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Battery State-of-Charge (SOC) Estimation

Курс от University of Colorado Boulder, University of Colorado System

Средний≈ 28.3 чАнглийский

О курсе

This course can also be taken for academic credit as ECEA 5732, part of CU Boulder’s Master of Science in Electrical Engineering degree. 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 a sigma-point Kalman filter on lab-test data and evaluate results - Implement method to detect and discard faulty voltage-sensor measurements

Навыки, которые вы освоите

Estimation Fine-tuning Mathematical Modeling Applied Mathematics Simulation and Simulation Software Model Optimization Model Evaluation Mathematical Software Numerical Analysis

Программа курса

7 модулей · 143 учебных материалов

01The importance of a good SOC estimator34 материалов

3.1.1: Welcome to the course!

Course Updates and Accessibility SupportЧтение Non-Credit Students: Welcome and Where to Find HelpЧтение Get help and meet other learners in this course. Join your discussion forums!Чтение Introduce YourselfОбсуждение

Учитесь у экспертов

Gregory Plett

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Обучение на Coursera

≈ 28.3 ч

7 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Ория, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Battery State-of-Charge (SOC) Estimation

О курсе

Навыки, которые вы освоите

Программа курса

3.1.1: Welcome to the course!

Учитесь у экспертов

Gregory Plett

3.1.2: What is the importance of a good SOC estimator?

3.1.3: How do we define SOC carefully?

3.1.4: What are some approaches to estimating battery cell SOC?

3.1.5: Understanding uncertainty via mean and covariance

3.1.6: Understanding joint uncertainty of two unknown quantities

3.1.7: Understanding time-varying uncertain quantities

3.1.8: Summary of "The importance of a good SOC estimator" and next steps

3.2.1: Predict/correct mechanism of sequential probabilistic inference

3.2.2: The Kalman-filter gain factor

3.2.3: Summarizing the six steps of generic probabilistic inference

3.2.4: Deriving the three Kalman-filter prediction steps

3.2.5: Deriving the three Kalman-filter correction steps

3.2.6: Summary of "Introducing the linear KF as a state estimator" and next steps

3.3.1: Visualizing the Kalman filter with a linearized cell model

3.3.2: Introducing Octave code to generate correlated random numbers

3.3.3: Introducing Octave code to implement KF for linearized cell model

3.3.4: How do we improve numeric robustness of Kalman filter?

3.3.5: Can we automatically detect bad measurements with a Kalman filter?

3.3.6: How do I initialize and tune a Kalman filter?

3.3.7: Summary of "Coming to understand the linear KF" and next steps

3.4.1: Introducing nonlinear variations to Kalman filters

3.4.2: Deriving the three extended-Kalman-filter prediction steps

3.4.3: Deriving the three extended-Kalman-filter correction steps

3.4.4: Introducing a simple EKF example, with Octave code

3.4.5: Preparing to implement EKF on an ECM

3.4.6: Introducing Octave code to initialize and control EKF for SOC estimation

3.4.7: Introducing Octave code to update EKF for SOC estimation

3.4.8: Summary of "Cell SOC estimation using an EKF" and next steps

3.5.1: Problems with EKF that are improved with sigma-point methods

3.5.2: Approximating uncertain variables using sigma points

3.5.3: Deriving the six sigma-point-Kalman-filter steps

3.5.4: Introducing a simple SPKF example with Octave code

3.5.5: Introducing Octave code to initialize and control SPKF for SOC estimation

3.5.6: Introducing Octave code to update SPKF for SOC estimation

3.5.7: Summary of "Cell SOC estimation using a SPFK" and next steps

3.6.1: Why do we need to be clever when estimating SOC for battery packs?

3.6.2: Developing the "bar" filter using an ECM

3.6.3: Developing the "delta" filters using an ECM

3.6.4-3.6.5: Introducing "desktop validation" as a method for predicting performance; summary

3.7 Capstone project