About this Course
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Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 22 hours to complete

Suggested: 5 hours/week...
Available languages

English

Subtitles: English...
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 22 hours to complete

Suggested: 5 hours/week...
Available languages

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Hours to complete
5 hours to complete

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 videos (Total 120 min), 13 readings, 7 quizzes
Video8 videos
3.1.2: What is the importance of a good SOC estimator?8m
3.1.3: How do we define SOC carefully?16m
3.1.4: What are some approaches to estimating battery cell SOC?26m
3.1.5: Understanding uncertainty via mean and covariance17m
3.1.6: Understanding joint uncertainty of two unknown quantities15m
3.1.7: Understanding time-varying uncertain quantities22m
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3m
Reading13 readings
Notes for lesson 3.1.11m
Frequently Asked Questions5m
Course Resources5m
How to Use Discussion Forums5m
Earn a Course Certificate5m
Notes for lesson 3.1.21m
Notes for lesson 3.1.31m
Notes for lesson 3.1.41m
Introducing a new element to the course!10m
Notes for lesson 3.1.51m
Notes for lesson 3.1.61m
Notes for lesson 3.1.71m
Notes for lesson 3.1.81m
Quiz7 practice exercises
Practice quiz for lesson 3.1.210m
Practice quiz for lesson 3.1.310m
Practice quiz for lesson 3.1.410m
Practice quiz for lesson 3.1.515m
Practice quiz for lesson 3.1.610m
Practice quiz for lesson 3.1.76m
Quiz for week 140m
Week
2
Hours to complete
3 hours to complete

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 videos (Total 97 min), 6 readings, 6 quizzes
Video6 videos
3.2.2: The Kalman-filter gain factor23m
3.2.3: Summarizing the six steps of generic probabilistic inference9m
3.2.4: Deriving the three Kalman-filter prediction steps21m
3.2.5: Deriving the three Kalman-filter correction steps16m
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2m
Reading6 readings
Notes for lesson 3.2.11m
Notes for lesson 3.2.21m
Notes for lesson 3.2.31m
Notes for lesson 3.2.41m
Notes for lesson 3.2.51m
Notes for lesson 3.2.61m
Quiz6 practice exercises
Practice quiz for lesson 3.2.112m
Practice quiz for lesson 3.2.210m
Practice quiz for lesson 3.2.310m
Practice quiz for lesson 3.2.410m
Practice quiz for lesson 3.2.510m
Quiz for week 230m
Week
3
Hours to complete
4 hours to complete

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 videos (Total 86 min), 7 readings, 7 quizzes
Video7 videos
3.3.2: Introducing Octave code to generate correlated random numbers15m
3.3.3: Introducing Octave code to implement KF for linearized cell model10m
3.3.4: How do we improve numeric robustness of Kalman filter?10m
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14m
3.3.6: How do I initialize and tune a Kalman filter?12m
3.3.7: Summary of "Coming to understand the linear KF" and next steps2m
Reading7 readings
Notes for lesson 3.3.11m
Notes for lesson 3.3.21m
Notes for lesson 3.3.31m
Notes for lesson 3.3.41m
Notes for lesson 3.3.51m
Notes for lesson 3.3.61m
Notes for lesson 3.3.71m
Quiz7 practice exercises
Practice quiz for lesson 3.3.110m
Practice quiz for lesson 3.3.210m
Practice quiz for lesson 3.3.310m
Practice quiz for lesson 3.3.410m
Practice quiz for lesson 3.3.510m
Practice quiz for lesson 3.3.610m
Quiz for week 330m
Week
4
Hours to complete
4 hours to complete

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 videos (Total 101 min), 8 readings, 7 quizzes
Video8 videos
3.4.2: Deriving the three extended-Kalman-filter prediction steps15m
3.4.3: Deriving the three extended-Kalman-filter correction steps6m
3.4.4: Introducing a simple EKF example, with Octave code15m
3.4.5: Preparing to implement EKF on an ECM20m
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13m
3.4.7: Introducing Octave code to update EKF for SOC estimation16m
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2m
Reading8 readings
Notes for lesson 3.4.11m
Notes for lesson 3.4.21m
Notes for lesson 3.4.31m
Notes for lesson 3.4.41m
Notes for lesson 3.4.51m
Notes for lesson 3.4.61m
Notes for lesson 3.4.71m
Notes for lesson 3.4.81m
Quiz7 practice exercises
Practice quiz for lesson 3.4.110m
Practice quiz for lesson 3.4.210m
Practice quiz for lesson 3.4.310m
Practice quiz for lesson 3.4.410m
Practice quiz for lesson 3.4.510m
Practice quiz for lesson 3.4.710m
Quiz for week 430m

Instructor

Gregory Plett

Professor
Electrical and Computer Engineering

About 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....

About the Algorithms for Battery Management Systems Specialization

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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.