Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Gladwin:2011:ijsysc,
  author =       "Dan Gladwin and Paul Stewart and Jill Stewart",
  title =        "Internal combustion engine control for series hybrid
                 electric vehicles by parallel and distributed genetic
                 programming/multiobjective genetic algorithms",
  journal =      "International Journal of Systems Science",
  volume =       "42",
  number =       "2",
  year =         "2011",
  pages =        "249--261",
  note =         "Computational Intelligence for Modelling and Control
                 of Advanced Automotive Drivetrains",
  keywords =     "genetic algorithms, genetic programming, automotive,
                 model-reference control, time-delay, hybrid vehicles,
                 parallel and distributed evolutionary computation,
                 mechanical systems, PID control, distrubed
                 evolutionary",
  ISSN =         "0020-7721",
  DOI =          "doi:10.1080/00207720903144479",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  URL =          "http://eprints.lincoln.ac.uk/3986/",
  URL =          "http://results.ref.ac.uk/Submissions/Output/1636812",
  size =         "13 pages",
  abstract =     "This article addresses the problem of maintaining a
                 stable rectified DC output from the three-phase AC
                 generator in a series-hybrid vehicle powertrain. The
                 series-hybrid prime power source generally comprises an
                 internal combustion (IC) engine driving a three-phase
                 permanent magnet generator whose output is rectified to
                 DC. A recent development has been to control the
                 engine/generator combination by an electronically
                 actuated throttle. This system can be represented as a
                 nonlinear system with significant time delay.
                 Previously, voltage control of the generator output has
                 been achieved by model predictive methods such as the
                 Smith Predictor. These methods rely on the
                 incorporation of an accurate system model and time
                 delay into the control algorithm, with a consequent
                 increase in computational complexity in the real-time
                 controller, and as a necessity relies to some extent on
                 the accuracy of the models. Two complementary
                 performance objectives exist for the control system.
                 Firstly, to maintain the IC engine at its optimal
                 operating point, and secondly, to supply a stable DC
                 supply to the traction drive inverters. Achievement of
                 these goals minimises the transient energy storage
                 requirements at the DC link, with a consequent
                 reduction in both weight and cost. These objectives
                 imply constant velocity operation of the IC engine
                 under external load disturbances and changes in both
                 operating conditions and vehicle speed set-points. In
                 order to achieve these objectives, and reduce the
                 complexity of implementation, in this article a
                 controller is designed by the use of Genetic
                 Programming methods in the Simulink modelling
                 environment, with the aim of obtaining a relatively
                 simple controller for the time-delay system which does
                 not rely on the implementation of real time system
                 models or time delay approximations in the controller.
                 A methodology is presented to use the myriad of
                 existing control blocks in the Simulink libraries to
                 automatically evolve optimal control structures.",
  oai =          "oai:eprints.lincoln.ac.uk:3986",
  uk_research_excellence_2014 = "D - Journal article",
}

Genetic Programming entries for Daniel Gladwin Paul Stewart Jill Stewart

Citations