Abstract
Opposed-piston free-piston engine generator (FPEG), as an energy conversion device, has attracted the attention of researchers with its advantages of variable compression ratio (CR) and good dynamic balance performance. At the same time, the variable compression ratio poses a challenge to the stable operation of the engine. The controller needs to overcome the interference caused by the combustion variations to the piston movement, so that the compression ratio of the engine remains stable. This paper proposes an opposed-piston synchronous motion control strategy based on master-slave position following and a compression ratio control strategy based on artificial neural networks. A test prototype and simulation model were established, and the model was verified by the prototype. The performance of the control strategy was studied through simulation analysis. The results showed that the engine achieved stable operation and the compression ratio was well controlled. Compared with the reported control strategies in the literature, the artificial neural network algorithm applied in free-piston engine generator system shows better control accuracy and good response characteristics.
Highlights
Free-Piston Engine Generator (FPEG) is an energy conversion device with simple structure and high thermal efficiency, which can be used as range extender for electric vehicles or portable power generation device [1]
Compared to traditional internal combustion engines, FPEG eliminates the crank-link mechanism, and the free-piston is directly connected to the linear electric machine (LEM) [2]
The controller adjusts the power generation current of the LEM online according to the control strategy, which can realize the stable control of the piston dead center position
Summary
Free-Piston Engine Generator (FPEG) is an energy conversion device with simple structure and high thermal efficiency, which can be used as range extender for electric vehicles or portable power generation device [1]. J. Lu et al.: CR Control of an Opposed-Piston FPEG Based on Artificial Neural Networks research on piston motion control strategies based on the prototype. The controller adjusts the power generation current of the LEM online according to the control strategy, which can realize the stable control of the piston dead center position. The control strategy was given fixed parameters through offline testing, and FPEG can only run with a specific trajectory or stroke length When environmental factors such as temperature and pressure change, the engine CR will be disturbed. As shown in Fig., the algorithm takes the OTC position and the ITC target position as input, and passes the neurons to obtain the output value of the generated current in the compression stroke. Where β is the learning rate, and α is the momentum factor, which can avoid oscillation during the weight update process
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