Zhi. (2015) Model-based intelligence multi-objective globally optimization for HCCI engines. IEEE Transactions on Vehicular Technology, 64 (9). pp. 4326-4331.
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I. INTRODUCTIONn order to improve the engine performance, more and more new technologies have been applied to modern engines, such as Variable Valve Timing (VVT), Gasoline Direct Injection (GDI), Exhaust Gas Recirculation (EGR), multiple injection, etc. As a result, the number of adjustable engine settings which can affect engine performance are becoming considerable. Moreover, due to the increasingly stringent emissions' regulations and the fierce competition between automotive manufactures, the number of performance references (Particulate Matter (PM) emissions, unregulated emissions, fuel consumption, noise, comfortability etc.) for modern engines are increasing [1]. With the increasing complexity of engines, and the combinatorial explosion of the parameter space, the traditional engine calibration approach is thus becoming more complex, expensive and time consuming. Vehicle manufactures have to spend more money and time on the engine optimization process [2]. Most calibrated set-points are trade-offs made within the limited time of engine testing, rather than globally optimization, to allow acceptable engine performance over a wider variety of operating conditions. It is desired to have an automated and intelligent engine globally optimization approach to replace the traditional manual optimization approach [3]...