Research on Control Strategy of Plug-in Hybrid Electric Vehicle Based on Improved Dynamic Programming 2023-01-0545
Because of the long driving range and good power performance, plug-in hybrid electric vehicles (PHEV) have drawn much attention. And the current fuel-saving effect of PHEV still has a lot of room for improvement. The complex powertrain structure of PHEV makes the requirements for control strategy be increasing. Therefore, it is crucial to develop an efficient control strategy to ensure that the PHEV operates at optimal performance with an improved driving range. This paper establishes a mathematical model for fuel economy control of PHEV, by treating the torque distribution problem as a multi-stage decision optimization problem, and establishes a global energy management strategy based on a dynamic programming (DP) algorithm. Based on the actual physical model, this paper creatively solves the correction range of battery SOC value according to the charge and discharge power of the motor, which greatly reduces the calculation time of the DP algorithm. Through further simulation study, it is found that the energy management strategy based on the improved DP algorithm can significantly improve the fuel economy of the whole vehicle compared with the rule-based energy management strategy. By comparing the working interval diagrams of the motor and the engine under the two strategies, it can be concluded that the higher the frequency of the engine working in the high efficiency zone, the more likely to get better fuel economy. This study provides the theoretical basis and essential guidance for the establishment and optimization of online energy management strategy.
Citation: He, Z. and Yu, Q., "Research on Control Strategy of Plug-in Hybrid Electric Vehicle Based on Improved Dynamic Programming," SAE Technical Paper 2023-01-0545, 2023, https://doi.org/10.4271/2023-01-0545. Download Citation
Author(s):
Zixuan He, Qinghua Yu
Affiliated:
Wuhan University of Technology
Pages: 10
Event:
WCX SAE World Congress Experience
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Hybrid electric vehicles
Fuel economy
Mathematical models
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