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The control of Homogeneous Charge Compression Ignition (HCCI) (also known as Controlled Auto Ignition (CAI)) has been a major research topic recently, since this type of combustion has the potential to be highly efficient and to produce low NOx and particulate matter emissions. Ion current has proven itself as a closed loop control feedback for SI engines. Based on previous work by the authors, ion current was acquired through HCCI operation too, with promising results. However, for best utilization of this feedback signal, advanced interpretation techniques such as artificial neural networks can be used. In this paper the use of these advanced techniques on experimental data is explored and discussed. The experiments are performed on a single cylinder cam-less (equipped with a Fully Variable Valve Timing (FVVT) system) research engine fueled with commercially available gasoline (95 ON).

Homogeneous charge compression ignition (HCCI), combustion has the potential to be highly efficient and to produce low NOx, carbon dioxide and particulate matter emissions, but experiences problems with cold start, running at idle and producing high power density. A solution to these is to operate the engine in a ‘hybrid mode’, where the engine operates in spark ignition mode at cold start, idle and high loads and HCCI mode elsewhere during the drive cycle, demanding a seamless transition between the two modes of combustion through spark assisted controlled auto ignition. Moreover; HCCI requires considerable control to maintain consistent start of combustion and heat release rate, which has thus far limited HCCI's practical application. In order to provide a suitable control method, a feedback signal is required.