltage to be lower or higher than the input voltage. In the buck converters, the main switch operates in conduction or cut-off mode and is triggered by a Pulse-Width Modulated (PWM) signal. The output and input voltage levels ratio are used to calculate the PWM signal’s duty cycle. Therefore, the duty cycle indicates the operation mode of the converter in steady-state operation. In this study, we analyze and control of a buck converter with the PWM signal. Besides, the bidirectional DC/DC converter has been achieved and optimized by PI control methods to control the battery charging and discharging modes. The simulation has been applied via the Matlab/Simulink environment. The results show the activity of each part of the designed circuits starting from the converters and the battery control system in charge and discharge modes

An intelligent power management system for unmanned earial vehicle propulsion applications

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results