The Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) is the key part to enhance optimal power distribution. Indeed, the most recent works are focusing on optimizing hydrogen consumption, without taking into consideration the degradation of embedded energy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize the battery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMS is normalized in order to address its multi-objective optimization. Then, weighting factors are assigned in the objective function to minimize the selected criteria. Compared to most EMSs based on optimization techniques, this proposed approach does not require any information about the speed profile, which allows it to be used for real-time control of FCHEV. The achieved simulation results show that the proposed approach reduces the economic cost up to 50% for some speed profile, keeping the battery pack in a safe range and significantly reducing energy sources degradation. The proposed health-conscious EMS has been validated experimentally and its online operation ability clearly highlighted on a PEMFC delivery postal vehicle.

As the world shifts toward a low-carbon future, green hydrogen has emerged as a critical pillar of the energy transition. It is produced using renewable energy to power water electrolysis, and it is a clean and flexible alternative to hydrogen made from fossil fuels. However it is still hard to roll out on a large scale because of technological limits, high costs, and the need for infrastructure. This review critically analyzes current electrolysis methods, including established systems like alkaline and PEM electrolyzers, as well as newly developed concepts such as AEMWE and SOWE. It discusses how they can be used in renewable energy systems, important techno-economic and durability problems, system modeling, and grid interaction. This work clarifies both the technological potential and the practical limitations of green-hydrogen electrolyzer systems while highlighting key directions for future research and implementation.

The present paper exhibits a real time assessment of a robust Energy Management Strategy (EMS) for battery-super capacitor (SC) Hybrid Energy Storage System (HESS). The proposed algorithm, dedicated to an electric vehicular application, is based on a self-gain scheduled controller, which guarantees the H performance for a class of linear parameter varying (LPV) systems. Assuming that the duty cycle of the involved DC-DC converters are considered as the variable parameters, that can be captured in real time, and forwarded to the controller to ensure both; the performance and robustness of the closed-loop system. The subsequent controller is therefore time-varying and it is automatically scheduled according to each parameter variation. This algorithm has been validated through experimental results provided by a tailor-made test bench including both the HESS and the vehicle traction emulation system. The experimental results demonstrate the overall stability of the system, where the proposed LPV supervisor successfully accomplishes a power frequency splitting in an adequate way, respecting the dynamic of the sources. The proposed solution provides significant performances for different speed levels.

This paper presents a reliability approach to analyze the degradation of proton exchange membrane fuel cell. This approach is based on the dependability analysis tools such as the causal and fault trees to establish an analysis of the internal state of the fuel cell energy conversion performance and evaluate its lifetime. The elaboration of causal tree offers powerful tools to a deductive analysis, which consists on seeking the various combinations of events leading to the fuel cell degradation. The parameters of fuel cell model are identified in order to found the degree of degradation. The experimental determination of the variation interval of the parameters is done according to each of degradation modes. A diagnostic method is proposed in order to identify the depth of each aging process of the fuel cell. The diagnosis is done by comparing the experimental output characteristic at beginning of life of the fuel cell with the used fuel cell to qualify and quantify the depth of degradation.