电池管理系统的算法

Algorithms for Battery Management Systems

Get Started in Algorithms for Battery Management. Learn how to model lithium-ion battery cells, and how to use those models to manage battery packs

专项课程电子
6385 次查看
科罗拉多大学系统
Coursera
  • 完成时间大约为 4 个月
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

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.

包含课程

课程1
Introduction to battery-management systems

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

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

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

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)

课程5
Battery Pack Balancing and Power Estimation

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

预备知识

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)

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