The implementations of fuel cells (FCs) in the vehicle industry have gained great attention for the last few decades owing to simple utilization, silent operation, high efficiency and modular structure. Technological advancements show that the use of FCs in electric vehicles (EVs) will increase rapidly and cause a revolution, and will be an alternative to traditional vehicles in the future. Commercial vehicles, projects and research show that work is underway to ensure that FCEVs have sufficient performance advances for their daily transportation needs. However, the lack of a detailed study that will shed light on researchers working in this field is obvious. It aims to provide a comprehensive scientific publication on the current status and future expectations to engineers and researchers interested in this field. In the current study, numerous studies have been examined in detail and added as supplementary to the bibliography. In this context, FCEVs are classified under headings of configurations, systems components, control/management, technical challenges, marketing and future aspects. First of all, FC types and electric motors are discussed in terms of their application areas, characteristic properties and operating conditions. Power converters, which are voltage regulation and motor drive topologies used in FCEVs, are detailed according to the structural frequency of use, structure, and complexity. In the next sections, control issues for converters and technical challenges are branched for FCEVs. In final section, the current status and future aspects are reported using a large number of marketing and target data.

A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects

This paper proposes a comprehensive framework for multiobjective design optimization of switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches. First, the definitive screening design was employed to perform sensitivity analyses to identify significant design variables without bias of interaction effects between design variables. Next, optimal third-order response surface (RS) models were constructed based on the Audze-Eglais Latin hypercube design using the selected significant design variables. The constructed optimal RS models consist of only significant regression terms, which were selected by using PSO. Then, a PSO-based multiobjective optimization coupled with the constructed RS models, instead of the finite-element analysis, was performed to generate the Pareto front with a significantly reduced computational cost. A sample SRM design with multiple optimization objectives, i.e., maximizing torque per active mass, maximizing efficiency, and minimizing torque ripple, was conducted to verify the effectiveness of the proposed optimal design framework.

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Multiobjective Optimization of Switched ReluctanceMotors Based on Design of Experiments and ParticleSwarm Optimization

This paper focuses on the multiobjective design optimization of the permanent magnet synchronous linear motors (PMSLMs), which are applied to a high-precision laser engraving machine. A novel efficient multiobjective design optimization method for a PMSLM is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds. First, based on the finite-element analysis (FEA) data, a regression machine learning algorithm, called an extreme learning machine (ELM), is introduced to solve the calculation modeling problem by mapping out the nonlinear and complex relationship between input structural factors and output motor performances. Comparative simulation experiments conducted using the traditional analytical modeling method and another machine learning modeling method, i.e., support vector machine, confirm the superiority of the ELM. Then, a new bionic intelligent optimization algorithm, called the gray wolf optimizer algorithm, is used to search the best optimization performances and structural parameters by performing iteration optimization calculation for multiobjective functions. Finally, FEA and prototype motor experiments prove the effectiveness and validity of the proposed method.

An Efficient Multiobjective Design Optimization Method for a PMSLM Based on an Extreme Learning Machine

In this article, a novel system-level robust design optimization method is presented to improve the performance of switched reluctance motor (SRM) drive systems under multiple operating conditions. Based on typical driving cycles of electric vehicles (EVs), five typical driving modes of the SRM are determined. The optimization objectives in each driving mode are established. The significant parameters of the motor and controller of each driving mode are selected as the optimization variables by using the sensitivity analysis. In order to simplify the optimization process, correlation analysis is performed to determine the coherence of the objective functions of all driving modes. Then, a sequential Taguchi method is applied to find an optimal design which is less sensitive to the noise factors. To verify the effectiveness of the proposed method, an SRM drive system applied in EVs with a 12/10 SRM and angle position control method is investigated. It is found that the proposed method can significantly reduce the torque ripple and improve the comprehensive performance. Finally, a 12/10 SRM is prototyped and tested to validate the simulation results.

System-Level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles

This article focuses on the local demagnetization fault recognition research of permanent magnet synchronous linear motor (PMSLM) and realizes the accurate identification of the position and degree of demagnetized permanent magnets. A fault recognition system based on S-transform (ST) and particle swarm optimization–least squares support vector machine (PSO–LSSVM) is proposed. The ST makes the induced electromotive force (EMF) signal with stronger signal characteristic expression ability, and the PSO–LSSVM model achieves better generalization ability and higher accuracy in the small sample state of PMSLM faults. In the process of fault identification: 1) the induced EMF analytical model for PMSLM under local demagnetization fault is presented; 2) the induced EMF signal is analyzed by ST, and the characteristic parameters are extracted from time–frequency curves. Then a characteristic vector is established by comparing the standard deviation values and similarities of different parameters; 3) a PSO–LSSVM classification model is established to realize the recognition of PMSLM faults. Prototype and finite element simulation experimental results confirm that the method can recognize the PMSLM faults accurately with a recognition rate of 100%.

Local Demagnetization Fault Recognition of Permanent Magnet Synchronous Linear Motor Based on S-Transform and PSO–LSSVM

This study focuses on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force. Traditional analytical optimisation method based on magnetic field with particle swarm optimisation algorithm was introduced to obtain the best combination of motor structure parameters. By contrast, the novel optimisation design method - Taguchi method based on orthogonal array was proposed to optimise the thrust and thrust ripple. After the design of experiments using finite-element analysis, the relative importance of each design parameter was estimated in detail. Experimental results of prototype can certify the superiority and validity of Taguchi optimisation method.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-epa.2016.0164

Optimal design of permanent magnet linear synchronous motors based on Taguchi method

High-resistance connection (HRC) is one of the common stator faults in motors. The HRC fault detection method for brushless direct current motors (BLDCMs) has not been reported yet. This study proposes a two-step strategy for the detection, localization, and evaluation of HRC fault in a BLDCM. In the first step, the zero sequence voltage component (ZSVC) of the motor is acquired and analyzed, and the HRC fault can be detected from the amplitude of the fundamental frequency component of the ZSVC spectrum. In the second step, once the HRC fault has been detected, three indicators are calculated from the ZSVC and three-phase currents to localize the fault phase. The resistance value of the HRC is then calculated. Theoretical, simulated, and experimental analyses have been performed, and the results of them are consistent with one another, thereby validating the effectiveness of the proposed strategy. Both the HRC fault detection algorithm and the motor control algorithm are implemented on a single microcontroller unit in real time, which makes the proposed strategy efficient and flexible in industrial applications.

Hui Wang

Papers

Optimal design of permanent magnet linear synchronous motors based on Taguchi method

A Two-Step Strategy for Online Fault Detection of High-Resistance Connection in BLDC Motor