Computational Co-optimization of Fuel and Spark-Ignition Engine

Spark-ignition engine efficiency can be increased by co-optimizing fuel and engine. First, computational fuel design can optimize fuel molecules or composition using predictive fuel property models, e.g., for high octane numbers. Then, the engine configuration can be optimized experimentally to maximize the achievable efficiency. However, such a sequential co-optimization based on fuel properties may yield suboptimal fuels, as the fuel properties do not fully capture the complex fuel–engine interaction. Therefore, we propose the computational, simultaneous co-optimization of fuel and engine. To this end, we derive a thermodynamic engine model that predicts the engine performance as a function of fuel composition and engine configuration. We calibrate the engine model against experimental data from a single-cylinder research engine, such that new candidate fuels require no model recalibration with additional experimental engine data. As a case study, we select 10 possible alternative fuel components identified in previous studies and create 39 binary and 60 ternary fuel mixtures. The composition of each fuel mixture is then co-optimized together with the compression ratio and the intake pressure of the engine considering knock and peak pressure constraints to ensure smooth and safe engine operation. The study reveals the small esters methyl acetate and ethyl acetate as promising fuel candidates for future spark-ignition engines. For methyl-acetate-rich blends, the engine model predicts knock-free operation at compression ratios of up to 20 and boost pressures of up to 1.8 bar, rendering methyl acetate a promising alternative to methanol. Considering significant model uncertainties, however, the findings require experimental validation.