关键词: 锂离子电池, 融合模型, 荷电状态估计, 无迹卡尔曼滤波

Abstract:

The accurate and efficient assessment of the state of charge (SOC) of lithium-ion batteries is critical to ensuring the satisfactory performance and safety of electric vehicles and energy storage devices. The equivalent circuit model is considered to be effective for describing complex reaction processes inside Li-ion batteries. To use the equivalent circuit model to address the difficult trade-off between accuracy and complexity in SOC estimation, we use the first-order RC model as the foundation of this study. In order to improve the performance of the model over the SOC interval, the RC model is optimized according to electrochemical principles. By adding an improved error term for the solid-phase diffusion process inside the reactive battery to the open-circuit voltage (OCV) module of the first-order RC model, we reduce the computational complexity. By adding a modified error term that reflects the solid-phase diffusion process inside the cell to the first-order RC model of the OCV module, we also reduce the error between the equivalent circuit model and the more accurate mechanism model while ensuring that the computational complexity remains low. Then, based on the multiplicity test and pulse test data, a particle swarm algorithm is used to reduce the complexity and improve the accuracy of parameter identification through parameter decoupling. At the same time, a polynomial method is used to fit the OCV-SOC curve based on the OCV data from a small-multiplication test. Subsequently, based on the model parameter identification results, SOC estimation research is carried out. To address the insufficient accuracy of conventional Kalman filtering, a weighted sliding window is used with traceless Kalman filtering to improve the accuracy and robustness of the SOC estimation, and the Kalman filtering algorithm is verified based on the UDDS and DST dynamic test data. The final estimation results show excellent accuracy and robustness, unlike the traditional method. The results quickly converge to the accurate value when the initial SOC has a large deviation.

Key words: lithium-ion battery, fusion model, state of charge estimation, untracked Kalman filtering