基于传感器融合的智能电池模块荷电状态估计

With of the growing emphasis on refined management of lithium-ion batteries (LIBs), there is a significant demand for low-cost estimation of the state of charge (SOC) at the individual LIB cell level. Following the emerging concept of smart batteries, a data and model dual-driven high-accuracy SOC estimation solution is proposed in this article. In particular, a cost-effective quasi-redundant current sensor configuration is proposed first, which incorporates the least-squares current adjustment technique to enable the fusion-based accurate current sensing of cells. Building upon this, an SOC estimation algorithm based on the iterative extended Kalman filter is proposed using smart battery modeling, which innovatively incorporates the cell electrical coupling for information enhancement in cell-level SOC estimation. Experimental results demonstrate that the integration of the sensing and algorithm enables precise SOC estimation, with a maximum SOC estimating error of only 1% for all in-pack cells.