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Model-based intelligence multi-objective globally optimization for HCCI engines
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Ma, He, Xu, Hongming, Wang, Jihong, Schnier, Thorsten, Neaves, Ben, Tan, Cheng and Wang, Zhi (2015) Model-based intelligence multi-objective globally optimization for HCCI engines. IEEE Transactions on Vehicular Technology, 64 (9). pp. 4326-4331. doi:10.1109/TVT.2014.2362954 ISSN 0018-9545.
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WRAP_Model-based Intelligence Multi-objective Globally Optimization for HCCI Engines %280212%29.pdf - Accepted Version - Requires a PDF viewer. Download (2550Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TVT.2014.2362954
Abstract
Modern engines feature a considerable number of adjustable control parameters. With this increasing number of Degrees of Freedom (DoF) for engines, and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated engine optimization approach is desired. In this paper, a self-learning evolutionary algorithm based multi-objective globally optimization approach for a Homogenous Charge Compression Ignition (HCCI) engine is developed. The performance of the HCCI engine optimizer is demonstrated by the co-simulation between an HCCI engine Simulink model and an Strength Pareto Evolutionary Algorithm 2 (SPEA2) based multi-objective optimizer developed in Java. The HCCI engine model is developed by integrating the physical gas exchange model, in-cylinder volume model and statistical combustion model. The model has been validated from 1500 rpm to 2250 rpm with different Indicated Mean Effective Pressure (IMEP). The model is able to simulate the performance of in-cylinder pressure, Indicated Specific Fuel Consumption (ISFC) and Indicated Specific Hydrocarbon (ISHC) emissions with acceptable accuracy in real-time within a wide engine operation window. The SPEA2 optimizer has been validated by the classic evaluation function SRN with constrains. The validation results show that the optimizer can find the Pareto Front of SRN efficiently. The introduced Intelligence optimization is an approach to optimize the engine ISFC and ISHC simultaneously by adjusting the engine actuators’ settings automatically through SPEA2. For this study, the HCCI engine actuators’ settings are Intake Valves Opening (IVO), Exhaust Valves Closing (EVC) and relative air to fuel ratio (λ). The co-simulation study and experimental validation results show that the intelligent multi-objective optimizer can find the optimal HCCI engine actuators’ settings with acceptable accuracy, and much lower time consumption than usual.
Item Type: | Journal Article | ||||||||
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Subjects: | T Technology > TL Motor vehicles. Aeronautics. Astronautics | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Automobiles -- Motors -- Cylinders, Automobiles -- Fuel consumption, Automobiles -- Motors -- Exhaust gas, Algorithms | ||||||||
Journal or Publication Title: | IEEE Transactions on Vehicular Technology | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 0018-9545 | ||||||||
Official Date: | 15 September 2015 | ||||||||
Dates: |
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Volume: | 64 | ||||||||
Number: | 9 | ||||||||
Page Range: | pp. 4326-4331 | ||||||||
DOI: | 10.1109/TVT.2014.2362954 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 20 May 2016 | ||||||||
Date of first compliant Open Access: | 20 May 2016 | ||||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC), Jaguar Cars Ltd, Shell Global Solutions (UK) | ||||||||
Grant number: | EP/J00930X/1 EP/J01043X/1 (EPSRC) |
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