Research Topic

Intelligent Control Techniques for Fuel Cell Systems for Mobile Applications

About this Research Topic

Nowadays, electric vehicles (EVs), hybrid EVs (HEVs), and most recently fuel cell HEVs (FCHEVs) have been widely considered as one of the most promising solutions to environmental pollution and energy crisis. Compared with conventional vehicles, the FCHEVs have zero emission and allow braking energy recovery, since they combine different power sources including fuel cells and batteries. As one type of fuel cell technology, proton exchange membrane fuel cells (PEMFCs) can be well-suited for FCHEVs due to their advantages of fast fueling time, compactness, high power density, and low operation temperature and pressure.

Nevertheless, before mass commercialization of FCHEVs, one of the major challenges is the design of appropriate energy management control techniques for an energy storage system in order to achieve their higher efficiency by reducing hydrogen consumption and increasing PEMFC lifetime. Specifically, PEMFCs are electrochemical energy conversion devices, which depend on slow electrochemical reactions. In order to achieve its longer lifetime, the PEMFC should provide a relatively stable power during FCHEV operation. Moreover, the battery state of charge (SOC) limitation should be considered during the FCHEV’s operation, in order to provide adequate power for a traction system and sufficient energy storage space to recover kinetic energy when braking phases occur.

Generally speaking, there are two categories of an energy management strategy: offline methods and online methods. Offline control methods are a global optimization strategy based on the known driving cycles. The offline control results are commonly considered as benchmark, such as dynamic programming (DP), equivalent consumption minimization strategy (ECMS), or Pontryagin's Maximum Principle (PMP). Online control methods can be applied in real-time applications with unknown driving cycles, such as load following control, neural network control, fuzzy logic control, or predictive control.

The scope of the Research Topic covers all aspects associated with the PEMFC technology, ranging from the molecular scale studies to the systems and applications. The objective of this Research Topic is to present the latest developments and advances of intelligent control techniques for fuel cell systems for mobile applications, focusing on theory, exploration, exploitation and applications.

We therefore welcome theoretical and experimental investigations on, but not limited to, the following areas:
· Development of the detailed, multiphysics modeling of PEMFCs, particularly using the approaches to modeling of complex electrical and thermal dynamics, including the models of auxiliary components;
· Development and design of the model-based control of PEMFC systems aimed at simultaneous optimization of efficiency and safe operational modes for the fuel cell and its auxiliary components;
· Development of advanced intelligent control techniques for fuel cell systems (fuzzy logic control, neural network control, expert control, deep learning control, hierarchical recursion and intelligent optimization algorithm);
· DC-DC converter design with a robust power control scheme suitable for fuel cell power conversion/controllability;
· Modeling of fuel cell/lithium-ion battery hybrid electric systems for mobile applications;
· Energy management strategies of fuel cell hybrid electric systems for hydrogen saving and fuel cell longer lifetime.


Keywords: intelligent control techniques, fuel cell hybrid power system, DC-DC converters, energy management strategies, mobile applications


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Nowadays, electric vehicles (EVs), hybrid EVs (HEVs), and most recently fuel cell HEVs (FCHEVs) have been widely considered as one of the most promising solutions to environmental pollution and energy crisis. Compared with conventional vehicles, the FCHEVs have zero emission and allow braking energy recovery, since they combine different power sources including fuel cells and batteries. As one type of fuel cell technology, proton exchange membrane fuel cells (PEMFCs) can be well-suited for FCHEVs due to their advantages of fast fueling time, compactness, high power density, and low operation temperature and pressure.

Nevertheless, before mass commercialization of FCHEVs, one of the major challenges is the design of appropriate energy management control techniques for an energy storage system in order to achieve their higher efficiency by reducing hydrogen consumption and increasing PEMFC lifetime. Specifically, PEMFCs are electrochemical energy conversion devices, which depend on slow electrochemical reactions. In order to achieve its longer lifetime, the PEMFC should provide a relatively stable power during FCHEV operation. Moreover, the battery state of charge (SOC) limitation should be considered during the FCHEV’s operation, in order to provide adequate power for a traction system and sufficient energy storage space to recover kinetic energy when braking phases occur.

Generally speaking, there are two categories of an energy management strategy: offline methods and online methods. Offline control methods are a global optimization strategy based on the known driving cycles. The offline control results are commonly considered as benchmark, such as dynamic programming (DP), equivalent consumption minimization strategy (ECMS), or Pontryagin's Maximum Principle (PMP). Online control methods can be applied in real-time applications with unknown driving cycles, such as load following control, neural network control, fuzzy logic control, or predictive control.

The scope of the Research Topic covers all aspects associated with the PEMFC technology, ranging from the molecular scale studies to the systems and applications. The objective of this Research Topic is to present the latest developments and advances of intelligent control techniques for fuel cell systems for mobile applications, focusing on theory, exploration, exploitation and applications.

We therefore welcome theoretical and experimental investigations on, but not limited to, the following areas:
· Development of the detailed, multiphysics modeling of PEMFCs, particularly using the approaches to modeling of complex electrical and thermal dynamics, including the models of auxiliary components;
· Development and design of the model-based control of PEMFC systems aimed at simultaneous optimization of efficiency and safe operational modes for the fuel cell and its auxiliary components;
· Development of advanced intelligent control techniques for fuel cell systems (fuzzy logic control, neural network control, expert control, deep learning control, hierarchical recursion and intelligent optimization algorithm);
· DC-DC converter design with a robust power control scheme suitable for fuel cell power conversion/controllability;
· Modeling of fuel cell/lithium-ion battery hybrid electric systems for mobile applications;
· Energy management strategies of fuel cell hybrid electric systems for hydrogen saving and fuel cell longer lifetime.


Keywords: intelligent control techniques, fuel cell hybrid power system, DC-DC converters, energy management strategies, mobile applications


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 September 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 September 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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