Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

<div><p>To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.</p></div

Working points of the engine.

<p>Working points of the engine.</p

A long naturalistic driving cycle.

<p>A long naturalistic driving cycle.</p

Power split.

<p>a1 is power of the engine, and a2 is the power of the battery.</p

The experimental driving schedule.

<p>The experimental driving schedule.</p

The powertrain structure.

<p>The powertrain structure.</p

SOC trajectories for the validation driving schedule.

<p>SOC trajectories for the validation driving schedule.</p

The facing-forward simulation model.

<p>The facing-forward simulation model.</p

<i>SOC</i> trajectory in different strategies.

<p><i>SOC</i> trajectory in different strategies.</p

Process of real-time energy management strategy design.

<p>Process of real-time energy management strategy design.</p