Ternary-material lithium-ion battery SOC estimation under various ambient temperature

Ambient temperature produces great effects on battery state-of-charge (SOC) estimation, due to the unstable estimation algorithm, the weakened traceability of battery model, and variable model parameters at various temperatures, especially lower temperatures. The widely used method based on the equivalent circuit model (ECM) offline in using different algorithm, like current integral, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), can obtain an accurate SOC estimation at room temperature, but it is difficult to guarantee the high precision at lower temperatures. To address this problem, the battery model is investigated at different temperature, and an offset item is proposed to develop the observer equation in the estimated model. Then, the square root of the Sigma points Kalman filter (SR-UKF) is applied, and on the basis of the individual model parameter-temperature table and the developed model, the high accuracy of SOC estimation is achieved. Additionally, considering the burden of original parameter modification (all model parameters modified) at various temperature which will increase the product cost and computational complexity of the battery management system (BMS), the relationship between individual model parameter and the error of SOC estimation is built, which is helpful for the simplification of parameter modification. The results indicate that the proposed method based on the developed estimated model and the simplified parameter modification can achieve an accurate, stable, and efficient SOC estimation.