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

Does a Higher Density of Active Sites Indicate a Higher Reaction Rate?

A consensus view in catalysis is that a higher density of catalytically active sites indicates a higher reaction rate. Using molecular dynamics simulations capable of mimicking the electrochemical formation of gas molecules, we herein demonstrate that this view is problematic for electrocatalytic gas production. Our simulation results show that a higher density of catalytic active sites does not necessarily indicate a higher reaction ratea high density of active sites could lead to a reduction in the rate of reaction. Further analysis reveals that this abnormal phenomenon is ascribed to aggregation of the produced gas molecules near catalytic sites. This work challenges the consensus view and lays the groundwork for better developing gas-producing reaction electrocatalysts

Process of real-time energy management strategy design.

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

The powertrain structure.

<p>The powertrain structure.</p

Working points of the engine.

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

A long naturalistic driving cycle.

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

SOC trajectories for the validation driving schedule.

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

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

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

Fuel consumption for the validation driving schedule.

<p>Fuel consumption for the validation driving schedule.</p

Working points of the engine.

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