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中級

中級

完成時間(小時)

完成時間大約為22 小時

建議:5 hours/week...
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英語(English)

字幕:英語(English)
100% 在線

100% 在線

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

完成時間(小時)

完成時間大約為22 小時

建議:5 hours/week...
可選語言

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
完成時間為 5 小時

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 個視頻 (總計 120 分鐘), 13 個閱讀材料, 7 個測驗
Video8 個視頻
3.1.2: What is the importance of a good SOC estimator?8分鐘
3.1.3: How do we define SOC carefully?16分鐘
3.1.4: What are some approaches to estimating battery cell SOC?26分鐘
3.1.5: Understanding uncertainty via mean and covariance17分鐘
3.1.6: Understanding joint uncertainty of two unknown quantities15分鐘
3.1.7: Understanding time-varying uncertain quantities22分鐘
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3分鐘
Reading13 個閱讀材料
Notes for lesson 3.1.11分鐘
Frequently Asked Questions5分鐘
Course Resources5分鐘
How to Use Discussion Forums5分鐘
Earn a Course Certificate5分鐘
Notes for lesson 3.1.21分鐘
Notes for lesson 3.1.31分鐘
Notes for lesson 3.1.41分鐘
Introducing a new element to the course!10分鐘
Notes for lesson 3.1.51分鐘
Notes for lesson 3.1.61分鐘
Notes for lesson 3.1.71分鐘
Notes for lesson 3.1.81分鐘
Quiz7 個練習
Practice quiz for lesson 3.1.210分鐘
Practice quiz for lesson 3.1.310分鐘
Practice quiz for lesson 3.1.410分鐘
Practice quiz for lesson 3.1.515分鐘
Practice quiz for lesson 3.1.610分鐘
Practice quiz for lesson 3.1.76分鐘
Quiz for week 140分鐘
2
完成時間(小時)
完成時間為 3 小時

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 個視頻 (總計 97 分鐘), 6 個閱讀材料, 6 個測驗
Video6 個視頻
3.2.2: The Kalman-filter gain factor23分鐘
3.2.3: Summarizing the six steps of generic probabilistic inference9分鐘
3.2.4: Deriving the three Kalman-filter prediction steps21分鐘
3.2.5: Deriving the three Kalman-filter correction steps16分鐘
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2分鐘
Reading6 個閱讀材料
Notes for lesson 3.2.11分鐘
Notes for lesson 3.2.21分鐘
Notes for lesson 3.2.31分鐘
Notes for lesson 3.2.41分鐘
Notes for lesson 3.2.51分鐘
Notes for lesson 3.2.61分鐘
Quiz6 個練習
Practice quiz for lesson 3.2.112分鐘
Practice quiz for lesson 3.2.210分鐘
Practice quiz for lesson 3.2.310分鐘
Practice quiz for lesson 3.2.410分鐘
Practice quiz for lesson 3.2.510分鐘
Quiz for week 230分鐘
3
完成時間(小時)
完成時間為 4 小時

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 個視頻 (總計 86 分鐘), 7 個閱讀材料, 7 個測驗
Video7 個視頻
3.3.2: Introducing Octave code to generate correlated random numbers15分鐘
3.3.3: Introducing Octave code to implement KF for linearized cell model10分鐘
3.3.4: How do we improve numeric robustness of Kalman filter?10分鐘
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14分鐘
3.3.6: How do I initialize and tune a Kalman filter?12分鐘
3.3.7: Summary of "Coming to understand the linear KF" and next steps2分鐘
Reading7 個閱讀材料
Notes for lesson 3.3.11分鐘
Notes for lesson 3.3.21分鐘
Notes for lesson 3.3.31分鐘
Notes for lesson 3.3.41分鐘
Notes for lesson 3.3.51分鐘
Notes for lesson 3.3.61分鐘
Notes for lesson 3.3.71分鐘
Quiz7 個練習
Practice quiz for lesson 3.3.110分鐘
Practice quiz for lesson 3.3.210分鐘
Practice quiz for lesson 3.3.310分鐘
Practice quiz for lesson 3.3.410分鐘
Practice quiz for lesson 3.3.510分鐘
Practice quiz for lesson 3.3.610分鐘
Quiz for week 330分鐘
4
完成時間(小時)
完成時間為 4 小時

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 個視頻 (總計 101 分鐘), 8 個閱讀材料, 7 個測驗
Video8 個視頻
3.4.2: Deriving the three extended-Kalman-filter prediction steps15分鐘
3.4.3: Deriving the three extended-Kalman-filter correction steps6分鐘
3.4.4: Introducing a simple EKF example, with Octave code15分鐘
3.4.5: Preparing to implement EKF on an ECM20分鐘
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13分鐘
3.4.7: Introducing Octave code to update EKF for SOC estimation16分鐘
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2分鐘
Reading8 個閱讀材料
Notes for lesson 3.4.11分鐘
Notes for lesson 3.4.21分鐘
Notes for lesson 3.4.31分鐘
Notes for lesson 3.4.41分鐘
Notes for lesson 3.4.51分鐘
Notes for lesson 3.4.61分鐘
Notes for lesson 3.4.71分鐘
Notes for lesson 3.4.81分鐘
Quiz7 個練習
Practice quiz for lesson 3.4.110分鐘
Practice quiz for lesson 3.4.210分鐘
Practice quiz for lesson 3.4.310分鐘
Practice quiz for lesson 3.4.410分鐘
Practice quiz for lesson 3.4.510分鐘
Practice quiz for lesson 3.4.710分鐘
Quiz for week 430分鐘

講師

Gregory Plett

Professor
Electrical and Computer Engineering

關於 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....

關於 Algorithms for Battery Management Systems 專項課程

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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