This course can also be taken for academic credit as ECEA 5734, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Este curso faz parte do Programa de cursos integrados Algorithms for Battery Management Systems
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oferecido por
Universidade do Colorado em Boulder
Sistema de Universidades do ColoradoUniversidade do Colorado
Programa de cursos integrados Algorithms for Battery Management SystemsUniversidade do Colorado em BoulderInformações sobre o curso
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Prazos flexíveis
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100% on-line
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Nível intermediário
Aprox. 23 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
O que você vai aprender
How to design balancers and power-limits estimators for lithium-ion battery packs
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 23 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
Universidade do Colorado em 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.
Sistema de Universidades do ColoradoUniversidade do Colorado
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.
Comece a trabalhar rumo ao seu mestrado
Este curso é parte da graduação 100% on-line Master of Science in Electrical Engineering da Universidade do Colorado em Boulder.
Caso seja aceito para o programa completo, seus cursos contarão para sua graduação.
Programa - O que você aprenderá com este curso
3 horas para concluir
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 horas para concluir
7 vídeos (Total 79 mín.), 12 leituras, 6 testes
3 horas para concluir
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 horas para concluir
6 vídeos (Total 73 mín.), 6 leituras, 6 testes
2 horas para concluir
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 horas para concluir
5 vídeos (Total 44 mín.), 5 leituras, 5 testes
4 horas para concluir
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 horas para concluir
6 vídeos (Total 69 mín.), 6 leituras, 6 testes
Avaliações
Principais avaliações do BATTERY PACK BALANCING AND POWER ESTIMATION
por SP15 de Jul de 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
por SG4 de Ago de 2020
Professor visualization is excellent and his explanation is extraordinary with the material.I am very Happy to complete this course and very Informative.
por SM27 de Jun de 2020
Excellent courses. Dr. Plett did a great job teaching this very relevant topic.
por AS1 de Ago de 2020
It is an excellent course for battery enthusiasts.
Sobre Programa de cursos integrados 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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