Battery State-of-Charge (SOC) Estimation

  • 4.8
Approx. 28 hours to complete
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Course Summary

Learn about the state of charge (SOC) of batteries and how to measure it. Explore different methods for estimating the SOC of various battery types and understand their limitations.

Key Learning Points

  • Understand the importance of battery SOC and its impact on battery life and performance.
  • Learn about the various methods and tools used to measure battery SOC.
  • Discover the limitations and challenges associated with measuring battery SOC accurately.

Related Topics for further study


Learning Outcomes

  • Understand the importance of battery SOC and its impact on battery life and performance.
  • Learn different methods for measuring battery SOC and their limitations.
  • Apply your knowledge to improve battery management and extend battery life.

Prerequisites or good to have knowledge before taking this course

  • Basic knowledge of electrical circuits
  • Familiarity with battery technologies

Course Difficulty Level

Intermediate

Course Format

  • Self-paced
  • Online
  • Video lectures
  • Quizzes

Similar Courses

  • Introduction to Battery Management Systems
  • Battery Technology

Related Education Paths


Notable People in This Field

  • CEO of Tesla
  • Professor of Physics and Atmospheric Science at Dalhousie University

Related Books

Description

This course can also be taken for academic credit as ECEA 5732, part of CU Boulder’s Master of Science in Electrical Engineering degree.

Knowledge

  • How to implement state-of-charge (SOC) estimators for lithium-ion battery cells

Outline

  • The importance of a good SOC estimator
  • 3.1.1: Welcome to the course!
  • 3.1.2: What is the importance of a good SOC estimator?
  • 3.1.3: How do we define SOC carefully?
  • 3.1.4: What are some approaches to estimating battery cell SOC?
  • 3.1.5: Understanding uncertainty via mean and covariance
  • 3.1.6: Understanding joint uncertainty of two unknown quantities
  • 3.1.7: Understanding time-varying uncertain quantities
  • 3.1.8: Summary of "The importance of a good SOC estimator" and next steps
  • Notes for lesson 3.1.1
  • Frequently asked questions
  • Course resources
  • How to use discussion forums
  • Earn a course certificate
  • Are you interested in earning an MSEE degree?
  • Notes for lesson 3.1.2
  • Notes for lesson 3.1.3
  • Notes for lesson 3.1.4
  • Introducing a new element to the course!
  • Notes for lesson 3.1.5
  • Notes for lesson 3.1.6
  • Notes for lesson 3.1.7
  • Notes for lesson 3.1.8
  • Practice quiz for lesson 3.1.2
  • Practice quiz for lesson 3.1.3
  • Practice quiz for lesson 3.1.4
  • Practice quiz for lesson 3.1.5
  • Practice quiz for lesson 3.1.6
  • Practice quiz for lesson 3.1.7
  • Quiz for week 1

  • Introducing the linear Kalman filter as a state estimator
  • 3.2.1: Predict/correct mechanism of sequential probabilistic inference
  • 3.2.2: The Kalman-filter gain factor
  • 3.2.3: Summarizing the six steps of generic probabilistic inference
  • 3.2.4: Deriving the three Kalman-filter prediction steps
  • 3.2.5: Deriving the three Kalman-filter correction steps
  • 3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps
  • Notes for lesson 3.2.1
  • Notes for lesson 3.2.2
  • Notes for lesson 3.2.3
  • Notes for lesson 3.2.4
  • Notes for lesson 3.2.5
  • Notes for lesson 3.2.6
  • Practice quiz for lesson 3.2.1
  • Practice quiz for lesson 3.2.2
  • Practice quiz for lesson 3.2.3
  • Practice quiz for lesson 3.2.4
  • Practice quiz for lesson 3.2.5
  • Quiz for week 2

  • Coming to understand the linear Kalman filter
  • 3.3.1: Visualizing the Kalman filter with a linearized cell model
  • 3.3.2: Introducing Octave code to generate correlated random numbers
  • 3.3.3: Introducing Octave code to implement KF for linearized cell model
  • 3.3.4: How do we improve numeric robustness of Kalman filter?
  • 3.3.5: Can we automatically detect bad measurements with a Kalman filter?
  • 3.3.6: How do I initialize and tune a Kalman filter?
  • 3.3.7: Summary of "Coming to understand the linear KF" and next steps
  • Notes for lesson 3.3.1
  • Notes for lesson 3.3.2
  • Notes for lesson 3.3.3
  • Notes for lesson 3.3.4
  • Notes for lesson 3.3.5
  • Notes for lesson 3.3.6
  • Notes for lesson 3.3.7
  • Practice quiz for lesson 3.3.1
  • Practice quiz for lesson 3.3.2
  • Practice quiz for lesson 3.3.3
  • Practice quiz for lesson 3.3.4
  • Practice quiz for lesson 3.3.5
  • Practice quiz for lesson 3.3.6
  • Quiz for week 3

