Power Allocation Strategy for Urban Rail HESS Based on Deep Reinforcement Learning Sequential Decision Optimization

Abstract

This is a repository copy of Power allocation strategy for urban rail HESS based on deep reinforcement learning sequential decision optimization.

Experimental Characterization of Hybrid Supercapacitor Under Different Operating Conditions Using EIS Measurements

“…{ marcantonio.catelani; lorenzo.ciani; fabio.corti, gabriele.patrizi, alberto.reatti }@unifi.it charging and discharging processes [6]. This efficiency, combined with their ability to handle high currents, makes SCs a valuable asset in applications that require fast response times, such as grid stabilization [7], energy management of hybrid vehicles [8], and peak load shaving [9].…”

Urban Rail System Modeling and Simulation Based on Dynamic Train Density

“…However, the overlap time study ignores the coupling relationship between multiple trains and traction substations, so it is impossible to analyze the electrical quantities of each node of the traction system under this mutual influence. The train simulation model based on the MATLAB/Simulink motor module [8,9] can realize the comprehensive modeling of the motor, power electronics, and traction system on the basis of kinematics. However, a single-motor model cannot simulate realistic magnitude urban rail power [10], while increasing the number of motors increases the simulation time significantly.…”

“…In order to improve the power simulation speed, current mainstream studies, such as the study by Saleh and Zhu et al [11,12], usually equate the train as a current source with the traction system components to form an equivalent circuit model and use the current injection method to perform the current calculation in order to obtain the RBE and traction system power flow distribution. Compared with the literature [9], Khodaparastan et al [13] used the equivalent circuit model to effectively improve the simulation speed and time span, which can realize the simulation analysis for 24 h. Zhu [14] investigated the problem of parameter tuning of the traction system and the energy storage system under different train operation scenarios. Sun [15] further combined the circuit model with the schedule optimization, equating the overlap time method to the overlap current method, and obtained a more accurate direct utilization of RBE through iterative current calculation, but the iterative calculation requires a lot of time.…”