An Improved State-of-Charge Estimation Method for Sodium-Ion Battery Based on Combined Correction of Voltage and Internal Resistance

iEnergy Pub Date : 2024-09-23 DOI:10.23919/IEN.2024.0017
Yongqi Li;Cheng Chen;Youwei Wen;Qikai Lei;Kaixuan Zhang;Yifei Chen;Rui Xiong
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Abstract

The accurate state-of-charge (SOC) estimation of sodium-ion batteries is the basis for their efficient application. In this paper, a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed, which considers the combination of open circuit voltage (OCV) and internal resistance correction. First, the optimal order of equivalent circuit model is analyzed and selected, and the monotonic and stable mapping relationships between OCV and SOC, as well as between ohmic internal resistance and SOC are determined. Then, a joint estimation algorithm for battery model parameters and SOC is established, and a joint SOC correction strategy based on OCV and ohmic internal resistance is established. The test results show that OCV correction is reliable when polarization is small, that the ohmic internal resistance correction is reliable when the current fluctuation is large, and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.
基于电压和内阻联合校正的钠离子电池充电状态估计改进方法
准确估计钠离子电池的充电状态(SOC)是其有效应用的基础。本文提出了一种适合钠离子电池及其应用条件的新 SOC 估算方法,该方法考虑了开路电压(OCV)和内阻校正的结合。首先,分析并选择了等效电路模型的最佳阶数,确定了开路电压与 SOC 之间以及欧姆内阻与 SOC 之间单调稳定的映射关系。然后,建立了电池模型参数和 SOC 的联合估计算法,并建立了基于 OCV 和欧姆内阻的 SOC 联合校正策略。测试结果表明,当极化较小时,OCV 修正是可靠的;当电流波动较大时,欧姆内阻修正是可靠的;所提出方法的 SOC 估计最大绝对误差不超过 2.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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