Battery Optimal Sizing Under a Synergistic Framework With DQN-Based Power Managements for the Fuel Cell Hybrid Powertrain

Li, Shuangqi; Wang, Hanxiao; He, Haibo; Wei, Zhongbao; Igić, Petar

doi:10.1109/tte.2021.3074792

Cited by 56 publications

(8 citation statements)

References 57 publications

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“…Consequently, numerous investigations have explored an alternative optimization objective: extending the lifespan of the power system [76]. The categorization of EMSs is illustrated in Figure 15.…”

Section: Energy Management Systemsmentioning

confidence: 99%

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

Ragab,

Marei,

Mokhtar

2023

Applied Sciences

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The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, and/or ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discusses various types of EMSs such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and mutative fuzzy logic controller (MFLC). It also discusses a systematic categorization of FCHEV topologies and delves into the unique characteristics of these topologies. Furthermore, it provides an in-depth comparative study of EMSs applied in FCHEVs, encompassing rule-based, optimization-based, and advanced learning-based approaches. However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This review aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as state of charge (SOC) and FC deterioration. This paper seeks a deeper comprehension of the recent advancements in EMSs for FCHEVs. It offers insights that can facilitate a more comprehensive grasp of the current state of research in this field, aiding researchers in staying up to date with the latest developments.

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Section: Energy Management Systemsmentioning

confidence: 99%

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

Ragab,

Marei,

Mokhtar

2023

Applied Sciences

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“…However, Hofstetter et al [58] add tailpipe NOx emissions as a constraint to the optimization problem. For a Fuel Cell -PHEV hybrid powertrain, Li et al [175] propose a framework for achieving optimal battery sizing parameters with minimal operation cost and component degradation.…”

Section: • a Huge Body Of Research Was Encountered Related Tomentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

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“…The RL-based method involves ongoing interactions between agents and their surroundings whilst the agent gradually formulates control rules that converge toward an optimal control strategy through the iterative process [32]. The most representative RL approach implemented in EMS is called the Q-learning method, where a Q-table should be well-trained based on sufficient previous data [33]. Consequently, the precision of the data model and the real-time efficiency are still crucial factors in this methodology, although it has been improved as the Deep Q-network method.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Control-Based Approach for Real-Time Energy Management of the Fuel Cell Electrical Bus Considering the Durability of the Fuel Cell System

Du,

Zhao,

Liu

et al. 2024

WEVJ

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The present study proposes a fuzzy logical control-based real-time energy management strategy (EMS) for a fuel cell electrical bus (FCEB), taking into account the durability of the fuel cell system (FCS), in order to enhance both the vehicle’s economic performance and the FCS’s service life. At first, the model of the FCEB is established whilst the power-following strategy is also formulated as a benchmark for the evaluation of the proposed strategy. Subsequently, a fuzzy logical controller is designed to improve the work efficiency of the FCS, in which the battery state-of-charge (SOC) and the vehicle’s desired power are considered the inputs, whilst the power of the FCS is the output. Then, a limitation method is integrated into the fuzzy logical controller to restrict the change rate of the FCS’s power to strengthen the FCS’s service life. At last, the evaluation is accessed based on the China city bus driving cycle (CCBC). The results indicate that the proposed fuzzy logical strategy can satisfy the dynamic performance of the FCEB well. Importantly, it also has a remarkable effectiveness in terms of promoting the FCEB’s economy. Despite a slight reduction in contrast to the fuzzy logical control, the improvements of the strategy in which the FCS’s durability is considered are still acceptable. The change rate of the FCS’s power can be confined to ±10 kW. Meanwhile, the promotion of economic performance can reach up to 8.43%, 7.69%, and 6.53% in the proposed durability consideration strategy in contrast to the power-following strategy under different battery SOCs. This will significantly benefit both the energy saving and the FCS’s durability.

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Battery Optimal Sizing Under a Synergistic Framework With DQN-Based Power Managements for the Fuel Cell Hybrid Powertrain

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 56 publications

References 57 publications

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

A Fuzzy Logic Control-Based Approach for Real-Time Energy Management of the Fuel Cell Electrical Bus Considering the Durability of the Fuel Cell System

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