The application of advanced technologies for engine efficiency improvement and emissions reduction also increase the occurrence possibility of abnormal combustions such as incomplete combustion, misfire, knock or pre-ignition. Novel promising combustion modes, which are basically dominated by chemical reaction kinetics show a major difficulty in combustion control. The challenge in precise combustion control is hard to overcome by the traditional engine map-based control method because it cannot monitor the combustion state of each cycle, hence, real-time cycle-resolved in-cylinder combustion diagnosis and control are required. In the past, cylinder pressure and ion current sensors, as the two most commonly used sensors for in-cylinder combustion diagnosis and control, have enjoyed a seemingly competitive relationship, so all related researches only use one of the sensors. However, these two sensors have their own unique features. In this study, the idea is to combine the information obtained from both sensors. At first, two kinds of ion current detection system are comprehensively introduced and compared at the hardware level and signal level. The most promising variant (the DC-Power ion current detection system) is selected for the subsequent experiments. Then, the concept of ion current/cylinder pressure cooperative combustion diagnosis and control system is illustrated and implemented on the engine prototyping control unit. One application case of employing this system for homogenous charge compression ignition abnormal combustion control and its stability improvement is introduced. The results show that a combination of ion current and cylinder pressure signals can provide richer and also necessary information for combustion control. Finally, ion current and cylinder pressure signals are employed as inputs of artificial neural network (ANN) models for combustion prediction. The results show that the combustion prediction performance is better when the inputs are a combination of both signals, instead of using only one of them. This offline analysis proves the feasibility of using an ANN-based model whose inputs are a combination of ion current and pressure signals for better prediction accuracy.
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