一文详解自适应无迹卡尔曼滤波（AUKF） - CSDN博客
网页2023年6月14日 · 文章介绍了自适应无迹卡尔曼滤波(aukf)算法，该算法能动态调整过程噪声和观测噪声的协方差，以适应系统实际行为。 AUKF通过比较预测误差来更新噪声协方差，提高了滤波性能，尤其适用于噪声特性变化的非线性系统。
【逐行注释】自适应Q和R的AUKF（自适应无迹卡尔曼滤 …
网页2024年9月13日 · 自适应无迹卡尔曼滤波（Adaptive Unscented Kalman Filter，AUKF）是一种用于状态估计的滤波算法。 自适应 无迹 卡尔曼滤波 适用于非线性和非高斯的系统状态估计问题，可以广泛应用于机器人导航、目标跟踪、航天器导航等领域。
An improved adaptive unscented kalman filtering for state of …
网页2020年12月1日 · Adaptive unscented Kalman filter (AUKF) has been intensively applied to estimate SOC due to its features of self-correction and high accuracy. Nevertheless, the estimation by traditional AUKF cannot proceed when error covariance matrix is non-positive definite, greatly influencing the stability of SOC estimation.
【逐行注释】自适应Q和R的AUKF（自适应无迹卡尔曼滤波），附 …
网页2024年9月12日 · 自适应无迹卡尔曼滤波（Adaptive Unscented Kalman Filter，AUKF）是一种用于状态估计的滤波算法。 自适应无迹卡尔曼滤波适用于非线性和非高斯的系统状态估计问题，可以广泛应用于机器人导航、目标跟踪、航天器导航等领域。
SOC estimation for lithium-ion battery based on AGA-optimized AUKF
网页2024年1月1日 · The window size for covariance matching (CM) of the adaptive unscented Kalman filter (AUKF) affects the state of charge (SOC) estimation performance due to changes with time in the distribution of error innovation sequence (EIS). A new adaptive genetic algorithm (AGA) to address this problem is proposed in this paper.
自适应无迹卡尔曼滤波动力电池的SOC估计 - BJTU
网页An AUKF is used to estimate the dynamic SOC of an electric vehicle. At first, an equivalent circuit model appropriate for SOC estimation is built and the corresponding...
State of charge estimation with the adaptive unscented Kalman …
网页2021年9月1日 · The accurate estimation of the state of charge (SOC) plays an important role in optimizing the energy management of electric vehicles. To improve the estimation accuracy of SOC, this study proposes a SOC estimation method with the adaptive unscented Kalman filter (AUKF) based on an accurate equivalent circuit model.
Lithium Polymer Battery State-of-Charge Estimation Based on …
网页2015年6月1日 · Abstract: An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately …
An Adaptive Unscented Kalman Filter With Selective Scaling (AUKF …
网页This article introduces an augmented adaptive unscented Kalman filter (KF). The proposed novel technique is suitable to simultaneously estimate both the diagonal process noise covariance matrix and the unknown inputs, thus combining previously reported KF estimators for unknown inputs (dual or joint KF) and for covariance matrices (adaptive KF).
A Novel State Estimation Approach Based on Adaptive …
网页2020年10月12日 · Accurately estimating the state-of-charge (SOC) of battery is of particular importance for real-time monitoring and safety control in electric vehicles. To obtain better SOC estimation accuracy, a joint modeling method based on adaptive unscented Kalman filter (AUKF) and least-squares support vector machine (LSSVM) is proposed.
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