An Electro-Mechanical Information Fusion-Based SOC Estimation Method for Lithium-Ion Batteries Enhanced by Advanced Optical Fiber Sensing

Accurate state-of-charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries. However, the weak voltage observability of lithium iron phosphate (LFP) batteries within the voltage plateau region limits the accuracy of conventional voltage-based methods. To address this issue, an electro–mechanical information fusion framework for SOC estimation is proposed. Fiber Bragg grating (FBG) sensors were employed to simultaneously measure the surface strain and temperature of prismatic LFP batteries. Experimental results showed that the strain signal exhibited a stronger correlation with SOC than the voltage signal, with an average absolute correlation coefficient of 0.92. A Thevenin equivalent circuit model combined with an adaptive forgetting factor recursive least squares (AFFRLS) algorithm was established for online voltage modeling, while a Mamba-based strain model was developed to capture the nonlinear temporal relationship between multidimensional sensing data and battery strain. The two models were further integrated with adaptive unscented Kalman filters (AUKFs) and fused through a dual-layer adaptive weighting strategy. Experimental results under the five operating conditions considered in this study demonstrated that the proposed method achieved average RMSE and MAE values of 0.98% and 0.80%, respectively, outperforming standalone voltage- and strain-based methods.