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
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Algorithms for Battery Management Systems
Battery Pack Balancing and Power Estimation
von
594 Angemeldet
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
13 Stunden zum Abschließen
Passive balancing methods for battery packs
In previous courses, you learned how to write algorithms to satisfy the estimation requirements of a battery management system. Now, you will learn how to write algorithms for two primary control tasks: balancing and power-limits computations. This week, you will learn why battery packs naturally become unbalanced, some balancing strategies, and how passive circuits can be used to balance battery packs.
7 Videos (Gesamt 79 min), 11 Lektüren, 6 Quiz
7 Videos
5.1.2: Introduction to battery-pack balancing10m
5.1.3: How do battery packs become imbalanced?15m
5.1.4: What are the criteria for specifying a balancing setpoint for a battery pack?13m
5.1.5: What are the criteria for specifying when to balance a battery pack?13m
5.1.6: What kinds of circuits can be used for passively balancing a battery pack?16m
5.1.7: Summary of "Passive balancing methods for battery packs"; what next?2m
11 Lektüren
Notes for lesson 5.1.11m
Frequently Asked Questions5m
Course Resources5m
How to Use Discussion Forums5m
Earn A Certificate5m
Notes for lesson 5.1.21m
Notes for lesson 5.1.31m
Notes for lesson 5.1.41m
Notes for lesson 5.1.51m
Notes for lesson 5.1.61m
Notes for lesson 5.1.71m
6 praktische Übungen
Practice quiz for lesson 5.1.29m
Practice quiz for lesson 5.1.39m
Practice quiz for lesson 5.1.49m
Practice quiz for lesson 5.1.59m
Practice quiz for lesson 5.1.69m
Quiz for week 130m
Woche
23 Stunden zum Abschließen
Active balancing methods for battery packs
Passive balancing can be effective, but wastes energy. Active balancing methods attempt to conserve energy and have other advantages as well. This week, you will learn about active-balancing circuitry and methods, and will learn how to write Octave code to determine how quickly a battery pack can become out of balance. This is useful for determining the dominant factors leading to imbalance, and for estimating how quickly the pack must be balanced to maintain it in proper operational condition.
6 Videos (Gesamt 73 min), 6 Lektüren, 6 Quiz
6 Videos
5.2.2: How to balance actively using transformer-based circuits7m
5.2.3: How to balance actively using a shared active bus14m
5.2.4: Using simulation to show how quickly we must balance a battery pack14m
5.2.5: Introducing Octave code to simulate balancing: The main program loop21m
5.2.6: Summary of "Active balancing methods for battery packs"; what next?3m
6 Lektüren
Notes for lesson 5.2.11m
Notes for lesson 5.2.21m
Notes for lesson 5.2.31m
Notes for lesson 5.2.41m
Notes for lesson 5.2.51m
Notes for lesson 5.2.61m
6 praktische Übungen
Practice quiz for lesson 5.2.19m
Practice quiz for lesson 5.2.29m
Practice quiz for lesson 5.2.39m
Practice quiz for lesson 5.2.49m
Practice quiz for lesson 5.2.515m
Quiz for week 230m
Woche
32 Stunden zum Abschließen
How to find available battery power using a simplified cell model
This week, we begin by reviewing the HPPC power-limit method from course 1. Then, you will learn how to extend the method to satisfy limits on SOC, load power, and electronics current. You will learn how to implement the power-limits computation methods in Octave code, and will see results for a representative scenario.
5 Videos (Gesamt 44 min), 5 Lektüren, 5 Quiz
5 Videos
5.3.2: How to compute available battery power based on cell terminal voltage8m
5.3.3: How to consider other performance limits when computing available battery power7m
5.3.4: Introducing Octave code to compute power limits using simplified cell model12m
5.3.5: Summary of "How to find available battery power using a simplified cell model"; what next?1m
5 Lektüren
Notes for lesson 5.3.11m
Notes for lesson 5.3.21m
Notes for lesson 5.3.31m
Notes for lesson 5.3.41m
Notes for lesson 5.3.51m
5 praktische Übungen
Practice quiz for lesson 5.3.19m
Practice quiz for lesson 5.3.29m
Practice quiz for lesson 5.3.39m
Practice quiz for lesson 5.3.415m
Quiz for week 330m
Woche
44 Stunden zum Abschließen
How to find available battery power using a comprehensive cell model
The HPPC method, even as extended last week, makes some simplifying assumptions that are not met in practice. This week, we explore a more accurate method that uses full state information from an xKF as its input, along with a full ESC cell model to find power limits. You will learn how to implement this method in Octave code and will compare its computations to those from the HPPC method you learned about last week.
6 Videos (Gesamt 69 min), 6 Lektüren, 6 Quiz
6 Videos
5.4.2: How to solve for a future battery condition using the bisection algorithm11m
5.4.3: How to use bisection to estimate available power using comprehensive cell model16m
5.4.4: Introducing Octave code to compute power limits using comprehensive cell model9m
5.4.5: Using simulation to compare and contrast different power-estimation methods12m
5.4.6: Concluding remarks for the specialization6m
6 Lektüren
Notes for lesson 5.4.11m
Notes for lesson 5.4.21m
Notes for lesson 5.4.31m
Notes for lesson 5.4.41m
Notes for lesson 5.4.51m
Notes for lesson 5.4.61m
6 praktische Übungen
Practice quiz for lesson 5.4.19m
Practice quiz for lesson 5.4.215m
Practice quiz for lesson 5.4.315m
Practice quiz for lesson 5.4.420m
Practice quiz for lesson 5.4.520m
Quiz for week 440m
Dozent
Gregory Plett
ProfessorElectrical and Computer Engineering
Über 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.
Über die Spezialisierung 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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Sobald Sie sich für ein Zertifikat angemeldet haben, haben Sie Zugriff auf alle Videos, Quizspiele und Programmieraufgaben (falls zutreffend). Aufgaben, die von anderen Kursteilnehmern bewertet werden, können erst dann eingereicht und überprüft werden, wenn Ihr Unterricht begonnen hat. Wenn Sie sich den Kurs anschauen möchten, ohne ihn zu kaufen, können Sie womöglich auf bestimmte Aufgaben nicht zugreifen.
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
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