A Reciprocating Motion Control Strategy of Single-Cylinder Free-Piston Engine Generator

Hu, Yan; Xu, Zhaoping; Lu, Jinkang; Liu, Dong; Jiang, Xin

doi:10.3390/electronics9020245

Cited by 13 publications

(16 citation statements)

References 15 publications

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“…Compared to theoretical studies, there are more industrial ones using ML algorithms. This section will introduce application of ML in industrial studies related to combustion, including power plant, [41][42][43][44][45] engine, [46][47][48][49][50][51][52][53][54] fuel [55][56][57][58] and other fields. [59][60][61][62][63][64] To reach the goal of efficient and clean combustion in electric generation, ML was used to classify coals, [41] monitor combustion process [42] and treat emissions [43][44][45] in power plants.…”

Section: Industrial Researchmentioning

confidence: 99%

Progress in the Application of Machine Learning in Combustion Studies

Zheng¹,

Lin²,

Yang³

et al. 2020

ES Energy Environ.

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Combustion is the main source of energy and environmental pollution. The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions. In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field. In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed. Combustion studies combined with ML can be divided into theoretical and industrial aspects. Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel. ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation. ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation. ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields. ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants. In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines. Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters. The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.

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Section: Industrial Researchmentioning

confidence: 99%

Progress in the Application of Machine Learning in Combustion Studies

Zheng¹,

Lin²,

Yang³

et al. 2020

ES Energy Environ.

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“…The linear motor system is the most important component of the FPEG. It must meet some special requirements of the FPEG, such as low moving mass, fast response, easy controllability and high energy conversion efficiency [16,17]. A single-phase moving-coil linear motor with embedded axial magnetized magnets is designed in this paper.…”

Section: Linear Motor Designmentioning

confidence: 99%

“…The PWM signals are calculated based on the current feedback of the motor, and a discrete proportional integral differential (PID) algorithm is used in this paper. The free-piston engine model is created according to the difference equations described in [16]. The free-piston engine model is used to output the piston motion trajectory to drive the linear motor finite element model.…”

Section: Combined Simulation Modelmentioning

confidence: 99%

Electromagnetic Loss Analysis of a Linear Motor System Designed for a Free-Piston Engine Generator

Yang

et al. 2020

Electronics

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A free-piston engine generator is a new type of power generating device, which has the advantages of high efficiency and simple structure. In this paper, a linear motor system composed of a moving-coil linear motor with axial magnetized magnets and a H-bridge pulse-width modulation (PWM) rectifier is designed for portable free-piston engine generators. Based on the finite-element model of the motor and physical model of the rectifier, the combined electromagnetic model is presented and then validated by the prototype-tested results. The electromagnetic processes of the linear motor system are simulated. The electromagnetic losses during the standard working cycle are analyzed. Under the rated reciprocating frequency of 50 Hz and the rated reciprocating stroke of 36 mm, the mechanical-to-electrical energy conversion efficiency of 86.3% can be obtained by the linear motor system, which meets the requirement of portable free-piston engine generators.Electronics 2020, 9, 621 2 of 11 mover assembly moves down from the TTC to BTC driving by the combustion pressure.During the stable running, the linear motor works in generating mode all the time. The electromotive force (EMF) in the coil of the motor is quasi-sine wave, and the generating current in the coil is controlled as standard sine wave by the rectifier, and the electrical energy will be stored in the battery in the form of direct current. During the starting of the device, the linear motor must work briefly in motoring mode to start the motion of the piston-mover assembly.

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“…A free-piston engine generation system (FPEG) recovers the combustion energy of the engine with a linear generator [10][11][12][13][14][15][16]. This system requires a highly efficient linear generator design, considering that almost the same combustion energy not randomly but repeatedly acts on the generator.…”

Section: Introductionmentioning

confidence: 99%

Generator Design Considering Mover Action to Improve Energy Conversion Efficiency in a Free-Piston Engine Generator

et al. 2021

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In a free-piston engine generator (FPEG), the power of the engine can be directly regenerated by linear generators without a crank. The mover motion of this system is interrelated with engine and power generation efficiencies due to the direct connection between the mover of the generator and the piston of the engine. The generator should be designed to improve the overall energy conversion efficiency. The dimensions and mass of the mover limit its operating stroke and drive frequency. Herein, we propose a method for designing linear generators and constructing FPEG systems, considering the mover operation to improve engine efficiency. We evaluated the effect of mover operation on the engine and generation efficiencies using thermal and electromagnetic field analysis software. The proposed design method improves the overall energy conversion efficiency compared with a generator that considers only the maximization of generation efficiency. Setting the mover operation for higher engine efficiency and designing a linear generator to realize the operation can effectively improve the energy conversion efficiency of FPEGs.

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A Reciprocating Motion Control Strategy of Single-Cylinder Free-Piston Engine Generator

Cited by 13 publications

References 15 publications

Progress in the Application of Machine Learning in Combustion Studies

Progress in the Application of Machine Learning in Combustion Studies

Electromagnetic Loss Analysis of a Linear Motor System Designed for a Free-Piston Engine Generator

Generator Design Considering Mover Action to Improve Energy Conversion Efficiency in a Free-Piston Engine Generator

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