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This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

The data has been provided by the protagonist in the form of interviews, tables and figures.

Abdul and his team, comprising MS Finance graduates from Lahore University of Management Sciences, embarked on a transformative process that led to the inception of their startup, Ingine. Originating from a discarded idea of importing recycling machines, the team pivoted to address a significant gap in the influencer marketing industry. They envisioned a subscription-based software-as-a-service platform that streamlines interactions between influencers and businesses, emphasizing secure payment processing, messaging and feedback features. The narrative underscores the intricate connection between influencer marketing and the return on investment for small businesses, recognizing the challenge of decoding tangible financial gains. Ingine’s mission is to unravel this puzzle, optimizing small businesses’ investments in influencer marketing while navigating the complexities of crafting a competitive influencer compensation model. The team’s background, strategic considerations and commitment to fostering sustainable relationships between influencers and businesses serve as a compelling backdrop to Ingine’s entrepreneurial aspirations.

The case can be used in entrepreneurship and entrepreneurial finance. The case can be used in undergrad, master’s, MBA, executive MBA and short executive programs. The complexity of a case can be increased or decreased depending on the level of class, i.e. start, middle or end of the course, and the time allocation, i.e. 90 min.

This paper aims to reviewed analytical methodologies, i.e. lumped parameter magnetic equivalent circuit (LPMEC), magnetic co-energy (MCE), Laplace equations (LE), Maxwell stress tensor (MST) method and sub-domain modelling for design of segmented PM(SPM) consequent pole flux switching machine (SPMCPFSM). Electric machines, especially flux switching machines (FSMs), are accurately modeled using numerical-based finite element analysis (FEA) tools; however, despite of expensive hardware setup, repeated iterative process, complex stator design and permanent magnet (PM) non-linear behavior increases computational time and complexity.

This paper reviews various alternate analytical methodologies for electromagnetic performance calculation. In above-mentioned analytical methodologies, no-load phase flux linkage is performed using LPMEC, magnetic co-energy for cogging torque, LE for magnetic flux density (MFD) components, i.e. radial and tangential and MST for instantaneous torque. Sub-domain model solves electromagnetic performance, i.e. MFD and torque behaviour.

The reviewed analytical methodologies are validated with globally accepted FEA using JMAG Commercial FEA Package v. 18.1 which shows good agreement with accuracy. In comparison of analytical methodologies, analysis reveals that sub-domain model not only get rid of multiples techniques for validation purpose but also provide better results by accounting influence of all machine parts which helps to reduce computational complexity, computational time and drive storage with overall accuracy of ∼99%. Furthermore, authors are confident to recommend sub-domain model for initial design stage of SPMCPFSM when higher accuracy and low computational cost are primal requirements.

The model is developed for high-speed brushless AC applications.

The SPMCPFSM enhances electromagnetic performance owing to segmented PMs configuration which makes it different than conventional designs. Moreover, developed analytical methodologies for SPMCPFSM reduce computational time compared with that of FEA.

The purpose of this paper is to analyze electromagnetic performance and develop an analytical approach to find the suitable coil combination and no-load flux linkage of the proposed hybrid excited consequent pole flux switching machine (HECPFSM) while minimizing the drive storage and computational time which is the main problem in finite element analysis (FEA) tools.

First, a new HECPFSM based on conventional consequent pole flux switching permanent machine (FSPM) is proposed, and lumped parameter magnetic network model (LPMNM) is developed for the initial analysis like coil combination and no-load flux linkage. In LPMNM, all the parts of one-third machine are modeled which helps in reduction of drive storage, computational complexity and computational time without affecting the accuracy. Second, self and mutual inductance are calculated in the stator, and dq-axis inductance is calculated using park transformation in the rotor of the proposed machine. Furthermore, on-load performance analysis, like average torque, torque density and efficiency, is done by FEA.

The developed LPMNM is validated by FEA via JMAG v. 19.1. The results obtained show good agreement with an accuracy of 96.89%.

The proposed HECPFSM is developed for high-speed brushless AC applications like electric vehicle (EV)/hybrid electric vehicle (HEV).

The proposed HECPFSM offers better flux regulation capability with enhanced electromagnetic performance as compared to conventional consequent pole FSPM. Moreover, the developed LPMNM reduces drive storage and computational time by modeling one-third of the machine.

The purpose of this paper is to investigate an alternative simplified analytical approach for the design of electric machines. Numerical-based finite element method (FEM) is a powerful tool for accurate modelling and electromagnetic performance analysis of electric machines. However, computational complexity, magnetic saturation, complex stator structure and time consumption compel researchers to adopt alternate analytical model for initial design of electric machine especially flux switching machines (FSMs).

In this paper, simplified lumped parameter magnetic equivalent circuit (LPMEC) model is presented for newly developed segmented PM consequent pole flux switching machine (SPMCPFSM). LPMEC model accounts influence of all machine parts for quarter of machine which helps to reduce computational complexity, computational time and drive storage without affecting overall accuracy. Furthermore, inductance calculation is performed in the rotor and stator frame of reference for accurate estimation of the self-inductance, mutual inductance and dq-axis inductance profile using park transformation.

