In this course, you will learn how to implement different state-of-health estimation methods and to evaluate their relative merits. By the end of the course, you will be able to:
- Identify the primary degradation mechanisms that occur in lithium-ion cells and understand how they work
- Execute provided Octave/MATLAB script to estimate total capacity using WLS, WTLS, and AWTLS methods and lab-test data, and to evaluate results
- Compute confidence intervals on total-capacity estimates
- Compute estimates of a cell’s equivalent-series resistance using lab-test data
- Specify the tradeoffs between joint and dual estimation of state and parameters, and steps that must be taken to ensure robust estimates (honors)
Este curso forma parte de Programa Especializado - Algorithms for Battery Management Systems
Battery State-of-Health (SOH) Estimation
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Programa - Qué aprenderás en este curso
Semana
14 horas para completar
How does lithium-ion cell health degrade?
As battery cells age, their total capacities generally decrease and their resistances generally increase. This week, you will learn WHY this happens. You will learn about the specific physical and chemical mechanisms that cause degradation to lithium-ion battery cells. You will also learn why it is relatively simple to estimate and track changes to resistance, but why it is difficult to track changes to total capacity accurately.
8 videos (Total 89 minutos), 12 readings, 7 quizzes
8 videos
4.1.2: What changes as a cell ages?16m
4.1.3: Negative-electrode aging processes at particle surface16m
4.1.4: Negative-electrode aging processes in bulk and composite electrode9m
4.1.5: Positive-electrode aging processes10m
4.1.6: Sensitivity of cell voltage to changes in equivalent series resistance (ESR)17m
4.1.7: Sensitivity of cell voltage to changes in cell total capacity9m
4.1.8: Summary of "How does lithium-ion cell health degrade?"; what next?2m
12 lecturas
Notes for lesson 4.1.11m
Frequently Asked Questions5m
Course Resources5m
How to Use Discussion Forums5m
Earn a Course Certificate5m
Notes for lesson 4.1.21m
Notes for lesson 4.1.31m
Notes for lesson 4.1.41m
Notes for lesson 4.1.51m
Notes for lesson 4.1.61m
Notes for lesson 4.1.71m
Notes for lesson 4.1.81m
7 ejercicios de práctica
Practice quiz for lesson 4.1.29m
Practice quiz for lesson 4.1.39m
Practice quiz for lesson 4.1.49m
Practice quiz for lesson 4.1.59m
Practice quiz for lesson 4.1.615m
Practice quiz for lesson 4.1.79m
Quiz for week 145m
Semana
24 horas para completar
Total-least-squares battery-cell capacity estimation
Total capacity is often estimated using ordinary-least-squares (OLS) methods. This week, you will learn that this is a fundamentally incorrect approach, and will learn that a total-least-squares (TLS) method should be used instead. You will learn how to derive a weighted OLS solution, to use as a benchmark, and how to derive a weighted TLS solution also.
7 videos (Total 68 minutos), 7 readings, 7 quizzes
7 videos
4.2.2: How to find the ordinary-least-squares solution as a benchmark9m
4.2.3: Making the ordinary-least-squares solution computationally efficient12m
4.2.4: Setting up weighted total-least-squares solution11m
4.2.5: Finding the solution to a weighted total-least-squares problem10m
4.2.6: Confidence intervals on least-squares solutions11m
4.2.7: Summary of "Total-least-squares battery-cell capacity estimation"; what next?2m
7 lecturas
Notes for lesson 4.2.11m
Notes for lesson 4.2.21m
Notes for lesson 4.2.31m
Notes for lesson 4.2.41m
Notes for lesson 4.2.51m
Notes for lesson 4.2.61m
Notes for lesson 4.2.71m
7 ejercicios de práctica
Practice quiz for lesson 4.2.19m
Practice quiz for lesson 4.2.215m
Practice quiz for lesson 4.2.315m
Practice quiz for lesson 4.2.49m
Practice quiz for lesson 4.2.515m
Practice quiz for lesson 4.2.615m
Quiz for week 245m
Semana
34 horas para completar
Simplified total-least-squares battery-cell capacity estimates
Unfortunately, the weighted TLS solution you learned in week 2 is not well suited for efficient computation on an embedded system like a BMS. As an intermediate step toward finding an efficient weighted TLS method, you will first learn a proportionally weighted TLS method this week. You will then learn how to generalize this to an "approximate weighted TLS" (AWTLS) method, which gives good estimates, and is feasible to implement on a BMS.
