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Battery State-of-Charge (SOC) Estimation に戻る

コロラド大学ボルダー校(University of Colorado Boulder) による Battery State-of-Charge (SOC) Estimation の受講者のレビューおよびフィードバック

4.8
135件の評価
29件のレビュー

コースについて

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...

人気のレビュー

NB
2021年8月12日

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
2020年8月10日

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

フィルター:

Battery State-of-Charge (SOC) Estimation: 1 - 25 / 29 レビュー

by John W

2019年5月17日

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 M. E

2020年1月8日

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

2021年8月29日

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.

by Albert S

2020年3月2日

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 Davide C

2020年5月1日

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 Kharan S

2020年8月23日

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

2021年8月13日

​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

2020年8月11日

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 Ameya K

2020年5月3日

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

by Shovan R S

2020年9月16日

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

by HAFIZ A A

2020年11月29日

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

by J S V S K

2020年9月15日

Nice Explanation and programming also easily understandable

by Nikhil B

2020年7月10日

A great explanation of SOC estimation using EKF and SPKF.

by Piotr M

2021年11月1日

Great knowledge to go deeper into battery world

by Nagapoornima S

2021年3月27日

The course was challenging.

by 2019BTEEL00034 M S S

2021年4月12日

good course to start upon

by Thang N

2020年8月20日

I like this course!

by Oscar D S B

2020年10月25日

Excellent course.

by VASUPALLI M

2020年9月25日

Excellent course

by Ryosuke I

2020年10月9日

とてもいい勉強になりました

by YE Z

2020年6月3日

Good course.

by BHARADWAZ B

2020年6月6日

.

by BHARADWAZ B

2020年6月6日

.

by varun k

2020年5月17日

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!

by Mario E

2021年4月22日

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.