As a carrier of hydrogen, ammonia has great potential in reducing pollution and carbon. This paper investigates the impact of diesel compression ignition on the combustion and emission performance of a natural gas/ammonia mixture RCCI engine through numerical simulation. The energy fraction of ammonia is set from 0% to 100% in the study to evaluate the feasibility of ammonia application in heavy‐duty natural gas/diesel RCCI engines. A natural gas/n‐heptane/ammonia reaction mechanism that includes 89 species and 611 reactions was innovatively established, which can accurately simulate the ignition process. The results indicate that under all engine load conditions, as the ammonia energy fraction increases, the cylinder pressure and thermal efficiency of the engine decrease, and the power performance gradually weakens. Under high load conditions, when ammonia is added to premixed natural gas, as the ammonia energy fraction increases, the characteristic frequency near the resonance mode gradually disappears, which is beneficial for reducing the knock trend and ringing intensity of engines. However, due to the increase in incomplete combustion increase, the ammonia energy fraction should not be too large at high loads.

Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications.