courseraИнженерия

Battery Pack Balancing and Power Estimation

Курс от University of Colorado Boulder, University of Colorado System

Средний≈ 23 чАнглийский

О курсе

This course can also be taken for academic credit as ECEA 5734, part of CU Boulder’s Master of Science in Electrical Engineering degree. In this course, you will learn how to design balancing systems and to compute remaining energy and available power for a battery pack. By the end of the course, you will be able to: - Evaluate different design choices for cell balancing and articulate their relative merits - Design component values for a simple passive balancing circuit - Use provided Octave/MATLAB simulation tools to evaluate how quickly a battery pack must be balanced - Compute remaining energy and available power using a simple cell model - Use provided Octave/MATLAB script to compute available power using a comprehensive equivalent-circuit cell model

Навыки, которые вы освоите

Estimation Matlab Mathematical Modeling Engineering Calculations Numerical Analysis Algorithms Scenario Testing Electronic Components Simulations Power Electronics Simulation and Simulation Software Mathematical Software Electrical Engineering Electrical Power Electrical Systems

Программа курса

6 модулей · 112 учебных материалов

01Passive balancing methods for battery packs 29 материалов

5.1.1: Welcome to the course!

Course Updates and Accessibility SupportЧтение Non-Credit Students: Welcome and Where to Find HelpЧтение Get help and meet other learners in this course. Join your discussion forums!Чтение Introduce YourselfОбсуждение

Учитесь у экспертов

Gregory Plett

Professor

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Обучение на Coursera

≈ 23 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 Pack Balancing and Power Estimation

О курсе

Навыки, которые вы освоите

Программа курса

5.1.1: Welcome to the course!

Учитесь у экспертов

Gregory Plett

5.1.2: Introduction to battery-pack balancing

5.1.3: How do battery packs become imbalanced?

5.1.4: What are the criteria for specifying a balancing setpoint for a battery pack?

5.1.5: What are the criteria for specifying when to balance a battery pack?

5.1.6: What kinds of circuits can be used for passively balancing a battery pack?

5.1.7: Summary of "Passive balancing methods for battery packs"; what next?

5.2.1: How to balance actively using capacitor-based circuits

5.2.2: How to balance actively using transformer-based circuits

5.2.3: How to balance actively using a shared active bus

5.2.4: Using simulation to show how quickly we must balance a battery pack

5.2.5: Introducing Octave code to simulate balancing: The main program loop

5.2.6: Summary of "Active balancing methods for battery packs"; what next?

5.3.1: What factors must we consider when finding available battery power?

5.3.2: How to compute available battery power based on cell terminal voltage

5.3.3: How to consider other performance limits when computing available battery power

5.3.4: Introducing Octave code to compute power limits using simplified cell model

5.3.5: Summary of "How to find available battery power using a simplified cell model"; what next?

5.4.1: What factors must we consider when finding available battery power?

5.4.2: How to solve for a future battery condition using the bisection algorithm

5.4.3: How to use bisection to estimate available power using comprehensive cell model

5.4.4: Introducing Octave code to compute power limits using comprehensive cell model

5.4.5: Using simulation to compare and contrast different power-estimation methods

5.4.6: Concluding remarks for the specialization

5.5.1: What BMS algorithm needs remain?

5.5.2: Physics-based ideal-cell models

5.5.3: Single-particle reduced-order models

5.5.4: 1-d physics-based reduced-order models

5.5.5: Models of degradation mechanisms

5.5.6: Optimized controls using physics-based models

6.1 Capstone project