基于数据-模型混合驱动的锂电池储能系统状态估计及预警方法

State estimation and early warning method for lithium battery energy storage system based on data-model hybrid drive

作为调控源-网-荷可靠运行的有效手段,电池储能已向更大规模迈进,而储能电池运行安全和可靠问题是制约其进一步发展和应用的关键环节。针对储能锂电池的运行状态估计和预警问题,本文提出一种基于数据-模型混合驱动的非线性状态估计方法。首先,以经典Thevenin电路模型和扩展卡尔曼滤波构建锂电池的数学模型;然后,提出基于实际系统与模型仿真系统运行状态偏差的非线性估计方法,实现对数-模偏差的有效估计进而实现电池的预警过程;最后,通过仿真算例验证本文所提方法受模型精度、误差以及充放电电流幅值等因素影响较小。本文首次将非线性状态估计(NSET)应用于锂电池预警中,利用模型驱动方法即扩展卡尔曼滤波结合数据驱动方法即NSET,可通过简单的计算精确估计储能锂电池的实时状态并进行预警,具有工程实际应用价值。

As an effective approach to operation of source network load,battery energy storage has been developed toward larger scale direction,and the safe and reliable operation of energy storage battery is the key to restrict its further development and application.Aiming at solving the problem of state estimation and early warning of lithium-ion battery,this paper proposes a nonlinear state estimation method based on data model hybrid drive.Firstly,the classical Thevenin circuit model and extended Kalman filter are used to build the mathematical model of lithium battery.Secondly,a nonlinear estimation method based on the deviation between the actual system and the model simulation system is proposed to realize the effective estimation of the log modulus deviation,and then realize the pre alarm process of the battery.Finally,the simulation example verifies that the proposed method is little affected by the model accuracy,error,and charge/discharge current amplitude.In this paper,it is the first time that the nonlinear state estimation technology(NSET)is applied to lithium battery early warning.The extended Kalman filter(EKF)combined with the data-driven method(nset)can accurately estimate the real-time state of lithium energy storage battery and give early warning through simple calculation,which has practical application value in engineering.