TY - JOUR
T1 - Research on parameter optimisation of control strategy for powertrain system of series hybrid electric bulldozer
AU - Song, Qiang
AU - Zeng, Pu
N1 - Publisher Copyright:
Copyright © 2016 Inderscience Enterprises Ltd.
PY - 2016
Y1 - 2016
N2 - To reduce the fuel consumption for a new type of series hybrid electric bulldozer, the parameters of control strategy for powertrain system should be optimised, especially for engine-generator system. In this paper, a new method based on multidisciplinary optimisation is proposed. The mathematical model of the series hybrid bulldozer system is established under MATLAB/Simulink software environment. On the basis of the idea of optimisation design, the parameters optimisation model for the control strategy is described. The optimised work flow is built by using the software of OPTIMUS, and adaptive genetic algorithm (AGA) is used to solve optimisation problem. The result shows that the bulldozer's fuel consumption after optimisation is reduced by about 6.74% compared with the former, and the method proposed in this paper can find the optimal solution in all global ranges, which greatly reduces the design and optimisation difficulties of the control strategy.
AB - To reduce the fuel consumption for a new type of series hybrid electric bulldozer, the parameters of control strategy for powertrain system should be optimised, especially for engine-generator system. In this paper, a new method based on multidisciplinary optimisation is proposed. The mathematical model of the series hybrid bulldozer system is established under MATLAB/Simulink software environment. On the basis of the idea of optimisation design, the parameters optimisation model for the control strategy is described. The optimised work flow is built by using the software of OPTIMUS, and adaptive genetic algorithm (AGA) is used to solve optimisation problem. The result shows that the bulldozer's fuel consumption after optimisation is reduced by about 6.74% compared with the former, and the method proposed in this paper can find the optimal solution in all global ranges, which greatly reduces the design and optimisation difficulties of the control strategy.
KW - Adaptive genetic algorithm
KW - Aga
KW - Control strategy
KW - Design on experiment
KW - Doe
KW - Dual-motor powertrain system
KW - Engine-generator system
KW - Fuel consumption
KW - Mathematical model
KW - Parameter optimisation
KW - Series hybrid electric bulldozer
KW - Ultracapacitor
UR - http://www.scopus.com/inward/record.url?scp=84994242553&partnerID=8YFLogxK
U2 - 10.1504/IJVD.2016.080018
DO - 10.1504/IJVD.2016.080018
M3 - Article
AN - SCOPUS:84994242553
SN - 0143-3369
VL - 72
SP - 132
EP - 142
JO - International Journal of Vehicle Design
JF - International Journal of Vehicle Design
IS - 2
ER -