基于滑动窗自适应滤波的锂电池SOC/SOH联合估计

Estimation of SOC/SOH for 18650-type lithium battery based on sliding-mode adaptive algorithm

以锂电池电化学-电路等效组合模型为基础,研究电池荷电状态(SOC)和健康状况(SOH)联合估计算法。电池组合模型包含电化学等效模型和电路等效模型两部分,两个RC并联电路分别表示电池工作过程中的瞬态响应和稳态响应。针对电池模型参数和性能参数的非线性特征,提出基于滑动窗滤波模型的非线性参数估计方法,该方法适用于锂电池的管理系统。同时,在模型参数和性能参数估计值的基础上,提出基于Kalman算法的电池SOC/SOH自适应在线联合估计方法。实验结果显示,新算法较好地解决了锂电池非线性模型引起的计算误差,保证电池SOC/SOH估计结果的实时性和有效性。

The estimation of the State of Charge （SOC） and State of Health （SOH） for 18650 lithium battery were considered. An inclusive model indicating the electrochemical characteristics of battery was taken into account and the nonlinear behavior of the open-circuit voltage versus SOC was also included in the model. The online estimation of battery parameters tackled the aforementioned problems to attain a reliable estimation of the battery SOC. Moreover, an analytical method based on sliding-mode observer was considered to estimate the additive nonlinear or uncertainty term in the model. This approach leaded to a very accurate model of the battery to be used in a battery management system. Lastly, an adaptive estimation algorithm based on parameter value was proposed to estimate the battery＇s SOH. The proposed scheme benefited from an adaptive rule for the online estimation of the series resistance in the lithium-ion battery based on the accurately identified model. Experimental tests certified the performance and feasibility of the proposed schemes.