锂离子电池SOC及容量的多尺度联合估计

锂离子电池的荷电状态（SOC）和电池容量估计是电池管理系统的核心。由于SOC和容量在估计过程中参数相互影响，提出一种适用于三元锂离子电池SOC及容量的多尺度联合估计方法。采用戴维宁等效电路模型，建立数学模型及状态空间方程。针对不同温度下电池特性不同的问题，在不同温度下开展了模型参数辨识，建立了参数随SOC及温度的变化关系。基于双扩展卡尔曼滤波（DEKF）算法建立了电池状态多尺度联合估计模型，对电池的SOC、极化电压在微观时间尺度上进行估计，对电池的容量在宏观时间尺度上进行估计，并对SOC估计中的容量进行更新，保证了电池长期估计的精度。在宽温度范围内进行验证，所建立的三元锂离子电池多尺度联合估计方法具有较高的精度。 

Multi-scale joint estimation of SOC and capacity of lithium-ion battery

The State of Charge (SOC) and battery capacity estimation of lithium-ion battery are the core of battery management system. Because the parameters of SOC and capacity affect each other in the estimation process, a multi-scale joint estimation method for SOC and capacity of ternary lithium-ion battery is proposed. In this paper, we use the equivalent circuit model of Thevenin to establish the mathematical model and state space equation. In order to solve the problem of different characteristics of battery at different temperatures, model parameters identification was carried out at different temperatures, and the two-dimensional pulse spectrograms of parameters related to SOC and temperature were established. Based on the Dual Extended Kalman Filter (DEKF), a multi-scale joint estimation model of battery state is established. The SOC and polarization voltage of the battery are estimated on the micro-time scale, the capacity of the battery is estimated on the macro-time scale, and the capacity of the SOC estimation is updated to ensure the accuracy of battery long-term estimation. Finally, the proposed multi-scale joint estimation algorithm for ternary lithium-ion battery is validated in a wide temperature range, and the result shows that it has high accuracy.