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

Co-Estimation of Lithium-Ion Battery State-of-Charge and Capacity Through the Temperature and Aging Awareness Model for Electric Vehicles

作为电动车辆的技术瓶颈,动力电池具有强时变非线性特性且可测量有限,使用时易受温度和老化的影响,全寿命周期和宽温度下精确状态估计一直是行业的技术难题。为此,该文首先使用不同温度和不同老化阶段的数据,建立了具有温度和老化意识的多阶段模型;然后利用概率密度函数计算单一模型的权值,提出了多阶段模型融合驱动的动力电池荷电状态(SOC)和容量协同估计方法;最后考虑不确定老化和温度因素的验证结果表明,提出的方法具有较高的SOC和容量估计精度,且对初值误差不敏感,-10%~50%初始误差时SOC估计误差小于2%,收敛速度快。

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.