RiseUpp Logo
Educator Logo

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.

4.8

(249 ratings)

19,230 already enrolled

Instructors:

English

21 languages available

Powered by

Provider Logo
Battery State-of-Charge (SOC) Estimation

This course includes

27 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

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

Battery Management Systems
State Estimation
Kalman Filtering
SOC Estimation
MATLAB Programming
Extended Kalman Filter
Sigma-Point Kalman Filter
Battery Systems
Algorithm Implementation
System Modeling

This course includes:

8.1 Hours PreRecorded video

37 assignments

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

Closed caption

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

Certificate

Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

icon-0icon-1icon-2icon-3icon-4

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

Gregory Plett
Gregory Plett

5 rating

22 Reviews

72,282 Students

9 Courses

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.

Battery State-of-Charge (SOC) Estimation

This course includes

27 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

4.8 course rating

249 ratings

Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.