Parameter Optimization of Hybrid Electric Vehicle Based on Multi-cycle Optimization Algorithm

doi:10.3901/JME.2017.16.061

Abstract

Abstract: Coordinated optimization for parameters of hybrid electrical vehicle (HEV) powertrain and control strategy is the key to improving fuel economy. In order to reduce fuel consumption, a methodology using simulated annealing particle swarm optimization (SAPSO) and multi-cycle is presented to achieve parameter optimization for both the powertrain and the control strategy based on the constraint of the dynamic performance requirements. The simulation have been carried out based on the MATLAB/Simulink software, and the result show that the proposed approach can find a set of control parameters to reduce fuel consumption with keeping power performance and balance of battery SOC. After optimization, the fuel consumption of vehicle will be reduced 5.49% at comprehensive driving cycle and 8.46%, 4.62%, 3.84%, 8.34%, 5.56% and 4.31% at cycles of HWFET, FTP, LA92, US06_HWY, UDDS, SC03, respectively. Without lose generality, the cycle of NEDC have been chose to validate the optimization control parameters and the fuel consumption will be reduced 8.36%. The road test is developed, and demonstrated the effectiveness of the energy management strategy in this work.

Key words: energy management strategy, hybrid electric vehicle (HEV), multi-cycle optimization, road test, SAPSO

CLC Number:

U469

LIU Yonggang, LI Jie, QIN Datong, LEI Zhenzhen, XIE Qingbo, YANG Yang. Parameter Optimization of Hybrid Electric Vehicle Based on Multi-cycle Optimization Algorithm[J]. Journal of Mechanical Engineering, 2017, 53(16): 61-69.

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