Voltar para Battery State-of-Health (SOH) Estimation

## ComentĂĄrios e feedback de alunos de Battery State-of-Health (SOH) Estimation da instituiĂ§ĂŁo Universidade do Colorado em Boulder

4.7
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89 classificaĂ§Ă”es

## Sobre o curso

This course can also be taken for academic credit as ECEA 5733, part of CU Boulderâs Master of Science in Electrical Engineering degree. In this course, you will learn how to implement different state-of-health estimation methods and to evaluate their relative merits. By the end of the course, you will be able to: - Identify the primary degradation mechanisms that occur in lithium-ion cells and understand how they work - Execute provided Octave/MATLAB script to estimate total capacity using WLS, WTLS, and AWTLS methods and lab-test data, and to evaluate results - Compute confidence intervals on total-capacity estimates - Compute estimates of a cellâs equivalent-series resistance using lab-test data - Specify the tradeoffs between joint and dual estimation of state and parameters, and steps that must be taken to ensure robust estimates (honors)...

## Melhores avaliaĂ§Ă”es

AK

22 de set de 2020

It was very new to me, and very interesting stuff. It became even better with the instructor's skill.

I would love recommending it to my friends

AS

8 de abr de 2020

A detailed course on battery capacity estimation, which covers overall perspectives, and complications in the SOH estimation of the battery.

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## 1 â 19 de 19 AvaliaĂ§Ă”es para o Battery State-of-Health (SOH) Estimation

por Davide C

âą

10 de mai de 2020

This course explains how to estimate battery SOH (State of Health) parameters: series resistance and total capacity, by using total least squares method and Kalman filters. Honestly, this course was quite boring compared to the other 4 courses of this specialization, but I found the mathematical methods explained in this course to be very useful. The Prof. explains very well and easily such complex concepts.

por Albert S

âą

2 de mar de 2020

This course provides detailed understanding into the state-of-health estimation theory. The course is a logical follow-up to the third course in this series (Battery State-of-Charge (SOC) Estimation). The underlying maths is somewhat more demanding than in the aforementioned course, therefore, taking more time to grasp on it would be benefitial. 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.

por John W

âą

31 de mai de 2019

excellent course in different statistical methods (different least squares methods) of estimating capacity. So much to learn in such a condense course. Aside from many coding examples, the main purpose is to teach statistical methods for optimizing capacity estimation and evaluate the performance of different methods. Its really up to the learner how much time they like to spend, either observing every little coding detail, or to just learning the main ideas.

por Mr S K R - P

âą

11 de mar de 2020

This Course is one of best technique in the literature point of view to compute the SOH of Lithium ion battery with Estimation and Probability techniques. I sincerely thank Dr.Plett and his team , and also Coursera team for providing this course to me.

Thanks and Yours Sincerely

Suresh Kumar.R

por Anant k

âą

23 de set de 2020

It was very new to me, and very interesting stuff. It became even better with the instructor's skill.

I would love recommending it to my friends

por Apurv S

âą

9 de abr de 2020

A detailed course on battery capacity estimation, which covers overall perspectives, and complications in the SOH estimation of the battery.

por Varun K

âą

30 de mai de 2020

Good course. Nice insight on optimization techniques. Problems and cases studies are really good

por JustinSmith

âą

24 de mai de 2022

Great course with a an emphasis on using the previous courses to create useful programs

por Suryakant A K

âą

24 de ago de 2020

Gave brief overview of SOH and helps in understanding the basic concepts.

por Shovan R S

âą

1 de out de 2020

great course. very insightful

por Vinayak K

âą

15 de ago de 2019

Exceptional Professor!!

por Vikram K V

âą

21 de abr de 2021

Excellent

por Fernando S Ă

âą

18 de fev de 2020

Personally, I believe that the capsone project is really impractical, as it is defined. You do not have to apply directly the knowledge you learned throughout the ourse, but instead try thousands of combinations of the pair (dz, gamma) to obtain a really precise value for the rms error. I have spent many hous (would say more than 10) trying to achieve so, and I think I'm not the only one, considering the discussion forums. Frankly, I was really disappointed. Appart from that, the course was great, but I hope that the fact mentioned above does not discourage many people.

por Cagatay C

âą

26 de mar de 2021

I think the content and the way Dr. Plett teaches is amazing. He has a great textbook and his quizzes that follow the lecture reinforces the learning. I only got 1 star off because of the programming assignments. I understand they were aimed for a wider audience but for those in research it wasn't as fruitful.

por Bernard R A

âą

23 de mai de 2020

Very good in-depth introduction to aging mechanisms of Li-Ion batteries, together with sound mathematical foundations.

In a future, revised version of this course, I'd like to have a few more details on the Dual- and Joint-Kalman filter approaches.

por Anton L

âą

18 de jul de 2021

The course is going very deep in to mathematical models. I like the offered code samples as they allow to understand the functions in more detail

por J S V S K

âą

26 de set de 2020

Course is good but its taking time to understand

por Klaus H

âą

13 de jun de 2020

Jupyter Notebook kernel often crashes, it is slow and bad for debugging.

por Tochukwu N

âą

5 de dez de 2021

Though it is generally a nice course, felt overwhelming