• Energy Research

Recently, due to having features like high energy density, high efficiency, superior capacity, and long-life cycle in comparison with the other kinds of dry batteries, lithium-ion batteries have been widely used for energy storage in many applications e.g., hybrid power micro grids, electric vehicles, and medical devices. However, performance degradation and safety issues exist because of abnormal temperature increment of the cell. So that to guarantee safety and enhance its performance; lithium-ion batteries must be preserved during the operation in a specific range of temperature. So, an adequate battery thermal management system (BTMS) is needed to reduce the maximum temperature of the battery. In the literature, BTMS can be classed as either internal or external thermal management systems based upon which battery's temperature must be treated, the internal or the external one. This article introduces an updated comprehensive review of research interested in the external thermal management strategies to provide a deep understanding of the recent key technologies used for external BTMS. In addition, practical guidelines have been recommended for enhancing the applicability of such technology.

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.