Metal hydride hydrogen tank (MHHT) presents a promising approach for achieving safe and environmentally sustainable hydrogen storage, particularly in embedded systems such as electric vehicles. Precise monitoring of the hydrogen content is critical for operational safety; however, direct measurement of the stored hydrogen remains a significant challenge. This work proposes a novel approach for estimating the state-of-charge (SoC) of an MHHT using a custom-developed model in MATLAB and integrating an unscented Kalman filter (UKF) for state estimation. Real-time pressure and temperature measurements are used to validate the accuracy of the UKF-based SoC estimation. To further enhance the filter’s performance and robustness, a particle swarm optimization (PSO) algorithm is employed to optimize UKF hyperparameters. The results demonstrate that the hybrid PSO-UKF framework offers a viable and precise method for real-time SoC estimation in MHHTs, achieving RMSE values of 5.1862 × +10−5and 6.1445 × +10−5at hydrogen flow rates of 4 and 8 Nl/min, repectively.

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With increasingly serious environmental issues such as global warming, fossil fuels are gradually being replaced by clean energy. Fuel cells (FC) combining with hydrogen as a sort of clean energy have already been employed in many applications. Due to the nonlinear current-voltage property of FC, a DC-DC boost converter and a suitable controller are necessary for the FC system. In this paper, an observer-based adaptive sliding mode control is designed by considering the actual vehicle applications with unknown load and variable FC voltage for interleaved boost converter (IBC). The designed controller shows strong robustness in output voltage because of the adaptive laws. The system stability is proofed by Lyapunov functions. Last, the simulations were carried out to further verify the effectiveness of the designed controller.

With the drastic inclination towards reduction of atmospheric issues, fuel-cell vehicles are becoming the major alternative for internal combustion. Due to the characteristics of the fuel cell, multiple-input converter is more attractive in redundancy and safety. In this paper, a multi-input three-phase interleaved boost converter (IBC) is designed by analyzing every state of its work to obtain its averaged state-space equation. Two control methods based on PI and super-twisting (ST) are designed and applied to the converter. A fault-tolerant method is proposed for the IBC. The simulation results of fault-tolerant based on two control methods are compared. The results show that it has better performance in fluctuation of DC bus voltage and inductor current when using ST method.

The accurate estimation of state of health (SOH) of Lithium-ion battery is critical to guarantee safety and reliability of whole system. Large number of researches on SOH have many strict constrained assumptions, such as repeated same load, which differ from real situation. In this paper, the study focus on variable load profile. An artificial neural network is designed to determine the battery SOH using operation times for 12 different currents and the beginning capacity for every cycle. Two kinds of validation samples are chosen to test the neural network. Simulation results show that for randomly chosen 20 validation samples from whole dataset, each sample errors don't exceed 4.37% and root mean squared error is 1.33%, for 20 sequential validation samples from one battery, maximum estimation error is 4.96% and root mean squared error is 1.25%, all below 5%, proving the good behavior of the designed neural network SOH estimation.

This paper describes a novel equivalent consumption minimization strategy (ECMS) for a fuel cell hybrid electric vehicle. The vehicle is composed of a fuel cell as main energy source, batteries and supercapacitors as peaking power sources. Through using fuel cell efficiency penalty coefficient, battery SOC coefficient and supercapacitor SOC coefficient, the designed new strategy aims to operate the fuel cell system at its best efficiency point, the battery as a long term energy buffer and supercapacitor to supply peak power. Considering the characteristic differences between battery and supercapacitor, different SOC penality coefficients are set to full play the potential of battery's large storage ability and supercapacitor's fast charge/discharge power. The proposed energy management is validated by simulation using driving cycle with urban pattern.

This paper proposes a novel control strategy for fuel cell electric vehicle including hydrid tanks using fuzzy logic controller. The aim of the study is to manage both thermal and electric energy with the same controller in order to use the fuel cell system as a range extender by preventing the batteries state of charge to drop too quickly. The presented controller use both batteries state of charge and thermal status of hydrid tank to control the fuel cell power. This work is a part of the Mobypost project, which aims to build and test a fleet of 10 fuel cell electric vehicles with their own refuelling hydrogen station based on hydrogen production by water electrolysis with solar panel.

The most adapted energy management in hybrid electric vehicles depends on the current driving situation. This paper describes a novel control strategy based on driving cycle recognition. A Driving Cycle Recognition Algorithm (DCRA) is firstly presented. It allows recognition between three driving modes: urban, suburban and highway. A real-time control strategy is then defined based on fuzzy logic using DCRA. Results are presented and compared to fuzzy logic controllers parametrized for urban or highway cycles.

Over the last few decades, the proton exchange membrane fuel cell (PEMFC) has been named by public and private researchers as excellent candidate for automotive applications with a high degree of autonomy and zero pollutant emissions (gases, noise). However, before the commercialization of fuel cell electric vehicles (FCEV), some challenging issues remain to be solved, especially the reliability of power trains in case of faults. In this perspective, the purpose of this paper is to study the influence of faults on the interactions between fuel cell and DC/DC converter. The main contribution of this work is to study different faults which could occur during the operation both for FC and DC/DC converter.

Fault tolerance in proton exchange membrane fuel cell (PEMFC) and power converters for automotive applications has become crucial in order to increase the reliability of the power train. As a matter of fact, the occurrence of faults in PEMFC and power converters has undesirable effects on the whole power train such as decreasing of the efficiency and lifetime of the components (PEMFC, converters). The purpose of this paper is to present a fault diagnosis method for PEMFC and DC/DC converter. This fault diagnosis is based on neural networks (NNs) modeling approach combined to numerical simulation in which a new developed sensitive model of PEMFC and an interleaved DC/DC converter have been especially used. Specifically, in this study drying and flooding faults that usually occured in PEMFC according to operations condition variation such as temperatue, humidity and pressure have been considered. Moreover, the power semiconductor failures in DC/DC converter have been taken into consideration in this study.

In this paper firstly, best method identification for data acquisition selection by aim of fault analysis is presented and in continue, a new pattern for a full bridge thyristor base AC-DC converter under failure in view of series hybrid electric vehicle (SHEV) application is identified and presented by the help of experimental test result. Open phase signatures are utilized to introduce a particular frequency pattern, and side-band components changes at proposed frequencies are employed as a proper index for fault recognition.