Increment-oriented online power distribution strategy for multi-stack proton exchange membrane fuel cell systems aimed at collaborative performance enhancement

Li, Xin; Shang, Zhiyu; Peng, Fei; Li, Liwei; Zhao, Yuanzhe; Liu, Zhixiang

doi:10.1016/j.jpowsour.2021.230512

Cited by 54 publications

(10 citation statements)

References 32 publications

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“…[43][44][45] Along these lines, designing and nding stable and highly active catalysts are considered as two main targets of the green chemistry objectives. [46][47][48][49][50] Recently, there has been an outstanding attention to chemical processes involving heterogeneous catalysts with extraordinary activity, high surface area to volume and good recyclability. 51,52 Heterogenized catalytic complexes are new organic-inorganic hybrid compounds prepared from metallic ions and organic linkers, bonded to the surface of inorganic insoluble nanomaterials.…”

Section: Introductionmentioning

confidence: 99%

Nanomagnetic macrocyclic Schiff-base–Mn(ii) complex: an efficient heterogeneous catalyst for click approach synthesis of novel β-substitued-1,2,3-triazoles

Jasim

Riadi

Majdi³

et al. 2022

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Nanomagnetic macrocyclic Schiff-base–Mn(ii) complex: an efficient heterogeneous catalyst for click approach synthesis of novel β-substitued-1,2,3-triazoles

Jasim

Riadi

Majdi³

et al. 2022

RSC Adv.

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“…According to formula (21), ω 0 usually has four roots, but since the intercept frequency can only be a positive value, not a complex value, the only relevant solution of formula ( 21) can be:…”

Section: Intercept Frequency Measurementbased Temperature Estimationmentioning

confidence: 99%

“…[17][18][19] Effective early temperature prediction in the initial stage is the key to avoid thermal runaway accidents. 20,21 At present, the main method adopted by battery management system is to install temperature sensors on the battery surface and estimate the average temperature of the battery by using the temperature measured by multiple sensors. However, due to the problems of battery polarization and heat conduction, this method cannot meet the early and sensitive requirements.…”

Section: Introductionmentioning

confidence: 99%

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Wang

Duan

et al. 2022

Intl J of Energy Research

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Summary With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights Use EIS to quickly and effectively predict the internal temperature changes of LIBs. No hardware temperature sensors and thermal model are required. The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed.

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“…Deep learning methods can extract complex feature information from the input data in layers by building a deep neural network with multiple layers of nonlinear transformations. 86,110 Some deep learning methods can handle time-series data considering the time dependence of SOC estimation. Training data and training methods have a significant impact on the accuracy of SOC prediction.…”

Section: Summarization Of Past Studiesmentioning

confidence: 99%

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

211

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Implementing carbon neutrality and emission peak policies requires a highlevel electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.

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Increment-oriented online power distribution strategy for multi-stack proton exchange membrane fuel cell systems aimed at collaborative performance enhancement

Cited by 54 publications

References 32 publications

Nanomagnetic macrocyclic Schiff-base–Mn(ii) complex: an efficient heterogeneous catalyst for click approach synthesis of novel β-substitued-1,2,3-triazoles

Nanomagnetic macrocyclic Schiff-base–Mn(ii) complex: an efficient heterogeneous catalyst for click approach synthesis of novel β-substitued-1,2,3-triazoles

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

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