A Review of Cross-Scale State Estimation Techniques for Power Batteries in Electric Vehicles: Evolution from Single-State to Multi-State Cooperative Estimation

Ning Chen (), Yihang Xie, Yuanhao Cheng, Huaiqing Wang, Yu Zhou, Xu Zhao, Jiayao Chen and Chunhua Yang
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Ning Chen: School of Automation, Central South University, Changsha 410083, China
Yihang Xie: School of Automation, Central South University, Changsha 410083, China
Yuanhao Cheng: School of Automation, Central South University, Changsha 410083, China
Huaiqing Wang: School of Automation, Central South University, Changsha 410083, China
Yu Zhou: School of Automation, Central South University, Changsha 410083, China
Xu Zhao: School of Automation, Central South University, Changsha 410083, China
Jiayao Chen: School of Automation, Central South University, Changsha 410083, China
Chunhua Yang: School of Automation, Central South University, Changsha 410083, China

Energies, 2025, vol. 18, issue 19, 1-27

Abstract: As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles.

Keywords: electric vehicle; lithium-ion battery; state of charge; state of health; remaining useful life (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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