An optimized energy management strategy for fuel cell hybrid power system based on maximum efficiency range identification

Wang, Tianhong; Li, Qi; Wang, Xiaotong; Qiu, Yibin; Li, Meng; Meng, Xiang; Li, Jincheng; Chen, Weirong

doi:10.1016/j.jpowsour.2019.227333

Cited by 85 publications

(22 citation statements)

References 40 publications

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“…Wang et al 55 think about the hydrogen consumption and to increase the efficiency and reliability of the fuel cell. Wang, Li et al 56 propose a research on maximum efficiency range (MER) for a battery/fuel cell “sightseeing car” with the goal of reducing the hydrogen consumption and to ensure an optimal power distribution between battery and the fuel cell system.…”

Section: Methodsmentioning

confidence: 99%

“…Several types of classifications of energy management strategies have been suggested in the revised literature considering the criteria of taxonomy, advantages and disadvantages, the natural‐inspired algorithm used, performance obtained, etc. In the following sections, we built two classifications: the first is based on the type of algorithm, and the second is based on the goal they seek to optimize. Rule‐based strategies Fuzzy control strategy 42,76,80,82 State machine control strategy 84 Classical PI control strategy 37,43,47,48,52‐55,58,60,65,67,69,79 Power prediction 24,30,47,87,89,91 Unscented Kalman filter 61 Optimisation‐based strategies Pontryagin's minimum principle (PMP) 39,64 Quadratic programming (QPo) 38,56,86 Stochastic dynamic programming (SDP) 71,92 Multi‐mode predictive 68,90 Dynamic particle swarm optimization 28,83 Equivalent consumption minimization strategy (ECMS) 31,38,41,98 Dynamic programming (DP) 25,59,64 Genetic algorithm (GA) 40,43,76 Efficiency optimization strategy 27,29,36,57,85,93,96 Learning‐based strategies Reinforcement learning 33,62,74 Hybrid 32,46,49,63,66,70,72,73,75,77,78,81,88,95,97 …”

Section: Classification Of Strategiesmentioning

confidence: 99%

“…Compared to the problems and strategies raised in the previews 77 references [24–97, 100–102], critically commented by the authors in their work using tools as tables, diagrams, and figures, the actual problems and requests, identified from the reviewed literature from 2019 and 2020, evolved with the trends, and they are more specific and harder to archive.…”

Section: Novelty Of the Subjectmentioning

confidence: 99%

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Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

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Summary The object of this research came from the continuing need to analyze and identify effective methods of reducing pollution and optimizing the functions of electric vehicles. So, a classic analysis of the management strategies proposed in the literature was performed to identify the main research directions that can be used to design an optimal strategy. The performed analysis was validated using different counting and representation methods in VOSviewer and Excel. VOSviewer is a program that visually presents the best research approaches for understanding and developing applications. Thus, analyzing the results obtained, it is possible to identify where the research of energy management strategies for electric vehicles is focused. For example, the most important factors for increasing the performance of a strategy are identified as related to optimization, efficiency, degradation, fuel economy, and systemic development. We identified 127 energy management strategies in the studied articles, which were analyzed and classified by multiple criteria. For example, 21.59% of the strategies have as main goal to increase the vehicles efficiency, followed by the 10% on fuel economy, 7.8% hydrogen consumption strategy, 6.29% work for performance optimization and emission reduction, and so on. So, using this tool, researchers can explore their field of research to identify challenging topics to address in the coming years.

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Section: Methodsmentioning

confidence: 99%

Section: Classification Of Strategiesmentioning

confidence: 99%

Section: Novelty Of the Subjectmentioning

confidence: 99%

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Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

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“…In [25], adaptive RLS is used to extract the ME and MP points of a FC system in an electric tram based on which the safe operating zone for the proposed EMS is updated. In [26], forgetting factor RLS combined with sequential QP is used to identify the maximum efficiency range for an ECMS. In [27], Ettihir et al have used RLS to extract the parameters of a current-dependent FC model, suggested by Squadrito et al [28], and in [29], unscented Kalman filter (KF) is used for updating the same model.…”

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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“…However, the perturbation signal is not simple to select during the cold startup of the PEMFC as it can increase the heating time and decrease the overall performance. The second solution is based on the use of an online parameter estimator coupled with an optimization algorithm [42,43]. In this respect, the estimator updates the parameters of the mode online and then the required characteristics, such as maximum power, is extracted from the updated model.…”

Section: Introductionmentioning

confidence: 99%

An Online Self Cold Startup Methodology for PEM Fuel Cells in Vehicular Applications

Amamou

Kandidayeni

Kélouwani

et al. 2020

IEEE Trans. Veh. Technol.

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This paper puts forward an adaptive cold start strategy for a proton exchange membrane fuel cell (PEMFC) based on maximum power mode. The proposed strategy consists of a water evacuation process after PEMFC shutdown and a selfheating process at PEMFC cold startup. To maximize the performance of the suggested strategy, an optimal operating condition for the cold start procedure is sought first. In this respect, an experimental parametric study is performed to explore the impact of fan velocity, micro-short circuit, anode pressure, and purge procedure on the PEMFC cold start performance. After laying down the proper conditions, the proposed cold start procedure is implemented on a test bench for experimental validations. The self-heating process is based on an online adaptive algorithm that maximizes the PEMFC's internal heat depending on its operating parameters' variation. In fact, this algorithm attempts to keep the current density at high levels, leading to PEMFC's performance improvement achieved by membrane hydration and temperature increase. The experimental results confirm the effectiveness of the proposed strategy, which presents a fast and cost-effective PEMFC's cold start.

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An optimized energy management strategy for fuel cell hybrid power system based on maximum efficiency range identification

Cited by 85 publications

References 40 publications

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

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

An Online Self Cold Startup Methodology for PEM Fuel Cells in Vehicular Applications

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