A comparative study of power allocation strategies used in fuel cell and ultracapacitor hybrid systems

Wang, Yujie; Sun, Zhen‐Dong; Li, Xiyun; Yang, Xiaoyu; Chen, Zonghai

doi:10.1016/j.energy.2019.116142

Cited by 62 publications

(24 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, battery-UC-based HESS's main issues are discussed in [23] that consider aging mechanism, state estimation, and lifetime prediction. A comparative analysis for FC-UC-based HESS is done in [24] to choose the optimal power allocation strategy from the PID-based and rule-based approach [24]. The DP algorithm and a semi-physical experimental platform are included to verify the two strategies' effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

et al. 2021

View full text Add to dashboard Cite

Choice of hybrid electric vehicles (HEVs) in transportation systems is becoming more prominent for optimized energy consumption. HEVs are attaining tremendous appreciation due to their ecofriendly performance and assistance in smart grid notion. The variation of energy storage systems in HEV (such as batteries, supercapacitors or ultracapacitors, fuel cells, and so on) with numerous control strategies create variation in HEV types. Therefore, choosing an appropriate control strategy for HEV applications becomes complicated. This paper reflects a comprehensive review of the imperative information of energy storage systems related to HEVs and procurable optimization topologies based on various control strategies and vehicle technologies. The research work classifies different control strategies considering four configurations: fuel cell-battery, battery-ultracapacitor, fuel cell-ultracapacitor, and battery-fuel cellultracapacitor. Relative analysis among different control techniques is carried out based on the control aspects and operating conditions to illustrate these techniques' pros and cons. A parametric comparison and a crosscomparison are provided for different hybrid configurations to present a comparative study based on dynamic performance, battery lifetime, energy efficiency, fuel consumption, emission, robustness, and so on. The study also analyzes the experimental platform, the amelioration of driving cycles, mathematical models of each control technique to demonstrate the reliability in practical applications. The presented recapitulation is believed to be a reliable base for the researchers, policymakers, and influencers who continuously develop HEVs with energy-efficient control strategies.

show abstract

Section: Introductionmentioning

confidence: 99%

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Their review indicates that energy management strategies such as rule-based, fuzzy logic, Equivalent Consumption Minimisation Strategy and waveletbased load sharing are the main EMS streams for hybrid fuel cell road vehicles. Wang et al [7] developed a rule-based on-line energy management strategy for hybrid fuel cell and ultracapacitor systems. Although near-optimal fuel economy has been achieved by their rule-based strategy in the two load profiles under investigation, the performance of the strategy in other profiles which can vary significantly is not clear.…”

Section: Introductionmentioning

confidence: 99%

Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Partridge

Bucknall

2020

Applied Energy

View full text Add to dashboard Cite

Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H 2 replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

show abstract

“…In recent years, many results on energy management strategies (EMSs) for FCHEVs have been obtained. In general, the proposed strategies can be categorized into two main approaches: rule-based strategy [9]- [11] and optimization-based strategy [12], [13]. Rule-based energy strategies are developed based on simple rules or maps extracted from optimized algorithms like dynamic programming algorithm [14].…”

Section: Introductionmentioning

confidence: 99%

“…Rule-based energy strategies are developed based on simple rules or maps extracted from optimized algorithms like dynamic programming algorithm [14]. In [9]- [11], in order to improve the FCHEV fuel economy, rule-based EMSs are proposed to maximize the output net power by employing the charge and discharge limits of power capabilities and residual capacity of the battery and ultracapacitor. For the optimization-based strategy, the main idea lies in minimizing a pre-defined cost function within feasible constraints such as convex optimization [15], model-predictive control [16], equivalent consumption minimization strategy (ECMS) [17] and other optimization algorithms [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Frequency Decoupling-Based Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Vehicle Using Fuzzy Control Method

Tao

Zhu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In order to extend fuel cell lifespan and improve fuel economy of electrical hybrid vehicle with fuel cell/battery/ultracapacitor (FCHEV), a frequency decoupling-based energy management strategy (EMS) for FCHEV using fuzzy control method is proposed. In detail, firstly, according to different characteristics of energy sources, required power of FCHEV is decomposed into three frequency ranges based on Harr wavelet transform and an adaptive-fuzzy filter. Secondly, based on the proposed frequency decoupling, the obtained three frequency required power is supplied by fuel cell-battery and ultracapacitor, respectively, which can guarantee power performance of vehicle and reduce pressure and power fluctuation on fuel cell and battery. Thirdly, for improving fuel economy, one fuzzy controller is proposed to split the power between fuel cell and battery. Finally, the proposed strategy in this paper is verified by advisor-simulink and experimental bench. Simulation and experimental results show that the proposed EMS can effectively reduce impact of power fluctuations on fuel cell, extend its lifespan and reduce fuel consumption on 7.94% compared to equivalent consumption minimization strategy.INDEX TERMS Fuel cell electric vehicle, energy management strategy, fuzzy control, frequency decoupling, fuel economy.

show abstract

A comparative study of power allocation strategies used in fuel cell and ultracapacitor hybrid systems

Cited by 62 publications

References 24 publications

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Frequency Decoupling-Based Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Vehicle Using Fuzzy Control Method

Contact Info

Product

Resources

About