Continuous and accurate state-of-charge estimation is essential for optimal reliability and performance in electric vehicle battery management systems. This work reviews state-of-charge estimation strategies, from straightforward methods like lookup tables and ampere-hour counting to advanced mathematical models, such as electrochemical, observer-assisted equivalent circuit, and impedance-based models that capture cell dynamics. Additionally, data-driven models including fuzzy logic, neural networks, and support vector machines are explored for their ability to leverage large datasets. This review highlights the strengths and limitations of each method, emphasizing the specific contexts in which these strategies can be applied to achieve optimal effectiveness.

The growth of electric vehicles (EVs) has prompted the need to enhance the technology of lithium-ion batteries (LIBs) in order to improve their response when subjected to external factors that can alter their performance, thereby affecting their safety and efficiency. Mechanical abuse has been considered one of the major sources of LIB failure due to the changes it provokes in the structural integrity of cells. Therefore, this article aims to review the main factors that aggravate the effects of mechanical loading based on the results of different laboratory tests that subjected LIBs to abusive testing. The results of different cell types tested under different mechanical loadings have been gathered in order to assess the changes in LIB properties and the main mechanisms responsible for their failure and permanent damage. The main consequences of mechanical abuse are the increase in LIB degradation and the formation of events such as internal short circuits (ISCs) and thermal runways (TRs). Then, a set of standards and regulations that evaluate the LIB under mechanical abuse conditions are also reviewed.

A variable bus voltage DC microgrid (MG) is simulated in Simulink for optimization purposes. It is initially controlled with a Voltage Event Control (VEC) algorithm supplemented with a State of Charge Event Control (SOCEC) algorithm. This control determines the power generated/consumed by each element of the MG based on bus voltage and battery State of Charge (SOC) values. Two supplementary strategies are proposed and evaluated to improve the DC-DC converters’ efficiency. First, bus voltage optimization control: a centralized Energy Management System (EMS) manages the battery power in order to make the bus voltage follow the optimal voltage reference. Second, online optimization of switching frequency: local drivers operate each converter at its optimal switching frequency. The two proposed optimization strategies have been verified in the simulations.