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 faz parte do Programa de cursos integrados Algorithms for Battery Management Systems
Battery State-of-Health (SOH) Estimation
oferecido por
Programa de cursos integrados Algorithms for Battery Management SystemsSistema de Universidades do ColoradoUniversidade do Colorado
Programa - O que você aprenderá com este curso
Semana
14 horas para concluir
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 vídeos (total de (Total 89 mín.) min), 12 leituras, 7 testes
8 videos
4.1.2: What changes as a cell ages?16min
4.1.3: Negative-electrode aging processes at particle surface16min
4.1.4: Negative-electrode aging processes in bulk and composite electrode9min
4.1.5: Positive-electrode aging processes10min
4.1.6: Sensitivity of cell voltage to changes in equivalent series resistance (ESR)17min
4.1.7: Sensitivity of cell voltage to changes in cell total capacity9min
4.1.8: Summary of "How does lithium-ion cell health degrade?"; what next?2min
12 leituras
Notes for lesson 4.1.11min
Frequently Asked Questions5min
Course Resources5min
How to Use Discussion Forums5min
Earn a Course Certificate5min
Notes for lesson 4.1.21min
Notes for lesson 4.1.31min
Notes for lesson 4.1.41min
Notes for lesson 4.1.51min
Notes for lesson 4.1.61min
Notes for lesson 4.1.71min
Notes for lesson 4.1.81min
7 exercícios práticos
Practice quiz for lesson 4.1.29min
Practice quiz for lesson 4.1.39min
Practice quiz for lesson 4.1.49min
Practice quiz for lesson 4.1.59min
Practice quiz for lesson 4.1.615min
Practice quiz for lesson 4.1.79min
Quiz for week 145min
Semana
24 horas para concluir
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 vídeos (total de (Total 68 mín.) min), 7 leituras, 7 testes
7 videos
4.2.2: How to find the ordinary-least-squares solution as a benchmark9min
4.2.3: Making the ordinary-least-squares solution computationally efficient12min
4.2.4: Setting up weighted total-least-squares solution11min
4.2.5: Finding the solution to a weighted total-least-squares problem10min
4.2.6: Confidence intervals on least-squares solutions11min
4.2.7: Summary of "Total-least-squares battery-cell capacity estimation"; what next?2min
7 leituras
Notes for lesson 4.2.11min
Notes for lesson 4.2.21min
Notes for lesson 4.2.31min
Notes for lesson 4.2.41min
Notes for lesson 4.2.51min
Notes for lesson 4.2.61min
Notes for lesson 4.2.71min
7 exercícios práticos
Practice quiz for lesson 4.2.19min
Practice quiz for lesson 4.2.215min
Practice quiz for lesson 4.2.315min
Practice quiz for lesson 4.2.49min
Practice quiz for lesson 4.2.515min
Practice quiz for lesson 4.2.615min
Quiz for week 245min
Semana
34 horas para concluir
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 vídeos (total de (Total 64 mín.) min), 7 leituras, 7 testes
7 videos
4.3.1: Simplifying the total-least-squares solution for cases having proportional uncertainties13min
4.3.2: Making simplified solution computationally efficient6min
4.3.3: Defining geometry for approximate full solution to weighted total least squares12min
4.3.4: Finding appropriate cost function for approximate full solution to WTLS problem8min
4.3.5: Finding solution to the AWTLS problem10min
4.3.6: Adding fading memory8min
4.3.7: Summary of "Simplified total-least-squares battery-cell capacity estimates"; what next?4min
7 leituras
Notes for lesson 4.3.11min
Notes for lesson 4.3.21min
Notes for lesson 4.3.31min
Notes for lesson 4.3.41min
Notes for lesson 4.3.51min
Notes for lesson 4.3.61min
Notes for lesson 4.3.71min
7 exercícios práticos
Practice quiz for lesson 4.3.115min
Practice quiz for lesson 4.3.220min
Practice quiz for lesson 4.3.39min
Practice quiz for lesson 4.3.49min
Practice quiz for lesson 4.3.515min
Practice quiz for lesson 4.3.615min
Quiz for week 345min
Semana
44 horas para concluir
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 vídeos (total de (Total 70 mín.) min), 6 leituras, 6 testes
6 videos
4.4.2: Demonstrating Octave code for HEV: Scenario 121min
4.4.3: Demonstrating Octave code for HEV: Scenarios 2–37min
4.4.4: Demonstrating Octave code for BEV: Scenario 16min
4.4.5: Demonstrating Octave code for BEV: Scenarios 2–310min
4.4.6: Summary of "How to write code for the different total-capacity estimators"; what next?9min
6 leituras
Notes for lesson 4.4.11min
Notes for lesson 4.4.21min
Notes for lesson 4.4.31min
Notes for lesson 4.4.41min
Notes for lesson 4.4.51min
Notes for lesson 4.4.61min
6 exercícios práticos
Practice quiz for lesson 4.4.115min
Practice quiz for lesson 4.4.215min
Practice quiz for lesson 4.4.315min
Practice quiz for lesson 4.4.415min
Practice quiz for lesson 4.4.515min
Quiz for week 445min
Instrutores
Gregory Plett
ProfessorElectrical and Computer Engineering
Sobre Sistema de Universidades do ColoradoUniversidade do 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.
Sobre o Programa de cursos integrados 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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