Recently, electric vehicles have gained enormous popularity due to their performance and efficiency. The investment in developing this new technology is justified by the increased awareness of the environmental impacts caused by combustion vehicles, such as greenhouse gas emissions, which have contributed to global warming and the depletion of oil reserves that are not renewable energy sources. Lithium-ion batteries are the most promising for electric vehicle (EV) applications. They have been widely used for their advantages, such as high energy density, many cycles, and low self-discharge. This work extensively investigates the main methods of estimating the state of charge (SoC) obtained through a literature review. A total of 109 relevant articles were found using the prism method. Some basic concepts of the state of health (SoH); a battery management system (BMS); and some models that can perform SoC estimation are presented. Challenges encountered in this task are discussed, such as the nonlinear characteristics of lithium-ion batteries that must be considered in the algorithms applied to the BMS. Thus, the set of concepts examined in this review supports the need to evolve the devices and develop new methods for estimating the SoC, which is increasingly more accurate and faster. This review shows that these tools tend to be continuously more dependent on artificial intelligence methods, especially hybrid algorithms, which require less training time and low computational cost, delivering real-time information to embedded systems.

Recently, the increased adoption of electric vehicles (EVs) has significantly demanded new energy storage systems (ESS) technologies. In this way, Lithium-ion batteries (LIB) are the mainstream technology for this application. Lithium presents several advantages compared with other chemicals because it can provide delivery energy for a long time, a long lifetime, and high density and capacity. The LIB comprises several cells connected in different configurations, such as parallel, series, or combinations. This variety of designs makes the monitoring control process more complex, complicating diagnosing and prognosis of abuses and failures. To observe these difficulties, this paper presents sixteen experiments of a mini-packing of four cells under the main abuses found in the LIB. The time series data were collected during the abuses and saved in a CSV file. The results indicated that the current, temperature, and voltage should be used to identify the external short-circuit (ESC) failures in the packing of batteries. On the other side, only the voltage signature is able to determine the Over-Charging (OC), and finally, the combination of temperature and voltage should be used to identify and locate the Over-Discharging (OD) failures in different arrangements of packing. This study also provides ways to build mechanisms to protect the cells and avoid loss of performance and safety issues.