Internal combustion engines (ICEs) remain central to global transportation and power generation, yet their thermodynamic irreversibilities impose fundamental limits on achievable efficiency. This study presents a comprehensive thermodynamic analysis and multi-objective optimization framework for ICEs, integrating first- and second-law methodologies with particle swarm optimization (PSO) and genetic algorithm (GA) techniques. A validated thermodynamic cycle model encompassing Otto, Diesel, and Dual cycles is employed to characterize the influence of compression ratio, equivalence ratio, and heat release rate on indicated thermal efficiency and exergy destruction. Experimental validation is conducted on a single-cylinder, four-stroke research engine instrumented with high-resolution in-cylinder pressure sensors and thermal mapping arrays. Results demonstrate that optimally selected compression ratios in the range of 9.5–13.5 yield thermal efficiency improvements of 8.3–14.7% relative to the baseline configuration, while combustion irreversibility remains the dominant exergy loss mechanism, accounting for 38.3–48.2% of total fuel exergy input depending on load. The proposed optimization framework reduces brake-specific fuel consumption by 11.6% and exhaust exergy losses by 9.4% simultaneously without violating emissions constraints. These findings provide actionable design guidelines for next-generation ICE platforms targeting improved fuel economy and reduced carbon intensity.

Keywords: Internal combustion engine; thermodynamic optimization; exergy analysis; compression ratio; particle swarm optimization; heat release rate; second-law efficiency