Battery State-of-Health (SOH) Estimation
Master advanced techniques for estimating and tracking battery health degradation using modern estimation methods.
Master advanced techniques for estimating and tracking battery health degradation using modern estimation methods.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Algorithms for Battery Management Systems Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Understand lithium-ion cell degradation mechanisms
Implement total capacity estimation methods
Calculate confidence intervals for estimates
Compute equivalent series resistance
Develop Kalman filter-based estimators
Evaluate different estimation approaches
Skills you'll gain
This course includes:
5.7 Hours PreRecorded video
31 assignments
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FullTime access
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There are 6 modules in this course
This comprehensive course explores methods for estimating and tracking battery state-of-health, focusing on lithium-ion cell degradation mechanisms and estimation techniques. Students learn about capacity decrease and resistance increase phenomena, implementing various estimation methods including weighted least squares and Kalman filtering approaches. The curriculum covers both theoretical foundations and practical implementation using Octave/MATLAB, including confidence interval computation and parameter estimation techniques.
How does lithium-ion cell health degrade?
Module 1 · 4 Hours to complete
Total-least-squares battery-cell capacity estimation
Module 2 · 3 Hours to complete
Simplified total-least-squares battery-cell capacity estimates
Module 3 · 3 Hours to complete
How to write code for the different total-capacity estimators
Module 4 · 4 Hours to complete
A Kalman-filter approach to total capacity estimation
Module 5 · 3 Hours to complete
Capstone project
Module 6 · 3 Hours to complete
Fee Structure
Instructor
Leading Expert in Battery Systems and Control Engineering
Gregory Plett serves as Professor of Electrical and Computer Engineering at the University of Colorado Colorado Springs, where he has established himself as a pioneering researcher in battery management systems since 1998. His academic credentials include a B.Eng. from Carleton University and M.S.E.E. and Ph.D. degrees from Stanford University. His research focuses on advanced control systems for high-capacity batteries used in hybrid and electric vehicles, encompassing physics-based modeling, system identification, and state estimation. He has authored three influential volumes on Battery Management Systems, covering battery modeling, equivalent-circuit methods, and physics-based methods. As Director of the UCCS High-Capacity Battery Research and Test Laboratory, he leads cutting-edge research in battery pack simulation and management systems. His teaching portfolio includes advanced courses in control systems, battery dynamics, and management algorithms. A senior member of IEEE and life member of the Electrochemical Society, his work has significantly influenced the field of electric vehicle battery technology. His research innovations include developing methods for state-of-charge estimation, degradation modeling, and fast-charging protocols for battery packs.
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4.7 course rating
155 ratings
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