Power Allocation Strategy Based on Decentralized Convex Optimization in Modular Fuel Cell Systems for Vehicular Applications

Khalatbarisoltani, Arash; Kandidayeni, Mohsen; Boulon, Loïc; Hu, Xiaosong

doi:10.1109/tvt.2020.3028089

Cited by 47 publications

(15 citation statements)

References 46 publications

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“…Similar to Equation (14), n is the number of the cells, F is the Faraday constant, m h is the molar mass of hydrogen. In addition, more attention was focused on the overall system efficiency rather than the efficiency of a single power converter or fuel cell battery, and the total efficiency could be defined as:…”

Section: Hydrogen Consumption and Overall Efficiencymentioning

confidence: 99%

“…There is no doubt that a well-designed EMS is indispensable for HEPS based on fuel cells, as an EMS can guarantee each power source operates with efficiency while meeting the load power requirements [7]. Nowadays, a host of EMSs for fuel-cell HEPS can be seen in the literature, especially in the fields of micro-grids (MG) [8,9], hybrid electrical vehicles (HEV) [10][11][12][13][14][15][16], electric ships [17,18], more electric aircraft (MEA) [19][20][21][22] and electrical propulsion aircraft and UAVs [23][24][25][26][27][28][29]. EMSs are represented by popular online control methods, such as rule-based control [19,[28][29][30], fuzzy-based control [9,23,[31][32][33][34], model predictive control [12,15,17,[35][36][37] and fuzzy logic model predictive control [13]; and other optimization-based methods, such as convex optimization [14], dynamic programming (DP) [24] and neural network (NN) methods [10].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, many studies concentrated more on the verification of a certain EMS for a fixed flight profile, while other evaluation criteria were left out of consideration. Some global optimization methods need a complex optimization model and solver, which can increase the computational burden of the EMS in question, and divergence of such algorithms is also a problem [13,14]. Although DRL and NNs can perform very well [10,18], real flight data sets are still needed.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

Lei

Jin

et al. 2022

Aerospace

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With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype（RCP）experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.

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Section: Hydrogen Consumption and Overall Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

Lei

Jin

et al. 2022

Aerospace

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show abstract

“…en, the parallel form is recommended when the problem scale is large. e auxiliary problem principle (APP), which was proposed by G. Cohen in 1980 [8], is an effective parallel distributed algorithm [9]. e iteration strategy of the auxiliary problem principle for solving (1) can be expressed as follows:…”

Section: Introductionmentioning

confidence: 99%

The Extension of Auxiliary Problem Principle for Solving Dynamic Economic Dispatch Problem in Power Systems

Ren

2022

Journal of Mathematics

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In this paper, we study the convex optimization problem with linear constraint, and the objective function is composed of m separable convex functions. Considering the special case where the objective function is composed of two separable convex functions, the auxiliary problem principle (APP) is an effective parallel distributed algorithm for solving the special case. Inspired by the principle of APP, a natural idea to solve separable convex optimization problem with m ≥ 3 is to extend the method of APP, resulting in the APP-like algorithm. The convergence of the APP-like algorithm is not clear yet. In this paper, we give a sufficient condition for the convergence of the APP-like algorithm. Specifically, the APP algorithm is a special case of the APP-like algorithm when m = 2. However, simulation results show that the convergence efficiency of the APP-like algorithm is affected by the selection of penalty parameter. Therefore, we propose an improved APP-like algorithm in this paper. Simulation results show that the improved APP-like algorithm is robust to the selection of penalty parameter and that the convergence efficiency of the improved APP-like algorithm is better when compared with the APP-like algorithm.

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“…In the heavy-duty HFCVs, the energy management strategy (EMS) unit is a crucial control part to reduce the total end-user costs and meet the powertrain components' requirements [2]. Several studies have been done in the literature on designing the EMSs for the HFCVs with an MFCS structure [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

Khalatbarisoltani¹,

Kandidayeni²,

Boulon³

et al. 2021

SAE Int. J. Elec. Veh.

Self Cite

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An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this paper puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack. The contribution of the suggested multi-agent approach lies in the development of a fully decentralized learning strategy composed of several connected local modules. The performance of the proposed approach is investigated through several simulations and experimental tests. The results indicate the advantage of the established Dec-RL control scheme in convergence speed and optimization criteria.

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Power Allocation Strategy Based on Decentralized Convex Optimization in Modular Fuel Cell Systems for Vehicular Applications

Cited by 47 publications

References 46 publications

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

The Extension of Auxiliary Problem Principle for Solving Dynamic Economic Dispatch Problem in Power Systems

A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

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