TY - JOUR
T1 - 基于系统综合效率最优的双轴并联PHEV联合优化控制策略
AU - Wang, Wei Da
AU - Wang, Xian Tao
AU - Yan, Zheng Jun
AU - Xu, Jin Song
AU - Wang, Yu
AU - Li, Xun Ming
N1 - Publisher Copyright:
© 2018, Materials Review Magazine. All right reserved.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - In view of the control strategy and parameter optimization of the dual-axis-parallel plug in hybrid electric vehicle (PHEV), a joint optimization control strategy was proposed, analyzing the impact of engine and motor working area on vehicle economy, taking the torque distributing ratio and transmission gear as the optimization parameters, and taking the highest comprehensive system efficiency as the optimization target. Meanwhile, the cost function was introduced to coordinate the control of comprehensive system efficiency and gear shifting cost. Taking the lowest fuel consumption under the typical cycle condition as optimized objective, an adaptive simulated annealing (ASA) algorithm was used to optimize the parameters of the joint optimization control strategy based on the Isight-Cruise-Matlab joint optimization simulation platform. The simulation results show that, compared with the pre-optimization strategy, the parameter optimization strategy saved the oil consumption by 7.7%, and saved 17.3% compared with the initial rule strategy.
AB - In view of the control strategy and parameter optimization of the dual-axis-parallel plug in hybrid electric vehicle (PHEV), a joint optimization control strategy was proposed, analyzing the impact of engine and motor working area on vehicle economy, taking the torque distributing ratio and transmission gear as the optimization parameters, and taking the highest comprehensive system efficiency as the optimization target. Meanwhile, the cost function was introduced to coordinate the control of comprehensive system efficiency and gear shifting cost. Taking the lowest fuel consumption under the typical cycle condition as optimized objective, an adaptive simulated annealing (ASA) algorithm was used to optimize the parameters of the joint optimization control strategy based on the Isight-Cruise-Matlab joint optimization simulation platform. The simulation results show that, compared with the pre-optimization strategy, the parameter optimization strategy saved the oil consumption by 7.7%, and saved 17.3% compared with the initial rule strategy.
KW - Adaptive simulated annealing (ASA) algorithm
KW - Joint optimization control strategy
KW - Plug in hybrid electric vehicle(PHEV)
UR - http://www.scopus.com/inward/record.url?scp=85062916307&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2018.08.044
DO - 10.15918/j.tbit1001-0645.2018.08.044
M3 - 文章
AN - SCOPUS:85062916307
SN - 1001-0645
VL - 38
SP - 205
EP - 210
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
ER -