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

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26 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...

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

AK

May 2, 2020

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

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 M. E

•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 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 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 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 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 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

创建者 YE Z

•Jun 3, 2020

Good course.

创建者 BHARADWAZ B

•Jun 6, 2020

.

创建者 BHARADWAZ B

•Jun 6, 2020

.

创建者 varun k

•May 17, 2020

Overall it was good course with detail explanation about state estimation using kalman filter, EKM and SPKF. Superb explanation of topics with optimum pace and trainer was expectionally good in presenting such complex topics.

But the final project was too easy. There was less challenge. A small variation could have been introduced in the project where one actually learns how to program Kalman filters. For the level of mathematical complexity involve during derivations, the final project is not a match. Keep challenging problems as projects it would be great!

创建者 Mario E

•Apr 22, 2021

Content wise very interesting but the math was really a challenge this time. So it takes really some energy to go through and solve all the quizes. Taking a break in between and listen to some of the lessons a second time helped me at the end.

创建者 Haoran ( W

•Jul 10, 2020

This part of the course is very mathematical and conceptual, while passing the course seems easy but it requires very strong math and programming ability to fully understand. Great course for an advanced learner.

创建者 Darun T B

•Apr 14, 2021

Excellent course, would be happy if those sigma points were explained too. But still got the main idea of sigma point and the steps to execute them.

创建者 Adhip S

•Jul 23, 2020

Capstone projects could be more demanding. Maybe you can provide a multiple temperatures example.

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