OMRON CP1E-NA20DR-A PLC Based Fish Scales and Manure Cleaner Prototype

In fish removal, most of them still use manual cleaning tools whose main crust is human labor so that it can take a not fast time, especially if it is large-scale in cleaning fish and in terms of safety, it is very risky for injured hands due to manual cleaning tools. Aka it is necessary to have a fish cleaning device and clean the dirt automatically, Design and build a prototype of a fish scales and dirt cleaner based on PLC OMRON CP1E-NA20DR-A is a solution in cleaning fish scales and large-scale fish excrement. The way the tool works is that the operator will put the fish into the machine, after the fish is inserted, the sensors at the entrance of the fish will respond to the fish by sending a signal to the PLC so that the PLC will turn on the induction motor as the main crust of the machine, after the engine starts the fish will start to clean the scales and dirt using a knife that has been designed to be used on the machine. Researchers used three fish samples in machine performance experiments to find out how efficient the process of combing and cleaning the dirt was compared to using manual cleaning. The results show that the tool has worked as a whole in scouring and cleaning the dirt as desired by the researcher

Investigating the stability and degradation of hydrogen PEM fuel cell

The hydrogen proton exchange membrane (PEM) fuel cells are promising to utilize fuel cells in electric vehicle (EV) applications. However, hydrogen PEM fuel cells are still encountering challenges regarding their functionality and degradation mechanism. Therefore, this paper aims to study the performance of a 3.2 kW hydrogen PEM fuel cell under accelerated operation conditions, including varying fuel pressure at a level of 0.1–0.5 bar, variable loading, and short-circuit contingencies. We will also present the results on the degradation estimation mechanism of four fuel cells working at different operational conditions, including high-to-low voltage range and high-to-low temperature variations. These experiments examine over 180 days of continuous fuel cell working cycle. We have observed that the drop in the fuel cells' efficiency is at around 7.2% when varying the stack voltage and up to 14.7% when the fuel cell's temperature is not controlled and remained at 95 °C

Operating cost comparison of a single-stack and a multi-stack hybrid fuel cell vehicle through an online hierarchical strategy

One of the recently suggested solutions for enhancing the fuel economy and lifetime in a fuel cell (FC) hybrid electric vehicle (HEV) is the use of a multi-stack (MS) structure for the FC system. However, to fully realize the potential of this structure, the design of an appropriate energy management strategy (EMS) is necessary. This paper aims to compare the operating cost, including hydrogen consumption and degradation of the FC, between a single-stack (SS) and an MSFC-HEV. To do so, a hierarchical EMS, composed of two layers, is devised for the MS system. In the first layer, a rule-based strategy determines how many FCs should be ON according to the requested power, battery state of charge (SOC), and FCs degradations. In the second layer, an equivalent consumption minimization strategy (ECMS) is developed to determine the output power of each activated FC according to the cost function and constraints. Regarding the SS structure, ECMS is employed for power distribution. The purpose of this strategy is to decrease fuel consumption and FC system degradation costs in both structures. The performance of the ECMS is compared with dynamic programming (DP) as a global optimization strategy for validation purposes. The obtained results using experimental data show that an FC-HEV with an MS structure reaches less hydrogen and degradation costs than an SS one

Energy management strategies for fuel cell vehicles: A comprehensive review of the latest progress in modeling, strategies, and future prospects

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains

Evaluation and optimisation of traction system for hybrid railway vehicles

Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)