PSO algorithm-based parameter optimization for HEV powertrain and its control strategy |
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Authors: | J Wu C -H Zhang N -X Cui |
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Institution: | (1) School of Control Science and Engineering, Shandong University, Jinan, 250061, China |
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Abstract: | 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 Swarm 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. |
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Keywords: | Parallel hybrid electric vehicle Particle swarm optimization Goal-attainment method Powertrain Control strategy |
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