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Retour à Battery State-of-Health (SOH) Estimation

Avis et commentaires pour d'étudiants pour Battery State-of-Health (SOH) Estimation par Université du Colorado à Boulder

4.8
étoiles
45 évaluations
10 avis

À propos du cours

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 cell’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)...

Meilleurs avis

AS

Apr 09, 2020

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

VK

May 30, 2020

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

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1 - 10 sur 10 Avis pour Battery State-of-Health (SOH) Estimation

par Davide C

May 10, 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.

par Albert S

Mar 02, 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.

par John W

Jun 01, 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.

par Suresh K R

Mar 11, 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

par Apurv S

Apr 09, 2020

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

par Varun K

May 30, 2020

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

par Vinayak S K

Aug 15, 2019

Exceptional Professor!!

par Fernando S Á

Feb 18, 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.

par Bernard R A

May 23, 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.

par Klaus H

Jun 13, 2020

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