Reference-power tracking control of Ultrabattery: Constrained Optimal Predictive Control Approach

Energy storage systems (ESSs) have a wide range of applications in the present scenario, including power system frequency and voltage regulation, electric vehicles, and renewable integration. Their performance and life depend on various constraints that must be satisfied during their operation. In addition, the control inputs are constrained. Therefore, in this study, we present state-and input-constrained control of an Ultrabattery ESS. The control objective is to make the ESS follow a reference power command while ensuring that the system operates within its regime of operation. The control problem was set a reference power tracking problem. The objective function was set to reduce the deviation between the reference power command and the actual power output. Moreover, the objective function contains a term that minimizes the control deviation. A novel predictive control algorithm—tracking model predictive static programming— was used to control the Ultrabattery ESS. The algorithm converts the control problem into a low-dimensional static programming problem, which is solved to obtain the control update. In addition, the algorithm is significantly fast because it involves recursive evaluation of sensitivity matrices. The Ultrabattery ESS was simulated in MATLAB using the modified Randle's model and averaged-modelled bidirectional DC-DC converter. The results indicated that the algorithm ensures that the reference power command is optimally tracked while satisfying the operational constraints.