This paper presents a type of noise-adaptive (NA) interacting multiple model (IMM) algorithm combined with an unscented Kalman filter (UKF) in order to address problems in poor filtering accuracy and filtering divergence of IMM caused by the statistical properties of noise. These properties further affect the estimation accuracy of state of charge (SOC) when IMM deals with dynamic changes in battery model parameters. Accordingly, the proposed algorithm can realize the accurate estimation of SOC when model parameters change dynamically and when the statistical properties of noise are unknown. By integrating a Sage-Husa noise estimator, NA-IMM-UKF enabled the whole UKF model set to estimate and correct noise information in real time in order for posteriori and unknown noise information to be adjusted adaptively. At the same time, a forgetting factor was introduced in order to optimize the proposed algorithm, thus improving the problem in which the Sage–Husa noise estimator converges slowly when used in conjunction with UKF. By conducting an experiment and simulation, NA-IMM-UKF was shown to carry out SOC estimation under multiple models, with an average error of only 0.4% and maximum error of only 1.08%. However, by comparing the estimated result of SOC under a single model with the Sage–Husa​ estimator minus the forgetting factor, the average error dropped by 0.15% while the maximum error decreased by 2.78%. In the final noise comparison experiment, following the addition of unknown random noise, the average error of the NA-IMM-UKF algorithm was found to be only 0.48%, while the maximum error was only 1.51%, far surpassing the estimation results of the IMM-UKF algorithm in the same state. As a result, even if the statistical properties of noise are uncertain, the proposed algorithm can still estimate SOC both accurately and effectively.

中文翻译：

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基于噪声自适应交互多重模型的锂离子电池荷电状态估计

针对噪声统计特性引起的IMM滤波精度差和滤波发散的问题，提出一种与无迹卡尔曼滤波器（UKF）相结合的噪声自适应（NA）交互多模型（IMM）算法。当IMM处理电池模型参数的动态变化时，这些特性进一步影响充电状态（SOC）的估计精度。因此，该算法可以实现模型参数动态变化和噪声统计特性未知时SOC的准确估计。通过集成 Sage-Husa 噪声估计器，NA-IMM-UKF 使整个 UKF 模型集能够实时估计和校正噪声信息，以便自适应调整后验和未知噪声信息。同时引入遗忘因子来优化所提出的算法，从而改善了Sage-Husa噪声估计器与UKF结合使用时收敛缓慢的问题。通过实验和仿真，证明NA-IMM-UKF可以在多种模型下进行SOC估计，平均误差仅为0.4%，最大误差仅为1.08%。然而，通过将单一模型下的 SOC 估计结果与减去遗忘因子的 Sage-Husa​估计器进行比较，平均误差下降了 0.15%，最大误差下降了 2.78%。在最终的噪声对比实验中，在加入未知随机噪声后，发现NA-IMM-UKF算法的平均误差仅为0.48%，而最大误差仅为1.51%，远远超过了算法的估计结果。 IMM-UKF 算法处于相同状态。因此，即使噪声的统计特性不确定，所提出的算法仍然可以准确有效地估计 SOC。