Continued increases in the
emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this
increase in production, there has been a parallel demand for continuously
improving strategies of hybrid electric vehicle control. The goal of an ideal
control strategy is to maximize fuel economy while minimizing emissions.
Methods exist by which the globally optimal control strategy may be found.
However, these methods are not applicable in real-world driving applications
since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the
global optimal as a benchmark against which performance can be evaluated. The
goal of this work is to use a previously defined strategy that has been shown
to closely approximate the global optimal and implement a radial basis function
(RBF) artificial neural network (ANN) that dynamically adapts the strategy
based on past driving conditions. The strategy
used is the Equivalent Consumption Minimization Strategy (ECMS), which
uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that
is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the
equivalence factor by examining a past time window of driving characteristics.
A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN.
For the majority of drive cycles examined, the RBF-ANN implementation is shown
to produce fuel economy values that are within ±2.5% of the fuel economy
obtained with the optimal equivalence factor. The advantage of the RBF-ANN is
that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting
or exceeding the performance of the optimal ECMS. Recommendations are made on
how the RBF-ANN could be improved to produce better results across a greater
array of driving conditions.