À propos de ce cours
5.0
1 notes
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100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 22 heures pour terminer

Recommandé : 5 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais...
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 22 heures pour terminer

Recommandé : 5 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais...

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
5 heures pour terminer

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....
Reading
8 vidéos (Total 120 min), 13 lectures, 7 quiz
Video8 vidéos
3.1.2: What is the importance of a good SOC estimator?8 min
3.1.3: How do we define SOC carefully?16 min
3.1.4: What are some approaches to estimating battery cell SOC?26 min
3.1.5: Understanding uncertainty via mean and covariance17 min
3.1.6: Understanding joint uncertainty of two unknown quantities15 min
3.1.7: Understanding time-varying uncertain quantities22 min
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3 min
Reading13 lectures
Notes for lesson 3.1.11 min
Frequently Asked Questions5 min
Course Resources5 min
How to Use Discussion Forums5 min
Earn a Course Certificate5 min
Notes for lesson 3.1.21 min
Notes for lesson 3.1.31 min
Notes for lesson 3.1.41 min
Introducing a new element to the course!10 min
Notes for lesson 3.1.51 min
Notes for lesson 3.1.61 min
Notes for lesson 3.1.71 min
Notes for lesson 3.1.81 min
Quiz7 exercices pour s'entraîner
Practice quiz for lesson 3.1.210 min
Practice quiz for lesson 3.1.310 min
Practice quiz for lesson 3.1.410 min
Practice quiz for lesson 3.1.515 min
Practice quiz for lesson 3.1.610 min
Practice quiz for lesson 3.1.76 min
Quiz for week 140 min
Semaine
2
Heures pour terminer
3 heures pour terminer

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....
Reading
6 vidéos (Total 97 min), 6 lectures, 6 quiz
Video6 vidéos
3.2.2: The Kalman-filter gain factor23 min
3.2.3: Summarizing the six steps of generic probabilistic inference9 min
3.2.4: Deriving the three Kalman-filter prediction steps21 min
3.2.5: Deriving the three Kalman-filter correction steps16 min
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2 min
Reading6 lectures
Notes for lesson 3.2.11 min
Notes for lesson 3.2.21 min
Notes for lesson 3.2.31 min
Notes for lesson 3.2.41 min
Notes for lesson 3.2.51 min
Notes for lesson 3.2.61 min
Quiz6 exercices pour s'entraîner
Practice quiz for lesson 3.2.112 min
Practice quiz for lesson 3.2.210 min
Practice quiz for lesson 3.2.310 min
Practice quiz for lesson 3.2.410 min
Practice quiz for lesson 3.2.510 min
Quiz for week 230 min
Semaine
3
Heures pour terminer
4 heures pour terminer

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....
Reading
7 vidéos (Total 86 min), 7 lectures, 7 quiz
Video7 vidéos
3.3.2: Introducing Octave code to generate correlated random numbers15 min
3.3.3: Introducing Octave code to implement KF for linearized cell model10 min
3.3.4: How do we improve numeric robustness of Kalman filter?10 min
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14 min
3.3.6: How do I initialize and tune a Kalman filter?12 min
3.3.7: Summary of "Coming to understand the linear KF" and next steps2 min
Reading7 lectures
Notes for lesson 3.3.11 min
Notes for lesson 3.3.21 min
Notes for lesson 3.3.31 min
Notes for lesson 3.3.41 min
Notes for lesson 3.3.51 min
Notes for lesson 3.3.61 min
Notes for lesson 3.3.71 min
Quiz7 exercices pour s'entraîner
Practice quiz for lesson 3.3.110 min
Practice quiz for lesson 3.3.210 min
Practice quiz for lesson 3.3.310 min
Practice quiz for lesson 3.3.410 min
Practice quiz for lesson 3.3.510 min
Practice quiz for lesson 3.3.610 min
Quiz for week 330 min
Semaine
4
Heures pour terminer
4 heures pour terminer

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....
Reading
8 vidéos (Total 101 min), 8 lectures, 7 quiz
Video8 vidéos
3.4.2: Deriving the three extended-Kalman-filter prediction steps15 min
3.4.3: Deriving the three extended-Kalman-filter correction steps6 min
3.4.4: Introducing a simple EKF example, with Octave code15 min
3.4.5: Preparing to implement EKF on an ECM20 min
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13 min
3.4.7: Introducing Octave code to update EKF for SOC estimation16 min
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2 min
Reading8 lectures
Notes for lesson 3.4.11 min
Notes for lesson 3.4.21 min
Notes for lesson 3.4.31 min
Notes for lesson 3.4.41 min
Notes for lesson 3.4.51 min
Notes for lesson 3.4.61 min
Notes for lesson 3.4.71 min
Notes for lesson 3.4.81 min
Quiz7 exercices pour s'entraîner
Practice quiz for lesson 3.4.110 min
Practice quiz for lesson 3.4.210 min
Practice quiz for lesson 3.4.310 min
Practice quiz for lesson 3.4.410 min
Practice quiz for lesson 3.4.510 min
Practice quiz for lesson 3.4.710 min
Quiz for week 430 min

Enseignant

Gregory Plett

Professor
Electrical and Computer Engineering

À propos de University of Colorado System

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....

À propos de la Spécialisation 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

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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