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Bewertung und Feedback des Lernenden für Battery State-of-Charge (SOC) Estimation von University of Colorado Boulder

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153 Bewertungen

Über den Kurs

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-Bewertungen

NB

12. Aug. 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

10. Aug. 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 von 37 Bewertungen für Battery State-of-Charge (SOC) Estimation

von John W

17. Mai 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.

von M. E

8. Jan. 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.

von Vigneshwaran T

29. Aug. 2021

D​on'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.

von Albert S

2. März 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.

von Davide C

1. Mai 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.

von Kharan S

23. Aug. 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.

von Nicolas B

13. Aug. 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

von Bhargav S

11. Aug. 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

von JustinSmith

9. Mai 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.

von Pawel M

28. Jan. 2022

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

von Zihao Z

18. Jan. 2022

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

von Ameya K

3. Mai 2020

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

von Shovan R S

16. Sep. 2020

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

von HAFIZ A A

29. Nov. 2020

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

von Rodrigo P S

24. Feb. 2022

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

von J S V S K

15. Sep. 2020

Nice Explanation and programming also easily understandable

von Nikhil B

10. Juli 2020

A great explanation of SOC estimation using EKF and SPKF.

von Piotr M

1. Nov. 2021

Great knowledge to go deeper into battery world

von JAVAID I E

6. Apr. 2022

It was great to improve the

von Nagapoornima S

27. März 2021

The course was challenging.

von 2019BTEEL00034 M S S

12. Apr. 2021

good course to start upon

von Thang N

20. Aug. 2020

I like this course!

von Oscar D S B

25. Okt. 2020

Excellent course.

von VASUPALLI M

25. Sep. 2020

Excellent course

von Ryosuke I

9. Okt. 2020

とてもいい勉強になりました