Battery State-of-Charge (SOC) Estimation
Master advanced battery state estimation techniques from voltage-based methods to Kalman filters.Battery State-of-Charge (SOC) Estimation
Master advanced battery state estimation techniques from voltage-based methods to Kalman filters.Battery State-of-Charge (SOC) Estimation
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
Implement voltage and current-based SOC estimators
Apply sequential probabilistic inference methods
Execute linear Kalman filter implementations
Develop extended Kalman filter solutions
Implement sigma-point Kalman filter techniques
Handle faulty sensor measurements
Skills you'll gain
This course includes:
8.1 Hours PreRecorded video
37 assignments
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FullTime access
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There are 7 modules in this course
This comprehensive course covers advanced techniques for estimating battery state-of-charge (SOC). Students learn to implement and evaluate various estimation methods, from basic voltage-based approaches to sophisticated Kalman filtering techniques. The curriculum progresses through linear Kalman filters, extended Kalman filters (EKF), and sigma-point Kalman filters (SPKF), with practical implementation in Octave/MATLAB. Special attention is given to handling sensor errors and improving computational efficiency for battery pack applications.
The importance of a good SOC estimator
Module 1 · 5 Hours to complete
Introducing the linear Kalman filter as a state estimator
Module 2 · 3 Hours to complete
Coming to understand the linear Kalman filter
Module 3 · 3 Hours to complete
Cell SOC estimation using an extended Kalman filter
Module 4 · 4 Hours to complete
Cell SOC estimation using a sigma-point Kalman filter
Module 5 · 4 Hours to complete
Improving computational efficiency using the bar-delta method
Module 6 · 2 Hours to complete
Capstone project
Module 7 · 4 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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4.8 course rating
249 ratings
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