Enroll Course: https://www.coursera.org/learn/battery-state-of-health
In today’s world, where electric vehicles and renewable energy storage systems are becoming increasingly prevalent, understanding battery technology is more important than ever. The Battery State-of-Health (SOH) Estimation course offered on Coursera is a fantastic opportunity for anyone looking to deepen their knowledge of lithium-ion batteries and their health estimation methods. This course is also available for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree.
### Course Overview
The course is structured to provide a comprehensive understanding of the degradation mechanisms that affect lithium-ion cells. It covers various state-of-health estimation methods, allowing students to evaluate their relative merits. By the end of the course, participants will be equipped to identify primary degradation mechanisms, execute Octave/MATLAB scripts for capacity estimation, and implement various estimation methods.
### Syllabus Breakdown
1. **How does lithium-ion cell health degrade?**
The course begins with an exploration of the physical and chemical mechanisms behind battery degradation. Understanding why total capacities decrease and resistances increase as batteries age is crucial for anyone working with battery technology.
2. **Total-least-squares battery-cell capacity estimation**
Students learn about the limitations of ordinary-least-squares (OLS) methods and the advantages of total-least-squares (TLS) methods. This section is vital for grasping the nuances of accurate capacity estimation.
3. **Simplified total-least-squares battery-cell capacity estimates**
The course then delves into efficient computation methods suitable for embedded systems, introducing proportionally weighted TLS and approximate weighted TLS (AWTLS) methods.
4. **How to write code for the different total-capacity estimators**
Practical coding sessions in Octave allow students to implement the learned methods and benchmark their performance in real-world scenarios, particularly in hybrid and battery-electric vehicle applications.
5. **A Kalman-filter approach to total capacity estimation**
Advanced techniques using extended Kalman filters (EKFs) and sigma-point Kalman filters (SPKFs) are introduced, providing students with cutting-edge tools for battery state estimation.
6. **Capstone project**
The course culminates in a capstone project where students apply their knowledge to determine optimal input data for total-capacity estimation methods, reinforcing their learning through practical application.
### Recommendation
I highly recommend the Battery State-of-Health (SOH) Estimation course for anyone interested in battery technology, whether you’re a student, a professional in the field, or simply a tech enthusiast. The course is well-structured, with a perfect blend of theoretical knowledge and practical application. The hands-on coding experience is particularly beneficial, allowing students to see the real-world implications of their learning.
In conclusion, this course not only enhances your understanding of battery health estimation but also equips you with valuable skills that are increasingly in demand in today’s technology-driven world. Don’t miss out on this opportunity to advance your knowledge and career in the field of electrical engineering and battery technology!
Enroll Course: https://www.coursera.org/learn/battery-state-of-health