This course can also be taken for academic credit as ECEA 5732, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Algorithms for Battery Management Systems
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Über diesen Kurs
Was Sie lernen werden
How to implement state-of-charge (SOC) estimators for lithium-ion battery cells
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University of Colorado Boulder
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University of Colorado System
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Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Lehrplan - Was Sie in diesem Kurs lernen werden
The importance of a good SOC estimator
This week, you will learn some rigorous definitions needed when discussing SOC estimation and some simple but poor methods to estimate SOC. As background to learning some better methods, we will review concepts from probability theory that are needed to be able to deal with the impact of uncertain noises on a system's internal state and measurements made by a BMS.
Introducing the linear Kalman filter as a state estimator
This week, you will learn how to derive the steps of the Gaussian sequential probabilistic inference solution, which is the basis for all Kalman-filtering style state estimators. While this content is highly theoretical, it is important to have a solid foundational understanding of these topics in practice, since real applications often violate some of the assumptions that are made in the derivation, and we must understand the implication this has on the process. By the end of the week, you will know how to derive the linear Kalman filter.
Coming to understand the linear Kalman filter
The steps of a Kalman filter may appear abstract and mysterious. This week, you will learn different ways to think about and visualize the operation of the linear Kalman filter to give better intuition regarding how it operates. You will also learn how to implement a linear Kalman filter in Octave code, and how to evaluate outputs from the Kalman filter.
Cell SOC estimation using an extended Kalman filter
A linear Kalman filter can be used to estimate the internal state of a linear system. But, battery cells are nonlinear systems. This week, you will learn how to approximate the steps of the Gaussian sequential probabilistic inference solution for nonlinear systems, resulting in the "extended Kalman filter" (EKF). You will learn how to implement the EKF in Octave code, and how to use the EKF to estimate battery-cell SOC.
Bewertungen
- 5 stars81,29Â %
- 4 stars16,12Â %
- 3 stars1,29Â %
- 2 stars0,64Â %
- 1 star0,64Â %
Top-Bewertungen von BATTERY STATE-OF-CHARGE (SOC) ESTIMATION
Excellent course that has very clear teaching material and engaging tests and assignments. A great foundational course for battery algorithms.
Capstone projects could be more demanding. Maybe you can provide a multiple temperatures example.
As an electrical engineer, I firmly state that this course is the best for anyone who would like to embark on this journey of battery energy storage. Well structured
With an excellent instructor
The concepts taught were absolutely crucial for the later parts of this specialization and they were explained properly.
Über den Spezialisierung Algorithms for Battery Management Systems
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.

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