Специализация Algorithms for Battery Management Systems

Начинается Sep 19

Специализация 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

Об этой специализации

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.

Автор:

courses
5 courses

Следуйте предложенному порядку или выберите свой.

projects
Проекты

Поможет на практике применить полученные навыки.

certificates
Сертификаты

Отметьте новые навыки в резюме и на LinkedIn.

Обзор проектов

Курсы
Intermediate Specialization.
Some related experience required.
  1. 1-Й КУРС

    Introduction to battery-management systems

    Субтитры
    English

    О курсе

    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
  2. 2-Й КУРС

    Equivalent Circuit Cell Model Simulation

    Субтитры
    English

    О курсе

    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 differen
  3. 3-Й КУРС

    Battery State-of-Charge (SOC) Estimation

    Субтитры
    English

    О курсе

    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 esti
  4. 4-Й КУРС

    Battery State-of-Health (SOH) Estimation

    Начинается October 2018
    Субтитры
    English

    О курсе

    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)
  5. 5-Й КУРС

    Battery Pack Balancing and Power Estimation

    Начинается January 2019
    Субтитры
    English

    О курсе

    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

Авторы

  • University of Colorado System

    The University of Colorado Colorado Springs is recognized as a world leader in its research program in practical engineering modeling and controls of lithium-ion battery systems.

    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.

  • Gregory Plett

    Gregory Plett

    Professor

FAQs