Enhanced energy management of dual-stage hybrid energy storage systems with a novel adaptive robust control algorithm

This paper presents an indirect adaptive robust control algorithm for a nonlinear hybrid energy storage system (NHESS) that can be used in electric vehicles. The NHESS consists of a fuel cell as a primary source and an ultra-capacitor as an additional energy source. A neural network approximation strategy (Indirect Adaptive Robust RBF Neural Network IAR-RBFNN) estimates state and unknown functions. The IAR-RBFNN for the NHESS is resilient and robust, subject to bounded but unknown disturbances that can affect the fuel-cell and ultra-capacitor currents and the output voltage of a DC bus. The proposed controller switches between the two power converters to track the ideal current levels and help regulate the DC-bus voltage to higher levels to improve efficiency. To overcome the effect of the disturbances, the proposed controller contains a robustifying term, and the adaptation laws are guaranteed to be ultimately bounded using an e-modification approach. Additionally, the approximation capacity of radial basis function neural network (RBFNN) systems is employed to estimate the entire system dynamics, unlike only estimation disturbance or a few system parameters. The performance of the proposed control strategy is further evaluated against other reported studies in the literature in terms of several performance indicators. Quantitative results demonstrate that the proposed controller achieves RMSE values of 0.13 Ω for I F C , 0.25 Ω for I P B , and 2.5 V for V D C , significantly outperforming the ATSMC and ORAT2F methods which recorded higher RMSEs of 0.41 Ω , 0.65 Ω , and 3.51 V, respectively. The results reveal that the proposed controller outperforms the benchmarking control strategies regarding the tracking performance for the fuel-cell and ultra-capacitor ideal currents. • A novel robust controller for energy management of hybrid electric vehicles. • IAR-RBFNN used for output voltage regulation of DC Bus of Fuel Cell HEVs. • Improved efficiency of DC-bus voltage of hybrid FC+UC energy system. • Adaptive estimation of the unknown model and external disturbances.