Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense th... [more]

The Levelized Cost of Energy for floating offshore wind must decrease significantly to be competitive with fixed offshore wind projects or even with onshore wind projects. This study focuses on the design optimization of cylindrical buoyancy bodies for floating substructures of offshore wind turbines. The presented work is based on a previously studied buoyancy body design that allows an efficient manufacturing process and integration into different substructures. In this study, an optimization framework based on genetic algorithm is developed to parameterize the buoyancy body’s geometry and optimize its design in terms of cost, considering loads acting on the structure as well as manufacturing and floater specific dimension restrictions. The implementation of the optimization process is detailed, and tested for a given study case. Two structurally different genetic algorithms are considered in order to compare the results obtained and asses the performance of the presented optimizatio... [more]

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are u... [more]

Clean heating has not been widely applied in rural Chinese areas. Considering the abundance of solar energy resources, harvesting solar energy for heating can be an effective solution to the problem of space heating in most rural areas. As the disperse building distribution in rural areas makes it difficult to implement centralized heating on a large scale, deploying centralized−decentralized hybrid solar heating system can achieve the best result from both the technical and economic perspectives. Taking a virtual village in Tibet as an example, this paper explores how to obtain optimal design of centralized−decentralized hybrid solar heating system based on building clustering. The results show that: (1) Compared with the fully centralized system and fully decentralized system, the centralized−decentralized hybrid solar heating system in the studied case could achieve a life cycle cost (LCC) saving of 4.8% and 2.3%, respectively; (2) The LCC of centralized−decentralized hybrid solar h... [more]

Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones. Furthermore, when detecting an indoor thermal discomfort, we provide a new genetic-based algorithm to find the most suitable values of i... [more]

As an important production equipment of the offshore platform, the operation reliability of submersible motors is critical to oil and gas production, natural gas energy supplies, and social and economic benefits, etc. In order to realize the health management and fault diagnosis of submersible motors, a motor fault-monitoring method based on multi-signal fusion is proposed. The current signals and vibration signals were selected as characteristic signals. Through fusion correlation analysis, the correlation between different signals was established to enhance the amplitude at the same frequency, so as to highlight the motor fault characteristic frequency components, reduce the difficulty of fault identification, and provide sample data for motor fault pattern identification. Furthermore, the wavelet packet node energy analysis and back propagation neural network were combined to identify the motor faults and realize the real-time monitoring of the operating status of the submersible mo... [more]

In modern electrical grids, the number of nonlinear grid elements and actively controlled loads is rising. Maintaining the power quality will therefore become a challenging task. This paper presents a power quality mitigation method via smart demand-side management. The mitigation method is based on a genetic algorithm guided optimization for smart operational planning of the grid elements. The algorithm inherits the possibility to solve multiple, even competing, objectives. The objective function uses and translates the fitness functions of the genetic algorithm into a minimization or maximization problem, thus narrowing down the complexity of the addressed high cardinality optimization problem. The NSGA-II algorithm is used to obtain feasible solutions for the auto optimization of the demand-side management. A simplified industrial grid with five different machines is used as a case study to showcase the minimization of the harmonic distortion to normative limits for all time steps d... [more]

An improved mathematical model and an improved particle swarm optimization (IPSO) are proposed for the complex design parameters and conflicting design goals of the indoor luminaire layout (ILL) problem. The ILL problem is formulated as a nonlinear constrained mixed-variable optimization problem that has four decision variables. For a general lighting scheme (GLS), the number and location of luminaires can be uniquely determined by optimizing four decision variables, which avoid using program loops to determine the number of luminaires. We improve the particle swarm optimization (PSO) in three aspects: (1) up-down probabilistic rounding (UDPR) method proposed to solve mixed integer, (2) improving the velocity of the best global particle, and (3) using nonlinear inertia weights with random items. The IPSO has better optimization results in an office study compared with the PSO and genetic algorithm (GA). The results are validated by DIALux simulation software, and a maximum deviation of... [more]

In magnetically coupled resonant wireless power transfer (MCR-WPT) systems, the nonhomogeneous magnetic field of the transmitting coil can lead to frequency splitting phenomena and lower efficiency. In this paper, a 3D transmitting coil (TX) with a homogeneous magnetic field distribution is proposed. The proposed coil structure consists of two layers with different numbers of turns per layer, i.e., with different current distributions. To achieve a homogeneous magnetic field distribution with a high magnetic field value and a low profile of the 3D coil structure, the optimal layer placement and current distribution were optimized using a genetic algorithm (GA). The prototype of the optimized coil was fabricated, and its magnetic field distribution was measured. The measurement results agreed more than 95% with the simulation results. The measured homogeneous area was at least 12.5% larger than reported in the literature. By using a different current distribution, the profile of the 3D... [more]

