CPU-FPGA based real-time simulation of fuel cell electric vehicle

Ma, Rui; Liu, Chen; Zheng, Zhixue; Gechter, Franck; Briois, Pascal; Gao, Fei

doi:10.1016/j.enconman.2018.08.099

Cited by 31 publications

(8 citation statements)

References 34 publications

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“…It should be noted that the modelling of fuel cells and battery are not within the scope of this paper, and they are replaced by two ideal voltage sources. This is feasible in the frame of multi‐rate simulation if the fuel cell model and battery model need to be interfaced to the developed power converter model [13].…”

Section: Partitioning‐based Real‐time Modellingmentioning

confidence: 99%

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Device‐level modelling and FPGA‐based real‐time simulation of the power electronic system in fuel cell electric vehicle

Bai

Liu

et al. 2019

IET Power Electronics

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Power electronic converters are essential components in the fuel cell electric vehicle (FCEV) powertrain, which precisely control the power output of the fuel cell stack, the charging and discharging process of the battery, and the operation of the traction motor. The hardware-in-the-loop (HIL) simulation plays an important role in the rapid validation of power converters in their early development stages. In this study, a device-level model of the power converters in FCEV powertrain is developed for the FPGA-based real-time simulation. By fulfilling the circuit topology partitioning and the switch model partitioning, the FCEV power electronic system can be simulated with sub-microsecond time-step while producing the detailed switching waveforms with 5 ns resolution. Therefore, the device electrical stress, and the switching power losses can be evaluated in real time. Furthermore, the electro-thermal converter model is developed accordingly, which makes it possible to evaluate the IGBT/ Diode thermal behaviour in the HIL simulation. In this study, the FPGA hardware is designed with the NI LABVIEW FPGA module and implemented on the NI-7975R FLEXRIO FPGA board. The accuracy of the FPGA-based simulation results is evaluated by the results from offline simulation tools. The effectiveness of the proposed model is then validated by the controller HIL experiments.

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Section: Partitioning‐based Real‐time Modellingmentioning

confidence: 99%

“…Considering the background of electric vehicles and FCEV, the real-time simulation of the power converters has been studied in [11][12][13][14]. However, the ideal switch model is commonly used where no power losses can be simulated [11][12][13]. Herrera et al [14] have proposed an electro-thermal model of the power converter.…”

Section: Introductionmentioning

confidence: 99%

Device‐level modelling and FPGA‐based real‐time simulation of the power electronic system in fuel cell electric vehicle

Bai

Liu

et al. 2019

IET Power Electronics

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“…In this paper, we extend the application of the approximate I/O linearization that can be applied for MP/NMP stable processes into the field‐programmable gate array (FPGA) hardware as a stand‐alone control device. The FPGA has been used for various real‐time applications, for example, movement control of autonomous mobile robots, soft sensors for calculating the confidence level of iron‐melting cupola furnaces, engine control units for motorcycle, the model predictive control hardware, and aircraft control systems with a linear model, and the co‐simulation of the fuel cell electric vehicle . An approximate I/O linearizing feedback is formulated and then truncated Taylor series expansion.…”

Section: Introductionmentioning

confidence: 99%

Input/output linearizing controller with Taylor series expansion for a nonminimum phase process by hardware‐in‐the‐loop approach

Panjapornpon

Kajornrungsilp

Rochpuang

2020

Asia-Pacific J Chem Eng

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Input/output (I/O) linearization is a model-based control method that receives much attention for many research studies in chemical industries. Despite many advances in control theory, the I/O linearization is not widespread for a real-time implementation when compared with other model-based techniques. It requires differentiation and inversion of the process model-which becomes cumbersome as the complexity of the model increases. Thus, this work presents the development and experiment of an embedded I/O feedback control device with a pilot process that emulates an exothermic reactor with nonminimum phase behavior. Algorithms of an approximate I/O linearization control system is truncated by using Taylor series expansion, and it is embedded into a field-programmable gate array control device. The effectiveness of the embedded approximate I/O controller is compared with a digital proportional-integral (PI) controller for a test with the pilot process with a hardwarein-the-loop concept. The results show that the embedded model-based controller provides high performance for the servo problem and disturbance rejection, compared with the PI controller. The proposed embedded model-based controller shows the application to apply the advance control method into the field-programmable gate array hardware as a stand-alone control device, and it can perform the computation of a complex algorithm.

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“…Modeling of components of hybrid power generation system and work styles of different control algorithms for the implementation of this system are described in the literature (Anoune et al, 2018; Bizon, 2014; Das et al, 2017; Rekioua, 2014). Real-time studies are mentioned less than the studies in simulation environment in the literature (Ma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The control of energy production system created from fuel cell and different solar panels via microcontroller

Karakan

Oğuz

Uslu

2019

Transactions of the Institute of Measurement and Control

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In the study, the energy that monocrystalline, polycrystalline and thin-film solar panels of equal power produce was combined on the electronic board and hybrid solar system was created. Fuel cell was added to this hybrid system, so providing diversity in energy production sources. The produced electrical energy flow was controlled by using different energy sources types together. Battery groups were connected to the control circuit for storing the produced energy. Also, the receiver loads, which would redirect the flow energy were added to the control circuit. The electrical energy each solar panel produced, the electric current the load pulled and battery charge-discharge conditions were monitored through the control system designed to control the flow energy. These data were recorded at regular intervals with C# software. The control algorithms that are appropriate for system’s energy flow were determined and consumers were fed with the electrical energy produced by the hybrid system, and the battery was charged at the same time. The recorded data were analyzed and the energy each solar panel produced, the rates of benefiting from produced electricity through applied algorithms, and how much consumers were fed with the energy accumulated on the battery group were approximately determined.

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CPU-FPGA based real-time simulation of fuel cell electric vehicle

Cited by 31 publications

References 34 publications

Device‐level modelling and FPGA‐based real‐time simulation of the power electronic system in fuel cell electric vehicle

Device‐level modelling and FPGA‐based real‐time simulation of the power electronic system in fuel cell electric vehicle

Input/output linearizing controller with Taylor series expansion for a nonminimum phase process by hardware‐in‐the‐loop approach

The control of energy production system created from fuel cell and different solar panels via microcontroller

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