This course can also be taken for academic credit as ECEA 5734, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Ce cours fait partie de la Spécialisation Algorithms for Battery Management Systems
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Université du Colorado à Boulder
University of Colorado System
Spécialisation Algorithms for Battery Management SystemsUniversité du Colorado à BoulderÀ propos de ce cours
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Cours 5 sur 5 dans le
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Approx. 23 heures pour terminer
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Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Ce que vous allez apprendre
How to design balancers and power-limits estimators for lithium-ion battery packs
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 5 sur 5 dans le
Niveau intermédiaire
Approx. 23 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Offert par
Université du Colorado à 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.
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Commencez à travailler pour obtenir votre master
Ce cours fait partie du diplôme intégralement en ligne Master of Science in Electrical Engineering de Université du Colorado à Boulder.
Si vous êtes admis(e) au programme complet, vos cours sont pris en compte dans votre apprentissage diplômant.
Programme de cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
7 vidéos (Total 79 min), 12 lectures, 6 quiz
3 heures pour terminer
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 heures pour terminer
6 vidéos (Total 73 min), 6 lectures, 6 quiz
2 heures pour terminer
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 heures pour terminer
5 vidéos (Total 44 min), 5 lectures, 5 quiz
4 heures pour terminer
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 heures pour terminer
6 vidéos (Total 69 min), 6 lectures, 6 quiz
Avis
Meilleurs avis pour BATTERY PACK BALANCING AND POWER ESTIMATION
par SP15 juil. 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
par SG4 août 2020
Professor visualization is excellent and his explanation is extraordinary with the material.I am very Happy to complete this course and very Informative.
par DC9 mai 2020
Especially the last Week 6 (Honors Course) was extremely useful, to better understand the advanced physical/chemical models for lithium batteries.
par SM27 juin 2020
Excellent courses. Dr. Plett did a great job teaching this very relevant topic.
À propos du Spécialisation 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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