@article{Li2026, 
author = {Xiaoyu Li and Shen Zhao and Xinyu Wei and Yanli Zhu and Jun Tian},
title = {Multi-dimensional Characterization of Lithium-ion Battery States via Voltage and Multiscale Mechanical Feature Integration from Electrochemical-thermal-mechanical Models},
year = {2026},
journal = {Chinese Journal of Electrical Engineering},
volume = {12},
number = {1},
pages = {50-72},
keywords = {Lithium-ion battery, electrochemical-thermal-mechanical coupled model, degradation mechanisms, multi-dimensional feature},
url = {https://www.sciopen.com/article/10.23919/CJEE.2025.000193},
doi = {10.23919/CJEE.2025.000193},
abstract = {Accurate state characterization of lithium-ion batteries (LIBs) is critical for enhancing their reliability and extending the lifespan of battery management systems. The inherent coupling of electrochemical, thermal, and mechanical phenomena necessitates multidimensional parameter sensing for a precise state assessment. Herein, a coupled electrochemical-thermal-mechanical (ETM) multi-physics modeling framework is proposed to reveal the multiscale parameter coevolution mechanism and explore novel state-of-charge (SOC) estimation indicators. By integrating a pseudo-two-dimensional (P2D) electrochemical model, thermogenesis equations, and elastic-plastic mechanics constitutive relations, an aging kinetic model considering the loss of active material (LAM) and loss of lithium inventory (LLI) is constructed. Various driving cycles, including constant current discharge, urban dynamometer driving schedule (UDDS) dynamics, and 1C cyclic charge/discharge experiments, are utilized to verify the proposed model with the two indices of voltage and strain. Based on the validated model, the aging kinetics of the coupled P2D electrochemical-3D thermo-mechanical system are first investigated to quantify the interaction mechanisms of mechanical stress, heat production, and electrochemical polarization under quasi-static and dynamic loading. The results demonstrate that displacement and maximum strain metrics detect microstructural damage earlier, and synergy with voltage signals reduces the estimation deviations of traditional methods under wide temperature and dynamic conditions, offering a novel paradigm for multidimensional state estimation.}
}