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
This course is part of the Algorithms for Battery Management Systems Specialization
Battery Pack Balancing and Power Estimation
Offered By
Syllabus - What you will learn from this course
Week
1Passive 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.
11 readings, 6 quizzes
11 readings
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 practice exercises
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
Week
2Active 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 readings, 6 quizzes
6 readings
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 practice exercises
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
Week
3How 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 readings, 5 quizzes
5 readings
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 practice exercises
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
Week
4How 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 readings, 6 quizzes
6 readings
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 practice exercises
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
Instructor
Gregory Plett
ProfessorElectrical and Computer Engineering
About 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.
About the Algorithms for Battery Management Systems Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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