Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles

Hongzhao Li (), Hongsheng Jia, Ping Xiao, Haojie Jiang and Yang Chen
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Hongzhao Li: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Hongsheng Jia: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Ping Xiao: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Haojie Jiang: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Yang Chen: National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Energies, 2025, vol. 18, issue 9, 1-30

Abstract: Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field.

Keywords: lithium-ion batteries; battery management system; state of charge; battery models; data-driven; state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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