Compression Ratio Control of an Opposed-Piston Free-Piston Engine Generator Based on Artificial Neural Networks

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.