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Volver a Battery State-of-Charge (SOC) Estimation

Opiniones y comentarios de aprendices correspondientes a Battery State-of-Charge (SOC) Estimation por parte de Universidad de Colorado en Boulder

4.8
estrellas
148 calificaciones
36 reseña

Acerca del Curso

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...

Principales reseñas

NB

12 de ago. de 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

10 de ago. de 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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26 - 36 de 36 revisiones para Battery State-of-Charge (SOC) Estimation

por YE Z

3 de jun. de 2020

Good course.

por BHARADWAZ B

6 de jun. de 2020

.

por BHARADWAZ B

6 de jun. de 2020

.

por Varun K

17 de may. de 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!

por RICARD B

9 de mar. de 2022

I​t's nice for the math development, maybe I think is very complex to perform this estimation in some real applications.

Probably with more wide view doing the remaining courses we can understand better why we need this large effort in calculation and to apply the more suitable estimation (accuracy) in each application. The error type and process time indicators are important guide to choose the "just enough".

T​hank you, excellent course!

por Mario E

22 de abr. de 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.

por Haoran ( W

10 de jul. de 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.

por Robert F

16 de dic. de 2021

It was overall an interesting course, but the mathematics could be improved with some real world examples. I think with no background in this topic you will be fastly lost.

por Darun T B

14 de abr. de 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.

por Adhip S

23 de jul. de 2020

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

por WJ C

3 de may. de 2021

The course was a bit difficult to follow.