Simulation research on a novel control strategy for fuel cell extended-range vehicles

Geng, C.; Jin, Xiaofei; Zhang, Xin

doi:10.1016/j.ijhydene.2018.04.038

Cited by 75 publications

(28 citation statements)

References 13 publications

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“…Ref. [137] proposed an ON-OFF strategy for fuel cell extended-range vehicles and designed a fuzzy logic control strategy for power following when the fuel cell is ON. To avoid the load variation and mode switching effects caused by logic shifts, Refs.…”

Section: Energy Managementmentioning

confidence: 99%

Challenges and developments of automotive fuel cell hybrid power system and control

Gao

et al. 2019

Sci. China Inf. Sci.

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Fuel cells are emerging as promising power sources and have attracted increasing attention from industries and academics worldwide. In particular, automotive manufacturers are replacing internal combustion engines in vehicles with fuel cell systems, which are advantaged by zero emissions, high efficiency, and various clean routes that generate pure hydrogen. However, current fuel cell systems are costly, and their corresponding infrastructures are not fully qualified to meet current market demand. This paper reviews the challenges and developments of automotive fuel cell hybrid power systems and their controls. It briefly summarizes the model, control, and optimization issue associated with the research and application of fuel cells in hybrid power systems. After presenting the basic knowledge and discussing the trending size and structure of fuel cells for automotive usage, the review describes models of automotive fuel cell systems, focusing on the electrochemical reaction dynamics and the key parameters influencing their efficiency and lifetime. The control problems associated with automotive fuel cell systems as well as the optimization issue associated with hybrid energy systems (comprising fuel cells, batteries, and ultra-capacitors) are elaborately analyzed. The review concludes with current problems and challenges faced by the energy control systems of fuel cell vehicles.

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Section: Energy Managementmentioning

confidence: 99%

Challenges and developments of automotive fuel cell hybrid power system and control

Gao

et al. 2019

Sci. China Inf. Sci.

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“…To increase the power density and to meet the demand for load power, it is necessary to integrate an energy management system. The energy management strategy of FCEVs is based on many important control techniques [12] such as finite state machine management strategies [13], grey wolf optimizer [14], model predictive control [15,16], fuzzy logic control [17,18], genetic algorithms [19,20], hierarchical prediction [21,22] as well as other control techniques developed so far for the energy management system.…”

Section: Introductionmentioning

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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“…The dual power source (fuel cell and battery pack) can make the fuel cell hybrid electric vehicles (FCHEVs) produce a better power performance, but how to make the power source power distribution more reasonable and better improve the economy is a research difficulty. Based on previous experience, researchers developed rule-based energy management algorithms, such as thermostatic control strategy (TCS) [1] and a power following control strategy (PFCS) [2,3]. Fuzzy control strategy (FCS) [4][5][6] and fuzzy control strategy optimized by other algorithms [7] can adapt to the requirements of vehicle nonlinear control and effectively distribute the power between the power sources of fuel cell hybrid vehicles.…”

Section: Introductionmentioning

confidence: 99%

Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

et al. 2020

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Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.

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Simulation research on a novel control strategy for fuel cell extended-range vehicles

Cited by 75 publications

References 13 publications

Challenges and developments of automotive fuel cell hybrid power system and control

Challenges and developments of automotive fuel cell hybrid power system and control

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

Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

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