This course can also be taken for academic credit as ECEA 5732, part of CU Boulderâ€™s Master of Science in Electrical Engineering degree.

This course is part of the Algorithms for Battery Management Systems Specialization

**11,120**already enrolled

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## About this Course

## What you will learn

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

## Offered by

### University of Colorado Boulder

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

### 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.

## Start working towards your degree

## Syllabus - What you will learn from this course

**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.

**5 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.

**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.

**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.

**4 hours to complete**

## Reviews

- 5 stars80.89%
- 4 stars16.56%
- 3 stars1.27%
- 2 stars0.63%
- 1 star0.63%

### TOP REVIEWS FROM BATTERY STATE-OF-CHARGE (SOC) ESTIMATION

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

Sir Gregory plett is an excellent Professor Ever and thanks to Coursera for such valuable plateform.

The course was well planned and organised! There is flexibility in the course deadline which is appreciable and suitable for students, Working professionals, faculties.

The concepts taught were absolutely crucial for the later parts of this specialization and they were explained properly.

## 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.

## Frequently Asked Questions

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