A method for estimating the state-of-charge of LFP pouch batteries based on force-electrical coupled signals

Estimating the state-of-charge (SOC) of LiFePO₄ (LFP) batteries is challenging due to their long open-circuit-voltage (OCV) plateau regions. In this paper, a SOC estimation method based on force-electrical coupled signals in the framework of filtering algorithm is designed by combining the first-order equivalent mechanical model (EMM) and the first-order RC equivalent circuit model (ECM). Combining the respective advantages of LFP battery force curve characteristics and OCV curve characteristics to achieve globally optimal SOC estimation with multi-sensor fusion. First, an innovative EMM is proposed to simulate the force variation of the battery with SOC change, and a particle swarm optimization (PSO) algorithm is used to identify the model parameters. Second, the ECM and EMM adopt a tandem structure to estimate the SOC using extended Kalman filtering (EKF), where a noise covariance adaptive updating step is introduced to dynamically adjust the estimation weights of the two models in order to address the problem that the non-monotonicity of the force-SOC curves may lead to erroneous convergence of the SOC. Finally, it is demonstrated that the root-mean-square error of the SOC estimation of this method is within 2%, which improves the accuracy by a factor of 2-3 compared to the traditional voltage feedback method.