Energy management and economic analysis for a fuel cell supercapacitor excavator

Li, Tianyu; Huang, Lan; Liu, Huiying

doi:10.1016/j.energy.2019.02.016

Cited by 56 publications

(27 citation statements)

References 36 publications

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“…Fathy et al [4] introduced a new approach based salp swarm algorithm (SSA) to optimally manage the energy between FCs, batteries, and SCs with considering hydrogen consumption as an objective function. Li et al [20] analyzed the energy management of a hybrid source of FC and SCs for supplying excavator based on three approaches of dynamic programming, model predictive control and minimum principle of Pontryagin considering hydrogen consumption as the objective function. Zhao et al [21] presented different metaheuristic optimization approaches for managing the energy of the fuel cell hybrid system for supplying aircraft.…”

Section: Introductionmentioning

confidence: 99%

Recent Coyote Algorithm-Based Energy Management Strategy for Enhancing Fuel Economy of Hybrid FC/Battery/SC System

2019

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An optimized energy management strategy (EMS) based on a recent coyote optimization algorithm (COA) applied to a hybrid electric power system is proposed in this paper. The proposed hybrid system comprises fuel cell (FC), battery storage bank (BSB) and supercapacitors (SCs). The FC has been selected to be the chief power source to meet the load demand at steady state. Whereas BSB is used as the chief energy buffer and to help the FC during deficit periods and SCs are employed to meet the transient maximum power. The performance of the hybrid electric power system mostly depends on how to distribute the demanded load through different kinds of power sources. Therefore, optimized EMS is highly required to do this job. The key objective of the proposed EMS is to reduce hydrogen consumption by the hybrid system and increase the durability of power sources. To investigate the superiority and validity of COA, a comparison with other approaches is carried based on minimum hydrogen consumption and high energy efficiency. Such methods include external energy maximization strategy (EEMS), particle swarm optimizer (PSO), genetic algorithm (GA), grey wolf optimizer (GWO), grasshopper optimization algorithm (GOA), multi-verse optimizer (MVO), salp swarm algorithm (SSA) and sunflower optimization (SFO). The obtained results confirmed the superiority of the proposed COA. Using COA reduced hydrogen consumption by 38.8% compared to the EEMS method. Based on the minimum hydrogen consumption, the strategies are ranked from the best as following; COA, GWO, SSA, GOA, MVO, GA, PSO, and EEMS.

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Section: Introductionmentioning

confidence: 99%

Recent Coyote Algorithm-Based Energy Management Strategy for Enhancing Fuel Economy of Hybrid FC/Battery/SC System

2019

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“…Depending on the applications they develop, many researchers have combined different control algorithms with the idea of maximizing optimization [165][166][167][168][169]. The characteristics of these techniques cannot be used individually, since each control algorithm besides its advantages also has a number of shortcomings that make it nonperforming in the energy optimization process.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies

et al. 2021

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With the development of technologies in recent decades and the imposition of international standards to reduce greenhouse gas emissions, car manufacturers have turned their attention to new technologies related to electric/hybrid vehicles and electric fuel cell vehicles. This paper focuses on electric fuel cell vehicles, which optimally combine the fuel cell system with hybrid energy storage systems, represented by batteries and ultracapacitors, to meet the dynamic power demand required by the electric motor and auxiliary systems. This paper compares the latest proposed topologies for fuel cell electric vehicles and reveals the new technologies and DC/DC converters involved to generate up-to-date information for researchers and developers interested in this specialized field. From a software point of view, the latest energy management strategies are analyzed and compared with the reference strategies, taking into account performance indicators such as energy efficiency, hydrogen consumption and degradation of the subsystems involved, which is the main challenge for car developers. The advantages and disadvantages of three types of strategies (rule-based strategies, optimization-based strategies and learning-based strategies) are discussed. Thus, future software developers can focus on new control algorithms in the area of artificial intelligence developed to meet the challenges posed by new technologies for autonomous vehicles.

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“…In [10], minimizing the hydrogen and fuel cell lifetime costs as the objective function is solved through stochastic dynamic programming (SDP). Three representative EMSs: DP, PMP, and MPC in [11] are developed to minimize hydrogen consumption and fuel cell durability. In [12] and [13], the fuel cell models are identified online to find the variation of fuel cell system performances and to operate the fuel cell in the best efficiency and power operating points through PMP.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Zhou

Gualous

et al. 2021

IEEE Trans. Ind. Inf.

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The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. A road, health, and price-conscious optimal cost minimization strategy based on decision making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of vehicles including the hydrogen cost and degradation costs of fuel cell and battery. Second, a decision making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate health states of power sources and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach optimal results on the overall cost and charge sustaining of battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed.

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Energy management and economic analysis for a fuel cell supercapacitor excavator

Cited by 56 publications

References 36 publications

Recent Coyote Algorithm-Based Energy Management Strategy for Enhancing Fuel Economy of Hybrid FC/Battery/SC System

Recent Coyote Algorithm-Based Energy Management Strategy for Enhancing Fuel Economy of Hybrid FC/Battery/SC System

Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

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