With the increasingly stringent CO2 emission regulations, the degree of strengthening of the engines is increasing. Under high-pressure conditions, the airway throat parts of the intake and exhaust systems have a great influence on the flow loss of the diesel engine. The reasonable distribution of the throat area of the intake and exhaust ports in the limited cylinder headspace is key to improving the performance of supercharged engines. This study took a large-bore, high-pressure ratio diesel engine as the research object. Firstly, the three-dimensional (3D) flow simulation method was used to reveal the influence law of different throat areas on the engine intake and exhaust flow under steady-state conditions, and a steady-flow test bench was built to verify the accuracy of the simulation model and law. Secondly, based on the 3D steady-state calculation and test results, a more accurate one-dimensional simulation model was constructed, and a joint optimization simulation platform was... [more]

The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization.

This paper presents a two-stage grid-connected PV system with reactive power management capability. The proposed model can send phase-shifted current to the grid during a low-voltage ride through (LVRT) to recover the voltage levels of the grid’s feeders. The novelty of the proposed algorithm, unlike the common methods, is that it does not need to disable the maximum power point tracking (MPPT) state while managing active and reactive power injection simultaneously. Moreover, the new method promotes a safety factor by offering overcurrent protection to the PV inverter. The phase-locked loop based on the synchronous reference frame (SRF-PLL) is optimized using a genetic algorithm (GA). The settling time of SRF-PLL’s step response is minimized, and the frequency dynamics are improved to enhance synchronization during LVRT. The system’s performance is tested and verified using MATLAB/Simulink simulations. The obtained results prove the effectiveness of the proposed control algorithm in ma... [more]

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experime... [more]

To save energy consumed by a building, utilizing optimal predictive control with model predictive control (MPC) makes the most of energy storage systems (ESSs) to reduce the electrical energy consumption of peak and heavy loads. This study evaluated MPC applicability in a multi-zone commercial building using the EnergyPlus model and conducted multi-objective optimization of thermal comfort and energy savings. As a result of the simulation, optimal ESS charging scenarios responded to the fluctuating electricity pricing system, and changing the peak load time reduced the electricity bill of the grid by 55% compared to the existing operating method. At the same time, room temperatures stayed within the thermal comfort range, and the Pareto curve showed a proper balance between energy saving and thermal comfort. Especially, the proposed method with a white model is applicable for MPC applications in commercial buildings, as it gave optimal solutions within the target time interval.

As a direct energy converter between heat and electricity, thermoelectric generators (TEGs) have potential applications including recovery of waste heat, and solar thermoelectric power generation. Geometric parameter and material are two critical factors to improve the TEG performance. However, the strategies base on structure design and material development are always separated. There are limited studies on the effects of consolidating them simultaneously. Here, an idea of segmented material coupled with irregularly variable cross-section design was conceived to further improve the TEG output power. The performance of TEGs with rectangular leg, segmented leg, variable cross-sectional leg, and the new design are compared. The coupling effects between various mechanisms are revealed, which are responsible for the superior performance provided by the developed design. Based on this knowledge, a multiparameters optimization was performed through the genetic algorithm to reach the optimal... [more]

The acceptance of hybrid energy storage system (HESS) Electric vehicles (EVs) is increasing rapidly because they produce zero emissions and have a higher energy efficiency. Due to the nonlinear and strong coupling relationships between the sizing parameters of the HESS components and the control strategy parameters and EV’s performances, energy consumption rate, running range and HESS cost, how to design the HESS EVs for different preferences is a key problem. How to get the real time performances from the HESS EV is a difficulty. The multiobjective optimization for the HESS EV considering the real time performances and the HESS cost is a solution. A Li-ion battery (BT) semi-active HESS and optimal energy control strategy were proposed for an EV. The multiobjectives include energy consumption over 100 km, acceleration time from 0−100 km per hour, maximum speed, running range and HESS cost of the EV. According to the degrees of impact on the multiobjectives, the scaled factors of BT cap... [more]

The work describes a systematic optimization strategy for designing hypersonic inlet intakes. A Reynolds-averaged Navier-Stokes database is mined using genetic algorithms to develop ideal designs for a priori defined targets. An intake geometry from the literature is adopted as a baseline. Thus, a steady-state numerical assessment is validated and the computational grid is tuned under nominal operating conditions. Following validation tasks, the model is used for multi-objective optimization. The latter aims at minimizing the drag coefficient while boosting the static and total pressure ratios, respectively. The Pareto optimal solutions are analyzed, emphasizing the flow patterns that result in the improvements. Although the approach is applied to a specific setup, the method is entirely general, offering a valuable flowchart for designing super/hypersonic inlets. Notably, because high-quality computational fluid dynamics strategies drive the innovation process, the latter accounts for... [more]

