An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.

The hybrid-energy storage systems (ESSs) are promising eco-friendly power converter devices used in a wide range of applications. However, their insufficient lifespan is one of the key issues by hindering their large-scale commercial application. In order to extend the lifespan of the hybrid-ESSs, the cost functions proposed in this paper include the degradation of the hydrogen devices and the battery. Indeed, this paper aims to develop a sophisticated model predictive control strategy for a grid-connected wind and solar microgrid, which includes a hydrogen-ESS, a battery-ESS, and the interaction with external consumers, e.g., battery/fuel cell electric vehicles. The integrated system requires the management of its energy production in different forms, i.e., the electric and the hydrogen ones. The proposed strategy consists of the economical and operating costs of the hybrid-ESSs, the degradation issues, and the physical and dynamic constraints of the system. The mixed-logic dynamic framework is required to model the operating modes of the hybrid-ESSs and the switches between them. The effectiveness of the controller is analyzed by numerical simulations which are conducted using solar and wind generation profiles of solar panels and wind farms located in Abu Dhabi, United Arab Emirates. Such simulations, indeed, show that the proposed strategy appropriately manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.

In this paper, an online energy management control strategy is proposed based on a novel fractional-order extremum seeking (ES) method. The proposed method is an online adaptive optimization algorithm, which can be effectively used in the applications of fuel cell hybrid electric vehicles. Compared with the traditional integer-order ES method, the presented method uses Oustaloup approximation based fractional-order calculus in order to achieve faster convergence speed and higher robustness. A detailed mathematical analysis of the proposed method is presented to give a stability proof and shows how the fractional-order calculus improves the integer-order ES method. In order to support the stability analysis results and demonstrate the effectiveness and robustness of the proposed method, a hardware-in-the-loop test bench is developed to provide two experimental case studies. Experimental results show that, by using the presented fractional-order ES approach, the operation points of a proton exchange membrane fuel cell stack system can be effectively controlled in its maximum efficiency area. In addition, the fuel cell system durability can be improved.