  • Cell SOC estimation using an extended Kalman filter
  • 3.4.1: Introducing nonlinear variations to Kalman filters
  • 3.4.2: Deriving the three extended-Kalman-filter prediction steps
  • 3.4.3: Deriving the three extended-Kalman-filter correction steps
  • 3.4.4: Introducing a simple EKF example, with Octave code
  • 3.4.5: Preparing to implement EKF on an ECM
  • 3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation
  • 3.4.7: Introducing Octave code to update EKF for SOC estimation
  • 3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps
  • Notes for lesson 3.4.1
  • Notes for lesson 3.4.2
  • Notes for lesson 3.4.3
  • Notes for lesson 3.4.4
  • Notes for lesson 3.4.5
  • Notes for lesson 3.4.6
  • Notes for lesson 3.4.7
  • Notes for lesson 3.4.8
  • Practice quiz for lesson 3.4.1
  • Practice quiz for lesson 3.4.2
  • Practice quiz for lesson 3.4.3
  • Practice quiz for lesson 3.4.4
  • Practice quiz for lesson 3.4.5
  • Practice quiz for lesson 3.4.7
  • Quiz for week 4

  • Cell SOC estimation using a sigma-point Kalman filter
  • 3.5.1: Problems with EKF that are improved with sigma-point methods
  • 3.5.2: Approximating uncertain variables using sigma points
  • 3.5.3: Deriving the six sigma-point-Kalman-filter steps
  • 3.5.4: Introducing a simple SPKF example with Octave code
  • 3.5.5: Introducing Octave code to initialize and control SPKF for SOC estimation
  • 3.5.6: Introducing Octave code to update SPKF for SOC estimation
  • 3.5.7: Summary of "Cell SOC estimation using a SPFK" and next steps
  • Notes for lesson 3.5.1
  • Notes for lesson 3.5.2
  • Notes for lesson 3.5.3
  • Notes for lesson 3.5.4
  • Notes for lesson 3.5.5
  • Notes for lesson 3.5.6
  • Notes for lesson 3.5.7
  • Practice quiz for lesson 3.5.1
  • Practice quiz for lesson 3.5.2
  • Practice quiz for lesson 3.5.3
  • Practice quiz for lesson 3.5.4
  • Practice quiz for lesson 3.5.6
  • Quiz for week 5

  • Improving computational efficiency using the bar-delta method
  • 3.6.1: Why do we need to be clever when estimating SOC for battery packs?
  • 3.6.2: Developing a "bar" filter using an ECM
  • 3.6.3: Developing the "delta" filters using an ECM
  • 3.6.4: Introducing "desktop validation" as a method for predicting performance
  • 3.6.5: Summary of "Improving computational efficiency using the bar-delta method" and next steps
  • Notes for lesson 3.6.1
  • Notes for lesson 3.6.2
  • Notes for lesson 3.6.3
  • Notes for lesson 3.6.4
  • Notes for lesson 3.6.5
  • Quiz for lesson 3.6.1
  • Quiz for lesson 3.6.2
  • Quiz for lesson 3.6.3
  • Quiz for lessons 3.6.4 and 3.6.5

  • Capstone project

Summary of User Reviews

Learn about Battery State of Charge on Coursera with this comprehensive course. Students praise the engaging content and knowledgeable instructor. The overall rating is positive.

Key Aspect Users Liked About This Course

engaging content

Pros from User Reviews

  • knowledgeable instructor
  • useful content
  • well-structured course
  • practical assignments
  • great community support

Cons from User Reviews

  • some technical jargon
  • not enough real-world examples
  • too basic for advanced learners
  • requires a lot of time commitment
  • some technical issues

Keywords

English
Available now
Approx. 28 hours to complete
Gregory Plett
University of Colorado Boulder, University of Colorado System
Coursera

Instructor

Gregory Plett

  • 4.8 Raiting

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