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

Wu, Xiaolong; Gao, Zilin; Xu, Yuanwu; Peng, Jingxuan; Xia, Zeyang; Hu, Lingyan; Hu, Jiangong; Wang, Zhuo; Li, Xi

doi:10.3390/atmos14060947

Cited by 2 publications

(1 citation statement)

References 53 publications

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“…Hence, the solid oxide electrolysis cell (SOEC) has emerged as a secure, eco-friendly, and effective technique for generating hydrogen [6] while pursuing the objective of discovering an environmentally friendly and efficient method for hydrogen production. However, it is difficult to achieve its working process accurately, which involves electrochemical reactions, modeling, and control tasks [7,8]. Model identification has become one of the effective and high-precision modeling methods for it.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Nonlinear SOEC Parameter System Based on Data-Driven Method

Hou,

Ma,

et al. 2023

Atmosphere

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Based on the basic nonlinear parameter system of the solid oxide electrolysis cell, the data-driven method was used for system identification. The basic model of the solid oxide electrolysis cell was accomplished in Simulink and experiments were performed under a diversified input/output operating environment. The experimental results of the solid oxide electrolysis cell basic parameter system generated 15 datasets. The system identification process involved the utilization of these datasets with the application of nonlinear autoregressive-exogenous models. Initially, data identification came from the Matlab mechanism model. Then, the nonlinear autoregressive-exogenous structures were estimated and selected exploratively through an individual operating condition. In terms of fitness, we conclude that the solid oxide electrolysis cell parameter system cannot be satisfied by a solitary autoregressive-exogenous model for all datasets. Nevertheless, the nonlinear autoregressive-exogenous model utilized S-type nonlinearities to fit a total of 2 validation datasets and 15 estimated datasets. The obtained results were compared with the basic parameter system of a solid oxide electrolysis cell, and the nonlinear autoregressive-exogenous projected output demonstrated an accuracy of over 93% across diverse operational circumstances—regardless of whether there was noise interference. This result has positive significance for the future use of the solid oxide electrolysis cell to achieve the dual carbon goal in China.

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Section: Introductionmentioning

confidence: 99%

Modeling of Nonlinear SOEC Parameter System Based on Data-Driven Method

Hou,

Ma,

et al. 2023

Atmosphere

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Research on intelligent active disturbance rejection control algorithm for shock train leading edge of dual-mode scramjet

Liang,

Guo,

Tian

et al. 2024

Physics of Fluids

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To prevent scramjet inlet unstart, stringent control over the shock train's leading edge in the isolator is necessary. Therefore, this study proposes a fuel flow active control scheme combining an adaptive particle swarm optimization algorithm (APSO) and active disturbance rejection control (ADRC) to enhance the traditional control (such as proportional-integral-derivative, PID) system's stability and anti-interference capabilities in complex and uncertain environments. Within this context, a simple, semi-empirical mathematical model of a dual-mode scramjet is constructed. An ADRC controller is designed to counteract the nonlinearity and disturbance of the dual-mode scramjet. ADRC, not requiring an accurate mathematical model, utilizes its extended state observer to estimate all uncertain factors affecting the controlled object based on the relationship between the shock train's leading edge and the controller's output, treating them as unknown disturbances for compensation, thereby exhibiting strong anti-interference capabilities. In order to optimize the performance, the APSO is introduced to fine-tune the ADRC controller parameters. The APSO reduces the rise time of ADRC from 22.28 to 11.39 ms, and the adjustment time from 40.05 to 13.64 ms. The APSO effectively tunes the parameters of the ADRC controller, which ensures rapidity comparable to the PID and all-coefficient adaptive control (ACAC) in managing the shock train's leading edge. At the same time, ADRC has stronger anti-interference ability and adaptive ability than ACAC and PID. This research preliminarily verifies the feasibility and advancement of the ADRC for shock train leading-edge control in scramjet isolators.

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

Cited by 2 publications

References 53 publications

Modeling of Nonlinear SOEC Parameter System Based on Data-Driven Method

Modeling of Nonlinear SOEC Parameter System Based on Data-Driven Method

Research on intelligent active disturbance rejection control algorithm for shock train leading edge of dual-mode scramjet

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