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
本課程是 Algorithms for Battery Management Systems 專項課程 專項課程的一部分
Battery Pack Balancing and Power Estimation
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1完成時間為 3 小時
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.
7 個視頻 （總計 79 分鐘）, 11 個閱讀材料, 6 個測驗
7 個視頻
5.1.2: Introduction to battery-pack balancing10分鐘
5.1.3: How do battery packs become imbalanced?15分鐘
5.1.4: What are the criteria for specifying a balancing setpoint for a battery pack?13分鐘
5.1.5: What are the criteria for specifying when to balance a battery pack?13分鐘
5.1.6: What kinds of circuits can be used for passively balancing a battery pack?16分鐘
5.1.7: Summary of "Passive balancing methods for battery packs"; what next?2分鐘
11 個閱讀材料
Notes for lesson 5.1.11分鐘
Frequently Asked Questions5分鐘
Course Resources5分鐘
How to Use Discussion Forums5分鐘
Earn A Certificate5分鐘
Notes for lesson 5.1.21分鐘
Notes for lesson 5.1.31分鐘
Notes for lesson 5.1.41分鐘
Notes for lesson 5.1.51分鐘
Notes for lesson 5.1.61分鐘
Notes for lesson 5.1.71分鐘
6 個練習
Practice quiz for lesson 5.1.29分鐘
Practice quiz for lesson 5.1.39分鐘
Practice quiz for lesson 5.1.49分鐘
Practice quiz for lesson 5.1.59分鐘
Practice quiz for lesson 5.1.69分鐘
Quiz for week 130分鐘
週
2完成時間為 3 小時
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.
6 個視頻 （總計 73 分鐘）, 6 個閱讀材料, 6 個測驗
6 個視頻
5.2.2: How to balance actively using transformer-based circuits7分鐘
5.2.3: How to balance actively using a shared active bus14分鐘
5.2.4: Using simulation to show how quickly we must balance a battery pack14分鐘
5.2.5: Introducing Octave code to simulate balancing: The main program loop21分鐘
5.2.6: Summary of "Active balancing methods for battery packs"; what next?3分鐘
6 個閱讀材料
Notes for lesson 5.2.11分鐘
Notes for lesson 5.2.21分鐘
Notes for lesson 5.2.31分鐘
Notes for lesson 5.2.41分鐘
Notes for lesson 5.2.51分鐘
Notes for lesson 5.2.61分鐘
6 個練習
Practice quiz for lesson 5.2.19分鐘
Practice quiz for lesson 5.2.29分鐘
Practice quiz for lesson 5.2.39分鐘
Practice quiz for lesson 5.2.49分鐘
Practice quiz for lesson 5.2.515分鐘
Quiz for week 230分鐘
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3完成時間為 2 小時
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.
5 個視頻 （總計 44 分鐘）, 5 個閱讀材料, 5 個測驗
5 個視頻
5.3.2: How to compute available battery power based on cell terminal voltage8分鐘
5.3.3: How to consider other performance limits when computing available battery power7分鐘
5.3.4: Introducing Octave code to compute power limits using simplified cell model12分鐘
5.3.5: Summary of "How to find available battery power using a simplified cell model"; what next?1分鐘
5 個閱讀材料
Notes for lesson 5.3.11分鐘
Notes for lesson 5.3.21分鐘
Notes for lesson 5.3.31分鐘
Notes for lesson 5.3.41分鐘
Notes for lesson 5.3.51分鐘
5 個練習
Practice quiz for lesson 5.3.19分鐘
Practice quiz for lesson 5.3.29分鐘
Practice quiz for lesson 5.3.39分鐘
Practice quiz for lesson 5.3.415分鐘
Quiz for week 330分鐘
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4完成時間為 4 小時
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.
6 個視頻 （總計 69 分鐘）, 6 個閱讀材料, 6 個測驗
6 個視頻
5.4.2: How to solve for a future battery condition using the bisection algorithm11分鐘
5.4.3: How to use bisection to estimate available power using comprehensive cell model16分鐘
5.4.4: Introducing Octave code to compute power limits using comprehensive cell model9分鐘
5.4.5: Using simulation to compare and contrast different power-estimation methods12分鐘
5.4.6: Concluding remarks for the specialization6分鐘
6 個閱讀材料
Notes for lesson 5.4.11分鐘
Notes for lesson 5.4.21分鐘
Notes for lesson 5.4.31分鐘
Notes for lesson 5.4.41分鐘
Notes for lesson 5.4.51分鐘
Notes for lesson 5.4.61分鐘
6 個練習
Practice quiz for lesson 5.4.19分鐘
Practice quiz for lesson 5.4.215分鐘
Practice quiz for lesson 5.4.315分鐘
Practice quiz for lesson 5.4.420分鐘
Practice quiz for lesson 5.4.520分鐘
Quiz for week 440分鐘
講師
Gregory Plett
ProfessorElectrical and Computer Engineering
關於 科罗拉多大学系统
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.
關於 Algorithms for Battery Management Systems 專項課程
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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