Distributed Maximum Power Extraction From Fuel Cell Stack Arrays Using Dedicated Power Converters in Series and Parallel Configuration

Somaiah, Boddu; Agarwal, Vivek

doi:10.1109/tec.2016.2557803

Cited by 53 publications

(14 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [12], four FCs are connected to the DC bus through DC-DC converters to reach higher output voltage while utilizing a maximum power (MP) point tracking tool for each stack. In [13], a hierarchical control method, composed of a control and a management layer, is proposed for a multiple FC system.…”

Section: Introductionmentioning

confidence: 99%

Online Health-Conscious Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell Vehicle Based on Game Theory

Ghaderi¹,

Kandidayeni²,

Soleymani³

et al. 2022

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FC-HEV with three FCs and a battery pack. GT is a wellapproved method for characterizing the interactions in multiagent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rulebased strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rulebased EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system.

show abstract

Section: Introductionmentioning

confidence: 99%

Online Health-Conscious Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell Vehicle Based on Game Theory

Ghaderi¹,

Kandidayeni²,

Soleymani³

et al. 2022

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…However, GWO has low accuracy and slow convergence. Somaiah and Agarwal [20] used phase-shifted converters as dedicated power converter for obtaining the maximum power from FC stack. An approach based on chicken swarm optimizer (CSO) has been used for simulating MPPT incorporated with FC installed with electric vehicles [21].…”

Section: Introductionmentioning

confidence: 99%

Recent Approach of Forensic-Based Investigation Algorithm for Optimizing Fractional Order PID-Based MPPT With Proton Exchange Membrane Fuel Cell

2021

View full text Add to dashboard Cite

The output power of the fuel cell (FC) is mainly depending on the membrane water content, temperature, the hydrogen and oxygen partial pressures. The polarization curve has one global maximum power to be tracked. Therefore, a robust maximum power point tracking (MPPT) is highly required to follow the optimal operating point under any operating conditions. In this article, a recent approach of forensic-based investigation (FBI) algorithm is proposed to identify the optimal parameters of fractional order PID-based MPPT with proton exchange membrane (PEM) fuel cell. FBI is selected due to its high accuracy and requirement of less computational efforts. The considered objective function to be minimized is the error between the voltage at maximum power (V MP ) and the actual one at FC terminals. To prove the robustness of the proposed methodology, four cases of operating conditions are analyzed which are constant membrane water content and temperature, constant membrane water content with variable temperature, variable membrane water content with constant temperature, and variable membrane water content with variable temperature. The obtained results are compared to other approaches such as incremental resistance (INCR), particle swarm optimizer (PSO), invasive weed optimizer (IWO), sin-cosine algorithm (SCA), and artificial ecosystem optimizer (AEO). In case (1), the proposed FBI-FOPID succeeded in achieving maximum power of 5185.101 W. While the minimum objective functions in the second, third, and fourth cases are 2.5736 V, 1.4436 V, and 1.1568 V respectively obtained via the proposed approach. The comparison confirmed the superiority of the proposed FBI-based MPPT compared with other methods.INDEX TERMS Fractional PID, MPPT, optimization, proton exchange membrane fuel cell.

show abstract

“…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%

“…9) where 𝑃𝑃 𝑚𝑚[𝑘𝑘] denotes the generated powers of the FC modules at time 𝑘𝑘. 𝑃𝑃 𝑚𝑚[𝑘𝑘] must take into account (2.a) and (2.b).…”

mentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Distributed Maximum Power Extraction From Fuel Cell Stack Arrays Using Dedicated Power Converters in Series and Parallel Configuration

Cited by 53 publications

References 20 publications

Online Health-Conscious Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell Vehicle Based on Game Theory

Online Health-Conscious Energy Management Strategy for a Hybrid Multi-Stack Fuel Cell Vehicle Based on Game Theory

Recent Approach of Forensic-Based Investigation Algorithm for Optimizing Fractional Order PID-Based MPPT With Proton Exchange Membrane Fuel Cell

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

Contact Info

Product

Resources

About