返回到 Battery State-of-Charge (SOC) Estimation

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36 条评论

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...

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\n\nWith 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

筛选依据：

创建者 John W

•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.

创建者 Elenchezhiyan M

•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.

创建者 Vigneshwaran T

•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.

创建者 Albert S

•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.

创建者 Davide C

•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.

创建者 Kharan S

•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.

创建者 Nicolas B

•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

创建者 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

创建者 JustinSmith

•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.

创建者 Pawel M

•Jan 28, 2022

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

创建者 Zihao Z

•Jan 18, 2022

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

创建者 Ameya K

•May 3, 2020

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

创建者 Shovan R S

•Sep 16, 2020

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

创建者 HAFIZ A A

•Nov 29, 2020

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

创建者 Rodrigo P S

•Feb 24, 2022

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

创建者 J S V S K

•Sep 15, 2020

Nice Explanation and programming also easily understandable

创建者 Nikhil B

•Jul 10, 2020

A great explanation of SOC estimation using EKF and SPKF.

创建者 Piotr M

•Nov 1, 2021

Great knowledge to go deeper into battery world

创建者 JAVAID I E

•Apr 6, 2022

It was great to improve the

创建者 Nagapoornima S

•Mar 27, 2021

The course was challenging.

创建者 2019BTEEL00034 M S S

•Apr 12, 2021

good course to start upon

创建者 Thang N

•Aug 20, 2020

I like this course!

创建者 Oscar D S B

•Oct 25, 2020

Excellent course.

创建者 VASUPALLI M

•Sep 25, 2020

Excellent course

创建者 Ryosuke I

•Oct 9, 2020

とてもいい勉強になりました