Este curso forma parte de Programa especializado: Algorithms for Battery Management Systems

4.7
estrellas
220 calificaciones

96 %

Gregory Plett

16.165 ya inscrito

Ofrecido Por

Universidad de Colorado en Boulder

Sistema Universitario de Colorado

Parte del título de grado Master of Science in Electrical Engineering
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Programa especializado: Algorithms for Battery Management SystemsUniversidad de Colorado en Boulder

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This course can also be taken for academic credit as ECEA 5732, part of CU Boulder’s Master of Science in Electrical Engineering degree.
In this course, you will learn how to implement different state-of-charge estimation methods and to evaluate their relative merits. By the end of the course, you will be able to:

-	Implement simple voltage-based and current-based state-of-charge estimators and understand their limitations
-	Explain the purpose of each step in the sequential-probabilistic-inference solution
-	Execute provided Octave/MATLAB script for a linear Kalman filter and evaluate results
-	Execute provided Octave/MATLAB script for state-of-charge estimation using an extended Kalman filter on lab-test data and evaluate results
-	Execute provided Octave/MATLAB script for state-of-charge estimation using a sigma-point Kalman filter on lab-test data and evaluate results
-	Implement method to detect and discard faulty voltage-sensor measurements

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Curso 3 de 5 en

Programa especializado: Algorithms for Battery Management Systems

Nivel intermedio

Aprox. 27 horas para completar

Inglés (English)

Qué aprenderás

How to implement state-of-charge (SOC) estimators for lithium-ion battery cells

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 3 de 5 en

Programa especializado: Algorithms for Battery Management Systems

Nivel intermedio

Aprox. 27 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,78/5 (70 calificaciones)

Gregory Plett

Professor

Electrical and Computer Engineering

59.394 alumnos

5 cursos

Ofrecido por

Universidad de Colorado en Boulder

Sistema Universitario de Colorado

Comienza a trabajar para obtener tu maestría

Este curso es parte de Master of Science in Electrical Engineering completamente en línea de Universidad de Colorado en Boulder. Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.

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Programa - Qué aprenderás en este curso

Calificación del contenido96%(2,830 calificaciones)

Semana1

Semana 1

5 horas para completar

The importance of a good SOC estimator

5 horas para completar

8 videos (Total 120 minutos), 14 lecturas, 7 cuestionarios

8 videos

3.1.1: Welcome to the course!8m3.1.2: What is the importance of a good SOC estimator?8m3.1.3: How do we define SOC carefully?16m3.1.4: What are some approaches to estimating battery cell SOC?26m3.1.5: Understanding uncertainty via mean and covariance17m3.1.6: Understanding joint uncertainty of two unknown quantities15m3.1.7: Understanding time-varying uncertain quantities22m3.1.8: Summary of "The importance of a good SOC estimator" and next steps3m

14 lecturas

Notes for lesson 3.1.11mFrequently asked questions5mCourse resources5mHow to use discussion forums5mEarn a course certificate5mAre you interested in earning an MSEE degree?5mNotes for lesson 3.1.21mNotes for lesson 3.1.31mNotes for lesson 3.1.41mIntroducing a new element to the course!10mNotes for lesson 3.1.51mNotes for lesson 3.1.61mNotes for lesson 3.1.71mNotes for lesson 3.1.81m

7 ejercicios de práctica

Practice quiz for lesson 3.1.210mPractice quiz for lesson 3.1.310mPractice quiz for lesson 3.1.410mPractice quiz for lesson 3.1.515mPractice quiz for lesson 3.1.610mPractice quiz for lesson 3.1.730mQuiz for week 140m

Semana2

Semana 2

3 horas para completar

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.

3 horas para completar

6 videos (Total 97 minutos), 6 lecturas, 6 cuestionarios

6 videos

3.2.1: Predict/correct mechanism of sequential probabilistic inference24m3.2.2: The Kalman-filter gain factor23m3.2.3: Summarizing the six steps of generic probabilistic inference9m3.2.4: Deriving the three Kalman-filter prediction steps21m3.2.5: Deriving the three Kalman-filter correction steps16m3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2m

6 lecturas

Notes for lesson 3.2.11mNotes for lesson 3.2.21mNotes for lesson 3.2.31mNotes for lesson 3.2.41mNotes for lesson 3.2.51mNotes for lesson 3.2.61m

6 ejercicios de práctica

Practice quiz for lesson 3.2.112mPractice quiz for lesson 3.2.210mPractice quiz for lesson 3.2.310mPractice quiz for lesson 3.2.410mPractice quiz for lesson 3.2.510mQuiz for week 230m

Semana3

Semana 3

4 horas para completar

Coming to understand the linear Kalman filter

4 horas para completar

7 videos (Total 86 minutos), 7 lecturas, 7 cuestionarios

7 videos

3.3.1: Visualizing the Kalman filter with a linearized cell model20m3.3.2: Introducing Octave code to generate correlated random numbers15m3.3.3: Introducing Octave code to implement KF for linearized cell model10m3.3.4: How do we improve numeric robustness of Kalman filter?10m3.3.5: Can we automatically detect bad measurements with a Kalman filter?14m3.3.6: How do I initialize and tune a Kalman filter?12m3.3.7: Summary of "Coming to understand the linear KF" and next steps2m

