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

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.

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.

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.

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

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

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

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

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

great course. very insightful

Exceptional Professor!!

Excellent

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.

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.

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.

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

Course is good but its taking time to understand

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

Though it is generally a nice course, felt overwhelming