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An effective online fault diagnosis system is of great significance to improve the reliability of fuel cell vehicles. In this paper, a fault diagnosis model for proton exchange membrane fuel cells is proposed. Firstly, the tests of electrochemical impedance spectroscopy under different fault types (flooding, drying, air starvation) and fault degrees (minor, moderate, severe) are carried out, and each polarization loss of the fuel cell is denoted by an equivalent circuit model (ECM). Then, the parameters of the ECM are identified by the proposed random mutation differential evolution algorithm. Furthermore, the parameters identified under different fault conditions are used to train and test a probabilistic neural network-based fault diagnosis model. The fault diagnosis model achieves diagnosis accuracies of 100% for the fault type and 96.67% for the fault degree. By setting operating conditions with different fault degrees, the fault diagnosis model proposed in this paper can realize the fault type and fault degree diagnosis, effectively avoiding the misjudgment of fault types, and is effective for improving the reliability of the fuel cell system. [Display omitted] • A RMDE algorithm is proposed to realize the impedance parameter identification. • A fault diagnosis model based on the probabilistic neural network is developed. • The diagnosis accuracies for fault type and fault degree reach 100% and 96.67%. [ABSTRACT FROM AUTHOR]

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match. • A new signal processing-based IDM is presented for hydrogen energy-based DGs. • A UWT-based IDM with an improved pyramidal algorithm was implemented in real-time. • The proposed IDM provides both zero NDZ and short detection time. • Threshold values can be selected easily and noise sensitivity is more reliable. • Only PCC voltage signal is required, thus, loss of computation time is minimum. [ABSTRACT FROM AUTHOR]

Establishing a reasonable energy management strategy (EMS) is the key to improve the service durability, power performance and fuel economy of the fuel cell hybrid electric vehicle (FCHEV). This paper obtains energy distribution optimal solution for the fuel cell hybrid bus (FCHB) based on Pontryagin's minimum principle (PMP) algorithm, and the problems of inaccurate estimation of motor power and difficult real-time application are solved. Firstly, the driving feature recognition is completed by collecting the motor output power directly when the FCHB stops at the station. On the basis of it, the sub-optimal co-state value is chosen. Secondly, the sub-optimal co-state is used to complete the real-time application of PMP algorithm in the driving segment. The results are acquired through the simulation and the actual comparison experiment, compared with rule-based simulation and rule-based actual experiment, the hydrogen consumption of the proposed strategy decreases by 20.3% and 28.9% on average. Moreover, the online computation time per step of the proposed strategy is 3.64 ms averagely, less than sampling time interval 1s. It is shown that the proposed method has lower hydrogen consumption rate and excellent real-time performance. [Display omitted] • A method of driving feature recognition based on DBSCAN algorithm is proposed. • A real-time PMP EMS based on bus driving feature recognition is proposed. • The proposed EMS can realize real-time optimization without known road condition. • Hydrogen consumption is close to that of known road condition PMP strategy. [ABSTRACT FROM AUTHOR]

Energy management is a critical issue for the economical and optimum operation of grid integrated renewable energy systems. In conventional grid integrated systems, the control methods are operated according to the design criteria for grid connection modes and cause surplus energy for low-loading conditions under grid-failure situations. For this purpose, in this paper, a flexible control strategy is improved basis on perturb & observe MPPT, and it is purposed to improve the system efficiency under grid-connection and grid-failure modes of renewable energy systems. In this way, it is aimed to ensure optimized power delivering capability with optimum load voltages/currents at desired values, and it also prevents overloading under grid-failure mode. To show the proposed method's validity, the algorithm of control method is embedded in a function block and performed for grid connection & grid-failure modes under different load conditions. The performance results show that the flexible control strategy prevents surplus energy under the grid-failure mode and reduces the power losses compared to the conventional method. Also, the proposed method increases the load-side power quality. The results show that THD values are significantly reduced to fewer than the conventional perturb & observe method. • A flexible control strategy for grid-connection and grid-failure modes. • Optimum energy generation for local loads under grid-failure mode. • Comparison with conventional perturb & observe method based system. • Enhancement of electrical energy quality at load-side. [ABSTRACT FROM AUTHOR]

Microbial electrolysis cell (MEC) is a promising reactor. However, currently, the reactor cannot be adapted for industrial-scale biohydrogen production. Nevertheless, this drawback can be overcome by modeling studies based on mathematical equations. The limitation of analytical instrumentation to record the non-linearity of the dynamic behavior for biohydrogen processes in an MEC has led to the introduction of computational approach that has the potential to reduce time constraints and optimize experimental costs. Reviews of comparative studies on bioelectrochemical models are widely reported, but there is less emphasis on the MEC model. Therefore, in this paper, a comprehensive review of the MEC mathematical model will be further discussed. The classification of the model with respect to the assumptions, model improvement, and extensive studies based on the model application will be critically analyzed to establish a methodology algorithm flow chart as a guideline for future implementation. [Display omitted] • Advancement of MEC mathematical models. • Classification of MEC mathematical models. • Proposed guideline for the MEC mathematical model implementation. [ABSTRACT FROM AUTHOR]