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PSO ALGORITHM-BASED PARAMETER OPTIMIZATION FOR HEV POWERTRAIN AND ITS CONTROL STRATEGY
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  • PSO ALGORITHM-BASED PARAMETER OPTIMIZATION FOR HEV POWERTRAIN AND ITS CONTROL STRATEGY
  • PSO ALGORITHM-BASED PARAMETER OPTIMIZATION FOR HEV POWERTRAIN AND ITS CONTROL STRATEGY
저자명
Wu. J.,Zhang. C.H.,Cui. N.X.
간행물명
International journal of automotive technology
권/호정보
2008년|9권 1호|pp.53-59 (7 pages)
발행정보
한국자동차공학회
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정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

The coordination between the powertrain and control strategy has significant impacts on the operating performance of hybrid electric vehicles (HEVs). A comprehensive methodology based on Particle Swann Optimization (PSO) is presented in this paper to achieve parameter optimization for both the powertrain and the control strategy, with the aim of reducing fuel consumption, exhaust emissions, and manufacturing costs of the HEV. The original multi-objective optimization problem is converted into a single-objective problem with a goal-attainment method, and the principal parameters of powertrain and control strategy are set as the optimized variables by PSO, with the dynamic performance index of HEVs being defined as the constraint condition. Computer simulations were carried out, which showed that the PSO scheme gives preferable results in comparison to the ADVISOR method. Therefore, fuel consumption and exhaust emissions of HEVs can be effectively reduced without sacrificing dynamic performance of HEVs.