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

Exceptional Professor!!

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.