Informações sobre o curso
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Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 30 horas para completar

Sugerido: 12 hours/week...

Inglês

Legendas: Inglês

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 30 horas para completar

Sugerido: 12 hours/week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
5 horas para concluir

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 vídeos (total de (Total 120 mín.) min), 13 leituras, 7 testes
8 videos
3.1.2: What is the importance of a good SOC estimator?8min
3.1.3: How do we define SOC carefully?16min
3.1.4: What are some approaches to estimating battery cell SOC?26min
3.1.5: Understanding uncertainty via mean and covariance17min
3.1.6: Understanding joint uncertainty of two unknown quantities15min
3.1.7: Understanding time-varying uncertain quantities22min
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3min
13 leituras
Notes for lesson 3.1.11min
Frequently Asked Questions5min
Course Resources5min
How to Use Discussion Forums5min
Earn a Course Certificate5min
Notes for lesson 3.1.21min
Notes for lesson 3.1.31min
Notes for lesson 3.1.41min
Introducing a new element to the course!10min
Notes for lesson 3.1.51min
Notes for lesson 3.1.61min
Notes for lesson 3.1.71min
Notes for lesson 3.1.81min
7 exercícios práticos
Practice quiz for lesson 3.1.210min
Practice quiz for lesson 3.1.310min
Practice quiz for lesson 3.1.410min
Practice quiz for lesson 3.1.515min
Practice quiz for lesson 3.1.610min
Practice quiz for lesson 3.1.76min
Quiz for week 140min
Semana
2
3 horas para concluir

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 vídeos (total de (Total 97 mín.) min), 6 leituras, 6 testes
6 videos
3.2.2: The Kalman-filter gain factor23min
3.2.3: Summarizing the six steps of generic probabilistic inference9min
3.2.4: Deriving the three Kalman-filter prediction steps21min
3.2.5: Deriving the three Kalman-filter correction steps16min
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2min
6 leituras
Notes for lesson 3.2.11min
Notes for lesson 3.2.21min
Notes for lesson 3.2.31min
Notes for lesson 3.2.41min
Notes for lesson 3.2.51min
Notes for lesson 3.2.61min
6 exercícios práticos
Practice quiz for lesson 3.2.112min
Practice quiz for lesson 3.2.210min
Practice quiz for lesson 3.2.310min
Practice quiz for lesson 3.2.410min
Practice quiz for lesson 3.2.510min
Quiz for week 230min
Semana
3
4 horas para concluir

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 vídeos (total de (Total 86 mín.) min), 7 leituras, 7 testes
7 videos
3.3.2: Introducing Octave code to generate correlated random numbers15min
3.3.3: Introducing Octave code to implement KF for linearized cell model10min
3.3.4: How do we improve numeric robustness of Kalman filter?10min
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14min
3.3.6: How do I initialize and tune a Kalman filter?12min
3.3.7: Summary of "Coming to understand the linear KF" and next steps2min
7 leituras
Notes for lesson 3.3.11min
Notes for lesson 3.3.21min
Notes for lesson 3.3.31min
Notes for lesson 3.3.41min
Notes for lesson 3.3.51min
Notes for lesson 3.3.61min
Notes for lesson 3.3.71min
7 exercícios práticos
Practice quiz for lesson 3.3.110min
Practice quiz for lesson 3.3.210min
Practice quiz for lesson 3.3.310min
Practice quiz for lesson 3.3.410min
Practice quiz for lesson 3.3.510min
Practice quiz for lesson 3.3.610min
Quiz for week 330min
Semana
4
4 horas para concluir

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 vídeos (total de (Total 101 mín.) min), 8 leituras, 7 testes
8 videos
3.4.2: Deriving the three extended-Kalman-filter prediction steps15min
3.4.3: Deriving the three extended-Kalman-filter correction steps6min
3.4.4: Introducing a simple EKF example, with Octave code15min
3.4.5: Preparing to implement EKF on an ECM20min
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13min
3.4.7: Introducing Octave code to update EKF for SOC estimation16min
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2min
8 leituras
Notes for lesson 3.4.11min
Notes for lesson 3.4.21min
Notes for lesson 3.4.31min
Notes for lesson 3.4.41min
Notes for lesson 3.4.51min
Notes for lesson 3.4.61min
Notes for lesson 3.4.71min
Notes for lesson 3.4.81min
7 exercícios práticos
Practice quiz for lesson 3.4.110min
Practice quiz for lesson 3.4.210min
Practice quiz for lesson 3.4.310min
Practice quiz for lesson 3.4.410min
Practice quiz for lesson 3.4.510min
Practice quiz for lesson 3.4.710min
Quiz for week 430min
Semana
5
4 horas para concluir

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 vídeos (total de (Total 116 mín.) min), 7 leituras, 6 testes
7 videos
3.5.2: Approximating uncertain variables using sigma points31min
3.5.3: Deriving the six sigma-point-Kalman-filter steps17min
3.5.4: Introducing a simple SPKF example with Octave code19min
3.5.5: Introducing Octave code to initialize and control SPKF for SOC estimation9min
3.5.6: Introducing Octave code to update SPKF for SOC estimation18min
3.5.7: Summary of "Cell SOC estimation using a SPFK" and next steps7min
7 leituras
Notes for lesson 3.5.11min
Notes for lesson 3.5.21min
Notes for lesson 3.5.31min
Notes for lesson 3.5.41min
Notes for lesson 3.5.51min
Notes for lesson 3.5.61min
Notes for lesson 3.5.71min
6 exercícios práticos
Practice quiz for lesson 3.5.110min
Practice quiz for lesson 3.5.210min
Practice quiz for lesson 3.5.310min
Practice quiz for lesson 3.5.46min
Practice quiz for lesson 3.5.610min
Quiz for week 530min
Semana
6
3 horas para concluir

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 vídeos (total de (Total 71 mín.) min), 5 leituras, 4 testes
5 videos
3.6.2: Developing a "bar" filter using an ECM6min
3.6.3: Developing the "delta" filters using an ECM15min
3.6.4: Introducing "desktop validation" as a method for predicting performance21min
3.6.5: Summary of "Improving computational efficiency using the bar-delta method" and next steps2min
5 leituras
Notes for lesson 3.6.11min
Notes for lesson 3.6.21min
Notes for lesson 3.6.31min
Notes for lesson 3.6.41min
Notes for lesson 3.6.51min
4 exercícios práticos
Quiz for lesson 3.6.115min
Quiz for lesson 3.6.210min
Quiz for lesson 3.6.310min
Quiz for lessons 3.6.4 and 3.6.515min
Semana
7
5 horas para concluir

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 testes

Instrutores

Gregory Plett

Professor
Electrical and Computer Engineering

Sobre Sistema de Universidades do ColoradoUniversidade do Colorado

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....

Sobre o Programa de cursos integrados 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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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