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Back to Battery State-of-Charge (SOC) Estimation

Learner Reviews & Feedback for Battery State-of-Charge (SOC) Estimation by University of Colorado Boulder

4.8
stars
244 ratings

About the Course

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...

Top reviews

NB

Aug 12, 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

BS

Aug 10, 2020

Good and a very challenging course. Really makes you work to understand even the basic concepts. Challenging theoretical and practical assignments. Lot of learning obtained from this course

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1 - 25 of 64 Reviews for Battery State-of-Charge (SOC) Estimation

By John W

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May 17, 2019

Overall, I good introductory course into Kalman Filtering for SOC estimation. However, the final project was a little bit to easy. In addition to tuning the initial covariance states, maybe add a different part 2 (other than tuning initial parameters) for developing to understand the kalman filter algorithm relating to battery estimation.

By Elenchezhiyan M

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Jan 8, 2020

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

By Vigneshwaran T

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Aug 29, 2021

Don't give up if you are intimidated by the abstract mathematics at the beginning of this course, which is challenging, but after the end of week #2 everything will make sense and the subsequent course content gets much easier. I am a computational chemist and I never even heard of sequential probabilistic inference prior to this course, and I am not that good at mathematics as well. So, believe me Prof. Gregory Plett has done an excellent job on explaining these complicated concepts, turst him and stick with the course until the end. I got everthing I hoped for from this course. I thank Prof. Gregory Plett and Coursera for offering this course.

By Erick A M D

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Feb 16, 2023

Excellent !!!

I've taken other courses that include parameter estimations using "Linear and Extended Kalman Filters" and they used to be very complicated, with a lot of missing information. But this course explains Kalman Filters in a very smooth way, with a lot more depth in basic concepts, in the underlying math involved, and building procedures and software step-by-step with a very pragmatic focus.

It covers Linear Kalman Filters, Extended Kalman Filters (EKF) and Sigma-points Kalman Filters.

Congratulations and Thank you, Professor!

By Davide C

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May 1, 2020

This course deeply explains about linear Kalman filter and its non-linear externsion: Estended KF and Sigma Point KF. The course also explains how to apply these powerful tools to battery cells State of Charge estimation, a physical quantity which cannot be measured directly and therefore has to be estimated indirectly based on electrical current, voltage, and temperature. The professor was capable to explain in a simple way such complex mathematics behind Kalman filters theory. I am looking forward to use this new knowledge at work.

By Roman F

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Mar 29, 2023

This is a remarkable course in which Kalman filter based methods for defining the battery state of charge are analyzed in details. Theoretical basis is clearly explained and practical implementation of EKF and SPKF is demonstrated. Special attention is paid to bar-delta filters for efficient and robust definition of SOCs of many cells within a battery pack

By Kharan S

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Aug 23, 2020

The course explains the Kalman filter in detail. The highlight of this course is that the professor explains all the complicated mathematics in small advancements that you can easily understand rather than putting a lot in front and confusing a lot.

By Nicolas B

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Aug 13, 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

By Bhargav S

•

Aug 11, 2020

Good and a very challenging course. Really makes you work to understand even the basic concepts. Challenging theoretical and practical assignments. Lot of learning obtained from this course

By Albert S

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Mar 2, 2020

This course is comprehensive introduction into the matter. The course explains in detail mathematical concepts behind Kalman filters (and can therefore serve very well for general understanding of estimation theory and Kalman filters), than it shift gently to Kalman filter approaches to state-of-charge. Even with minimum pre-knowledge, after the course ends, one is fully equipped to deal with ECM-based state-of-charges. This course requires dilligent work at home as well. I would recommend it to anyone dealing with battery control algorithms, both at the university, as well as in the private sector.

By Thiyagarajan

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Nov 16, 2023

the best course for anyone who works in battery management area or EV technology in general. Professor Plett is an extra ordinary and gifted teacher. immensely thankful for the way he simplifies and teaches complex engineering problems like Kalman filtering.

By JustinSmith

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May 9, 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.

By Pawel M

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Jan 28, 2022

Excellent course that has very clear teaching material and engaging tests and assignments. A great foundational course for battery algorithms.

By Zihao Z

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Jan 18, 2022

Linear Kalman Filter, Extend Kalman Filter, Sigma-point Kalman Filter, very practical, very good course for battery SOC estimation

By Ameya K

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May 3, 2020

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

By derick m

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Jan 19, 2023

The course is both detailed and systematic in explaining the fundamental concepts behind SOC estimation.

By Shovan R S

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Sep 16, 2020

Great course!!! I got hands on experience with all types of kalman filter for battery state estimation.

By HAFIZ A A

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Nov 29, 2020

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

By Rodrigo P S

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Feb 24, 2022

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

By HIMANSHU M

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Jun 17, 2023

Wonderful course and truly knowledgeable and engaging instructor. Thank you so much.

By J S V S K

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Sep 15, 2020

Nice Explanation and programming also easily understandable

By Nikhil B

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Jul 10, 2020

A great explanation of SOC estimation using EKF and SPKF.

By Nikolaos D

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Feb 19, 2023

Best introduction on kalman filters ever created

By Piotr M

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Nov 1, 2021

Great knowledge to go deeper into battery world

By Batteryand S

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May 12, 2023

It was hard but fantastic and worthy it