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中级

完成时间(小时)

完成时间大约为22 小时

建议:5 hours/week...
可选语言

英语(English)

字幕:英语(English)
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
中级

中级

完成时间(小时)

完成时间大约为22 小时

建议:5 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 5 小时

The importance of a good SOC estimator

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....
Reading
8 个视频 (总计 120 分钟), 13 个阅读材料, 7 个测验
Video8 个视频
3.1.2: What is the importance of a good SOC estimator?8分钟
3.1.3: How do we define SOC carefully?16分钟
3.1.4: What are some approaches to estimating battery cell SOC?26分钟
3.1.5: Understanding uncertainty via mean and covariance17分钟
3.1.6: Understanding joint uncertainty of two unknown quantities15分钟
3.1.7: Understanding time-varying uncertain quantities22分钟
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3分钟
Reading13 个阅读材料
Notes for lesson 3.1.11分钟
Frequently Asked Questions5分钟
Course Resources5分钟
How to Use Discussion Forums5分钟
Earn a Course Certificate5分钟
Notes for lesson 3.1.21分钟
Notes for lesson 3.1.31分钟
Notes for lesson 3.1.41分钟
Introducing a new element to the course!10分钟
Notes for lesson 3.1.51分钟
Notes for lesson 3.1.61分钟
Notes for lesson 3.1.71分钟
Notes for lesson 3.1.81分钟
Quiz7 个练习
Practice quiz for lesson 3.1.210分钟
Practice quiz for lesson 3.1.310分钟
Practice quiz for lesson 3.1.410分钟
Practice quiz for lesson 3.1.515分钟
Practice quiz for lesson 3.1.610分钟
Practice quiz for lesson 3.1.76分钟
Quiz for week 140分钟
2
完成时间(小时)
完成时间为 3 小时

Introducing the linear Kalman filter as a state estimator

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....
Reading
6 个视频 (总计 97 分钟), 6 个阅读材料, 6 个测验
Video6 个视频
3.2.2: The Kalman-filter gain factor23分钟
3.2.3: Summarizing the six steps of generic probabilistic inference9分钟
3.2.4: Deriving the three Kalman-filter prediction steps21分钟
3.2.5: Deriving the three Kalman-filter correction steps16分钟
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2分钟
Reading6 个阅读材料
Notes for lesson 3.2.11分钟
Notes for lesson 3.2.21分钟
Notes for lesson 3.2.31分钟
Notes for lesson 3.2.41分钟
Notes for lesson 3.2.51分钟
Notes for lesson 3.2.61分钟
Quiz6 个练习
Practice quiz for lesson 3.2.112分钟
Practice quiz for lesson 3.2.210分钟
Practice quiz for lesson 3.2.310分钟
Practice quiz for lesson 3.2.410分钟
Practice quiz for lesson 3.2.510分钟
Quiz for week 230分钟
3
完成时间(小时)
完成时间为 4 小时

Coming to understand the linear Kalman filter

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....
Reading
7 个视频 (总计 86 分钟), 7 个阅读材料, 7 个测验
Video7 个视频
3.3.2: Introducing Octave code to generate correlated random numbers15分钟
3.3.3: Introducing Octave code to implement KF for linearized cell model10分钟
3.3.4: How do we improve numeric robustness of Kalman filter?10分钟
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14分钟
3.3.6: How do I initialize and tune a Kalman filter?12分钟
3.3.7: Summary of "Coming to understand the linear KF" and next steps2分钟
Reading7 个阅读材料
Notes for lesson 3.3.11分钟
Notes for lesson 3.3.21分钟
Notes for lesson 3.3.31分钟
Notes for lesson 3.3.41分钟
Notes for lesson 3.3.51分钟
Notes for lesson 3.3.61分钟
Notes for lesson 3.3.71分钟
Quiz7 个练习
Practice quiz for lesson 3.3.110分钟
Practice quiz for lesson 3.3.210分钟
Practice quiz for lesson 3.3.310分钟
Practice quiz for lesson 3.3.410分钟
Practice quiz for lesson 3.3.510分钟
Practice quiz for lesson 3.3.610分钟
Quiz for week 330分钟
4
完成时间(小时)
完成时间为 4 小时

Cell SOC estimation using an extended Kalman filter

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....
Reading
8 个视频 (总计 101 分钟), 8 个阅读材料, 7 个测验
Video8 个视频
3.4.2: Deriving the three extended-Kalman-filter prediction steps15分钟
3.4.3: Deriving the three extended-Kalman-filter correction steps6分钟
3.4.4: Introducing a simple EKF example, with Octave code15分钟
3.4.5: Preparing to implement EKF on an ECM20分钟
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13分钟
3.4.7: Introducing Octave code to update EKF for SOC estimation16分钟
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2分钟
Reading8 个阅读材料
Notes for lesson 3.4.11分钟
Notes for lesson 3.4.21分钟
Notes for lesson 3.4.31分钟
Notes for lesson 3.4.41分钟
Notes for lesson 3.4.51分钟
Notes for lesson 3.4.61分钟
Notes for lesson 3.4.71分钟
Notes for lesson 3.4.81分钟
Quiz7 个练习
Practice quiz for lesson 3.4.110分钟
Practice quiz for lesson 3.4.210分钟
Practice quiz for lesson 3.4.310分钟
Practice quiz for lesson 3.4.410分钟
Practice quiz for lesson 3.4.510分钟
Practice quiz for lesson 3.4.710分钟
Quiz for week 430分钟

讲师

Gregory Plett

Professor
Electrical and Computer Engineering

关于 University of Colorado System

The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond....

关于 Algorithms for Battery Management Systems 专项课程

In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack....
Algorithms for Battery Management Systems

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