Modelling of lithium-ion battery for online energy management systems
Modelling of lithium-ion battery for online energy management systems
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DOI：
10.1049/iet-est.2012.0008
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发表时间：
2012-11
影响因子：
2.3
通讯作者：
S. X. Chen;H. Gooi;Nan Xia;Mingqiang Wang
中科院分区：
文献类型：
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作者：
S. X. Chen;H. Gooi;Nan Xia;Mingqiang Wang
提出了一种新的用于在线能量管理系统的等效锂离子电池模型。它有一个平衡电势E和一个等效内阻Rint。平衡电势E表示为荷电状态（SOC）、电流和温度的函数。等效内阻R int包括R1和R2。R1定义为电阻，它可以由放电电流和温度来表示. R2定义为温度变化引起的电阻.采用自适应扩展卡尔曼滤波器实现了基于所提出的锂离子电池模型的在线能量管理系统。SOC被认为是锂离子电池充放电过程的状态变量。自适应地更新处理噪声和观测误差的协方差参数。在能量在线管理系统中，通过在线测量电压和电流，可以预测锂离子电池的SOC。验证了所提锂离子电池模型的有效性和鲁棒性。实验结果表明，SOC的估计是准确的各种操作条件。在实验结果和分析部分中还将所提出的方法与其他SOC估计方法进行了比较。
This study presents a new equivalent lithium-ion (Li-ion) battery model for online energy management system. It has an equilibrium potential E and an equivalent internal resistance R int . The equilibrium potential E is expressed as a function of state-of-charge (SOC), current and temperature. The equivalent internal resistance R int includes R 1 and R 2 . R 1 is defined as the resistance, which can be formulated by the discharging current and temperature. R 2 is defined as the resistance which is because of the change of temperature. The adaptive extended Kalman filter is employed to implement the online energy management system based on the proposed Li-ion battery model. The SOC is considered as the state variable for the charging or discharging process of the Li-ion battery. The covariance parameters of the processing noise and observation errors are updated adaptively. The SOC of the Li-ion battery can be predicted by the online measured voltage and current in the online energy management system. The effectiveness and robustness of the proposed Li-ion battery model is validated. Experimental results show that the estimated SOC is accurate for various operating conditions. A comparison between the proposed method and other SOC estimation methods is also shown in the experimental results and analysis section.