卡尔曼滤波器
荷电状态
扩展卡尔曼滤波器
电池(电)
算法
转化(遗传学)
计算机科学
控制理论(社会学)
锂离子电池
工程类
功率(物理)
人工智能
化学
生物化学
物理
控制(管理)
量子力学
基因
作者
Jie Xiao,Yonglian Xiong,Pengju Lei,Ting Yi,Quanhui Hou,Yongsheng Fan,Chunsheng Li,Yan Sun
标识
DOI:10.1149/1945-7111/acf621
摘要
Accurately estimating the state of charge (SOC) is imperative for ensuring safe and dependable battery utilization. However, accurately calculating SOC for LiMn 0.6 Fe 0.4 PO 4 /LiNi 0.5 Co 0.2 Mn 0.3 O 2 (LMFP/NCM) batteries can be challenging due to their two flat voltage platforms and significant temperature dependence. To improve estimation accuracy, a battery SOC estimation method based on a dual Kalman filter (DKF) was proposed. The adaptive unscented Kalman filter (AUKF) process starts with the introduction of Schmidt orthogonal transform, which is subsequently employed in the algorithm’s sampling point selection procedure to mitigate computational complexity. Moreover, the utilization of the multi-innovation theory serves to enhance the accuracy of algorithmic estimation. The extended Kalman filter is used to identify the parameters of the equivalent circuit model online while simultaneously carrying out battery SOC estimation. This approach mitigates the impact of variations in battery model parameters during charging and discharging processes. Under complex conditions, the algorithm’s average error is less than 0.53%, demonstrating its effectiveness in improving SOC estimation accuracy as evidenced by comparison between experiment and simulation results. It has reference significance for optimizing LMFP/NCM battery SOC estimation.
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