access icon free Optimisation of reference state-of-charge curves for the feed-forward charge/discharge control of energy storage systems on-board DC electric railway vehicles

© The Institution of Engineering and Technology
Received 04/04/2012, Accepted 18/06/2014, Revised 13/09/2013, Published 01/10/2014

The charge/discharge control of OBESS's (on-board energy storage systems) on-board DC electric railway vehicles based on the feed-forward data is proposed in this study. The feed-forward data, called the reference SOC (state-of-charge) curve, determines the SOC of an OBESS against the train position. The detailed simulation model of this charge/discharge control scheme has been implemented in a multi-train power network simulator. A rule-based approach to the design of the SOC curve is proposed, in which the trajectory of charge/discharge power of an OBESS on a train is generated using a simple set of rules from the trajectory of input/output power of the traction system. From the evaluation using the developed simulation model, it can be concluded that the system having OBESS's with the proposed feed-forward control effectively reduces energy consumption. However, the proposed SOC curve design process includes hand-adjusting of many parameters; therefore, the numerical optimisation of these parameters is proposed, in which the developed simulation model is embedded for calculating the evaluation function. Results yielded from several attempts of optimisation show that the optimisation yields sufficiently effective SOC curves, although the improvement from the result yielded by hand-adjusted parameters is small.

Inspec keywords: feedforward; traction; knowledge based systems; railways; optimisation; energy storage

Other keywords: optimisation show; on-board energy storage systems; multitrain power network simulator; feedforward charge-discharge control; DC electric railway vehicles; reference state-of-charge curves; rule based approach; traction system; OBESS

Subjects: Control of electric power systems; Other energy storage; Optimisation techniques; Optimisation techniques; Transportation; Expert systems and other AI software and techniques

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