Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance

Tan, Jiaqi; Hu, Haoqin; Liu, Shengnan; Chen, Cong; Xuan, Dongji

doi:10.1016/j.ijhydene.2022.08.154

Cited by 36 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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

show abstract

Section: Results Analysismentioning

confidence: 80%

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

Jin,

Xu,

Wang

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Neural networks have been shown to be effective at modeling complex systems and can be used to predict various variables in PEMFCs, including temperature [41,42]. In particular, NNs have been used to model the relationship between temperature and other variables such as current density, gas flow rate, and humidity [43]. This allows for the development of control strategies that can maintain desired temperature and humidity levels and improve the overall performance and efficiency of PEMFCs [44].…”

Section: Literature Reviewmentioning

confidence: 99%

Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies

Mehmood

Iqbal

Uppal

2023

Energy

View full text Add to dashboard Cite

“…Consequently, their accuracy is insufficient for achieving precise adaptive control. As a model-free algorithm, NNC finds extensive application in fuel cell control, including artificial neural network control [17] and BP neural network control [18]. Nevertheless, despite its simple control principle, the performance of NNC exhibits substantial variations under different operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Temperature Control of Fuel Cell Based on PEI-DDPG

Lu,

Yan

2024

Energies

View full text Add to dashboard Cite

Proton exchange membrane fuel cells (PEMFCs) constitute nonlinear systems that are challenging to model accurately. Therefore, a controller with robustness and adaptability is imperative for temperature control within the PEMFC stack. This paper introduces a data-driven controller utilizing deep reinforcement learning for stack temperature control. Given the PEMFC system’s characteristics, such as nonlinearity, uncertainty, and environmental conditions, we propose a novel deep reinforcement learning algorithm—the deep deterministic policy gradient with priority experience playback and importance sampling method (PEI-DDPG). Algorithm design incorporates technologies such as priority experience playback, importance sampling, and optimized sample data storage structure, enhancing the controller’s performance. Simulation results demonstrate the proposed algorithm’s superior effectiveness in temperature control for PEMFC, leveraging the PEI-DDPG algorithm’s high adaptability and robustness. The proposed algorithm’s effectiveness is additionally validated on the RT-LAB experimental platform. The proposed PEI-DDPG algorithm reduces the average adjustment time by 8.3%, 17.13%, and 24.56% and overshoots by 2.12 times, 4.16 times, and 4.32 times compared to the TD3, GA-PID, and PID algorithms, respectively.

show abstract

Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance

Cited by 36 publications

References 43 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

Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies

Temperature Control of Fuel Cell Based on PEI-DDPG

Contact Info

Product

Resources

About