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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v10i2.14329</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>A Dual-Adaptive Electronic Monitoring System for Robust State-of-Charge Estimation of LiFePO4 Batteries in High-Precision Applications</title><url>https://artdesignp.com/journal/JERA/10/2/10.26689/jera.v10i2.14329</url><author>YueChaomin,ZhangYongpeng,ZhuZhicheng</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>2</issue><history><date date-type="pub"><published-time>2026-03-31</published-time></date></history><abstract>Electronic battery management systems (BMS) require high-precision state-of-charge (SOC) estimation to ensure the reliability of integrated electronic applications. However, the flat voltage plateau of LiFePO4 batteries poses a significant challenge for electronic sensing and state observation. This paper proposes a synergistic dual-adaptive framework designed for real-time electronic monitoring. The framework integrates a thermodynamic-gradient gain-re-allocation (TG-GRA) mechanism into the recursive least squares algorithm to enhance parameter identification fidelity. Furthermore, a current-adaptive augmented extended Kalman filter (CAEKF) is developed to optimize the electronic control loop by dynamically adjusting noise covariance and compensating for voltage hysteresis. Experimental validation across 32 dynamic cycles demonstrates that the proposed electronic sensing strategy reduces the root-mean-square error (RMSE) to 1.3%. With its low computational overhead, this framework provides a robust and efficient system-level solution for embedded electronic research and applications.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Barik S, Saravanan B, 2024, Recent Developments and Challenges in State-of-Charge Estimation Techniques for Electric Vehicle Batteries: A Review. Journal of Energy Storage, 2024(100).</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Demirci O, Taskin S, Schaltz E, et al., 2024, Review of Battery State Estimation Methods for Electric Vehicles: Part I: SOC Estimation. 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