锂(药物)
锂离子电池
电池容量
订单(交换)
电池(电)
离子
容量损失
可靠性工程
环境科学
材料科学
化学
热力学
工程类
物理
功率(物理)
经济
医学
有机化学
财务
内分泌学
作者
Shuxian Li,Minghui Hu,Yunxiao Li,Changchao Gong
摘要
For state-of-charge (SOC) estimation, the resistance deterioration and continuous capacity loss can lead to erroneous estimation results. In this paper, an SOC estimator of lithium-ion battery based on the fractional-order model and adaptive dual Kalman filtering algorithm is proposed first. Then, to improve the accuracy of SOC estimation considering capacity loss, the particle filter algorithm is applied to update capacity online in real time. Then, an SOC estimation method is proposed considering battery capacity loss. The simulation results show that the accuracy of battery capacity prediction based on particle filter is high under the condition of capacity loss.
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