courseraИнженерия

Battery State-of-Health (SOH) Estimation

Курс от University of Colorado Boulder, University of Colorado System

Средний≈ 23.3 чАнглийский

О курсе

This course can also be taken for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree. 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)

Навыки, которые вы освоите

Estimation Applied Mathematics Simulations Mathematical Software Mathematical Modeling Failure Analysis Algorithms Matlab Scripting Numerical Analysis Scripting Languages Materials science Model Optimization Laboratory Research Statistical Process Controls Electric Power Systems Laboratory Testing Model Evaluation

Программа курса

6 модулей · 126 учебных материалов

01How does lithium-ion cell health degrade?32 материалов

4.1.1: Welcome to the course!

Course Updates and Accessibility SupportЧтение Non-Credit Students: Welcome and Where to Find HelpЧтение Introduce YourselfОбсуждение Notes for lesson 4.1.1Чтение

Учитесь у экспертов

Gregory Plett

Professor

В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 23.3 ч

6 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Ория, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Battery State-of-Health (SOH) Estimation

О курсе

Навыки, которые вы освоите

Программа курса

4.1.1: Welcome to the course!

Учитесь у экспертов

Gregory Plett

4.1.2: What changes as a cell ages?

4.1.3: Negative-electrode aging processes at particle surface

4.1.4: Negative-electrode aging processes in bulk and composite electrode

4.1.5: Positive-electrode aging processes

4.1.6: Sensitivity of cell voltage to changes in equivalent series resistance (ESR)

4.1.7: Sensitivity of cell voltage to changes in cell total capacity

4.1.8: Summary of "How does lithium-ion cell health degrade?"; what next?

4.2.1: What’s wrong with using ordinary least squares to estimate total capacity?

4.2.2: How to find the ordinary-least-squares solution as a benchmark

4.2.3: Making the ordinary-least-squares solution computationally efficient

4.2.4: Setting up weighted total-least-squares solution

4.2.5: Finding the solution to a weighted total-least-squares problem

4.2.6: Confidence intervals on least-squares solutions

4.2.7: Summary of "Total-least-squares battery-cell capacity estimation"; what next?

4.3.1: Simplifying the total-least-squares solution for cases having proportional uncertainties

4.3.2: Making simplified solution computationally efficient

4.3.3: Defining geometry for approximate full solution to weighted total least squares

4.3.4: Finding appropriate cost function for approximate full solution to WTLS problem

4.3.5: Finding solution to the AWTLS problem

4.3.6: Adding fading memory

4.3.7: Summary of "Simplified total-least-squares battery-cell capacity estimates"; what next?

4.4.1: Introducing Octave code to estimate cell total capacity

4.4.2: Demonstrating Octave code for HEV: Scenario 1

4.4.3: Demonstrating Octave code for HEV: Scenarios 2–3

4.4.4: Demonstrating Octave code for BEV: Scenario 1

4.4.5: Demonstrating Octave code for BEV: Scenarios 2–3

4.4.6: Summary of "How to write code for the different total-capacity estimators"; what next?

4.5.1: Deriving SPKF method for parameter estimation

4.5.2: Deriving EKF method for parameter estimation

4.5.3-4.5.4: How to estimate states and parameters at the same time; steps to do so

4.5.5-4.5.6: Addressing issues of robustness and speed; summary

4.6 Capstone project