State-of-charge estimation in Li-SOCl2 batteries via electrochemical impedance spectroscopy and a type-2 fuzzy logic framework based on the mean aggregation interval approach

Peyman Bayat and Pezhman Bayat

Energy, 2025, vol. 341, issue C

Abstract: Accurate and resilient state-of-charge (SoC) estimation is essential for lithium–thionyl chloride (Li-SOCl2) batteries operating under real-world uncertainty. This study presents a novel context-aware framework, mean aggregation interval approach (MAIA) integrated with interval type-2 fuzzy sets (IT2FSs), that intelligently merges advanced electrochemical characterization with adaptive fuzzy modeling. Distinctively, our approach leverages electrochemical impedance spectroscopy (EIS) data collected from a practical onboard setup rather than laboratory-grade systems, bridging the gap between algorithmic rigor and realistic deployment. Five novel EIS-derived parameters are extracted, offering deeper insight into battery behavior across varying SoC levels. MAIA performs robust multi-stage filtering to autonomously exclude anomalies linked to faulty cells, preserving data integrity without manual supervision. Subsequently, layered IT2FS reasoning applies midline interval calculations and smooth fuzzy rule aggregation, yielding interpretability and precision under nonlinear discharge conditions. The integrated methodology delivers fault-tolerant SoC tracking that is resilient to sensor ambiguity and operational fluctuation, validated through fourteen real-world experiments. Compared to existing methods, MAIA–IT2FSs demonstrates superior accuracy and flexibility, offering a high-fidelity foundation for smart meters, electric mobility systems, and next-generation energy platforms. These innovations mark a significant step toward scalable and intelligent battery management. The framework is computationally efficient, suitable for real-time embedded implementation, with execution times of under 50 ms on resource-constrained hardware.

Keywords: Electrochemical impedance spectroscopy; Energy systems; Interval type-2 fuzzy sets; Lithium-thionyl chloride; State-of-charge estimation; Smart meters (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:341:y:2025:i:c:s0360544225052053

DOI: 10.1016/j.energy.2025.139563

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