Model-Based Multiobjective Evolutionary Algorithm Optimization for HCCI Engines

Abstract

Zhi. (2015) Model-based intelligence multi-objective globally optimization for HCCI engines. IEEE Transactions on Vehicular Technology, 64 (9). pp. 4326-4331. Permanent WRAP URL:http://wrap.warwick.ac.uk/79180 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:"© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. 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]...

Innovative Calibration Method for System Level Simulation Models of Internal Combustion Engines

“…Additionally, calibration loop 2 covered 720 simulations at part-load operating points whereas calibration loop 3 covered 320 simulations. (13), (19) and (20)) are presented ( Table 1).…”

“…where is given by Equation (19) in [29] (p. 2898). Scheme of the gas flow transfer element with the finite length consisting of the orifice followed by the element with length [29].…”

“…FrMp was identified as a CBP of the air cleaner, intercooler and EGR cooler for the pressure drop calibration according to Equation (20), whereas the orifice flow coefficient ( ) was identified for the catalyst according to Equation (13). In addition, HTMp was selected as a CBP for tuning of the temperature difference across the intercooler and EGR cooler according to Equation (19). A flowchart of the evaluation procedure of the CBPs is presented in Figure 6, covering the required input data (step s1) and workflow of the CBPs' evaluation (step s2).…”

“…It has already been shown that optimization methods are suitable for determining optimized configurations of the components, e.g., manifold geometry [6], valve lift profiles and timings [7][8][9][10][11][12], shape of the injection rate [13], exhaust gas recirculation (EGR) rates and multiple injections [14], injection timings [15], constants of the heat transfer model [16] and parameters of the combustion model [17][18][19]. These analyses either use a calibrated ICE simulation model as a starting point for evaluation of the optimized configuration [6][7][8][9][10][11][12] or tune a specific sub-model by optimization methods and compare simulation results to the measurement data [17][18][19].…”

“…These analyses either use a calibrated ICE simulation model as a starting point for evaluation of the optimized configuration [6][7][8][9][10][11][12] or tune a specific sub-model by optimization methods and compare simulation results to the measurement data [17][18][19].…”