Об этом курсе
4.6
Оценки: 9
Рецензии: 1

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Прибл. 30 часа на выполнение

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Английский

Субтитры: Английский

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Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Промежуточный уровень

Прибл. 30 часа на выполнение

Предполагаемая нагрузка: 12 hours/week...

Английский

Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
5 ч. на завершение

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 видео ((всего 120 мин.)), 13 материалов для самостоятельного изучения, 7 тестов
8 видео
3.1.2: What is the importance of a good SOC estimator?8мин
3.1.3: How do we define SOC carefully?16мин
3.1.4: What are some approaches to estimating battery cell SOC?26мин
3.1.5: Understanding uncertainty via mean and covariance17мин
3.1.6: Understanding joint uncertainty of two unknown quantities15мин
3.1.7: Understanding time-varying uncertain quantities22мин
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3мин
13 материала для самостоятельного изучения
Notes for lesson 3.1.11мин
Frequently Asked Questions5мин
Course Resources5мин
How to Use Discussion Forums5мин
Earn a Course Certificate5мин
Notes for lesson 3.1.21мин
Notes for lesson 3.1.31мин
Notes for lesson 3.1.41мин
Introducing a new element to the course!10мин
Notes for lesson 3.1.51мин
Notes for lesson 3.1.61мин
Notes for lesson 3.1.71мин
Notes for lesson 3.1.81мин
7 практического упражнения
Practice quiz for lesson 3.1.210мин
Practice quiz for lesson 3.1.310мин
Practice quiz for lesson 3.1.410мин
Practice quiz for lesson 3.1.515мин
Practice quiz for lesson 3.1.610мин
Practice quiz for lesson 3.1.76мин
Quiz for week 140мин
Неделя
2
3 ч. на завершение

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 видео ((всего 97 мин.)), 6 материалов для самостоятельного изучения, 6 тестов
6 видео
3.2.2: The Kalman-filter gain factor23мин
3.2.3: Summarizing the six steps of generic probabilistic inference9мин
3.2.4: Deriving the three Kalman-filter prediction steps21мин
3.2.5: Deriving the three Kalman-filter correction steps16мин
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2мин
6 материала для самостоятельного изучения
Notes for lesson 3.2.11мин
Notes for lesson 3.2.21мин
Notes for lesson 3.2.31мин
Notes for lesson 3.2.41мин
Notes for lesson 3.2.51мин
Notes for lesson 3.2.61мин
6 практического упражнения
Practice quiz for lesson 3.2.112мин
Practice quiz for lesson 3.2.210мин
Practice quiz for lesson 3.2.310мин
Practice quiz for lesson 3.2.410мин
Practice quiz for lesson 3.2.510мин
Quiz for week 230мин
Неделя
3
4 ч. на завершение

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 видео ((всего 86 мин.)), 7 материалов для самостоятельного изучения, 7 тестов
7 видео
3.3.2: Introducing Octave code to generate correlated random numbers15мин
3.3.3: Introducing Octave code to implement KF for linearized cell model10мин
3.3.4: How do we improve numeric robustness of Kalman filter?10мин
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14мин
3.3.6: How do I initialize and tune a Kalman filter?12мин
3.3.7: Summary of "Coming to understand the linear KF" and next steps2мин
7 материала для самостоятельного изучения
Notes for lesson 3.3.11мин
Notes for lesson 3.3.21мин
Notes for lesson 3.3.31мин
Notes for lesson 3.3.41мин
Notes for lesson 3.3.51мин
Notes for lesson 3.3.61мин
Notes for lesson 3.3.71мин
7 практического упражнения
Practice quiz for lesson 3.3.110мин
Practice quiz for lesson 3.3.210мин
Practice quiz for lesson 3.3.310мин
Practice quiz for lesson 3.3.410мин
Practice quiz for lesson 3.3.510мин
Practice quiz for lesson 3.3.610мин
Quiz for week 330мин
Неделя
4
4 ч. на завершение

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 видео ((всего 101 мин.)), 8 материалов для самостоятельного изучения, 7 тестов
8 видео
3.4.2: Deriving the three extended-Kalman-filter prediction steps15мин
3.4.3: Deriving the three extended-Kalman-filter correction steps6мин
3.4.4: Introducing a simple EKF example, with Octave code15мин
3.4.5: Preparing to implement EKF on an ECM20мин
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13мин
3.4.7: Introducing Octave code to update EKF for SOC estimation16мин
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2мин
8 материала для самостоятельного изучения
Notes for lesson 3.4.11мин
Notes for lesson 3.4.21мин
Notes for lesson 3.4.31мин
Notes for lesson 3.4.41мин
Notes for lesson 3.4.51мин
Notes for lesson 3.4.61мин
Notes for lesson 3.4.71мин
Notes for lesson 3.4.81мин
7 практического упражнения
Practice quiz for lesson 3.4.110мин
Practice quiz for lesson 3.4.210мин
Practice quiz for lesson 3.4.310мин
Practice quiz for lesson 3.4.410мин
Practice quiz for lesson 3.4.510мин
Practice quiz for lesson 3.4.710мин
Quiz for week 430мин
Неделя
5
4 ч. на завершение

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 видео ((всего 116 мин.)), 7 материалов для самостоятельного изучения, 6 тестов
7 видео
3.5.2: Approximating uncertain variables using sigma points31мин
3.5.3: Deriving the six sigma-point-Kalman-filter steps17мин
3.5.4: Introducing a simple SPKF example with Octave code19мин
3.5.5: Introducing Octave code to initialize and control SPKF for SOC estimation9мин
3.5.6: Introducing Octave code to update SPKF for SOC estimation18мин
3.5.7: Summary of "Cell SOC estimation using a SPFK" and next steps7мин
7 материала для самостоятельного изучения
Notes for lesson 3.5.11мин
Notes for lesson 3.5.21мин
Notes for lesson 3.5.31мин
Notes for lesson 3.5.41мин
Notes for lesson 3.5.51мин
Notes for lesson 3.5.61мин
Notes for lesson 3.5.71мин
6 практического упражнения
Practice quiz for lesson 3.5.110мин
Practice quiz for lesson 3.5.210мин
Practice quiz for lesson 3.5.310мин
Practice quiz for lesson 3.5.46мин
Practice quiz for lesson 3.5.610мин
Quiz for week 530мин
Неделя
6
3 ч. на завершение

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 видео ((всего 71 мин.)), 5 материалов для самостоятельного изучения, 4 тестов
5 видео
3.6.2: Developing a "bar" filter using an ECM6мин
3.6.3: Developing the "delta" filters using an ECM15мин
3.6.4: Introducing "desktop validation" as a method for predicting performance21мин
3.6.5: Summary of "Improving computational efficiency using the bar-delta method" and next steps2мин
5 материала для самостоятельного изучения
Notes for lesson 3.6.11мин
Notes for lesson 3.6.21мин
Notes for lesson 3.6.31мин
Notes for lesson 3.6.41мин
Notes for lesson 3.6.51мин
4 практического упражнения
Quiz for lesson 3.6.115мин
Quiz for lesson 3.6.210мин
Quiz for lesson 3.6.310мин
Quiz for lessons 3.6.4 and 3.6.515мин
Неделя
7
5 ч. на завершение

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 тестов

Преподаватели

Gregory Plett

Professor
Electrical and Computer Engineering

О Система университетов штата Колорадо

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....

О специализации ''Algorithms for Battery Management Systems'

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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