7 videos (Total 64 minutos), 7 readings, 7 quizzes
7 videos
4.3.2: Making simplified solution computationally efficient6m
4.3.3: Defining geometry for approximate full solution to weighted total least squares12m
4.3.4: Finding appropriate cost function for approximate full solution to WTLS problem8m
4.3.5: Finding solution to the AWTLS problem10m
4.3.6: Adding fading memory8m
4.3.7: Summary of "Simplified total-least-squares battery-cell capacity estimates"; what next?4m
7 lecturas
Notes for lesson 4.3.11m
Notes for lesson 4.3.21m
Notes for lesson 4.3.31m
Notes for lesson 4.3.41m
Notes for lesson 4.3.51m
Notes for lesson 4.3.61m
Notes for lesson 4.3.71m
7 ejercicios de práctica
Practice quiz for lesson 4.3.115m
Practice quiz for lesson 4.3.220m
Practice quiz for lesson 4.3.39m
Practice quiz for lesson 4.3.49m
Practice quiz for lesson 4.3.515m
Practice quiz for lesson 4.3.615m
Quiz for week 345m
Semana
44 horas para completar
How to write code for the different total-capacity estimators
So far this course, you have learned a number of methods for estimating total capacity. This week, you will learn how to implement those methods in Octave code. You will also explore different simulation scenarios to benchmark how well each method works, in comparison with the others. The scenarios are representative of hybrid-electric-vehicle (HEV) and battery-electric-vehicle (BEV) applications, but the principles learned can be extrapolated to other similar application domains.
6 videos (Total 70 minutos), 6 readings, 6 quizzes
6 videos
4.4.2: Demonstrating Octave code for HEV: Scenario 121m
4.4.3: Demonstrating Octave code for HEV: Scenarios 2–37m
4.4.4: Demonstrating Octave code for BEV: Scenario 16m
4.4.5: Demonstrating Octave code for BEV: Scenarios 2–310m
4.4.6: Summary of "How to write code for the different total-capacity estimators"; what next?9m
6 lecturas
Notes for lesson 4.4.11m
Notes for lesson 4.4.21m
Notes for lesson 4.4.31m
Notes for lesson 4.4.41m
Notes for lesson 4.4.51m
Notes for lesson 4.4.61m
6 ejercicios de práctica
Practice quiz for lesson 4.4.115m
Practice quiz for lesson 4.4.215m
Practice quiz for lesson 4.4.315m
Practice quiz for lesson 4.4.415m
Practice quiz for lesson 4.4.515m
Quiz for week 445m
Semana
53 horas para completar
A Kalman-filter approach to total capacity estimation
In the third course of the specialization, you learned how to use extended Kalman filters (EKFs) and sigma-point Kalman filters (SPKFs) to estimate the state of a battery cell. In this honors week, you will learn how to extend those concepts to apply EKF and SPKF to estimating the parameters of a battery-cell model if the state is known, and also how to simultaneously estimate both the state and parameters of a cell model.
6 videos (Total 54 minutos), 6 readings, 4 quizzes
6 videos
4.5.2: Deriving EKF method for parameter estimation8m
4.5.3: How to estimate states and parameters at the same time9m
4.5.4: Defining the steps for EKF and SPFK joint and dual estimation4m
4.5.5: Addressing issues of robustness and speed12m
4.5.6: Summary of "A Kalman-filter approach to total capacity estimation"; what next?2m
6 lecturas
Notes for lesson 4.5.11m
Notes for lesson 4.5.21m
Notes for lesson 4.5.31m
Notes for lesson 4.5.41m
Notes for lesson 4.5.51m
Notes for lesson 4.5.61m
4 ejercicios de práctica
Quiz for lesson 4.5.115m
Quiz for lesson 4.5.215m
Quiz for lessons 4.5.3 and 4.5.412m
Quiz for lesson 4.5.525m
Semana
64 horas para completar
Capstone project
You have learned several different total-capacity estimation methods. Some of these methods work better than others in general, but any method is only as good as the data you give it. In this project, you will explore a different way to determine the "x" and "y" data you use as input to the total-capacity estimation methods.
1 quiz
Instructor
Gregory Plett
ProfessorElectrical and Computer Engineering
Acerca de Sistema Universitario de Colorado
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.
Acerca del programa especializado 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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