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

Wang, Pengyu; Ma, Yangyang; Li, Jianhua; Gao, Yukun; Zhang, Yunrui; Ma, Denghui

doi:10.3390/pr10081499

Cited by 7 publications

(5 citation statements)

References 31 publications

(32 reference statements)

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“…Because the exhaust gas energy of the system is less under the low power load condition, this paper does not consider its control. Furthermore, under high power load conditions and medium power load conditions, compared with the electrical efficiency results of the fuel cell system obtained by controlling the air supply system to optimize the response characteristics and power density of the fuel cell in literature [47][48][49], the efficiency of the fuel cell system optimized based on the cathode exhaust gas energy recovery control in this study is at a reasonable level. According to the study of the control optimization findings presented above, the sensorless estimating method proposed can substitute the sensor function and significantly enhance the efficiency of exhaust energy recovery and fuel cell system.…”

Section: Results Analysismentioning

confidence: 80%

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: Results Analysismentioning

confidence: 80%

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

Jin,

Xu,

Wang

2024

IEEE Access

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“…The proton exchange membrane fuel cell (PEMFC) is one of the fuel cells widely used in automobiles. The PEMFC system consists of the fuel cell stack and auxiliary subsystems including the air supply subsystem, hydrogen supply subsystem, and cooling subsystem [2]. The fuel cell system net power P f cs can be obtained in Formula (23):…”

Section: Fuel Cell System Modelmentioning

confidence: 99%

“…Energy saving and emission reduction are hot research topics in the field of automobiles. In recent years, the technological progress of hydrogen energy and fuel cells has greatly promoted the performance improvement of fuel cell vehicles (FCVs) [1,2]. Compared with traditional internal combustion engine vehicles (ICEVs), FCVs have the advantages of zero carbon emission, high efficiency, and low noise in driving processes, which is beneficial for reducing emissions [3].…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Consumption Generation Method of Fuel Cell Vehicles: Based on Naturalistic Driving Data Mining

Wang

et al. 2022

Machines

Self Cite

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In this paper, an energy consumption generation method is proposed to accurately calculate the energy consumption of fuel cell vehicles (FCVs). A specific driver drives on a route (from Jilin University to FAW Volkswagen) for 331 working days (1 April 2020 to 28 July 2021) and collects more than 40,000 s of naturalistic driving data by means of a GPS receiver (FRII-D). To accurately calculate the energy consumption data of FCVs under actual driving cycles, naturalistic driving data mining is first studied. The principal component analysis (PCA) algorithm is used to reduce the dimension of the extracted driving cycle characteristic parameters, the K-means algorithm is used for driving cycle clustering, and the LVQ is used for driving cycle identification. Then, the characteristic parameters correlated to energy consumption are obtained based on the FCV model and regression analysis method. In addition, an energy consumption generation method is designed and proposed based on the characteristic parameters and identification results. Furthermore, the proposed energy consumption generation method can accurately calculate the energy consumption of FCVs, which also provides a reference for further research on the efficient energy management of FCVs.

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“…In order to ensure the operability of the control scheme at a later stage, researchers have proposed an ADRC method for hybrid Li-ion battery and fuel cell systems [14][15][16][17]. This method is an evolution of traditional PID control [18][19][20], which does not require high model accuracy and is widely used in industrial power applications.…”

Section: Introductionmentioning

confidence: 99%

Robust Control of RSOC/Li-ion Battery Hybrid System Based on Modeling and Active Disturbance Rejection Technology

Gao

et al. 2023

Atmosphere

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The application of new energy systems for industrial production to advance air pollution prevention and control has become an irreversible trend. This development includes hybrid systems consisting of reversible solid oxide cells (RSOC) and a Li-ion battery; however, at present the energy dispatching of such systems has an unstable factor in the form of poor heat/electricity/gas controllability. Therefore, the system studied in this paper uses the Li-ion battery as the energy supply/storage case, and uses the RSOC to supply power for the Li-ion battery charge or the Li-ion battery supply power to the RSOC for hydrogen production by water electrolysis. In this hybrid system, Li-ion battery thermoelectric safety and RSOC hydrogen production stability are extremely important. However, system operation involves the switching of multiple operating conditions, and the internal thermoelectric fluctuation mechanism is not yet clear. Therefore, in this paper we propose a separate control with a dual mode for hybrid systems. Active disturbance rejection control (ADRC) with a simple structure is used to achieve Li-ion battery module thermoelectric safety and control the hydrogen production/consumption of the RSOC module in the hybrid system. The results show that the required Li-ion battery thermoelectric safety and RSOC hydrogen consumption/production requirements can be met using the proposed controller, leading to a hybrid system with high stability control.

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

Cited by 7 publications

References 31 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 on Energy Consumption Generation Method of Fuel Cell Vehicles: Based on Naturalistic Driving Data Mining

Robust Control of RSOC/Li-ion Battery Hybrid System Based on Modeling and Active Disturbance Rejection Technology

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