This course can also be taken for academic credit as ECEA 5734, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Algorithms for Battery Management Systems
8.035 bereits angemeldet
von
University of Colorado Boulder
University of Colorado System
Spezialisierung Algorithms for Battery Management SystemsUniversity of Colorado BoulderÜber diesen Kurs
13.703 kürzliche Aufrufe
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Zertifikat zur Vorlage
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100 % online
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Kurs 5 von 5 in
Stufe „Mittel“
Ca. 23 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
Was Sie lernen werden
How to design balancers and power-limits estimators for lithium-ion battery packs
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 5 von 5 in
Stufe „Mittel“
Ca. 23 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
von
University of Colorado Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Dieses Kurs ist Teil des reinen Onlineabschlusses Master of Science in Electrical Engineering von University of Colorado Boulder.
Wenn Sie in das komplette Programm aufgenommen werden, werden Ihre Kurse auf Ihren Abschluss angerechnet.
Lehrplan - Was Sie in diesem Kurs lernen werden
3 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.
3 Stunden zum Abschließen
7 Videos (Gesamt 79 min), 12 Lektüren, 6 Quiz
3 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.
3 Stunden zum Abschließen
6 Videos (Gesamt 73 min), 6 Lektüren, 6 Quiz
2 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.
2 Stunden zum Abschließen
5 Videos (Gesamt 44 min), 5 Lektüren, 5 Quiz
4 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.
4 Stunden zum Abschließen
6 Videos (Gesamt 69 min), 6 Lektüren, 6 Quiz
Bewertungen
- 5 stars93,58 %
- 4 stars3,84 %
- 3 stars1,28 %
- 1 star1,28 %
Top-Bewertungen von BATTERY PACK BALANCING AND POWER ESTIMATION
von DC9. Mai 2020
Especially the last Week 6 (Honors Course) was extremely useful, to better understand the advanced physical/chemical models for lithium batteries.
von SG4. Aug. 2020
Professor visualization is excellent and his explanation is extraordinary with the material.I am very Happy to complete this course and very Informative.
von SP15. Juli 2020
This is one of the best and most useful specialization in my eyes. I would encourage every person interested in EV domain to learn it. Thank you Dr Gregory Plett for this course
von SM27. Juni 2020
Excellent courses. Dr. Plett did a great job teaching this very relevant topic.
Über den 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.
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