Performance optimization of fuel cell hybrid power robot based on power demand prediction and model evaluation

Lü, Xueqin; Deng, Ruiyu; Chen, Chao; Wu, Yinbo; Meng, Ruidong; Long, Liyuan

doi:10.1016/j.apenergy.2022.119087

Cited by 14 publications

(3 citation statements)

References 22 publications

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“…There were many raw data, and after the initial processing of the data, the features were ranked by random forest, and the final features were selected. Lu et al [88] studied the system energy optimization of a fuel cell hybrid power robot using comprehensive performance evaluation and random forest prediction methods. With improved power stability and reduced hydrogen consumption, this control method was ideally suited for applications in hybrid welding robots.…”

Section: Random Forestmentioning

confidence: 99%

Application of Machine Learning in Fuel Cell Research

Zheng

et al. 2023

Energies

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A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.

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Section: Random Forestmentioning

confidence: 99%

Application of Machine Learning in Fuel Cell Research

Zheng

et al. 2023

Energies

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“…The system energy optimization was investigated by X. Lü and colleagues using a thorough performance assessment and random forest prediction approach in order to enhance the stability, real-time performance, and economy of the PEMFC hybrid welding robot system [111]. The optimal control strategy was built on the basis of rule partition, using the entropy weight technique and the cloud model comprehensive performance testing procedure; the random forest prediction method was implemented in the energy management system, and the model parameters with the least mean square error were determined using particle swarm optimization, and the robot's load power was estimated.…”

Section: Random Forestmentioning

confidence: 99%

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

et al. 2022

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In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.

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“…Xiaomi's air charging technology, in particular, represents a significant advancement by facilitating long-distance charging for multiple devices simultaneously within a radius of several meters. In the robotics sector, WPT markedly enhances robots' operational range and working hours, ensuring uninterrupted task execution and effectively addressing the constraints imposed by tethered charging solutions [18,19]. In the transportation industry, amid an ongoing energy transition, electric locomotives are swiftly supplanting conventional transportation methods.…”

Section: Introductionmentioning

confidence: 99%

Research on Wireless Power Transfer Method for Intelligent Sensing Device of Non-Directly Buried Distribution Cables

He,

Zhang,

Zhou

et al. 2024

Electronics

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This paper presents a study on the impact of circuit parameters on the transmission of electrical energy in wireless power transfer systems designed for intelligent sensing devices within the urban electric power Internet of Things (IoT). Relying on the essential principles of resonant mutual inductance models, the paper conducts an analytical investigation into the phenomena of power-frequency splitting characteristics, efficiency-frequency splitting characteristics, and efficacy synchronization characteristics within wireless energy transmission technologies. The investigation includes a detailed analysis of a wireless power transfer system model operating at 100 kHz, delineating how varying circuit parameters influence the system’s efficiency. Via the utilization of graphical software and computational programming for simulation modeling, this research delved into the dynamics between key parameters such as equivalent load and coupling coefficient and their influence on distinct splitting phenomena. This rigorous approach substantiated the validity of the proposed power-frequency and efficiency-frequency splitting characteristics outlined in the study. Based on the analytical results, it is shown that selecting an appropriate equivalent load or utilizing impedance matching networks to adjust the equivalent load to a suitable size is crucial in consideration of the system’s output power, voltage withstand level, and transmission efficiency. The research findings provide a theoretical basis for the design of wireless power supply systems for non-directly buried cable front-end sensing devices.

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Performance optimization of fuel cell hybrid power robot based on power demand prediction and model evaluation

Cited by 14 publications

References 22 publications

Application of Machine Learning in Fuel Cell Research

Application of Machine Learning in Fuel Cell Research

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

Research on Wireless Power Transfer Method for Intelligent Sensing Device of Non-Directly Buried Distribution Cables

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