E-Vehicles are used for transportation and, with a vehicle-to-grid optimization approach, they may be used for supplying a backup source of energy for renewable energy sources. Renewable energy sources are integrated to maintain the demand of consumers, mitigate the active and reactive power losses, and maintain the voltage profile. Renewable energy sources are not supplied all day and, to meet the peak demand, extra electricity may be supplied through e-Vehicles. E-Vehicles with random integration may cause system unbalancing problems and need a solution. The objective of this paper is to integrate e-Vehicles with the grid as a backup source of energy through the grid-to-vehicle optimization approach by reducing active and reactive power losses and maintaining voltage profile. In this paper, three case studies are discussed: (i) integration of renewable energy sources alone; (ii) integration of e-Vehicles alone; (iii) integration of renewable energy sources and e-Vehicles in hybrid mo... [more]

Entropy generation is always a matter of concern in a heat transfer system. It denotes the amount of energy lost as a result of irreversibility. As a result, it must be reduced. The present work considers an investigation on the turbulent forced convective heat transfer and entropy generation of Al2O3-Ethylene glycol (EG) nanofluid inside a circular tube subjected to constant wall temperature. The study is focused on the development of an analytical framework by using mathematical models to simulate the characteristics of nanofluids in the as-mentioned thermal system. The simulated result is validated using published data. Further, Genetic algorithm (GA) and DIRECT algorithm are implemented to determine the optimal condition which yields minimum entropy generation. According to the findings, heat transfer increases at a direct proportion to the mass flow, Reynolds number (Re), and volume concentration of nanoparticles. Furthermore, as Re increases, particle concentration should be decr... [more]

Recent research reveals that multi-phase motors in electric propulsion systems are highly recommended due to their improved reliability and efficiency over traditional three phase motors. This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A six phase squirrel cage induction motor is designed and simulated by ANSYS Motor-CAD. In order to find the best fit method, simulation results are compared and applied to the motors for electric propulsion, considering the influence of design upon the motor performance. The six-phase squirrel cage induction motor is more energy efficient, reliable and cost effective for the electric propulsion compared to the conventional three phase motor. In this study, first the initial parameters of the six phase squirrel cage induction motor have been determined and then these parameters have been compared wi... [more]

The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the we... [more]

The prediction of heat transfers in Reynolds-Averaged Navier−Stokes (RANS) simulations requires corrections for rough surfaces. The turbulence models are adapted to cope with surface roughness impacting the near-wall behaviour compared to a smooth surface. These adjustments in the models correctly predict the skin friction but create a tendency to overpredict the heat transfers compared to experiments. These overpredictions require the use of an additional thermal correction model to lower the heat transfers. Finding the correct numerical parameters to best fit the experimental results is non-trivial, since roughness patterns are often irregular. The objective of this paper is to develop a methodology to calibrate the roughness parameters for a thermal correction model for a rough curved channel test case. First, the design of the experiments allows the generation of metamodels for the prediction of the heat transfer coefficients. The polynomial chaos expansion approach is used to crea... [more]

Load and supply parameters may be uncertain in microgrids (MGs) due for instance to the intermittent nature of renewable energy sources among others. Guaranteeing reliable and stable MGs despite parameter uncertainties is crucial for their correct operation. Their stability and dynamical features are directly related to the controllers’ parameters and power-sharing coefficients. Hence, to maintain power good quality within the desirable range of system parameters and to have a satisfactory response to sudden load changes, careful selection of the controllers and power-sharing coefficients are necessary. In this paper, a simple design approach for the optimal design of controllers’ parameters is presented in an islanded MG. To that aim, an optimization problem is formulated based on a small-signal state-space model and solved by three different optimization techniques including particle swarm optimization (PSO), genetic algorithm (GA), and a proposed approach based on the combination of... [more]

A large-scale two-dimensional computational fluid dynamics study is conducted in order to maximise the power output and smoothness of power delivery of a free-stream water wheel, a low-impact hydropower device. Based on models and methods developed in previous research, the study uses a genetic algorithm to optimise the geometry of a wheel with a given radius and depth, maximising two objective functions simultaneously. After convergence and suitable post-processing, a single optimal design is identified, featuring eight shortened blades that become fully immersed at the nadir point. The design results in a 71% reduction in blade material and a 113% increase in the work ratio while improving the hydraulic power by 8% compared to the previous best design. These characteristics are applied retroactively to a broad family of designs, resulting in significant improvements in performance. Analysis of the resulting designs indicates that when either the hydraulic power coefficient, rotor pow... [more]

With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 3... [more]