In this course, you will learn how to implement different state-of-charge estimation methods and to evaluate their relative merits. By the end of the course, you will be able to:
- Implement simple voltage-based and current-based state-of-charge estimators and understand their limitations
- Explain the purpose of each step in the sequential-probabilistic-inference solution
- Execute provided Octave/MATLAB script for a linear Kalman filter and evaluate results
- Execute provided Octave/MATLAB script for state-of-charge estimation using an extended Kalman filter on lab-test data and evaluate results
- Execute provided Octave/MATLAB script for state-of-charge estimation using an sigma-point Kalman filter on lab-test data and evaluate results
- Implement method to detect and discard faulty voltage-sensor measurements
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Algorithms for Battery Management Systems
Battery State-of-Charge (SOC) Estimation
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Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
15 Stunden zum Abschließen
The importance of a good SOC estimator
This week, you will learn some rigorous definitions needed when discussing SOC estimation and some simple but poor methods to estimate SOC. As background to learning some better methods, we will review concepts from probability theory that are needed to be able to deal with the impact of uncertain noises on a system's internal state and measurements made by a BMS.
8 Videos (Gesamt 120 min), 13 Lektüren, 7 Quiz
8 Videos
3.1.2: What is the importance of a good SOC estimator?8m
3.1.3: How do we define SOC carefully?16m
3.1.4: What are some approaches to estimating battery cell SOC?26m
3.1.5: Understanding uncertainty via mean and covariance17m
3.1.6: Understanding joint uncertainty of two unknown quantities15m
3.1.7: Understanding time-varying uncertain quantities22m
3.1.8: Summary of "The importance of a good SOC estimator" and next steps3m
13 Lektüren
Notes for lesson 3.1.11m
Frequently Asked Questions5m
Course Resources5m
How to Use Discussion Forums5m
Earn a Course Certificate5m
Notes for lesson 3.1.21m
Notes for lesson 3.1.31m
Notes for lesson 3.1.41m
Introducing a new element to the course!10m
Notes for lesson 3.1.51m
Notes for lesson 3.1.61m
Notes for lesson 3.1.71m
Notes for lesson 3.1.81m
7 praktische Übungen
Practice quiz for lesson 3.1.210m
Practice quiz for lesson 3.1.310m
Practice quiz for lesson 3.1.410m
Practice quiz for lesson 3.1.515m
Practice quiz for lesson 3.1.610m
Practice quiz for lesson 3.1.76m
Quiz for week 140m
Woche
23 Stunden zum Abschließen
Introducing the linear Kalman filter as a state estimator
This week, you will learn how to derive the steps of the Gaussian sequential probabilistic inference solution, which is the basis for all Kalman-filtering style state estimators. While this content is highly theoretical, it is important to have a solid foundational understanding of these topics in practice, since real applications often violate some of the assumptions that are made in the derivation, and we must understand the implication this has on the process. By the end of the week, you will know how to derive the linear Kalman filter.
6 Videos (Gesamt 97 min), 6 Lektüren, 6 Quiz
6 Videos
3.2.2: The Kalman-filter gain factor23m
3.2.3: Summarizing the six steps of generic probabilistic inference9m
3.2.4: Deriving the three Kalman-filter prediction steps21m
3.2.5: Deriving the three Kalman-filter correction steps16m
3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps2m
6 Lektüren
Notes for lesson 3.2.11m
Notes for lesson 3.2.21m
Notes for lesson 3.2.31m
Notes for lesson 3.2.41m
Notes for lesson 3.2.51m
Notes for lesson 3.2.61m
6 praktische Übungen
Practice quiz for lesson 3.2.112m
Practice quiz for lesson 3.2.210m
Practice quiz for lesson 3.2.310m
Practice quiz for lesson 3.2.410m
Practice quiz for lesson 3.2.510m
Quiz for week 230m
Woche
34 Stunden zum Abschließen
Coming to understand the linear Kalman filter
The steps of a Kalman filter may appear abstract and mysterious. This week, you will learn different ways to think about and visualize the operation of the linear Kalman filter to give better intuition regarding how it operates. You will also learn how to implement a linear Kalman filter in Octave code, and how to evaluate outputs from the Kalman filter.
7 Videos (Gesamt 86 min), 7 Lektüren, 7 Quiz
7 Videos
3.3.2: Introducing Octave code to generate correlated random numbers15m
3.3.3: Introducing Octave code to implement KF for linearized cell model10m
3.3.4: How do we improve numeric robustness of Kalman filter?10m
3.3.5: Can we automatically detect bad measurements with a Kalman filter?14m
3.3.6: How do I initialize and tune a Kalman filter?12m
3.3.7: Summary of "Coming to understand the linear KF" and next steps2m
7 Lektüren
Notes for lesson 3.3.11m
Notes for lesson 3.3.21m
Notes for lesson 3.3.31m
Notes for lesson 3.3.41m
Notes for lesson 3.3.51m
Notes for lesson 3.3.61m
Notes for lesson 3.3.71m
7 praktische Übungen
Practice quiz for lesson 3.3.110m
Practice quiz for lesson 3.3.210m
Practice quiz for lesson 3.3.310m
Practice quiz for lesson 3.3.410m
Practice quiz for lesson 3.3.510m
Practice quiz for lesson 3.3.610m
Quiz for week 330m
Woche
44 Stunden zum Abschließen
Cell SOC estimation using an extended Kalman filter
A linear Kalman filter can be used to estimate the internal state of a linear system. But, battery cells are nonlinear systems. This week, you will learn how to approximate the steps of the Gaussian sequential probabilistic inference solution for nonlinear systems, resulting in the "extended Kalman filter" (EKF). You will learn how to implement the EKF in Octave code, and how to use the EKF to estimate battery-cell SOC.
