State-of-Charge Estimation in Lithium-Ion Battery for Electric Vehicle Applications: A Comparative Review

Abstract

The demand for electric energy storage has been growing over the last decades. Computing technologies include lithium-ion batteries and ultra-super capacitors. The lithium-ion batteries have been adopted by industries to a large extent. High energy and power density, low discharge rate, and longer life are the advantageous features of lithium-ion batteries which have accelerated their adoption by industries. Lithium-ion battery systems include a battery management system that monitors the state-of-charge among numerous other battery parameters. The remaining capacity of accessible stored energy in the battery, battery life, and cell balancing is determined by the state-of-charge (SOC). Because SOC is extensively adopted for safer operations without overcharging, discharging, and battery life design, precise SOC measurement is critical for Battery Management Systems (BMS). As a result, SOC has been an attractive research topic for electric vehicles (EVs) in recent years, but there are significant challenges: The configuration of lithium-ion battery (LIB) is not linear, making it difficult to predict the SOC-related parameters accurately. It is difficult to analyze a LIB’s internal surroundings which might change between laboratory and real-world situations. As a result, research is necessary to improve the accuracy of SOC estimation in LIB for EV applications. The exact modeling and state prediction will increase the EV’s ability to operate steadily. The current methodologies used in LIB modeling and SOC estimation are discussed in this paper. In this paper, a brief discussion of the problems associated with different methods of SOC prediction is analyzed. The authors have reviewed the SOC-related lithium over the last three years.