Developing Artificial Intelligence (AI) and Machine Learning (ML) Based Soft Sensors for In-Cylinder Predictions with a Real-Time Simulator and a Crank Angle Resolved Engine Model

Currently, there are no safe and suitable fuel sources with comparable power density to traditional combustible fuels capable of replacing Internal Combustion Engines (ICEs). For the foreseeable future, civilian and military systems are likely to be reliant on traditional combustible fuels. Hybridization of the vehicle powertrains is the most likely avenue which can reduce emissions, minimize system inefficiencies, and build more sustainable vehicle systems that support the United States Army modernization priorities. Vehicle systems may further be improved by the creation and implementation of artificial intelligence and machine learning (AI/ML) in the form of advanced predictive capabilities and more robust control policies. AI/ML requires numerous characterized and complete datasets, given the sensitive nature of military systems, such data is unlikely to be known or accessible limiting the reach to develop and deploy AI/ML to military systems. With the absence of data, AI/ML may still be developed and deployed to military systems if supported by near-real-time or real-time computationally efficient and effective hardware and software or cloud-based computing. In this research, an OPAL real-time (OPAL-RT) simulator was used to emulate a compression ignition (CI) engine simulation architecture capable of developing and deploying advanced AI/ML predictive algorithms. The simulation architecture could be used for developing online predictive capabilities required to maximize the effectiveness or efficiency of a vehicle. The architecture includes a real-time simulator (RTS), a host PC, and a secondary PC. The RTS simulates a crank angle resolved engine model which utilized pseudo engine dynamometer data in the form of multi-dimensional matrices to emulate quasi-steady state conditions of the engine. The host PC was used to monitor and control the engine while the secondary PC was used to train the AI/ML to predict the per-cylinder generated torque from the crank shaft torque, which was then used to predict the in-cylinder temperature and pressure. The results indicate that using minimal sensor data and pretrained predictive algorithms, in-cylinder characterizations for unobserved engine variables may be achievable, providing an approximate characterization of quasi-steady state in-cylinder conditions.