The real-time and accuracy problems of
power distribution exist in plug-in hybrid electric
vehicle energy management. The existing off-line power
prediction model fails to fully consider the influence of multi-objective changes in dynamic performance of hybrid electric vehicle
battery such as SOH and power fluctuation on the prediction results, and the
existing machine learning algorithm has insufficient performance in power
distribution calculation and prediction of hybrid electric vehicles. In order
to solve these problems, the off-line dynamic
programming and online XGBoost algorithm was used to model the power
distribution of hybrid electric vehicles and realize the power distribution
prediction. Firstly, the power system model of plug-in
hybrid electric vehicle was built, and the typical mixed driving conditions of
vehicle were obtained by cluster analysis method. Secondly, the optimal
distribution ratio of engine power and lithium battery under the working
condition was calculated off-line by dynamic
programming algorithm. Finally, XGBoost algorithm was used as training data to
verify the model. The calculation results show that the considered multi-objective optimization in the off-line
dynamic planning makes the model training in the online stage have sufficient
data support. Compared to the random forest algorithm, the XGBoost algorithm
reduces the maximum error by 28% and increases the computational speed by 62%,
which enables accurate estimation of the power distribution of plug-in hybrid vehicles.
TIAN Ke, MA Xiao
. Energy Management Strategy of Combining Dynamic
Programming and XGBoost Algorithm for Hybrid Electric Vehicle[J]. Vehicle Engine, 2025
, 0(2)
: 80
.
DOI: 10.3969/j.issn.1001-2222.2025.02.012
