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.
專項課程 的運作方式
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實踐項目
每個專項課程都包括實踐項目。您需要成功完成這個（些）項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程，則需要在開始之前完成其他所有課程。
獲得證書
在結束每門課程並完成實踐項目之後，您會獲得一個證書，您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。
此專項課程包含 5 門課程
課程1
Introduction to battery-management systems
4.8
56 個評分
This course will provide you with a firm foundation in lithium-ion cell terminology and function and in battery-management-system requirements as needed by the remainder of the specialization. After completing this course, you will be able to:
- List the major functions provided by a battery-management system and state their purpose
- Match battery terminology to a list of definitions
- Identify the major components of a lithium-ion cell and their purpose
- Understand how a battery-management system “measures” current, temperature, and isolation, and how it controls contactors
- Identify electronic components that can provide protection and specify a minimum set of protections needed
- Compute stored energy in a battery pack
- List the manufacturing steps of different types of lithium-ion cells and possible failure modes
課程2
Equivalent Circuit Cell Model Simulation
4.4
12 個評分
In this course, you will learn the purpose of each component in an equivalent-circuit model of a lithium-ion battery cell, how to determine their parameter values from lab-test data, and how to use them to simulate cell behaviors under different load profiles. By the end of the course, you will be able to:
- State the purpose for each component in an equivalent-circuit model
- Compute approximate parameter values for a circuit model using data from a simple lab test
- Determine coulombic efficiency of a cell from lab-test data
- Use provided Octave/MATLAB script to compute open-circuit-voltage relationship for a cell from lab-test data
- Use provided Octave/MATLAB script to compute optimized values for dynamic parameters in model
- Simulate an electric vehicle to yield estimates of range and to specify drivetrain components
- Simulate battery packs to understand and predict behaviors when there is cell-to-cell variation in parameter values
課程3
Battery State-of-Charge (SOC) Estimation
5.0
4 個評分
In this course, you will learn how to implement different state-of-charge estimation methods and to evaluate their relative merits. By the end of the course, you will be able to:
- Implement simple voltage-based and current-based state-of-charge estimators and understand their limitations
- Explain the purpose of each step in the sequential-probabilistic-inference solution
- Execute provided Octave/MATLAB script for a linear Kalman filter and evaluate results
- Execute provided Octave/MATLAB script for state-of-charge estimation using an extended Kalman filter on lab-test data and evaluate results
- Execute provided Octave/MATLAB script for state-of-charge estimation using an sigma-point Kalman filter on lab-test data and evaluate results
- Implement method to detect and discard faulty voltage-sensor measurements
課程4
Battery State-of-Health (SOH) Estimation
5.0
2 個評分
In this course, you will learn how to implement different state-of-health estimation methods and to evaluate their relative merits. By the end of the course, you will be able to:
- Identify the primary degradation mechanisms that occur in lithium-ion cells and understand how they work
- Execute provided Octave/MATLAB script to estimate total capacity using WLS, WTLS, and AWTLS methods and lab-test data, and to evaluate results
- Compute confidence intervals on total-capacity estimates
- Compute estimates of a cell’s equivalent-series resistance using lab-test data
- Specify the tradeoffs between joint and dual estimation of state and parameters, and steps that must be taken to ensure robust estimates (honors)
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.
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此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
完成专项课程后我会获得大学学分吗？
此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。
完成专项课程需要多长时间？
The standard version of this specialization is 20 weeks in duration. The honors track requires an additional 4 weeks of study.
What background knowledge is necessary?
This is a graduate-level specialization that assumes that learners already hold a technical undergraduate degree: a Bachelor's degree in Electrical Engineering, Computer Engineering, or Mechanical Engineering or a Bachelor's degree in a closely related engineering discipline plus undergraduate-level competency in the following areas: Math (differential and integral calculus, linear algebra, and differential equations), Science (calculus-based physics and general chemistry), and Engineering (linear circuits, electronics, and linear systems)
Do I need to take the courses in a specific order?
The courses are designed to be taken in order, from Course 1 through Course 5. Course 1 gives a broad overview, background concepts, and context for the others; Course 2 is a strong prerequisite for the remaining courses since it describes the mathematical and programming frameworks that will be used; Course 3 includes topics in random variables that are important for Course 4. Course 5 is the only exception, and may be taken any time after completing Course 2.
What will I be able to do upon completing the Specialization?
After completing the specialization, you will be able to: use laboratory data to create mathematical models of battery cells, and use these models to implement state-of-charge, state-of-health, available power (state-of-function), available energy, and balancing algorithms.
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