Informações sobre o curso
10,537 visualizações recentes

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: 11 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: 11 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 120 mín.)), 13 leituras, 7 testes
8 videos
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 97 mín.)), 6 leituras, 6 testes
6 videos
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 86 mín.)), 7 leituras, 7 testes
7 videos
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 101 mín.)), 8 leituras, 7 testes
8 videos
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

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.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.