Simulation study on the PEMFC oxygen starvation based on the coupling algorithm of model predictive control and PID

Wang, Yuhang; Li, Haotian; Feng, Hua; Han, Kuihua; He, Suoying; Gao, Ming

doi:10.1016/j.enconman.2021.114851

Cited by 65 publications

(18 citation statements)

References 38 publications

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“…The parameters used in the PEMFC model are presented in Table 2. (12) where 𝑚 𝑖𝑛 and 𝑚 𝑜𝑢𝑡 are inlet and outlet mass flow rates; ℎ 𝑖𝑛 and ℎ 𝑜𝑢𝑡 are specific enthalpies; 𝑊 and 𝑄 are electrical and thermal power; 𝑇 𝑖𝑛 and 𝑇 𝑜𝑢𝑡 are temperature; 𝑈𝐴 is the heat transfer coefficient multiplied by area; 𝐿𝑀𝑇𝐷 is the heat transfer logarithmic mean temperature difference.…”

Section: Load Profiles Of Buildingmentioning

confidence: 99%

Life cycle assessment of a PEMFC-based distributed energy system for hotel application

Jiangyi

Xia

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Hydrogen fuel cell can not only generate electricity efficiently, but also provide a large amount of thermal energy by recovering the waste heat of the fuel cell stack. This study proposed a PEMFC-based distributed energy systems (PEMFC-DES) containing of input energy, PEMFC-based combined cooling, heating and power (PEMFC-CCHP) system and building loads. The performances of energy, environment, economics of the proposed system are investigated through life cycle assessment (LCA) under different hydrogen-electricity contribution rates (RH ), hydrogen production methods and hydrogen gas prices. The results show that the hydrogen production method has a greater impact on primary energy consumption and greenhouse gas emissions. At RH = 1.0, the greenhouse gas emission of coal-to-hydrogen is 2.1 times higher than that of the baseline system. The life cycle primary energy consumption and greenhouse gas emissions of the wind-to-hydrogen are the smallest at RH = 1, which are 85.14% and 87.64% lower than that of the baseline system respectively. Additionally, when the hydrogen price is lower than 20 CNY/kg, the total life cycle cost is lower than that of the baseline system.

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Section: Load Profiles Of Buildingmentioning

confidence: 99%

Life cycle assessment of a PEMFC-based distributed energy system for hotel application

Jiangyi

Xia

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Problem 2: Assuming that a nonlinear model is used in MPC, predictions ŷ(k + p|k) are nonlinear functions of the calculated decision vector (19), which means that the MPC-L 1 or MPC-L 2 cost function is nonlinear. As a result, a nonlinear optimisation task is obtained and it must be repeatedly solved at each sampling instant.…”

Section: Computationally Efficient Nonlinear Mpc Using the L 1 Cost-f...mentioning

confidence: 99%

“…Solution to problem 2: The J 1 (k) cost function after applying an analytical approximator is still nonlinear in terms of the computed control moves (19). An advanced trajectory linearisation method adopted from [35] is used to improve the derivation of a simple quadratic optimisation task from the nonlinear MPC-L 1 optimisation task.…”

Section: Computationally Efficient Nonlinear Mpc Using the L 1 Cost-f...mentioning

confidence: 99%

“…The source of nonlinearity is the model of the controlled process. Namely, the predictions of the future values of the controlled variable and hence the predicted control errors are nonlinear in terms of the calculated future control increments (19). In order to solve this problem, an advanced on-line trajectory linearisation is carried out.…”

Section: Advanced Trajectory Linearisation Of the Mpc-l 1 Cost-functionmentioning

confidence: 99%

“…In recent years, applications of MPC algorithms [16][17][18] to PEM fuel cells have been reported. A linear model can be used for MPC prediction [19]. However, typically, a nonlinear model is used, meaning that a nonlinear optimisation task must be solved at each sampling instant on-line [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

Nebeluk

Ławryńczuk

2022

Energies

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This work describes the development of a fast Model Predictive Control (MPC) algorithm for a Proton Exchange Membrane (PEM) fuel cell. The MPC cost-function used considers the sum of absolute values of predicted control errors (the L1 norm). Unlike previous approaches to nonlinear MPC-L1, in which quite complicated neural approximators have been used, two analytical approximators of the absolute value function are utilised. An advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is derived. All implementation details of the discussed algorithm are detailed for two considered approximators. Furthermore, the algorithm is thoroughly compared with the classical MPC-L2 method in which the sum of squared predicted control errors is minimised. A multi-criteria control quality assessment is performed as the MPC-L1 and MPC-L2 algorithms are compared using four control quality indicators. It is shown that the presented MPC-L1 scheme gives better results for the PEM.

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Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge‐ and Choke‐Constrained Awareness

Ye,

Zhong,

Shen

et al. 2024

Advcd Theory and Sims

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The efficiency and dynamic response of air compressors are crucial for stability and lifespan of hydrogen fuel cells. A predictive control scheme with surge‐ and choke‐constrained awareness is proposed to ensure safe and efficient operation of air compressors in this study. The proposed scheme consists of an efficiency enhancement model predictive control (EE‐MPC), and an improved active disturbance rejection control (IADRC). Surge‐ and choke‐constrained awareness is achieved by comparing predicted air flow with surge and choke limitations. Simultaneously, the EE‐MPC is constrained with oxygen excess ratio (OER) and obtains optimal solution by searching active set. The reference flow and supply manifold pressure trajectories for IADRC are generated by EE‐MPC. A designed piecewise differentiable nonlinear smoothing function is embedded in IADRC. The disturbances are estimated for coordinating flow and pressure control. Under China heavy‐duty commercial vehicle test cycle for bus conditions, root‐mean‐squared errors (RMSEs) of flow and pressure are 3.27 g s−1 and 1.88 × 103 Pa, respectively, and the mean efficiency can be enhanced by 13.4% compared to the MPC with fixed OER. Finally, a controller hardware‐in‐the‐loop test is conducted, with flow and pressure RMSEs of 2.48 g s−1 and 4.28 × 103 Pa between the test and simulation, respectively.

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Simulation study on the PEMFC oxygen starvation based on the coupling algorithm of model predictive control and PID

Cited by 65 publications

References 38 publications

Life cycle assessment of a PEMFC-based distributed energy system for hotel application

Life cycle assessment of a PEMFC-based distributed energy system for hotel application

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge‐ and Choke‐Constrained Awareness

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