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)
Этот курс входит в специализацию ''Специализация Algorithms for Battery Management Systems'
Battery State-of-Health (SOH) Estimation
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Программа курса: что вы изучите
Неделя
14 ч. на завершение
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 видео ((всего 89 мин.)), 12 материалов для самостоятельного изучения, 7 тестов
8 видео
4.1.2: What changes as a cell ages?16мин
4.1.3: Negative-electrode aging processes at particle surface16мин
4.1.4: Negative-electrode aging processes in bulk and composite electrode9мин
4.1.5: Positive-electrode aging processes10мин
4.1.6: Sensitivity of cell voltage to changes in equivalent series resistance (ESR)17мин
4.1.7: Sensitivity of cell voltage to changes in cell total capacity9мин
4.1.8: Summary of "How does lithium-ion cell health degrade?"; what next?2мин
12 материала для самостоятельного изучения
Notes for lesson 4.1.11мин
Frequently Asked Questions5мин
Course Resources5мин
How to Use Discussion Forums5мин
Earn a Course Certificate5мин
Notes for lesson 4.1.21мин
Notes for lesson 4.1.31мин
Notes for lesson 4.1.41мин
Notes for lesson 4.1.51мин
Notes for lesson 4.1.61мин
Notes for lesson 4.1.71мин
Notes for lesson 4.1.81мин
7 практического упражнения
Practice quiz for lesson 4.1.29мин
Practice quiz for lesson 4.1.39мин
Practice quiz for lesson 4.1.49мин
Practice quiz for lesson 4.1.59мин
Practice quiz for lesson 4.1.615мин
Practice quiz for lesson 4.1.79мин
Quiz for week 145мин
Неделя
24 ч. на завершение
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 видео ((всего 68 мин.)), 7 материалов для самостоятельного изучения, 7 тестов
7 видео
4.2.2: How to find the ordinary-least-squares solution as a benchmark9мин
4.2.3: Making the ordinary-least-squares solution computationally efficient12мин
4.2.4: Setting up weighted total-least-squares solution11мин
4.2.5: Finding the solution to a weighted total-least-squares problem10мин
4.2.6: Confidence intervals on least-squares solutions11мин
4.2.7: Summary of "Total-least-squares battery-cell capacity estimation"; what next?2мин
7 материала для самостоятельного изучения
Notes for lesson 4.2.11мин
Notes for lesson 4.2.21мин
Notes for lesson 4.2.31мин
Notes for lesson 4.2.41мин
Notes for lesson 4.2.51мин
Notes for lesson 4.2.61мин
Notes for lesson 4.2.71мин
7 практического упражнения
Practice quiz for lesson 4.2.19мин
Practice quiz for lesson 4.2.215мин
Practice quiz for lesson 4.2.315мин
Practice quiz for lesson 4.2.49мин
Practice quiz for lesson 4.2.515мин
Practice quiz for lesson 4.2.615мин
Quiz for week 245мин
Неделя
34 ч. на завершение
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 видео ((всего 64 мин.)), 7 материалов для самостоятельного изучения, 7 тестов
7 видео
4.3.1: Simplifying the total-least-squares solution for cases having proportional uncertainties13мин
4.3.2: Making simplified solution computationally efficient6мин
4.3.3: Defining geometry for approximate full solution to weighted total least squares12мин
4.3.4: Finding appropriate cost function for approximate full solution to WTLS problem8мин
4.3.5: Finding solution to the AWTLS problem10мин
4.3.6: Adding fading memory8мин
4.3.7: Summary of "Simplified total-least-squares battery-cell capacity estimates"; what next?4мин
7 материала для самостоятельного изучения
Notes for lesson 4.3.11мин
Notes for lesson 4.3.21мин
Notes for lesson 4.3.31мин
Notes for lesson 4.3.41мин
Notes for lesson 4.3.51мин
Notes for lesson 4.3.61мин
Notes for lesson 4.3.71мин
7 практического упражнения
Practice quiz for lesson 4.3.115мин
Practice quiz for lesson 4.3.220мин
Practice quiz for lesson 4.3.39мин
Practice quiz for lesson 4.3.49мин
Practice quiz for lesson 4.3.515мин
Practice quiz for lesson 4.3.615мин
Quiz for week 345мин
Неделя
44 ч. на завершение
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 видео ((всего 70 мин.)), 6 материалов для самостоятельного изучения, 6 тестов
6 видео
4.4.2: Demonstrating Octave code for HEV: Scenario 121мин
4.4.3: Demonstrating Octave code for HEV: Scenarios 2–37мин
4.4.4: Demonstrating Octave code for BEV: Scenario 16мин
4.4.5: Demonstrating Octave code for BEV: Scenarios 2–310мин
4.4.6: Summary of "How to write code for the different total-capacity estimators"; what next?9мин
6 материала для самостоятельного изучения
Notes for lesson 4.4.11мин
Notes for lesson 4.4.21мин
Notes for lesson 4.4.31мин
Notes for lesson 4.4.41мин
Notes for lesson 4.4.51мин
Notes for lesson 4.4.61мин
6 практического упражнения
Practice quiz for lesson 4.4.115мин
Practice quiz for lesson 4.4.215мин
Practice quiz for lesson 4.4.315мин
Practice quiz for lesson 4.4.415мин
Practice quiz for lesson 4.4.515мин
Quiz for week 445мин
Неделя
53 ч. на завершение
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 видео ((всего 54 мин.)), 6 материалов для самостоятельного изучения, 4 тестов
6 видео
4.5.2: Deriving EKF method for parameter estimation8мин
4.5.3: How to estimate states and parameters at the same time9мин
4.5.4: Defining the steps for EKF and SPFK joint and dual estimation4мин
4.5.5: Addressing issues of robustness and speed12мин
4.5.6: Summary of "A Kalman-filter approach to total capacity estimation"; what next?2мин
6 материала для самостоятельного изучения
Notes for lesson 4.5.11мин
Notes for lesson 4.5.21мин
Notes for lesson 4.5.31мин
Notes for lesson 4.5.41мин
Notes for lesson 4.5.51мин
Notes for lesson 4.5.61мин
4 практического упражнения
Quiz for lesson 4.5.115мин
Quiz for lesson 4.5.215мин
Quiz for lessons 4.5.3 and 4.5.412мин
Quiz for lesson 4.5.525мин
Неделя
64 ч. на завершение
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 тест
Преподаватели
Gregory Plett
ProfessorElectrical and Computer Engineering
О Система университетов штата Колорадо
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.
О специализации ''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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