AI-Based Control of Storage Capacity in High-Power-Density Energy Storage Systems, Used in Electric Vehicles

Exempting batteries from supplying power transients in electric vehicles (EVs) is beneficial to extend their useful lifespan. The adaptive capacity of high-power-density energy storage systems (HPESSs), such as ultracapacitors (UCs) or high-speed flywheel energy storage systems (FESSs), could fulfill the targets in this context. This article proposes a sizing/control methodology and real-time artificial intelligence (AI)-based control of the storage capacity (SC) for the adaptive capacity HPESSs, used in EVs. The sizing approach consists of an optimal energy management strategy and a sizing algorithm applied to a variable-step HPESS (VS-HPESS). This methodology derives the battery/VS-HPESS power split and sizes of SCs. In addition, a nonlinear autoregressive neural network with eXogenous inputs (NARX-NN) is trained offline to switch the desirable capacity of the VS-HPESS in real-time operation. Finally, an experiment is designed to evaluate the proposed real-time control scheme, in which the EV power transients are emulated and applied to a dual-capacitance UC as the VS-HPESS. The results confirm the capability of the proposed approach to meet the considered targets.