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State of Charge Estimation for Lithium-Ion Battery Based on Hybrid Compensation Modeling and Adaptive H-Infinity Filter
IEEE Transactions on Transportation Electrification ( IF 7 ) Pub Date : 2022-06-03 , DOI: 10.1109/tte.2022.3180077
Xing Shu 1 , Zheng Chen 1 , Jiangwei Shen 1 , Fengxiang Guo 1 , Yuanjian Zhang 2 , Yonggang Liu 3
Affiliation  

Accurate estimation of state of charge (SOC) is crucial for operation performance promotion of lithium-ion batteries. However, the variations of temperature and loading current directly impact the estimation accuracy of SOC. To fully account for these influences, this study proposes a hybrid compensation model and exploits an advanced algorithm for high-performance SOC estimation. First, a fractional-order model (FOM) is constructed to delineate the electrochemical behaviors of batteries with higher accuracy, compared with traditional integral-order model (IOM). Then, the relationship among discharge rate, temperature, and available capacity is explored, and a capacity compensation model is established via the random forest (RF) algorithm. Based on the trustworthy parameter identification and capacity recognition, the SOC is estimated by the adaptive H-infinity filter (AHIF) to fully cope with the model and operation condition variations raised by different temperatures and loading currents. By this manner, the presented method enhances the robustness to parameter uncertainty and modeling errors and promotes the estimation accuracy of SOC in wide temperature range. The experimental results highlight that compared with the traditional IOM and adaptive extended Kalman filter (AEKF), the proposed method can highly boost the temperature adaptability, convergence speed, and estimation accuracy of SOC.

中文翻译:

基于混合补偿模型和自适应 H-Infinity 滤波器的锂离子电池荷电状态估计

准确估算荷电状态(SOC)对于提升锂离子电池的运行性能至关重要。然而,温度和负载电流的变化直接影响SOC的估计精度。为了充分考虑这些影响,本研究提出了一种混合补偿模型,并开发了一种用于高性能 SOC 估计的高级算法。首先,与传统的积分阶模型(IOM)相比,构建了分数阶模型(FOM)以更准确地描述电池的电化学行为。然后,探索放电率、温度和可用容量之间的关系,并通过随机森林 (RF) 算法建立容量补偿模型。基于可信参数识别和容量识别,SOC由自适应H-infinity滤波器(AHIF)估算,以充分应对不同温度和负载电流引起的模型和操作条件变化。通过这种方式,所提出的方法增强了对参数不确定性和建模误差的鲁棒性,并提高了宽温度范围内SOC的估计精度。实验结果表明,与传统的 IOM 和自适应扩展卡尔曼滤波器 (AEKF) 相比,所提出的方法可以显着提高 SOC 的温度适应性、收敛速度和估计精度。所提出的方法增强了对参数不确定性和建模误差的鲁棒性,提高了宽温度范围内SOC的估计精度。实验结果表明,与传统的 IOM 和自适应扩展卡尔曼滤波器 (AEKF) 相比,所提出的方法可以显着提高 SOC 的温度适应性、收敛速度和估计精度。所提出的方法增强了对参数不确定性和建模误差的鲁棒性,提高了宽温度范围内SOC的估计精度。实验结果表明,与传统的 IOM 和自适应扩展卡尔曼滤波器 (AEKF) 相比,所提出的方法可以显着提高 SOC 的温度适应性、收敛速度和估计精度。
更新日期:2022-06-03
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