Adaptive Energy Management Strategy of Fuel Cell Electric Vehicle

Yan, Sun; Xia, Changgao; Yin, Bifeng; Yu, Yingxiao; Jian, Han; Gao, Haiyu

doi:10.1007/s12239-022-0122-y

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…The reduction of hydrogen is achieved in fuel-celland supercapacitor-fed EVs [18]. The same sources used under the bee colony algorithm were tested with standard drive cycles, even though the motor selected for this system was a DC machine, and it is not suitable for EVs due to the risk of fire accidents [19]. A random decision forest (RDF) is mainly used to control the management of power.…”

Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

Mathesh,

Saravanakumar,

Salgotra

2024

Energies

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Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disconnect the input power to load. To ensure optimal performance and avoid overloading issues, the EV system needs sophisticated control. This work introduces a machine-learning-based controller using an artificial neural network to solve these problems. This paper describes the detailed power management control methodology using multiple sources like solar PV, fuel cells, and batteries. Novel control with an instantaneous reference current scheme is used to manage the input power sources to satisfy the power demand of electric vehicles. The proposed work executes the power split-up operation with standard and actual drive cycles and maximum power point tracking for PV panels using MATLAB Simulink. Finally, power management with a machine learning technique is implemented in an experimental analysis with the LabVIEW software, and an FPGA controller is used to control a 48 V, 1 kW permanent-magnet synchronous machine.

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Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

Mathesh,

Saravanakumar,

Salgotra

2024

Energies

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“…There are two main categories of fuel cell vehicle energy control strategies, rule based and optimization based [6][7][8]. The biggest advantage of rule-based energy control strategies is that they can achieve real-time control, including fuzzy control strategies, logic threshold control strategies, and sliding film control [9].…”

Section: Introductionmentioning

confidence: 99%

Dual Fuzzy Energy Control Study of Automotive Fuel Cell Hybrid Power System with Three Energy Sources

et al. 2022

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A dual fuzzy control strategy is proposed for the complex energy management problem in a multi-energy-source hybrid power system vehicle with fuel cell + Li-ion battery + super capacitor. According to the efficiency characteristic of each energy source in a fuel cell hybrid power system (referred to as FCHPS), a control scheme with a dual fuzzy control strategy is devised, in which the main fuzzy controller controls the fuel cell to ensure its power output. It is controlled by a sub-fuzzy controller that regulates the power output and braking energy recovery of the Li-ion battery and super capacitor. Simulation verification and comparative analysis were carried out under a world light vehicle test cycle (referred to as WLTC) conditions using MATLAB+ADVISOR. With a dual fuzzy control strategy, fuel cell hybrid vehicles meet dynamics requirements and the fuel economy of these vehicles is generally 6.7% and 6.4% better than power following control and single fuzzy control, respectively. Li-ion batteries are also capable of handling reduced average currents.

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Promising application of MXene-based materials in direct methanol fuel cells: A review

Ravichandran,

Liu

et al. 2024

Journal of Alloys and Compounds

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Adaptive Energy Management Strategy of Fuel Cell Electric Vehicle

Cited by 3 publications

References 33 publications

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

Dual Fuzzy Energy Control Study of Automotive Fuel Cell Hybrid Power System with Three Energy Sources

Promising application of MXene-based materials in direct methanol fuel cells: A review

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