关于此 专项课程
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100% 在线课程

立即开始,按照自己的计划学习。

灵活的计划

设置并保持灵活的截止日期。

中级

完成时间大约为4 个月

建议 10 小时/周

英语(English)

字幕:英语(English)

100% 在线课程

立即开始,按照自己的计划学习。

灵活的计划

设置并保持灵活的截止日期。

中级

完成时间大约为4 个月

建议 10 小时/周

英语(English)

字幕:英语(English)

专项课程 的运作方式

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how it works

此专项课程包含 5 门课程

课程1

Introduction to battery-management systems

4.7
162 个评分
41 个审阅

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

...
课程2

Equivalent Circuit Cell Model Simulation

4.7
25 个评分
3 个审阅

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

...
课程3

Battery State-of-Charge (SOC) Estimation

4.7
18 个评分
1 个审阅

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

...
课程4

Battery State-of-Health (SOH) Estimation

5.0
7 个评分
1 个审阅

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)

...

讲师

Gregory Plett

Professor
Electrical and Computer Engineering

关于 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....

常见问题

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  • 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。

  • The standard version of this specialization is 20 weeks in duration. The honors track requires an additional 4 weeks of study.

  • 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)

  • 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.

  • No.

  • 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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