Lithium-ion batteries are the most used these days for charging electric vehicles (EV). It is important to study the aging of batteries because the deterioration of their characteristics largely determines the cost, efficiency, and environmental impact of electric vehicles, especially full-electric ones. The estimation of batteries' state-condition is also very important for improving energy efficiency, lengthening the life cycle, minimizing costs and ensuring safe implementation of batteries

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... electric vehicles. However, batteries with large temporal variables and non-linear characteristics are often affected by random factors affecting the equivalent internal resistance (EIR), battery state of charge (SoC), and state of health (SoH) in EV applications. The estimation of batteries' parameters is a complex process, due to its dependence on various factors such as batteries age and ambient temperature, among others. A good estimate of SoC and internal resistance leads to long battery life and disaster prevention in the event of a battery failure. The classification of estimation methodologies for internal parameters and the charging status of batteries will be very helpful in choosing the appropriate method for the development of a reliable and secure battery management system (BMS) and an energy management strategy for electric vehicles.