Hybrid LSTM+1DCNN Approach to Forecasting Torque Internal Combustion Engines

Innovative solutions are now being researched to manage the ever-increasing amount of data required to optimize the performance of internal combustion engines. Machine learning approaches have shown to be a valuable tool for signal prediction due to their real-time and cost-effective deployment. Among them, the architecture consisting of long short-term memory (LSTM) and one-dimensional convolutional neural networks (1DCNNs) has emerged as a highly promising and effective option to replace physical sensors. This architecture combines the capacity of LSTM to detect patterns and relationships in smaller segments of a signal with the ability of 1DCNNs to detect patterns and relationships in larger segments of a signal. The purpose of this work is to assess the feasibility of substituting a physical device dedicated to calculating the torque supplied by a spark-ignition engine. The suggested architecture was trained and tested using signals from the field during a test campaign conducted under transient operating conditions. The results reveal that LSTM + 1DCNN is particularly well suited for signal prediction with considerable variability. It constantly outperforms other architectures used for comparison, with average error percentages of less than 2%, proving the architecture's ability to replace physical sensors.