一文详解自适应无迹卡尔曼滤波（AUKF） - CSDN博客
2023年6月14日 · 通过结合车辆动力学、路面附着系数识别、轮胎模型等方面的知识，aukf算法能够更准确地估计车辆的状态，提供精准的车辆姿态、位置、速度等信息，为自动驾驶系统的决策与控制提供必要的数据支持。
无迹卡尔曼滤波UKF的理解与应用（附Matlab实例） - 知乎
无迹卡尔曼滤波（Unscented Kalman Filter，UKF），是无损变换（Unscented Transform，UT变换）与标准卡尔曼滤波体系的结合，通过无损变换变换使非线性系统方程适用于线性假设下的标准卡尔曼体系。 也就是说UKF是在KF的基础上加入了UT变换而已。 2 UKF有什么用？ 2.1 UKF使用背景. （1）对于某个系统，你拥有准确的数学模型（状态方程和观测方程），也就是说，给出这个系统的输入，你必然能算出这个系统的输出；但是，在现实生活中往往拿到的第一手测试数据 …
A method for state of charge and state of health estimation of …
2022年11月1日 · State of charge (SOC) and state of health (SOH) of the power cell are the main parameters of the BMS during operation. In this paper, an adaptive unscented Kalman filter algorithm (AUKF) is presented for the joint estimation of SOC and SOH of lithium-ion batteries.
基于 AUKF-BP 神经网络的锂电池 SOC 估算 - cip
本文以锂离子电池为研究对象，提出一种基于自适应无迹卡尔曼滤波(aukf)和bp神经网络相结合的电池soc估算方法。 该方法通过采样策略自适应性提高了UKF的估算精度，并利用训练好的BP神经网络SOC输出值作为UKF的观测量。
An improved adaptive unscented kalman filtering for state of …
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.
A Novel State Estimation Approach Based on Adaptive …
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.
SOC estimation for lithium-ion battery based on AGA-optimized AUKF
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.
AUKF-Based Collision Avoidance of Autonomous Vehicles …
To address these problems, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed to obtain accurate relative distance and velocity of preceding vehicles. Next, this information is fed to the decision module to determine whether to perform path planning or not.
Lithium Polymer Battery State-of-Charge Estimation Based on …
2015年6月1日 · 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 establish the battery model with limited initial training ...
An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for …
2023年8月17日 · In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF ...
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