Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

Yuan, Haibo; Zou, Wen-Jiang; Jung, Seunghun; Kim, Youngbae

doi:10.1016/j.ijhydene.2021.12.121

Cited by 67 publications

(13 citation statements)

References 40 publications

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“…The energy management strategy (EMS) has a big impact on FC longevity, battery charge maintenance, and fuel usage. Hai-BoYuan et al proposed a GA-based optimized rule-based EMS for efficient power allocation between the FC and the battery system [98]. Control variables in real-time rule-based EMS are optimized to maintain battery charge while taking FC durability and efficiency into account.…”

Section: Genetic Algorithmmentioning

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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Section: Genetic Algorithmmentioning

confidence: 99%

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

et al. 2022

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“…2. The mathematical model and specifications of a PEMFC model is demonstrated in our previous work [24].…”

Section: B Pemfc Systemmentioning

confidence: 99%

A Real-Time Rule-Based Energy Management Strategy With Multi-Objective Optimization for a Fuel Cell Hybrid Electric Vehicle

et al. 2022

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Energy management strategy (EMS) has a great impact on securing fuel cell durability, battery charge sustenance, and fuel saving in fuel cell hybrid electric vehicles (FCHEVs). This study aims to develop EMS that can be applied in real-time to satisfy above conditions. Real time power separation was performed using rule-based EMS. A genetic algorithm (GA) was implemented to calculate the optimal battery charge/discharge criterion that simultaneously satisfies the minimum hydrogen consumption rate, battery charge rate preservation, and high fuel cell efficiency. The battery charge/discharge parameter values vary according to driving patterns, and in this paper, typical suburban, urban, and highway driving conditions are considered. For the real-time application of this research method, the effectiveness was demonstrated by applying the driving conditions of unknown patterns. The effect on the initial battery SOC on EMS was analyzed, and in order to verify the superiority of this method, it was compared and analyzed with EMS results using dynamic programming and fuzzy logic under the same driving cycles. The effectiveness of this research method was verified through simulation, and it was confirmed through experiments for real-time application. Since there is a limit to the experiment using an actual fuel cell vehicle, the experiment was performed using a fuel cell and battery. This method can be applied to real fuel cell vehicles in the same way.INDEX TERMS Battery charge sustenance, energy management, fuel cell hybrid electric vehicle, hydrogen consumption, multi-objective GA.

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“…The state of charge (SOC) of the power cell is an important parameter for energy management. The SOC value at each moment can be obtained by the following euqation: (2) where SOC(t) represents the SOC value of the power battery at the current time t; SOC (0) represents the SOC value of the power battery at the initial time; Q represents the capacity of the power battery [12].…”

Section: Power Battery Modelmentioning

confidence: 99%

“…The new individuals obtained after crossover at k points are as follows: (12) The difference between multi-point crossing and one-point crossing is that multiple crossing points need to be randomly selected, for example, k1,...,kz∈(2,3,…k-1), when z is an odd number, increase the crossing position k =l. After the parent individuals A and B are crossed at the z point, the generated offspring individuals can be obtained by Equation ( 13).…”

Section: Crossover Operatormentioning

confidence: 99%

Optimization of energy management strategy for fuel cell vehicles based on hybrid genetic algorithm

Zhang

Li²

2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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Genetic algorithm is an algorithm that searches for the optimal solution by simulating the natural evolutionary process. The development of fuel cell vehicles is of great significance to the traditional automobile industry as well as the energy industry, and is an important initiative to protect the environment and conserve resources. However, fuel cell vehicles still face the problem of insufficient range. One solution is to form a multi-energy source system by combining fuel cells with secondary charging auxiliary power or other power sources. Therefore, to give full play to the advantages of hybrid power, it is necessary to solve the energy management problems brought about by hybrid power. In this study, the applicability of energy management strategy optimization for fuel cell vehicles is analyzed with the help of hybrid genetic algorithms.

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Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

Cited by 67 publications

References 40 publications

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

A Real-Time Rule-Based Energy Management Strategy With Multi-Objective Optimization for a Fuel Cell Hybrid Electric Vehicle

Optimization of energy management strategy for fuel cell vehicles based on hybrid genetic algorithm

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