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中級

完成時間大約為26 小時

建議:11 hours/week...

英語(English)

字幕:英語(English)

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

根據您的日程表重置截止日期。

中級

完成時間大約為26 小時

建議:11 hours/week...

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....
8 個視頻 (總計 120 分鐘), 13 個閱讀材料, 7 個測驗
8 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
13 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
7 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....
6 個視頻 (總計 97 分鐘), 6 個閱讀材料, 6 個測驗
6 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
6 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
6 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....
7 個視頻 (總計 86 分鐘), 7 個閱讀材料, 7 個測驗
7 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
7 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
7 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....
8 個視頻 (總計 101 分鐘), 8 個閱讀材料, 7 個測驗
8 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
8 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
7 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
5
完成時間為 4 小時

Cell SOC estimation using a sigma-point Kalman filter

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....
7 個視頻 (總計 116 分鐘), 7 個閱讀材料, 6 個測驗
7 個視頻
3.5.2: Approximating uncertain variables using sigma points31分鐘
3.5.3: Deriving the six sigma-point-Kalman-filter steps17分鐘
3.5.4: Introducing a simple SPKF example with Octave code19分鐘
3.5.5: Introducing Octave code to initialize and control SPKF for SOC estimation9分鐘
3.5.6: Introducing Octave code to update SPKF for SOC estimation18分鐘
3.5.7: Summary of "Cell SOC estimation using a SPFK" and next steps7分鐘
7 個閱讀材料
Notes for lesson 3.5.11分鐘
Notes for lesson 3.5.21分鐘
Notes for lesson 3.5.31分鐘
Notes for lesson 3.5.41分鐘
Notes for lesson 3.5.51分鐘
Notes for lesson 3.5.61分鐘
Notes for lesson 3.5.71分鐘
6 個練習
Practice quiz for lesson 3.5.110分鐘
Practice quiz for lesson 3.5.210分鐘
Practice quiz for lesson 3.5.310分鐘
Practice quiz for lesson 3.5.46分鐘
Practice quiz for lesson 3.5.610分鐘
Quiz for week 530分鐘
6
完成時間為 3 小時

Improving computational efficiency using the bar-delta method

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....
5 個視頻 (總計 71 分鐘), 5 個閱讀材料, 4 個測驗
5 個視頻
3.6.2: Developing a "bar" filter using an ECM6分鐘
3.6.3: Developing the "delta" filters using an ECM15分鐘
3.6.4: Introducing "desktop validation" as a method for predicting performance21分鐘
3.6.5: Summary of "Improving computational efficiency using the bar-delta method" and next steps2分鐘
5 個閱讀材料
Notes for lesson 3.6.11分鐘
Notes for lesson 3.6.21分鐘
Notes for lesson 3.6.31分鐘
Notes for lesson 3.6.41分鐘
Notes for lesson 3.6.51分鐘
4 個練習
Quiz for lesson 3.6.115分鐘
Quiz for lesson 3.6.210分鐘
Quiz for lesson 3.6.310分鐘
Quiz for lessons 3.6.4 and 3.6.515分鐘
7
完成時間為 5 小時

Capstone project

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. ...
2 個測驗

講師

Gregory Plett

Professor
Electrical and Computer Engineering

關於 科罗拉多大学系统

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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