温度和老化意识融合驱动的电动车辆锂离子动力电池电量和容量协同估计

As the technical bottleneck of electric vehicles, batteries have strong time-varying nonlinear characteristics and limited measurability. They are easily affected by temperature and aging during use. Accurate state estimation under the full life cycle and the wide temperature has always been a technical problem in the industry. Therefore, this paper first uses the data of different temperatures and different aging stages to establish a multi-stage model with temperature and aging awareness; then uses the probability density function to calculate the weight of the single models and proposes a multi-stage model fusion-driven battery state of charge (SOC) and capacity estimation method. Finally, the verification results considering uncertainty of aging and temperature factors show that the proposed method has high SOC and capacity estimation accuracy and is not sensitive to the initial error. The SOC estimation error is less than 2% with the -10% to 50% of the initial SOC errors, and the convergence is fast.