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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/battery-state-of-health
课程评论：没有评论
课程名称：电池健康状态（SOH）估计 课程概述：本课程也可以作为学分课程，编号 ECEA 5733，属于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，您将学习如何实施不同的健康状态估计方法，并评估它们的相对优缺点。课程结束时，您将能够： - 识别锂离子电池的主要降解机制，并理解其工作原理 - 执行提供的Octave/MATLAB脚本，以使用WLS、WTLS和AWTLS方法及实验室测试数据来估计总容量，并评估结果 - 计算总容量估计值的置信区间 - 利用实验室测试数据计算电池的等效串联电阻估计值 - 指定状态和参数的联合与双重估计之间的权衡，并确保稳健估计所需采取的步骤（荣誉课程） 课程大纲： 1. 锂离子电池健康的降解机制： - 随着电池老化，总容量通常减少，电阻通常增加。本周您将学习这一现象的原因，了解导致锂离子电池降解的具体物理和化学机制，以及估计和跟踪电池电阻变更的简单性与总容量变更难以准确跟踪之间的区别。 2. 总最小二乘法电池容量估计： - 本周您将学习，使用普通最小二乘法（OLS）估计总容量是一种根本错误的方法，而应使用总最小二乘法（TLS）。您将学习如何推导加权OLS解决方案作为基准，并推导加权TLS解决方案。 3. 简化的总最小二乘法电池容量估计： - 加权TLS解决方案不适合在嵌入式系统（如BMS）上高效计算。本周，您将学习一种比例加权TLS方法，并进一步推广到“近似加权TLS”（AWTLS）方法，以便在BMS上实现可行的良好估计。 4. 如何为不同的总容量估计器编写代码： - 本周，您将学习如何在Octave代码中实现多个总容量估计方法，并探索不同的模拟场景，以基准比较每种方法的工作效果。 5. 基于卡尔曼滤波的总容量估计： - 在第三门专业课程中，您学习了如何使用扩展卡尔曼滤波器（EKF）和西格玛点卡尔曼滤波器（SPKF）来估计电池单元状态。本周的荣誉课程将概述如何将这些概念扩展到在已知状态下估计电池单元模型的参数，以及如何同时估计模型的状态和参数。 6. 整合项目： - 通过学习的不同总容量估计方法，您将探索不同的方法来确定总容量估计所需的“x”和“y”数据输入，从而提升方法的有效性。 本课程旨在使学生深入理解电池健康状态估计的理论与实践，适用于电动汽车等多个领域。
Name:How does lithium-ion cell health degrade?
Description:As battery cells age, their total capacities generally decrease and their resistances generally increase. This week, you will learn WHY this happens. You will learn about the specific physical and chemical mechanisms that cause degradation to lithium-ion battery cells. You will also learn why it is relatively simple to estimate and track changes to resistance, but why it is difficult to track changes to total capacity accurately.
Name:Total-least-squares battery-cell capacity estimation
Description:Total capacity is often estimated using ordinary-least-squares (OLS) methods. This week, you will learn that this is a fundamentally incorrect approach, and will learn that a total-least-squares (TLS) method should be used instead. You will learn how to derive a weighted OLS solution, to use as a benchmark, and how to derive a weighted TLS solution also.
Name:Simplified total-least-squares battery-cell capacity estimates
Description:Unfortunately, the weighted TLS solution you learned in week 2 is not well suited for efficient computation on an embedded system like a BMS. As an intermediate step toward finding an efficient weighted TLS method, you will first learn a proportionally weighted TLS method this week. You will then learn how to generalize this to an "approximate weighted TLS" (AWTLS) method, which gives good estimates, and is feasible to implement on a BMS.
Name:How to write code for the different total-capacity estimators
Description:So far this course, you have learned a number of methods for estimating total capacity. This week, you will learn how to implement those methods in Octave code. You will also explore different simulation scenarios to benchmark how well each method works, in comparison with the others. The scenarios are representative of hybrid-electric-vehicle (HEV) and battery-electric-vehicle (BEV) applications, but the principles learned can be extrapolated to other similar application domains.
Name:A Kalman-filter approach to total capacity estimation
Description:In the third course of the specialization, you learned how to use extended Kalman filters (EKFs) and sigma-point Kalman filters (SPKFs) to estimate the state of a battery cell. In this honors week, you will learn how to extend those concepts to apply EKF and SPKF to estimating the parameters of a battery-cell model if the state is known, and also how to simultaneously estimate both the state and parameters of a cell model.
Name:Capstone project
Description:You have learned several different total-capacity estimation methods. Some of these methods work better than others in general, but any method is only as good as the data you give it. In this project, you will explore a different way to determine the "x" and "y" data you use as input to the total-capacity estimation methods.
This course can also be taken for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree. In this course, you will learn how to implement different state-of-health estimation methods and to evaluate their relative merits. By the end of the course, you will be able to: - Identify the primary degradation mechanisms that occur in lithium-ion cells and understand how they work - Execute provided Octave/MATLAB script to estimate total capacity using WLS, WTLS, and AWTLS methods and lab-test data, and to evaluate results - Compute confidence intervals on total-capacity estimates - Compute estimates of a cell’s equivalent-series resistance using lab-test data - Specify the tradeoffs between joint and dual estimation of state and parameters, and steps that must be taken to ensure robust estimates (honors)
