Iterative Estimation of Lithium-Ion Battery State of Charge Based on Developed Multivariate Joint t-Distribution Particle Filter

State of Charge (SOC) estimation for lithium-ion batteries is a critical component of battery health management systems. Accurate SOC estimation is essential for ensuring the safe operation of batteries. However, SOC estimation is a multimodal, nonlinear, and non-Gaussian problem. Given that particle filters are highly effective in modeling nonlinear and non-Gaussian systems, and that the t-distribution exhibits heavy-tailed characteristics, this paper proposes an iterative SOC estimation method based on a developed multivariate joint t-distribution particle filter(DMT-PF). First, a dual-polarization equivalent circuit model is used to construct the state and observation equations for SOC estimation. A novel observation is made that the state variables involved in SOC estimation are correlated. To address this, a multivariate t-distribution is used to model the state probability density function, enabling accurate state estimation and effective resampling, thereby mitigating particle degeneracy while keeping computational cost manageable. Furthermore, when large fluctuations occur between adjacent SOC estimates, an adaptive iterative strategy is introduced to adjust the scale matrix of the t-distribution, effectively reducing estimation errors caused by frequent disturbances. Experimental results demonstrate that the proposed method outperforms conventional approaches in terms of both estimation accuracy and robustness, particularly under frequent mode-switching scenarios.