This paper examines the effectiveness of optimizing energy management in hybrid
electric vehicles by integrating adaptive machine learning algorithms with the
energy management electronic control unit (ECU). Existing traditional rule-based
energy management and control strategies of power distribution between internal
combustion and battery struggle to adapt to dynamic driving conditions, such as
rapid acceleration, frequent stop-and-go traffic, and varying terrain. These
scenarios often result in sub-optimal energy utilization and performance, as the
fixed rules struggle to account for the immediate demands and inefficiencies
that arise in such conditions. In conditions like that, rapid acceleration
demands a sudden increase in power, which can lead to inefficient fuel
consumption if not managed properly, while frequent stop-and-go traffic
conditions can cause the battery to drain and lead to increased fuel
consumption. Varying terrain can also lead to improper power distribution, with
steep inclines demanding more power from the internal combustion engine than
normal terrain. In contrast, the proposed system utilizes real-time feedback
from the energy management ECU, based on that it decides the power distribution
between the battery and internal combustion engine in the subsequent cycle,
ensuring optimal performance across a wide range of driving conditions by
efficiently distributing the power between internal combustion engine and
battery.
In this paper, the approach toward the development of a machine learning
algorithm for energy management of hybrid electric vehicles will be shown in
MATLAB. The procedure involves data collection from the energy management ECU,
including parameters such as battery state of charge (SOC), vehicle speed,
engine load, torque, fuel consumption rate, and temperature. This data is then
used to extract relevant patterns and relationships. Subsequent machine learning
algorithms are selected based on their suitability for the data characteristics
and problem requirements and trained using MATLAB. After training, the model
will receive real-time feedback from the energy management ECU based on
collected parameters. Using this feedback the model decides the power
distribution between the internal combustion engine and battery. Results,
including metrics like mean absolute error (MAE) and root mean square error
(RMSE) regarding fuel consumption and overall fuel consumption rates, will be
plotted to validate the system’s performance before and after implementing the
machine learning model. These results will be plotted on performance graphs to
illustrate the improvement achieved.
In conclusion, this article showcases a comprehensive methodology utilizing
MATLAB for the development and validation of an adaptive machine learning-based
energy management system for hybrid electric vehicles.