7 lecturas

Notes for lesson 3.3.11mNotes for lesson 3.3.21mNotes for lesson 3.3.31mNotes for lesson 3.3.41mNotes for lesson 3.3.51mNotes for lesson 3.3.61mNotes for lesson 3.3.71m

7 ejercicios de práctica

Practice quiz for lesson 3.3.110mPractice quiz for lesson 3.3.210mPractice quiz for lesson 3.3.310mPractice quiz for lesson 3.3.410mPractice quiz for lesson 3.3.510mPractice quiz for lesson 3.3.610mQuiz for week 330m

Semana4

Semana 4

4 horas para completar

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.

4 horas para completar

8 videos (Total 101 minutos), 8 lecturas, 7 cuestionarios

8 videos

3.4.1: Introducing nonlinear variations to Kalman filters11m3.4.2: Deriving the three extended-Kalman-filter prediction steps15m3.4.3: Deriving the three extended-Kalman-filter correction steps6m3.4.4: Introducing a simple EKF example, with Octave code15m3.4.5: Preparing to implement EKF on an ECM20m3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13m3.4.7: Introducing Octave code to update EKF for SOC estimation16m3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2m

8 lecturas

Notes for lesson 3.4.11mNotes for lesson 3.4.21mNotes for lesson 3.4.31mNotes for lesson 3.4.41mNotes for lesson 3.4.51mNotes for lesson 3.4.61mNotes for lesson 3.4.71mNotes for lesson 3.4.81m

7 ejercicios de práctica

Practice quiz for lesson 3.4.110mPractice quiz for lesson 3.4.210mPractice quiz for lesson 3.4.310mPractice quiz for lesson 3.4.410mPractice quiz for lesson 3.4.510mPractice quiz for lesson 3.4.710mQuiz for week 430m

Semana5

Semana 5

5 horas para completar

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.

5 horas para completar

7 videos (Total 116 minutos), 7 lecturas, 6 cuestionarios

7 videos

3.5.1: Problems with EKF that are improved with sigma-point methods11m3.5.2: Approximating uncertain variables using sigma points31m3.5.3: Deriving the six sigma-point-Kalman-filter steps17m3.5.4: Introducing a simple SPKF example with Octave code19m3.5.5: Introducing Octave code to initialize and control SPKF for SOC estimation9m3.5.6: Introducing Octave code to update SPKF for SOC estimation18m3.5.7: Summary of "Cell SOC estimation using a SPFK" and next steps7m

7 lecturas

Notes for lesson 3.5.11mNotes for lesson 3.5.21mNotes for lesson 3.5.31mNotes for lesson 3.5.41mNotes for lesson 3.5.51mNotes for lesson 3.5.61mNotes for lesson 3.5.71m

6 ejercicios de práctica

Practice quiz for lesson 3.5.110mPractice quiz for lesson 3.5.210mPractice quiz for lesson 3.5.310mPractice quiz for lesson 3.5.430mPractice quiz for lesson 3.5.610mQuiz for week 530m

Semana6

Semana 6

3 horas para completar

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.

3 horas para completar

5 videos (Total 71 minutos), 5 lecturas, 4 cuestionarios

5 videos

3.6.1: Why do we need to be clever when estimating SOC for battery packs?24m3.6.2: Developing a "bar" filter using an ECM6m3.6.3: Developing the "delta" filters using an ECM15m3.6.4: Introducing "desktop validation" as a method for predicting performance21m3.6.5: Summary of "Improving computational efficiency using the bar-delta method" and next steps2m

5 lecturas

Notes for lesson 3.6.11mNotes for lesson 3.6.21mNotes for lesson 3.6.31mNotes for lesson 3.6.41mNotes for lesson 3.6.51m

4 ejercicios de práctica

Quiz for lesson 3.6.115mQuiz for lesson 3.6.210mQuiz for lesson 3.6.310mQuiz for lessons 3.6.4 and 3.6.515m

Semana7

Semana 7

5 horas para completar

Capstone project

5 horas para completar

Reseñas

4.7

60 reseña

5 stars
78,73 %
4 stars
18,55 %
3 stars
1,80 %
2 stars
0,45 %
1 star
0,45 %

Principales reseñas sobre BATTERY STATE-OF-CHARGE (SOC) ESTIMATION

por DB14 de abr. de 2021

Excellent course, would be happy if those sigma points were explained too. But still got the main idea of sigma point and the steps to execute them.

por JJ8 de may. de 2022

Using computer models to simulate battery behavior and estimate SOH was a skill I did not have before this course. It was taught in a gradual pace that was comfortable.

por NB12 de ago. de 2021

As an electrical engineer, I firmly state that this course is the best for anyone who would like to embark on this journey of battery energy storage. Well structured

With an excellent instructor

por RS23 de feb. de 2022

Useful to understand Kalman Filters and continue with the Battery Management System specialization.

Ver todas las reseñas

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Battery State-of-Charge (SOC) Estimation

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Qué aprenderás