State of charge estimation techniques for battery management system used in electric vehicles: a review

Nowadays electric vehicles (EVs) have become one of the most emerging technologies. In comparison to conventional vehicles in terms of emission, EVs are zero-emission vehicles (ZEVs) as they are powered by batteries whereas conventional fossil fuel-based vehicles emit a considerable amount of pollutants into the atmosphere. Depletion of the levels of fossil fuels and air pollution motivates countries throughout the world to use EVs. EVs are divided into four subsystems. The first one is the vehicle body. The second is the controller circuit along with the motor. The third is input from the driver and the fourth one is the most crucial part, the battery bank. The primary part of the EV's power source i.e., the battery. There are many types of batteries used in EVs such as Lithium-ion (Li-ion) batteries, Nickel metal hydride batteries, Lead-acid batteries, Solid-state batteries, as well as ultra-capacitors. The power resources of EVs are mainly dependent on the battery, but due to an inefficient battery management system (BMS), the user of the EV may face critical challenges. The major challenge is long charging times with shorter travel ranges. To reduce the charging time fast, charging is needed. The fast charging increases the charging current which increases the battery temperature. This in return will reduce the life span of the battery. Rapid charging and discharging simultaneously a with regenerative braking facility will also reduce the battery life. A BMS is used to observe parameters such as current, operating voltage, state of charge (SOC), total power consumption, state of health, battery aging, internal impedance, and the temperature at the time of charging and discharging. This review paper focuses on the different SOC estimation methods especially conventional methods and computer-based computational techniques along with their classification used in EV batteries. Finally, the most desired estimation method for SOC in BMS for EVs has been concluded at the end of this paper.