Approximate Cost-Optimal Energy Management of Hydrogen Electric Multiple Unit Trains Using Double Q-Learning Algorithm

Li, Qi; Meng, Xiang; Gao, Fei; Zhang, Guorui; Chen, Weirong

doi:10.1109/tie.2021.3113021

Cited by 78 publications

(23 citation statements)

References 31 publications

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“…HIL experiment and on-road test show that the overall efficiency reaches 52% under the proposed strategy, which gains 8% higher than that of the state machine. To ensure long-endurance, an improved double Q learning for the energy management of Hybrid UAVs is proposed [79], [100].…”

Section: Intelligent-based Methodsmentioning

confidence: 99%

A Comprehensive Review on Fuel Cell UAV Key Technologies: Propulsion System, Management Strategy, and Design Procedure

Huangfu

et al. 2022

IEEE Trans. Transp. Electrific.

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Unmanned aerial vehicles (UAVs) can be regarded as one of the most emerging technologies. Compared with piloted aerial vehicles, UAVs qualify for higher safety and lower cost, and they are more suitable for dangerous missions. Recently, fuel cell UAVs have obtained more and more attention due to their relatively higher energy density. However, their propulsion system still needs to be further improved to meet different requirements of performing difficult tasks. This article aims to give a comprehensive review of the key technologies of fuel cell UAVs. Firstly, various architectures of the propulsion system and corresponding advantages and disadvantages are introduced. Then, three kinds of management strategies for fuel cell UAVs are reviewed. Next, some special applications and considered design procedures are summarized. Compared with other review papers, this paper tries to cover more aspects of fuel cell UAVs. Since these aspects have a tight relationship with each other, giving a comprehensive review can be more beneficial to us to understand the state-of-the-art development of propulsion systems for fuel cell UAVs. The conclusions show that advanced propulsion architectures, efficient online energy management strategies, and professional design procedures are still in urgent demand in the commercial process of fuel cell UAVs.

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Section: Intelligent-based Methodsmentioning

confidence: 99%

A Comprehensive Review on Fuel Cell UAV Key Technologies: Propulsion System, Management Strategy, and Design Procedure

Huangfu

et al. 2022

IEEE Trans. Transp. Electrific.

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“…A chemical reaction under certain circumstances is the governor of the fuel cells' operation [4,5]. The construction of fuel cells includes an electrode, a cathode and an electrolyte.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

“…Vehicles’ manufacturers and heating companies have revealed the technology and aimed to benefit from its constructive side. A chemical reaction under certain circumstances is the governor of the fuel cells’ operation [4, 5]. The construction of fuel cells includes an electrode, a cathode and an electrolyte.…”

Section: Introductionmentioning

confidence: 99%

“…They can be integrated into the power grid as a smart micro‐grid, as well [1–3]. Nevertheless, owing to their stochastic nature and the need for support during peak hours, energy storage equipment and backup sources (such as batteries, fuel cells, and virtual plants) are needed [4, 5]. Thus, fuel cells can play a vital role in such forthcoming power grids.…”

Section: Introductionmentioning

confidence: 99%

“…This paper will identify and examine the application of different optimisation algorithms for estimating the parameters of PEM-FCs models and consider evidence for evaluating whether algorithms address this problem and give an accurate model. the need for support during peak hours, energy storage equipment and backup sources (such as batteries, fuel cells, and virtual plants) are needed [4,5]. Thus, fuel cells can play a vital role in such forthcoming power grids.…”

Section: Introductionmentioning

confidence: 99%

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An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Diab

Abdul-Ghaffar²,

Ahmed

et al. 2021

IET Renewable Power Gen

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This paper introduces the application of the variants of the Grey Wolf Optimisation algorithm for the sake of assessing unknown parameters of Proton Exchange Membrane Fuel Cells models. Three versions of Grey Wolf Optimisation algorithm are applied: Conventional Grey Wolf Optimisation, Improved Grey Wolf Optimisation based on dimension learning-based hunting, and Selective Opposition-based Grey Wolf Optimisation. Moreover, Optimisation algorithms of Ant Lion Optimiser, Atom search optimisation, Dragonfly algorithm, and Multi-Verse Optimiser have been applied to estimate the parameters of Proton Exchange Membrane Fuel Cells models for the validation purpose. These algorithms are applied to three commercial Proton Exchange Membrane Fuel Cells stacks: BCS 500W-PEM, 500W-SR-12PEM and 250W-stack. The analyses are executed considering several operational circumstances. Sum of square errors value of the results based on parameters estimation and those experimentally tested are calculated. The objective function is chosen as sum of square errors value. The results are compared with those obtained using well-known methods in the literature to validate the effectiveness of the proposed methods. It is noticeable that the simulated I-V curves momentously match the datasheet curves for all the studied cases. In addition, considering the accuracy of the solution and the convergence speed, the Proton Exchange Membrane Fuel Cells model based on the Improved Grey Wolf Optimisation algorithm excels all other algorithms. Based on the simulation results, the Improved Grey Wolf Optimisation algorithm can improve the optimisation efficiency to 99.97 for the 250 W stack while the efficiency with Grey Wolf Optimisation and Selective Opposition-based Grey Wolf Optimisation are 98.83 and 98.84, respectively, for the 250 W stack case study.

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Methods for estimating the accumulated nitrogen concentration in anode of proton exchange membrane fuel cell stacks based on back propagation neural network

Chen

et al. 2022

Intl J of Energy Research

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Excellent performance is an important indicator to ensure the commercial development of fuel cells. During the operation of a fuel cell system, nitrogen accumulation will occur at the anode of the fuel cell stack because of hydrogen circulation and nitrogen permeation across the membrane. Nitrogen accumulation will lead to the degradation of fuel cell performance. A suitable purge strategy can effectively reduce the excessive nitrogen concentration. The formulation of the purge strategy is closely related to the change in nitrogen concentration, so the accurate estimation of nitrogen concentration is quite important. In this paper, the effect of nitrogen concentration on fuel cell performance under different current densities is studied, and the anode nitrogen concentration in the fuel cell stack is estimated by back propagation neural network. The identification result of nitrogen concentration based on the voltage of every single cell is more accurate. Without considering aging and working condition changes, mean absolute errors of estimated results are 0.75%, 0.67%, 0.62%, and 0.73%, and root mean square errors are 1.09%, 0.97%, 0.88%, and 0.99% at different current densities of 0.6, 0.8, 1.0, and 1.2 AÁcm À2 , respectively. The results indicate that the estimation method of nitrogen concentration for fuel cell anode based on back propagation neural network has a high accuracy, which can provide a new method for formulating purge strategy for fuel cell system.

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Approximate Cost-Optimal Energy Management of Hydrogen Electric Multiple Unit Trains Using Double Q-Learning Algorithm

Cited by 78 publications

References 31 publications

A Comprehensive Review on Fuel Cell UAV Key Technologies: Propulsion System, Management Strategy, and Design Procedure

A Comprehensive Review on Fuel Cell UAV Key Technologies: Propulsion System, Management Strategy, and Design Procedure

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Methods for estimating the accumulated nitrogen concentration in anode of proton exchange membrane fuel cell stacks based on back propagation neural network

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