Intelligent Controller Based on Distributed Deep Reinforcement Learning for PEMFC Air Supply System

Li, Jiawen; Yu, Tao

doi:10.1109/access.2021.3049162

Cited by 19 publications

(5 citation statements)

References 34 publications

(35 reference statements)

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“…In past few years, as the problems of energy scarcity and environmental degradation are becoming more and more apparent, countries all over the world have begun to search for new clean energy sources as substitutes for fossil energy sources and are actively exploring low carbon and high efficiency energy development routes. Hydrogen, as a secondary energy carrier, has been widely recognized and paid attention to because of its many advantages, like high efficiency, renewability, and stable energy conversion process [1][2][3]. PEMFCs, which utilizes hydrogen as its primary fuel, has increasingly gained attention and is seen as one of the most promising technologies to replace conventional internal combustion engines as the power source for vehicles in the future [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Sensorless Control Method for Energy Recovery of EGTAC to Improve PEMFC Efficiency

Jin,

Xu,

Wang

2024

IEEE Access

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The efficient operation of the air supply system, particularly the air compressor, is crucial in ensuring the performance of the proton exchange membrane fuel cells (PEMFCs). Nevertheless, its high parasitic power consumption is the main reason for the efficiency decline of the PEMFC. While exhaust gas energy recovery is a viable approach to enhance system efficiency, conventional exhaust gas recovery systems are not well-suited for PEMFC and the cathode exhaust gas is difficult to monitor in real-time. Therefore, this study proposes a sensorless method based on reinforcement learning for energy recovery of the exhaust gas turbine air compressor (EGTAC) in the PEMFC. Firstly, the air supply system with EGTAC and stack model are established. Subsequently, the relationship between the exhaust gas energy and the working performance of both the EGTAC and the PEMFC is elucidated. Additionally, a method based on a state observer is devised to estimate the characteristics of the exhaust gas in a PEMFC. Finally, compared to the model predictive control (MPC), this method enhances the EGTAC exhaust gas recovery rate by 19.1% and the fuel cell system efficiency by 3.7%. The optimization differs by a maximum of 6.5% compared to the control with sensors.INDEX TERMS Exhaust gas energy recovery, Estimation of exhaust gas characteristics, Reinforcement learning, Sensorless control

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Section: Introductionmentioning

confidence: 99%

A Sensorless Control Method for Energy Recovery of EGTAC to Improve PEMFC Efficiency

Jin,

Xu,

Wang

2024

IEEE Access

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“…In recent years, with the rise of intelligent algorithm control strategies, scholars have applied intelligent algorithm control strategies to the PEMFC system. These include collective intelligence exploration distributed multi-delay deep deterministic policy gradient algorithm control strategies to manage gas supply flow [12,13] and deep learning algorithms combined with traditional PID control strategies to manage gas supply flow [14] and temperature [15]. These studies have yielded impressive results.…”

Section: Introductionmentioning

confidence: 99%

Research Progress on Gas Supply System of Proton Exchange Membrane Fuel Cells

Cheng,

Wu,

Mou

et al. 2024

Processes

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Proton exchange membrane fuel cells (PEMFCs) are attracting attention for their green, energy-saving, and high-efficiency advantages, becoming one of the future development trends of renewable energy utilization. However, there are still deficiencies in the gas supply system control strategy that plays a crucial role in PEMFCs, which limits the rapid development and application of PEMFCs. This paper provides a comprehensive and in-depth review of the PEMFC air delivery system (ADS) and hydrogen delivery system (HDS) operations. For the ADS, the advantages and disadvantages of the oxygen excess ratio (OER), oxygen pressure, and their decoupling control strategies are systematically described by the following three aspects: single control, hybrid control, and intelligent algorithm control. Additionally, the optimization strategies of the flow field or flow channel for oxygen supply speeds and distribution uniformity are compared and analyzed. For the HDS, a systematic review of hydrogen recirculation control strategies, purge strategies, and hydrogen flow control strategies is conducted. These strategies contribute a lot to improving hydrogen utilization rates. Furthermore, hydrogen supply pressure is summarized from the aspects of hybrid control and intelligent algorithm control. It is hoped to provide guidance or a reference for research on the HDS as well as the ADS control strategy and optimization strategy.

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“…A higher oxygen supply will greatly increase the power consumed by the compressor and reduce the net power of PEMFC, which leads to "Oxygen Saturation" [5]. As an important component of the air supply subsystem, the compressor can ensure that the air supply subsystem can supply appropriate oxygen to the PEMFC stack [6]. Therefore, researchers often optimize oxygen supply and reduce compressor power consumption by quickly and accurately controlling the compressor to improve the dynamic response performance of PEMFC.…”

Section: Introductionmentioning

confidence: 99%

A Novel Control Algorithm of the Air Supply Subsystem: Based on Dynamic Modeling of Proton Exchange Membrane Fuel Cell

Wang

et al. 2022

Processes

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In this paper, a novel second-order active disturbance rejection control (2-ADRC) algorithm is proposed to optimize the control of the air supply subsystem for Proton Exchange Membrane Fuel Cell (PEMFC). To improve the optimal control effect of the air supply subsystem for PEMFC, the modeling theory of the air supply subsystem considering dynamic characteristics of the PEMFC system is first studied, and the dynamic Simulink model of the PEMFC system is established and verified. Then, the optimal oxygen excess ratio (OER) parameters under different load currents are obtained, and the optimal OER parameters are also used as the OER control reference for the designed algorithms. In addition, a 2-ADRC algorithm is designed and proposed to make the actual OER parameters close to the optimal OER in real time. Furthermore, compared with PID and MPC algorithms, the 2-ADRC algorithm can comprehensively consider the two parameters of mass flow and pressure ratio to make the compressor work in the high-efficiency zone and improve the net power and efficiency of the PEMFC system.

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Intelligent Controller Based on Distributed Deep Reinforcement Learning for PEMFC Air Supply System

Cited by 19 publications

References 34 publications

A Sensorless Control Method for Energy Recovery of EGTAC to Improve PEMFC Efficiency

A Sensorless Control Method for Energy Recovery of EGTAC to Improve PEMFC Efficiency

Research Progress on Gas Supply System of Proton Exchange Membrane Fuel Cells

A Novel Control Algorithm of the Air Supply Subsystem: Based on Dynamic Modeling of Proton Exchange Membrane Fuel Cell

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