Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy

Shen, Ying; Cui, Pengfei; Wang, Xuechao; Han, Xuefeng; Wang, Yaxiong

doi:10.1016/j.ijhydene.2020.02.083

Cited by 42 publications

(13 citation statements)

References 50 publications

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“…An NN was used to learn the best SOC curve and realize the planning and control of the battery working state according to future driving information from the online ITS and navigation system. In Shen et al (2020), the efficiency and power change rate of the fuel cell were considered to balance the load of the power system, and the power slope of the fuel cell was limited to prevent the abrupt change. An incremental fuzzy logic EMS was proposed to ensure that the fuel cell was working in the high-efficiency range and to prolong its life.…”

Section: The Fuzzy Logic Rule-based Energy Management Strategymentioning

confidence: 99%

“…The EF was used as the intermediate variable to solve the global optimization sequence, and BPNN was then used to extract the optimal sequence and form an online EMS. In Han et al (2020), an RNN-based EMS was proposed. This strategy could adjust the ratio of energy consumption and battery loss in the EMS in real time.…”

Section: The Nn-based Energy Management Strategymentioning

confidence: 99%

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Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Zhu

Liu

et al. 2022

Mech. Sci.

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Abstract. In order to prevent the aggravation of global environmental problems, all industries are facing the challenge of green development. In the automotive field, the development of “new-energy vehicles” (plug-in electric vehicles) is particularly necessary. Hybrid electric vehicles (HEVs) have been proven to be an efficient way of solving environmental and energy problems. As the core of HEVs, the energy management strategy (EMS) plays an important role in fuel economy, power performance, and drivability. However, considering the randomness of actual driving conditions, there are great challenges involved in the establishment of an EMS. Therefore, it is critical to develop an efficient and adaptable EMS. This paper presents a systematic review of EMSs for HEVs. First, different issues that can affect the performance of EMSs are summarized. Second, recent studies on EMSs for HEVs are reviewed. Third, the advantages and disadvantages of different categories of EMSs are compared in detail. Finally, promising EMS research topics for future study are put forward.

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Section: The Fuzzy Logic Rule-based Energy Management Strategymentioning

confidence: 99%

Section: The Nn-based Energy Management Strategymentioning

confidence: 99%

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Zhu

Liu

et al. 2022

Mech. Sci.

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“…Rule-based strategies realize EMS goals according to the rules which are established based on the characteristics of the concerned powertrain and load. Among those proposed, fuzzy logic rule-based EMS has been demonstrated to be an effective one in a wide range of hybrid electric vehicles [2] [3]. Easy implementation and reliable performance make the rule-based EMS the most widely used strategy.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning based Energy Management for Fuel Cell Hybrid Electric Vehicles

Guo

Outbib

2021

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society

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In the paper, a self-learning energy management strategy is proposed for fuel cell hybrid electric vehicles (FCHEV). The studied energy system for FCHEV is composed of fuel cells and lithium batteries. A reinforcement learning (RL) based energy management strategy (EMS) for FCHEV is studied to achieve the power allocation of the two energy sources. The objective is to learn a satisfactory EMS from scratch and only through the interaction of environments. Specifically, Q-Learning, one of the RL methods, is applied to minimize fuel consumption and ensure battery sustainability. Compare with Dynamic Programming (DP), which can reach the best performance of sequential decision problems theoretically, Q-Learning based EMS can achieve results close to DP based EMS. During the process, different objective functions are optimized to be suitable for Q-Learning. Finally, the simulation results with python verify the effectiveness of the method proposed in this paper.

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“…Fuel cell vehicle (FCV) combines the benefits of fuel cell stack and ESS to achieve fuel economy and zero emission. The power system of FCV needs energy management strategy (EMS) to distribute energy between two sources, and to achieve the purpose of regulating power flow from FCS and ESS 20,21 …”

Section: Introductionmentioning

confidence: 99%

Optimization of fuzzy control energy management strategy for fuel cell vehicle power system using a multi‐islandgenetic algorithm

Zhao

Wang

et al. 2020

Energy Science & Engineering

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Energy storage system can be used to increase the fuel economy of fuel cell system (FCS). In this study, a new method was introduced for optimizing the energy management strategy (EMS) for fuel cell vehicle (FCV) to reduce fuel consumption. The membership function (MF) in fuzzy control is subjective; thus, 12 design variables in the input‐output MFs were selected using sensitivity analysis, and elliptical basis function neural network method was used to establish a high‐precision approximate model of FCV. Multi‐island genetic algorithm was used to optimize the MFs. The effectiveness of the optimized fuzzy control EMS and the proposed optimization method were demonstrated in simulations of two EMSs under four driving cycles. The simulation results confirmed that the optimized fuzzy control EMS provided smoother and more stable output power from FCS reducing hydrogen consumption by 8.4%, 1.1%, 5.1%, and 7.6%, respectively, compared to the original fuzzy control EMS; and hydrogen saved by the optimized EMS provided extra range of 9.15, 1.10, 5.37, and 8.25 km per 100 km in the four driving cycles, respectively. The optimized EMS can reduce hydrogen consumption to increase fuel economy and extend the life span of the fuel cell.

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Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy

Cited by 42 publications

References 50 publications

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Reinforcement Learning based Energy Management for Fuel Cell Hybrid Electric Vehicles

Optimization of fuzzy control energy management strategy for fuel cell vehicle power system using a multi‐islandgenetic algorithm

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