Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles

Due to cell-to-cell variations in battery pack, it is hard to model the behavior of the battery pack accurately; as a result, accurate State of Charge (SoC) estimation of battery pack remains very challenging and problematic. This paper tries to put effort on estimating the SoC of cells series lithium-ion battery pack for electric vehicles with adaptive data-driven based SoC estimator. First, a lumped parameter equivalent circuit model is developed. Second, to avoid the drawbacks of cell-to-cell variations in battery pack, a filtering approach for ensuring the performance of capacity/resistance conformity in battery pack has been proposed. The multi-cells "pack model" can be simplified by the unit model. Third, the adaptive extended Kalman filter algorithm has been used to achieve accurate SoC estimates for battery packs. Last, to analyze the robustness and the reliability of the proposed approach for cells and battery pack, the federal urban driving schedule and dynamic stress test have been conducted respectively. The results indicate that the proposed approach not only ensures higher voltage and SoC estimation accuracy for cells, but also achieves desirable prediction precision for battery pack, both the pack's voltage and SoC estimation error are less than 2%.