Об этом курсе: 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)
Для кого этот курс: Typical learners in this course will hold a technical undergraduate degree: a Bachelor's degree in Electrical Engineering, Computer Engineering, or Mechanical Engineering or a Bachelor's degree in a closely related engineering discipline plus undergraduate-level competency in the following areas: ⁃ Math: Calculus (differential and integral), Linear Algebra, and Differential Equations ⁃ Science: Physics (calculus based) and General Chemistry ⁃ Engineering: Linear Circuits, Electronics, and Linear Systems Learners will have an interest in battery management, most likely via a primary interest in some application domain (such as hybrid and electric vehicles) that requires careful management of a battery system. They will desire to learn state-of-the-art advanced methods of battery management and to know how to apply these methods to a practical design. Many of them will aspire to future employment in a field where they will design and implement battery controls.
Автор: University of Colorado System
Преподаватели: Gregory Plett, Professor
Electrical and Computer Engineering
| Основные сведения | Курс 4 из 5 — Algorithms for Battery Management Systems Specialization |
| Уровень | Учащийся среднего уровня |
| Язык | Английский |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
Программа курса
НЕДЕЛЯ 1
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, 12 материала для самостоятельного изучения, 6 тренировочных теста
- Reading: Notes for lesson 4.1.1
- Reading: Frequently Asked Questions
- Reading: Course Resources
- Reading: How to Use Discussion Forums
- Reading: Earn a Course Certificate
- Reading: Notes for lesson 4.1.2
- Видео: 4.1.2: What changes as a cell ages?
- Practice Quiz: Practice quiz for lesson 4.1.2
- Reading: Notes for lesson 4.1.3
- Видео: 4.1.3: Negative-electrode aging processes at particle surface
- Practice Quiz: Practice quiz for lesson 4.1.3
- Reading: Notes for lesson 4.1.4
- Видео: 4.1.4: Negative-electrode aging processes in bulk and composite electrode
- Practice Quiz: Practice quiz for lesson 4.1.4
- Reading: Notes for lesson 4.1.5
- Видео: 4.1.5: Positive-electrode aging processes
- Practice Quiz: Practice quiz for lesson 4.1.5
- Reading: Notes for lesson 4.1.6
- Видео: 4.1.6: Sensitivity of cell voltage to changes in equivalent series resistance (ESR)
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.1.6
- Reading: Notes for lesson 4.1.7
- Видео: 4.1.7: Sensitivity of cell voltage to changes in cell total capacity
- Practice Quiz: Practice quiz for lesson 4.1.7
- Reading: Notes for lesson 4.1.8
- Видео: 4.1.8: Summary of "How does lithium-ion cell health degrade?"; what next?
Оцениваемый: Quiz for week 1
НЕДЕЛЯ 2
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, 7 материала для самостоятельного изучения, 6 тренировочных теста
- Reading: Notes for lesson 4.2.1
- Practice Quiz: Practice quiz for lesson 4.2.1
- Reading: Notes for lesson 4.2.2
- Видео: 4.2.2: How to find the ordinary-least-squares solution as a benchmark
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.2.2
- Reading: Notes for lesson 4.2.3
- Видео: 4.2.3: Making the ordinary-least-squares solution computationally efficient
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.2.3
- Reading: Notes for lesson 4.2.4
- Видео: 4.2.4: Setting up weighted total-least-squares solution
- Practice Quiz: Practice quiz for lesson 4.2.4
- Reading: Notes for lesson 4.2.5
- Видео: 4.2.5: Finding the solution to a weighted total-least-squares problem
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.2.5
- Reading: Notes for lesson 4.2.6
- Видео: 4.2.6: Confidence intervals on least-squares solutions
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.2.6
- Reading: Notes for lesson 4.2.7
- Видео: 4.2.7: Summary of "Total-least-squares battery-cell capacity estimation"; what next?
Оцениваемый: Quiz for week 2
НЕДЕЛЯ 3
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, 7 материала для самостоятельного изучения, 6 тренировочных теста
- Reading: Notes for lesson 4.3.1
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.3.1
- Reading: Notes for lesson 4.3.2
- Видео: 4.3.2: Making simplified solution computationally efficient
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.3.2
- Reading: Notes for lesson 4.3.3
- Видео: 4.3.3: Defining geometry for approximate full solution to weighted total least squares
- Practice Quiz: Practice quiz for lesson 4.3.3
- Reading: Notes for lesson 4.3.4
- Видео: 4.3.4: Finding appropriate cost function for approximate full solution to WTLS problem
- Practice Quiz: Practice quiz for lesson 4.3.4
- Reading: Notes for lesson 4.3.5
- Видео: 4.3.5: Finding solution to the AWTLS problem
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.3.5
- Reading: Notes for lesson 4.3.6
- Видео: 4.3.6: Adding fading memory
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.3.6
- Reading: Notes for lesson 4.3.7
- Видео: 4.3.7: Summary of "Simplified total-least-squares battery-cell capacity estimates"; what next?
Оцениваемый: Quiz for week 3
НЕДЕЛЯ 4
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, 6 материала для самостоятельного изучения, 5 тренировочных теста
- Reading: Notes for lesson 4.4.1
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.4.1
- Reading: Notes for lesson 4.4.2
- Видео: 4.4.2: Demonstrating Octave code for HEV: Scenario 1
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.4.2
- Reading: Notes for lesson 4.4.3
- Видео: 4.4.3: Demonstrating Octave code for HEV: Scenarios 2–3
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.4.3
- Reading: Notes for lesson 4.4.4
- Видео: 4.4.4: Demonstrating Octave code for BEV: Scenario 1
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.4.4
- Reading: Notes for lesson 4.4.5
- Видео: 4.4.5: Demonstrating Octave code for BEV: Scenarios 2–3
- Notebook: Notebook to run before attempting practice quiz
- Practice Quiz: Practice quiz for lesson 4.4.5
- Reading: Notes for lesson 4.4.6
- Видео: 4.4.6: Summary of "How to write code for the different total-capacity estimators"; what next?
Оцениваемый: Quiz for week 4
НЕДЕЛЯ 5
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, 6 материала для самостоятельного изучения
- Reading: Notes for lesson 4.5.1
- Reading: Notes for lesson 4.5.2
- Видео: 4.5.2: Deriving EKF method for parameter estimation
- Reading: Notes for lesson 4.5.3
- Видео: 4.5.3: How to estimate states and parameters at the same time
- Reading: Notes for lesson 4.5.4
- Видео: 4.5.4: Defining the steps for EKF and SPFK joint and dual estimation
- Reading: Notes for lesson 4.5.5
- Видео: 4.5.5: Addressing issues of robustness and speed
- Notebook: Robustness and speed
- Reading: Notes for lesson 4.5.6
- Видео: 4.5.6: Summary of "A Kalman-filter approach to total capacity estimation"; what next?
- Notebook: Notebook to run before attempting quiz
Оцениваемый: Quiz for lesson 4.5.1
Оцениваемый: Quiz for lesson 4.5.2
Оцениваемый: Quiz for lessons 4.5.3 and 4.5.4
Оцениваемый: Quiz for lesson 4.5.5
НЕДЕЛЯ 6
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.
2 элемента
- Notebook: Jupyter notebook for capstone project
Оцениваемый: Tuning xLS algorithms for total-capacity estimation
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Авторы
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.
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