Dual-channel dynamic event-triggered data-driven control for energy storage system using quantised information

This paper proposes a data-driven adaptive control method for energy management in battery energy storage systems (BESSs), with a specific focus on optimising the utilisation of communication resources. First, nonlinear dynamics models of BESSs are converted into equivalent linear data models through dynamic linearisation techniques that utilise only input and output data. Then, an encoding-decoding scheme incorporating a novel quantizer based on the softplus function is designed to mitigate quantisation errors. Building on this, a semi-independent dual-channel dynamic event-triggered mechanism is proposed, effectively eliminating prolonged dormancy periods while reducing communication overhead. Finally, the boundedness of quantisation errors and convergence of tracking errors are proven, with simulation experiments further demonstrating the effectiveness of the proposed scheme.