8 Videos (Gesamt 101 min), 8 Lektüren, 7 Quiz
8 Videos
3.4.2: Deriving the three extended-Kalman-filter prediction steps15m
3.4.3: Deriving the three extended-Kalman-filter correction steps6m
3.4.4: Introducing a simple EKF example, with Octave code15m
3.4.5: Preparing to implement EKF on an ECM20m
3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation13m
3.4.7: Introducing Octave code to update EKF for SOC estimation16m
3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps2m
8 Lektüren
Notes for lesson 3.4.11m
Notes for lesson 3.4.21m
Notes for lesson 3.4.31m
Notes for lesson 3.4.41m
Notes for lesson 3.4.51m
Notes for lesson 3.4.61m
Notes for lesson 3.4.71m
Notes for lesson 3.4.81m
7 praktische Übungen
Practice quiz for lesson 3.4.110m
Practice quiz for lesson 3.4.210m
Practice quiz for lesson 3.4.310m
Practice quiz for lesson 3.4.410m
Practice quiz for lesson 3.4.510m
Practice quiz for lesson 3.4.710m
Quiz for week 430m
Woche
54 Stunden zum Abschließen
Cell SOC estimation using a sigma-point Kalman filter
The EKF is the best known and most widely used nonlinear Kalman filter. But, it has some fundamental limitations that limit its performance for "very nonlinear" systems. This week, you will learn how to derive the sigma-point Kalman filter (sometimes called an "unscented Kalman filter") from the Gaussian sequential probabilistic inference steps. You will also learn how to implement this filter in Octave code and how to use it to estimate battery cell SOC.
7 Videos (Gesamt 116 min), 7 Lektüren, 6 Quiz
7 Videos
3.5.2: Approximating uncertain variables using sigma points31m
3.5.3: Deriving the six sigma-point-Kalman-filter steps17m
3.5.4: Introducing a simple SPKF example with Octave code19m
3.5.5: Introducing Octave code to initialize and control SPKF for SOC estimation9m
3.5.6: Introducing Octave code to update SPKF for SOC estimation18m
3.5.7: Summary of "Cell SOC estimation using a SPFK" and next steps7m
7 Lektüren
Notes for lesson 3.5.11m
Notes for lesson 3.5.21m
Notes for lesson 3.5.31m
Notes for lesson 3.5.41m
Notes for lesson 3.5.51m
Notes for lesson 3.5.61m
Notes for lesson 3.5.71m
6 praktische Übungen
Practice quiz for lesson 3.5.110m
Practice quiz for lesson 3.5.210m
Practice quiz for lesson 3.5.310m
Practice quiz for lesson 3.5.46m
Practice quiz for lesson 3.5.610m
Quiz for week 530m
Woche
63 Stunden zum Abschließen
Improving computational efficiency using the bar-delta method
Kalman filtering requires that noises have zero mean. What do we do if the current-sensor has a dc bias error, as is often the case? How can we implement Kalman-filter type SOC estimators in a computationally efficient way for a battery pack comprising many cells? This week you will learn how to compensate for current-sensor bias error and how to implement the bar-delta method for computational efficiency. You will also learn about desktop validation as an approach for initial testing and tuning of BMS algorithms.
5 Videos (Gesamt 71 min), 5 Lektüren, 4 Quiz
5 Videos
3.6.2: Developing a "bar" filter using an ECM6m
3.6.3: Developing the "delta" filters using an ECM15m
3.6.4: Introducing "desktop validation" as a method for predicting performance21m
3.6.5: Summary of "Improving computational efficiency using the bar-delta method" and next steps2m
5 Lektüren
Notes for lesson 3.6.11m
Notes for lesson 3.6.21m
Notes for lesson 3.6.31m
Notes for lesson 3.6.41m
Notes for lesson 3.6.51m
4 praktische Übungen
Quiz for lesson 3.6.115m
Quiz for lesson 3.6.210m
Quiz for lesson 3.6.310m
Quiz for lessons 3.6.4 and 3.6.515m
Woche
75 Stunden zum Abschließen
Capstone project
You have already learned that Kalman filters must be "tuned" by adjusting their process-noise, sensor-noise, and initial state-estimate covariance matrices in order to give acceptable performance over a wide range of operating scenarios. This final course module will give you some experience hand-tuning both an EKF and SPKF for SOC estimation.
2 Quiz
Dozent
Gregory Plett
ProfessorElectrical and Computer Engineering
Über University of Colorado System
The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond.
Über die Spezialisierung Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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