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 forma parte de Programa especializado: Algorithms for Battery Management Systems
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Universidad de Colorado en Boulder
Sistema Universitario de Colorado
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Programa especializado: Algorithms for Battery Management SystemsUniversidad de Colorado en BoulderAcerca de este Curso
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Fechas lÃmite flexibles
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Certificado para compartir
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100Â % en lÃnea
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Nivel intermedio
Aprox. 23 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Qué aprenderás
H​ow to design balancers and power-limits estimators for lithium-ion battery packs
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 23 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Colorado en 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 Universitario de 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.
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Este curso forma parte de un programa de grado ofrecido por Universidad de Colorado en Boulder. Si te inscribes en un curso sin tÃtulo de grado a través de la universidad y lo completas en lÃnea, cuenta como horas de crédito para un programa de grado en CU-Boulder. Todo lo que tienes que hacer es solicitarlo a través de la universidad.
Programa - Qué aprenderás en este curso
3 horas para completar
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 completar
7 videos (Total 79 minutos), 12 lecturas, 6 cuestionarios
3 horas para completar
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 completar
6 videos (Total 73 minutos), 6 lecturas, 6 cuestionarios
2 horas para completar
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 completar
5 videos (Total 44 minutos), 5 lecturas, 5 cuestionarios
4 horas para completar
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 completar
6 videos (Total 69 minutos), 6 lecturas, 6 cuestionarios
Reseñas
- 5 stars93,90Â %
- 4 stars3,65Â %
- 3 stars1,21Â %
- 1 star1,21Â %
Principales reseñas sobre 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 NG10 de sep. de 2022
great to having this course help me out through such vast and eternal experines as in simple and effective way.
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 DC9 de may. de 2020
Especially the last Week 6 (Honors Course) was extremely useful, to better understand the advanced physical/chemical models for lithium batteries.
Acerca de Programa especializado: 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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