Energy State Estimation for Series-Connected Battery Packs Based on Online Curve Construction of Pack Comprehensive OCV

Lei Pei, Yuhong Wu, Xiaoling Shen, Cheng Yu, Zhuoran Wen and Tiansi Wang ()
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Lei Pei: Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Yuhong Wu: Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Xiaoling Shen: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Cheng Yu: Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Zhuoran Wen: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Tiansi Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2025, vol. 18, issue 7, 1-20

Abstract: Accurate estimation of the state of energy (SOE) in lithium-ion batteries is crucial for determining the output power and driving range of electric vehicles. However, in series-connected battery packs, inconsistencies among individual cells pose significant challenges for precise SOE estimation. This issue is particularly pronounced for lithium iron phosphate (LFP) batteries. Their relatively flat open-circuit voltage (OCV) curve makes the classic method of directly weighting the SOE of representative cells—commonly used for ternary batteries—ineffective. This is because the traditional method relies heavily on a linear relationship between the SOE and the voltage, which is not present in LFP batteries. To address this challenge, a novel SOE estimation approach based on the online construction of the battery pack’s comprehensive OCV curve is proposed in this paper. In this new approach, the weighting of representative cells shifts from a result-oriented mode to a key-parameter-oriented mode. By adopting this mode, the whole pack’s comprehensive OCV can be obtained training free and the pack’s SOE can be estimated online within an equivalent circuit model framework. The experimental results demonstrate that the proposed method effectively controls the SOE estimation error within 3% for series battery packs composed of cells with varying degrees of aging.

Keywords: series battery pack; state of energy; representative cells; OCV curve construction; dynamic weight distribution (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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