This course can also be taken for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree.

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

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Offered By

## About this Course

### What you will learn

How to implement state-of-health (SOH) 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 Master's degree

## Syllabus - What you will learn from this course

**4 hours to complete**

## How does lithium-ion cell health degrade?

As battery cells age, their total capacities generally decrease and their resistances generally increase. This week, you will learn WHY this happens. You will learn about the specific physical and chemical mechanisms that cause degradation to lithium-ion battery cells. You will also learn why it is relatively simple to estimate and track changes to resistance, but why it is difficult to track changes to total capacity accurately.

**4 hours to complete**

**4 hours to complete**

## Total-least-squares battery-cell capacity estimation

Total capacity is often estimated using ordinary-least-squares (OLS) methods. This week, you will learn that this is a fundamentally incorrect approach, and will learn that a total-least-squares (TLS) method should be used instead. You will learn how to derive a weighted OLS solution, to use as a benchmark, and how to derive a weighted TLS solution also.

**4 hours to complete**

**4 hours to complete**

## Simplified total-least-squares battery-cell capacity estimates

Unfortunately, the weighted TLS solution you learned in week 2 is not well suited for efficient computation on an embedded system like a BMS. As an intermediate step toward finding an efficient weighted TLS method, you will first learn a proportionally weighted TLS method this week. You will then learn how to generalize this to an "approximate weighted TLS" (AWTLS) method, which gives good estimates, and is feasible to implement on a BMS.

**4 hours to complete**

**4 hours to complete**

## How to write code for the different total-capacity estimators

So far this course, you have learned a number of methods for estimating total capacity. This week, you will learn how to implement those methods in Octave code. You will also explore different simulation scenarios to benchmark how well each method works, in comparison with the others. The scenarios are representative of hybrid-electric-vehicle (HEV) and battery-electric-vehicle (BEV) applications, but the principles learned can be extrapolated to other similar application domains.

**4 hours to complete**

## Reviews

### TOP REVIEWS FROM BATTERY STATE-OF-HEALTH (SOH) ESTIMATION

It was very new to me, and very interesting stuff. It became even better with the instructor's skill. I would love recommending it to my friends

A detailed course on battery capacity estimation, which covers overall perspectives, and complications in the SOH estimation of the battery.

Good course. Nice insight on optimization techniques. Problems and cases studies are really good

Gave brief overview of SOH and helps in understanding the basic concepts.

## 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.

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