Transfer Learning Based Prediction of Knock Intensity in a Hybrid Dedicated Engine Using Higher-Octane Gasoline for Thermal Efficiency Improvement

Abstract

Higher-octane gasoline has the potential to improve engine thermal efficiency via suppressing knock but requires re-calibration, which optimizes control parameters with the constraint of knock intensity. To improve engine efficiency during the re-calibration with limited experimental data, this paper proposed a new modelling approach for knock intensity prediction, termed as expertise-oriented adaptive transfer learning. Different from conventional data-driven modelling, which utilizes a linear sampling strategy to acquire training samples for a non-transfer neural network, this approach utilizes an expertise-oriented sampling strategy to acquire representative training samples for a domain adaptive neural network. Two gasoline fuels with different octane numbers were tested in a hybrid dedicated engine, where the knock intensity under swept spark advances was measured. A data-driven model was established through the proposed modelling approach to predict knock intensity, with the constraint of which the engine control parameters were optimized. A significant domain discrepancy of the dataset was found, which made transfer learning based on fine-tuning produce negative transfer, while the transfer learning based on the domain adaptative neural network produced positive transfer. The proposed methodology reduced the prediction error of knock intensity by 50.5% compared with the conventional methodology. Increasing the research octane number from 93.1 to 98.0 increased the engine efficiency by 2.0%, from 36.9% to 38.9%, where 0.3% benefited from the lower prediction error of knock intensity by the proposed model.

Keywords: Internal combustion engine, Fuel octane number, Knock intensity, Transfer learning, Domain adaptation, Sampling strategy