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自分のスケジュールですぐに学習を始めてください。

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中級レベル

約30時間で修了

推奨:11 hours/week...

英語

字幕:英語

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約30時間で修了

推奨:11 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
5時間で修了

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.

...
8件のビデオ (合計120分), 13 readings, 7 quizzes
8件のビデオ
3.1.2: What is the importance of a good SOC estimator?8 分
3.1.3: How do we define SOC carefully?16 分
3.1.4: What are some approaches to estimating battery cell SOC?26 分
3.1.5: Understanding uncertainty via mean and covariance17 分
3.1.6: Understanding joint uncertainty of two unknown quantities15 分
3.1.7: Understanding time-varying uncertain quantities22 分
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3 分
13件の学習用教材
Notes for lesson 3.1.11 分
Frequently Asked Questions5 分
Course Resources5 分
How to Use Discussion Forums5 分
Earn a Course Certificate5 分
Notes for lesson 3.1.21 分
Notes for lesson 3.1.31 分
Notes for lesson 3.1.41 分
Introducing a new element to the course!10 分
Notes for lesson 3.1.51 分
Notes for lesson 3.1.61 分
Notes for lesson 3.1.71 分
Notes for lesson 3.1.81 分
7の練習問題
Practice quiz for lesson 3.1.210 分
Practice quiz for lesson 3.1.310 分
Practice quiz for lesson 3.1.410 分
Practice quiz for lesson 3.1.515 分
Practice quiz for lesson 3.1.610 分
Practice quiz for lesson 3.1.76 分
Quiz for week 140 分
2
3時間で修了

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.

...
6件のビデオ (合計97分), 6 readings, 6 quizzes
6件のビデオ
3.2.2: The Kalman-filter gain factor23 分
3.2.3: Summarizing the six steps of generic probabilistic inference9 分
3.2.4: Deriving the three Kalman-filter prediction steps21 分
3.2.5: Deriving the three Kalman-filter correction steps16 分
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2 分
6件の学習用教材
Notes for lesson 3.2.11 分
Notes for lesson 3.2.21 分
Notes for lesson 3.2.31 分
Notes for lesson 3.2.41 分
Notes for lesson 3.2.51 分
Notes for lesson 3.2.61 分
6の練習問題
Practice quiz for lesson 3.2.112 分
Practice quiz for lesson 3.2.210 分
Practice quiz for lesson 3.2.310 分
Practice quiz for lesson 3.2.410 分
Practice quiz for lesson 3.2.510 分
Quiz for week 230 分
3
4時間で修了

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.

...
7件のビデオ (合計86分), 7 readings, 7 quizzes
7件のビデオ
3.3.2: Introducing Octave code to generate correlated random numbers15 分
3.3.3: Introducing Octave code to implement KF for linearized cell model10 分
3.3.4: How do we improve numeric robustness of Kalman filter?10 分
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14 分
3.3.6: How do I initialize and tune a Kalman filter?12 分
3.3.7: Summary of "Coming to understand the linear KF" and next steps2 分
7件の学習用教材
Notes for lesson 3.3.11 分
Notes for lesson 3.3.21 分
Notes for lesson 3.3.31 分
Notes for lesson 3.3.41 分
Notes for lesson 3.3.51 分
Notes for lesson 3.3.61 分
Notes for lesson 3.3.71 分
7の練習問題
Practice quiz for lesson 3.3.110 分
Practice quiz for lesson 3.3.210 分
Practice quiz for lesson 3.3.310 分
Practice quiz for lesson 3.3.410 分
Practice quiz for lesson 3.3.510 分
Practice quiz for lesson 3.3.610 分
Quiz for week 330 分
4
4時間で修了

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.

...
8件のビデオ (合計101分), 8 readings, 7 quizzes
8件のビデオ
3.4.2: Deriving the three extended-Kalman-filter prediction steps15 分
3.4.3: Deriving the three extended-Kalman-filter correction steps6 分
3.4.4: Introducing a simple EKF example, with Octave code15 分
3.4.5: Preparing to implement EKF on an ECM20 分
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13 分
3.4.7: Introducing Octave code to update EKF for SOC estimation16 分
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2 分
8件の学習用教材
Notes for lesson 3.4.11 分
Notes for lesson 3.4.21 分
Notes for lesson 3.4.31 分
Notes for lesson 3.4.41 分
Notes for lesson 3.4.51 分
Notes for lesson 3.4.61 分
Notes for lesson 3.4.71 分
Notes for lesson 3.4.81 分
7の練習問題
Practice quiz for lesson 3.4.110 分
Practice quiz for lesson 3.4.210 分
Practice quiz for lesson 3.4.310 分
Practice quiz for lesson 3.4.410 分
Practice quiz for lesson 3.4.510 分
Practice quiz for lesson 3.4.710 分
Quiz for week 430 分

講師

Gregory Plett

Professor
Electrical and Computer Engineering

University of Colorado Systemについて

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....

Algorithms for Battery Management Systemsの専門講座について

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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