The developed LPMEC model is validated with corresponding FEA using JMAG Commercial FEA Package v. 18.1 which shows good agreement with accuracy of ∼98.23%, and park transformation precisely estimates the inductance profile in rotor and stator frame of reference.

The model is developed for high-speed brushless AC applications.

The proposed SPMCPFSM enhance electromagnetic performance owing to partitioned PMs configuration which make it different than conventional designs. Moreover, the developed LPMEC model reduces computational time by solving quarter of machine.

For compactness and ease in assembling, a novel miniature size tubular moving magnet linear oscillating actuator (MT-MMLOA) design for miniature linear compressor application is proposed in this paper.

This MT-MMLOA design possesses a modular C-core stator structure having separation at the middle. Axially magnetized tubular permanent magnets are accommodated on the mover. To improve the output parameters of the linear oscillating actuators (LOA), all the design parameters are optimized using a parametric sweep. Finite element analysis of the proposed design is performed to examine the magnetic flux density as well as thrust force under both static and dynamic analysis within the intended stroke range.

Compared to conventional LOA for miniature compressors, the motor constant of the proposed LOA is 37 N/A that is 85% greater while keeping the same size of LOA. Permanent magnet volume used in the investigated topology of LOA is 26% reduced. Additionally, the overall volume of the machine is 10.3% decreased. Furthermore, the proposed topology is simple, inexpensive and easy to manufacture.

Electromagnetic performance comparison with different topologies proposed earlier in literature is carried out to prove the performance superiority of the proposed design.

The purpose of this paper is to modernize the generator system of wind turbine concept that not only improves the efficiency and power density but also reduces the system cost making design simpler and less expensive, especially in large-scale production.

This paper presents a new permanent magnet transverse flux generator (PMTFG) for wind energy production. The key feature of its composition is the double armature coil in a semi-closed stator core. The main structural difference of the presented design is the use of double coil in the same space of semi-closed stator core and reduced number of stator pole pairs and rotor magnets from 12/24 to 10/20. 3D simulations are performed using finite element analysis (FEA) to measure induced voltage and magnetic field distribution at no load. The FEA is performed to quantify the change in flux linkage, induced voltage and output power as a function of different speeds and load current.

Results show that PMTFG with double coil configuration has improved electromagnetic performance in terms of flux linkage, induced voltage, output power and efficiency. The power density of 10/20 PMTFG with the double coil is 0.0524 KW/Kg, about an 18% increase compared to the conventional design.

The proposed PMTFG is highly recommended for direct drive applications such as wind power.

Four models are simulated by FEA with single and double coil configuration, and load analysis is performed on all simulated models. Finally, results are compared with conventional PMTFG.

This paper aims to provide an overview of the recent developments and new topologies of single-phase moving magnet linear oscillating actuators (MMLOAs). The key advantage of the MMLOA when compared with conventional LOA is the absence of screws, gears and crankshaft mechanism, which results in fewer mechanical parts, simple structure, easy fabrication, lower noise levels and negligible frictional losses.

The review included papers up to August 2021. The structural designs of alternative topologies are deliberated in detail, and their relative merits and demerits are evaluated. Specific design issues, including pole and tooth number combinations, stroke length, magnet pole ratio and split ratio, are investigated. The imperative phenomena of the resonance, as well as the adjustable stroke, are also discussed in detail.

The electromagnetic performance in terms of thrust force of selected MMLOA topologies is compared. It is observed that the MMLOA with flux bridge topology has the highest thrust force of 365 N because of the large volume of the permanent magnets (PMs) used, which consequently increased the mass of the mover but based on overall performance analysis, single-phase end ferromagnetic Halbach surface-mounted PM LOA has the highest efficiency around 92%.

This review provides a comparative analysis for different tubular MMLOA topologies based on design construction and their electromagnetic performances.

This paper aims to develop an analytical approach to validate the finite element analysis (FEA) results. FEA itself is a powerful tool to evaluate the performance of electrical machines but takes more time and requires more drive storage. To overcome this issue, subdomain modeling (SDM) is used for the proposed machine.

SDM is developed to validate the electromagnetic performance of a new linear hybrid excited flux switching machine (LHEFSM) with ferrite magnets. In SDM, the problem is divided into different physical regions called subdomains. Maxwell's governing equation is solved analytically for each region, where the magnetic flux density (MFD) is generated. From the generated MFD, x and y components are calculated, which are then used to find the useful force along the x-axis.

FEA validates the developed SDM via JMAG v. 20.1. The results obtained show excellent agreement with an accuracy of 95.13%.

The proposed LHEFSM is developed for long stroke applications like electric trains.

The proposed LHEFSM uses low-cost ferrite magnets with DC excitation, which offers better flux regulation capability with improved electromagnetic performance. Moreover, the developed SDM reduces drive storage and computational time by modeling different parts of the machine.

The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed.

A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered.

For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit.

A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application.

Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations.

Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost.

Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.