期刊
ENERGIES
卷 14, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/en14010252
关键词
fuel cell electric vehicle; DC/DC converter topologies; energy management strategy; rule-based; global optimization; real-time optimization
资金
- Ministry of Education and Research, Romania within PNCDI III [PN 19 11 02 02, PN-III-P1-1.2-PCCDI-2017-0194/25 PCCDI]
This paper focuses on the latest topologies and energy management strategies for fuel cell electric vehicles, comparing the advantages and disadvantages of different control strategies and looking forward to new algorithms developed by software developers in the field of artificial intelligence to meet the challenges posed by new technologies.
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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