University of Colorado System
Battery State-of-Charge (SOC) Estimation
University of Colorado System

Battery State-of-Charge (SOC) Estimation

This course is part of Algorithms for Battery Management Systems Specialization

Taught in English

Some content may not be translated

Gregory Plett

Instructor: Gregory Plett

18,371 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(244 reviews)

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96%

Intermediate level
Some related experience required
27 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

What you'll learn

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

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Assessments

37 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(244 reviews)

|

96%

Intermediate level
Some related experience required
27 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

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This course is part of the Algorithms for Battery Management Systems Specialization
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There are 7 modules in this course

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.

What's included

8 videos15 readings7 quizzes1 discussion prompt1 ungraded lab

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.

What's included

6 videos6 readings6 quizzes

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.

What's included

7 videos7 readings7 quizzes2 ungraded labs

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.

What's included

8 videos8 readings7 quizzes3 ungraded labs

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.

What's included

7 videos7 readings6 quizzes2 ungraded labs

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.

What's included

5 videos5 readings4 quizzes1 ungraded lab

You have already learned that Kalman filters must be "tuned" by adjusting their process-noise, sensor-noise, and initial state-estimate covariance matrices in order to give acceptable performance over a wide range of operating scenarios. This final course module will give you some experience hand-tuning both an EKF and SPKF for SOC estimation.

What's included

2 programming assignments2 ungraded labs

Instructor

Instructor ratings
4.8 (79 ratings)
Gregory Plett
University of Colorado System
5 Courses67,765 learners

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