Real-Time Implementation of Maximum Net Power Strategy Based on Sliding Mode Variable Structure Control for Proton-Exchange Membrane Fuel Cell System

Li, Qi; Yang, Wei; Yin, Liangzhen; Chen, Weirong

doi:10.1109/tte.2020.2970835

Cited by 77 publications

(20 citation statements)

References 39 publications

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“…Nevertheless, once the concurrent identification of several operating points is in demand, like in an online EMS, the complexity of these algorithms also increases. There are also some other techniques, such as hierarchical technique (multi-objective optimization and data-driven constraint predictive control) [18], sliding mode variable structure [19], and systemic management of FC current and temperature in [20], to provide efficient and stable operation of the FC system. While these methods have shown robust and satisfactory results, they are dependent on a wide range of experimental maps to be used as an optimal reference.…”

Section: B Literature Studymentioning

confidence: 99%

Adaptive Parameter Identification of a Fuel Cell System for Health-Conscious Energy Management Applications

Kandidayeni

Chaoui

Boulon

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Since a proton exchange membrane (PEM) fuel cell (FC) has time-varying characteristics, its online characteristics estimation (voltage, power, internal resistance, etc.) is becoming a key step in designing an energy management strategy (EMS) for hybrid FC vehicles. In this respect, this paper proposes a new method based on Lyapunov adaptation law to estimate the linear and nonlinear parameters of a renowned PEMFC model in the literature. Unlike most of similar estimators, the suggested approach determines the maximum current, which is a nonlinear parameter, online while guaranteeing the system closed-loop stability. This parameter is normally assumed to be constant while it changes through time owing to degradation and operating conditions variation. This alteration makes the model imprecise while extracting some important characteristics, such as maximum power and polarization curve. Therefore, it needs to be regularly updated along with other parameters. To demonstrate the capability of the suggested method, a detailed comparison is provided with the well-known extended Kalman filter (EKF) as an attested nonlinear estimator. Moreover, to highlight the effectiveness of the nonlinearity consideration, a comparison with KF is performed where the nonlinear parameter is considered constant. The performed experiments on a 500-W PEMFC show that the proposed method can be over twice as accurate as EKF and KF concerning the estimation of maximum power and current while its runtime is nearly half of them. Index Terms-Kalman filter, health assessment, Lyapunov stability, online modeling, fuel cell I. INTRODUCTION A. General context YBRID fuel cell (FC) vehicles are considered as one the most promising technical solutions in the battle to confront the climate change crisis [1]. The performance of this multiple energy source system highly depends on the design of an

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Section: B Literature Studymentioning

confidence: 99%

Adaptive Parameter Identification of a Fuel Cell System for Health-Conscious Energy Management Applications

Kandidayeni

Chaoui

Boulon

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…Proton Exchange Membrane Fuel Cell (PEMFC) has many excellent properties such as rapid startup, high power density, environmentally friendly, and low working temperature [1][2][3]. It is considered a promising power conversion device in the areas of aerospace, vehicle transportations, portable devices, distributed generation, and so on [4][5][6]. Nevertheless, expensive cost and limited lifetime are two large obstacles for their extensive commercial application [7].…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of PEMFC systems under dynamic operating conditions

Hua

Zheng

Pahon

et al. 2021

Energy Conversion and Management

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The Prognostic and Health Management (PHM) has been developed for more than two decades. It is capable to anticipate the impending failures and make decisions in advance to extend the lifespan of the target systems, such as Proton Exchange Membrane Fuel Cell (PEMFC) systems. Prognostic is a critical stage of PHM. Among various prognostic methods, the data-driven ones could predict the system lifespan based on the device's knowledge and historical data. In the Remaining Useful Life (RUL) prediction, the Health Indicators (HIs) should be able to reflect the health states of the PEMFC stack. Moreover, an effective HI could help to define an explicit degradation state and improve the prediction accuracy. The HIs of voltage and power are usually used under static conditions due to their monotonic decreasing characteristics. Besides, the measurements of voltage and current are implemented easily in practice. Nevertheless, the static HIs are unable to be directly used under the dynamic operating conditions because they are sensitive to the mission profiles. To overcome the weakness of static HIs, a convenient and practical HI named Relative Power-loss Rate (RPLR) is proposed herein. According to the polarization curve at the beginning of life, the initial power under different mission profiles can be identified. Then the actual power is obtained by monitoring the current and voltage continuously. Finally, the RPLR is calculated based on the initial power and actual power. Afterward, the RUL of PEMFC is predicted by some Artificial Intelligence (AI) prognostic algorithms. Among the various data-driven prognostic approaches, Echo State Network (ESN) has provided an efficient and promising solution for the RUL prediction of PEMFC systems. Compared with classical Recurrent Neural Network (RNN), it could accelerate the convergence rate and reduce the computational complexity. Nevertheless, the traditionally used single-input ESN structure is feeble to handle the varying mission profiles. As a scheduling variable, the current is an interesting parameter since it represents the working properties to some extent. Considering the system's dynamic characteristics, the stack current is regarded as another input of ESN, and the output matrix's dimension is increased at the same time. Therefore, a double-input ESN structure is proposed to enhance the prediction performance. Based on the dynamic HI of RPLR, three dynamic micro-cogeneration (μ-CHP) durability tests of PEMFC systems are used to verify the improved ESN prediction structure.

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“…The proton exchange membrane fuel cell (PEMFC), which runs on hydrogen, is the most widely studied fuel cell type. PEMFCs have several advantages, including short start-up times, high specific power, long average lifetimes and low working temperatures, which together render this type of fuel cell a potentially suitable candidate for automobile power generation [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Distributed deep reinforcement learning for optimal voltage control of PEMFC

2021

IET Renewable Power Gen

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Proton exchange membrane fuel cells (PEMFCs) are promising components in the renewable energy field due to their high energy efficiency and low pollution output. However, these cells are also characterized by considerable nonlinearity, which in turn adversely affects the PEMFC output voltage. Conventional control algorithms cannot guarantee sufficient output voltage control, as they lack the robust adaptive ability required for adapting to these fluctuations in PEMFCs. In this paper, an optimal output voltage controller based on distributed deep reinforcement learning, which controls the output voltage by regulating the fuel input of the PEMFC, is proposed. In addition, an ensemble intelligence exploration multi-delay deep deterministic policy gradient (EIM-DDPG) algorithm is proposed for this controller. An ensemble intelligence exploration policy plus a classification experience replay mechanism are included within the EIM-DDPG algorithm to improve the exploration ability of the algorithm and thus increase the robustness and adaptive capability of the controller. As a result, the model-free optimal output voltage controller offers high robustness and adaptability. The simulation results in this paper demonstrate that the proposed optimal controller can realize the effective control of PEMFC output voltage.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Real-Time Implementation of Maximum Net Power Strategy Based on Sliding Mode Variable Structure Control for Proton-Exchange Membrane Fuel Cell System

Cited by 77 publications

References 39 publications

Adaptive Parameter Identification of a Fuel Cell System for Health-Conscious Energy Management Applications

Adaptive Parameter Identification of a Fuel Cell System for Health-Conscious Energy Management Applications

Remaining useful life prediction of PEMFC systems under dynamic operating conditions

Distributed deep reinforcement learning for optimal voltage control of PEMFC

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