王萍　张吉昂　程泽　于耀先

摘   要：锂离子电池的荷电状态（SOC）、健康状态（SOH）和剩余使用使命（RUL）是锂离子电池安全稳定运行的重要状态参数，本文提出一种基于充电电压上升片段的锂离子电池状态联合估计方法，实现对电池预测起点（SP）到寿命终点（EOL）的较长运行周期内SOC、SOH和RUL的联合估计. 该框架在充电阶段进行SOH和RUL估计，在放电阶段进行SOC估计. 首先提取电池恒流充电电压曲线片段的上升时间作为健康特征（HF），以HF作为输入，循环容量作为输出，建立最小二乘支持向量机（LSSVM）电池老化模型，对当前健康状态进行估计;采用等效电路模型对该电压区段进行非线性拟合，用拟合参数建立状态空间模型，结合无迹卡尔曼滤波算法进行SOC估计;用高斯过程回归时间序列模型对电池的健康特征序列进行建模，通过循环次数外推预测健康特征的变化趋势，并结合LSSVM老化模型，对RUL进行预测并给出置信区间. 实验结果表明，所提方法具有较高的估计精度和较好的稳定性.

关键词：荷电状态;健康状态;剩余使用寿命;等效电路模型;数据驱动方法

中图分类号：TM912.1                   文献标志码：A

A Coupled State Estimation Method of Lithium

Batteries Based on Partial Charging Voltage Segment

WANG Ping，ZHANG Jiang，CHENG Ze，YU Yaoxian？覮

（School of Electrical and Information Engineering，Tianjin University，Tianjin 300072，China ）

Abstract：The state of charge （SOC）， state of health （SOH） and residual mission （RUL） of lithium-ion battery are important state parameters for the safe and stable operation of lithium-ion battery. In this paper， a coupled estimation method of lithium-ion battery state based on the rising segment of charging voltage is proposed to realize the coupled estimation of SOC， SOH and RUL in a long operation cycle from the starting point of battery prediction （SP） to the end of life （EOL）. The framework estimates SOH and RUL in the charging phase and SOC in the discharge phase. Firstly， the rising time of constant current charging voltage curve segment is extracted as the health feature （HF）， and the HF as the input and cycle capacity as the output are used to establish the  least squares support vector machine （LSSVM） battery aging model for SOH estimation; The equivalent circuit model is used for nonlinear fitting of the voltage segment， and the state space model is established with the fitting parameters， which is combined with the unscented Kalman filter algorithm to estimate SOC; Gaussian process regression time series model is used to model the health feature series， and the change trend of HF is predicted by extrapolation of cycle times，which is combined with LSSVM model to predict RUL and the corresponding confidence interval. The experimental results show that the proposed method has high estimation accuracy and good stability.

Key words：state of charge;sate of health;remaining useful life;equivalent circuit model;data-driven method

鋰离子电池具有成本低、能量密度高、循环寿命长的优点，在全球能源和环境危机不断加剧的背景下，正逐渐成为电动汽车、直流变电站和光伏电网的重要储能装置[1]. 对锂离子电池配备电池管理系统（battery management system，BMS）可以对电池进行科学评估、风险预警和定期更换，保障电池的健康稳定运行. 锂离子电池的荷电……