Overall, I good introductory course into Kalman Filtering for SOC estimation. However, the final project was a little bit to easy. In addition to tuning the initial covariance states, maybe add a different part 2 (other than tuning initial parameters) for developing to understand the kalman filter algorithm relating to battery estimation.

The course was well planned and organised! There is flexibility in the course deadline which is appreciable and suitable for students, Working professionals, faculties.

Don't give up if you are intimidated by the abstract mathematics at the beginning of this course, which is challenging, but after the end of week #2 everything will make sense and the subsequent course content gets much easier. I am a computational chemist and I never even heard of sequential probabilistic inference prior to this course, and I am not that good at mathematics as well. So, believe me Prof. Gregory Plett has done an excellent job on explaining these complicated concepts, turst him and stick with the course until the end. I got everthing I hoped for from this course. I thank Prof. Gregory Plett and Coursera for offering this course.

Excellent !!!

I've taken other courses that include parameter estimations using "Linear and Extended Kalman Filters" and they used to be very complicated, with a lot of missing information. But this course explains Kalman Filters in a very smooth way, with a lot more depth in basic concepts, in the underlying math involved, and building procedures and software step-by-step with a very pragmatic focus.

It covers Linear Kalman Filters, Extended Kalman Filters (EKF) and Sigma-points Kalman Filters.

Congratulations and Thank you, Professor!

This course deeply explains about linear Kalman filter and its non-linear externsion: Estended KF and Sigma Point KF. The course also explains how to apply these powerful tools to battery cells State of Charge estimation, a physical quantity which cannot be measured directly and therefore has to be estimated indirectly based on electrical current, voltage, and temperature. The professor was capable to explain in a simple way such complex mathematics behind Kalman filters theory. I am looking forward to use this new knowledge at work.

This is a remarkable course in which Kalman filter based methods for defining the battery state of charge are analyzed in details. Theoretical basis is clearly explained and practical implementation of EKF and SPKF is demonstrated. Special attention is paid to bar-delta filters for efficient and robust definition of SOCs of many cells within a battery pack

The course explains the Kalman filter in detail. The highlight of this course is that the professor explains all the complicated mathematics in small advancements that you can easily understand rather than putting a lot in front and confusing a lot.

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

With an excellent instructor

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

This course is comprehensive introduction into the matter. The course explains in detail mathematical concepts behind Kalman filters (and can therefore serve very well for general understanding of estimation theory and Kalman filters), than it shift gently to Kalman filter approaches to state-of-charge. Even with minimum pre-knowledge, after the course ends, one is fully equipped to deal with ECM-based state-of-charges. 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.

the best course for anyone who works in battery management area or EV technology in general. Professor Plett is an extra ordinary and gifted teacher. immensely thankful for the way he simplifies and teaches complex engineering problems like Kalman filtering.

Using computer models to simulate battery behavior and estimate SOH was a skill I did not have before this course. It was taught in a gradual pace that was comfortable.

Excellent course that has very clear teaching material and engaging tests and assignments. A great foundational course for battery algorithms.

Linear Kalman Filter, Extend Kalman Filter, Sigma-point Kalman Filter, very practical, very good course for battery SOC estimation

The concepts taught were absolutely crucial for the later parts of this specialization and they were explained properly.

The course is both detailed and systematic in explaining the fundamental concepts behind SOC estimation.

Great course!!! I got hands on experience with all types of kalman filter for battery state estimation.

Sir Gregory plett is an excellent Professor Ever and thanks to Coursera for such valuable plateform.

Useful to understand Kalman Filters and continue with the Battery Management System specialization.

Wonderful course and truly knowledgeable and engaging instructor. Thank you so much.