Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference

噪音(视频) 控制理论(社会学) 均方误差 荷电状态 估计员 电池(电) 等效电路 电压 近似误差 奇异值分解 干扰(通信) 工程类 电子工程 功率(物理) 算法 计算机科学 数学 电气工程 物理 统计 频道(广播) 控制(管理) 量子力学 人工智能 图像(数学)
作者
Xiaoyong Yang,Shunli Wang,Paul Takyi‐Aninakwa,Xiao Guang Yang,Carlos Fernandez
出处
期刊:Journal of energy storage [Elsevier BV]
卷期号:73: 108974-108974 被引量:1
标识
DOI:10.1016/j.est.2023.108974
摘要

Strong electromagnetic interference, which has a significant impact on the performance and safety of the lithium-ion battery, usually affects the accurate state of charge (SOC). Different optimization strategies are used to estimate the model parameters and the SOC due to the unknown nonlinear characteristics caused by noise. However, the impact of sensor and model errors is treated separately. To express the sensor and model uncertainties, a noise bias compensation-equivalent circuit model (NBC-ECM) is proposed, in which sensor noise and model error voltages are employed in the model structure and the SOC estimation process of the lithium-ion battery. For parameter identification, a singular value decomposition-bias compensation recursive least squares (SVD-BCRLS) algorithm is proposed to identify the characteristic micro-parameters of the battery. Then, a moving window adaptive extended Kalman filtering (MWAEKF) algorithm based on window functions is proposed for accurate SOC estimation of lithium-ion batteries. The stability of the model parameters and the reliability of the proposed algorithm in estimating the SOC are evaluated using different noise factors: current and voltage sensor noises of 10 and 50 mA. Using the proposed SVD-BCRLS-MWAEKF algorithm, the maximum SOC error is 1.3 %, the root mean square error (RMSE) is 0.3972 %, and the mean absolute error (MAE) is 0.2316 % using the noise of 0.05 V/A under the hybrid power pulse characterization (HPPC) operating condition. With the same noise value under the Beijing bus dynamic stress test (BBDST) operating condition, the proposed algorithm SOC has a maximum SOC error of 1.57 %, an RMSE of 0.5638 %, and an MAE of 0.4475 %. Under noise interference conditions, estimation is more accurate compared to static conditions, proving that the proposed algorithm can overcome the uncertainties encountered by lithium-ion batteries for real-time BMS applications.
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