Battery State-of-Charge (SOC) Estimation 

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

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Notes for lesson 3.1.2

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Introducing a new element to the course!

Notes for lesson 3.1.5

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Practice quiz for lesson 3.1.2

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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

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

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Practice quiz for lesson 3.2.1

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Practice quiz for lesson 3.2.5

Quiz for week 2

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

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Practice quiz for lesson 3.3.1

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Practice quiz for lesson 3.3.5

Practice quiz for lesson 3.3.6

Quiz for week 3

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

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Practice quiz for lesson 3.4.1

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Practice quiz for lesson 3.4.7

Quiz for week 4

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

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Practice quiz for lesson 3.5.1

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Practice quiz for lesson 3.5.6

Quiz for week 5

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

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Quiz for lesson 3.6.1

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Quiz for lessons 3.6.4 and 3.6.5