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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/battery-state-of-charge
课程评论：没有评论
课程名称：电池充电状态(SOC)估计 概述：本课程可以作为学分课程选择，课程代码为ECEA 5732，是科罗拉多大学博尔德分校电气工程硕士学位的一部分。在本课程中，您将学习如何实现不同的充电状态估计方法，并评估它们的相对优缺点。课程结束时，您将能够： - 实现基于电压和电流的简单充电状态估计器，并理解其局限性 - 解释序列概率推理解决方案中每一步的目的 - 执行提供的Octave/MATLAB脚本，用于线性卡尔曼滤波器，并评估结果 - 执行提供的Octave/MATLAB脚本，使用扩展卡尔曼滤波器对实验数据进行充电状态估计并评估结果 - 执行提供的Octave/MATLAB脚本，使用sigma点卡尔曼滤波器对实验数据进行充电状态估计并评估结果 - 实施方法以检测和剔除故障的电压传感器测量数据 课程大纲： 1. SOC估计的重要性：学习SOC估计的严格定义以及一些简单但效果不佳的估计方法，并回顾所需的概率理论概念。 2. 线性卡尔曼滤波器作为状态估计器的介绍：推导高斯序列概率推理解决方案的步骤，构建线性卡尔曼滤波器的基础。 3. 理解线性卡尔曼滤波器：学习线性卡尔曼滤波器的不同思维方式和可视化操作，并实现其Octave代码。 4. 使用扩展卡尔曼滤波器对电池单元SOC进行估计：学习如何处理非线性系统并实现扩展卡尔曼滤波器（EKF）。 5. 使用sigma点卡尔曼滤波器对电池单元SOC进行估计：推导和实现sigma点卡尔曼滤波器，克服EKF的基本限制。 6. 使用条形-增量方法提高计算效率：处理电流传感器直流偏差误差，学习实现条形-增量方法的计算效率。 7. 最终项目：通过调整EKF和SPKF的过程噪声、传感器噪声和初始状态估计协方差矩阵，实践手动调谐。 通过参与本课程，学员将具备电池充电状态估计的理论基础和实践技能。
Name:The importance of a good SOC estimator
Description:This week, you will learn some rigorous definitions needed when discussing SOC estimation and some simple but poor methods to estimate SOC. As background to learning some better methods, we will review concepts from probability theory that are needed to be able to deal with the impact of uncertain noises on a system's internal state and measurements made by a BMS.
Name:Introducing the linear Kalman filter as a state estimator
Description:This week, you will learn how to derive the steps of the Gaussian sequential probabilistic inference solution, which is the basis for all Kalman-filtering style state estimators. While this content is highly theoretical, it is important to have a solid foundational understanding of these topics in practice, since real applications often violate some of the assumptions that are made in the derivation, and we must understand the implication this has on the process. By the end of the week, you will know how to derive the linear Kalman filter.
Name:Coming to understand the linear Kalman filter
Description:The steps of a Kalman filter may appear abstract and mysterious. This week, you will learn different ways to think about and visualize the operation of the linear Kalman filter to give better intuition regarding how it operates. You will also learn how to implement a linear Kalman filter in Octave code, and how to evaluate outputs from the Kalman filter.
Name:Cell SOC estimation using an extended Kalman filter
Description:A linear Kalman filter can be used to estimate the internal state of a linear system. But, battery cells are nonlinear systems. This week, you will learn how to approximate the steps of the Gaussian sequential probabilistic inference solution for nonlinear systems, resulting in the "extended Kalman filter" (EKF). You will learn how to implement the EKF in Octave code, and how to use the EKF to estimate battery-cell SOC.
Name:Cell SOC estimation using a sigma-point Kalman filter
Description:The EKF is the best known and most widely used nonlinear Kalman filter. But, it has some fundamental limitations that limit its performance for "very nonlinear" systems. This week, you will learn how to derive the sigma-point Kalman filter (sometimes called an "unscented Kalman filter") from the Gaussian sequential probabilistic inference steps. You will also learn how to implement this filter in Octave code and how to use it to estimate battery cell SOC.
Name:Improving computational efficiency using the bar-delta method
Description:Kalman filtering requires that noises have zero mean. What do we do if the current-sensor has a dc bias error, as is often the case? How can we implement Kalman-filter type SOC estimators in a computationally efficient way for a battery pack comprising many cells? This week you will learn how to compensate for current-sensor bias error and how to implement the bar-delta method for computational efficiency. You will also learn about desktop validation as an approach for initial testing and tuning of BMS algorithms.
Name:Capstone project
Description:You have already learned that Kalman filters must be "tuned" by adjusting their process-noise, sensor-noise, and initial state-estimate covariance matrices in order to give acceptable performance over a wide range of operating scenarios. This final course module will give you some experience hand-tuning both an EKF and SPKF for SOC estimation.
This course can also be taken for academic credit as ECEA 5732, part of CU Boulder’s Master of Science in Electrical Engineering degree. In this course, you will learn how to implement different state-of-charge estimation methods and to evaluate their relative merits. By the end of the course, you will be able to: - Implement simple voltage-based and current-based state-of-charge estimators and understand their limitations - Explain the purpose of each step in the sequential-probabilistic-inference solution - Execute provided Octave/MATLAB script for a linear Kalman filter and evaluate results - Execute provided Octave/MATLAB script for state-of-charge estimation using an extended Kalman filter on lab-test data and evaluate results - Execute provided Octave/MATLAB script for state-of-charge estimation using a sigma-point Kalman filter on lab-test data and evaluate results - Implement method to detect and discard faulty voltage-sensor measurements
