This course can also be taken for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree.
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Battery State-of-Health (SOH) Estimation
Ce cours fait partie de Spécialisation Algorithms for Battery Management Systems
Instructeur : Gregory Plett
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(155 avis)
Ce que vous apprendrez
How to implement state-of-health (SOH) estimators for lithium-ion battery cells
Compétences que vous acquerrez
- Catégorie : How to implement state-of-health (SOH) estimators for lithium-ion battery cells
Détails à connaître
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Il y a 6 modules dans ce cours
As battery cells age, their total capacities generally decrease and their resistances generally increase. This week, you will learn WHY this happens. You will learn about the specific physical and chemical mechanisms that cause degradation to lithium-ion battery cells. You will also learn why it is relatively simple to estimate and track changes to resistance, but why it is difficult to track changes to total capacity accurately.
Inclus
8 vidéos13 lectures7 devoirs1 sujet de discussion1 laboratoire non noté
Total capacity is often estimated using ordinary-least-squares (OLS) methods. This week, you will learn that this is a fundamentally incorrect approach, and will learn that a total-least-squares (TLS) method should be used instead. You will learn how to derive a weighted OLS solution, to use as a benchmark, and how to derive a weighted TLS solution also.
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7 vidéos7 lectures7 devoirs4 laboratoires non notés
Unfortunately, the weighted TLS solution you learned in week 2 is not well suited for efficient computation on an embedded system like a BMS. As an intermediate step toward finding an efficient weighted TLS method, you will first learn a proportionally weighted TLS method this week. You will then learn how to generalize this to an "approximate weighted TLS" (AWTLS) method, which gives good estimates, and is feasible to implement on a BMS.
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7 vidéos7 lectures7 devoirs4 laboratoires non notés
So far this course, you have learned a number of methods for estimating total capacity. This week, you will learn how to implement those methods in Octave code. You will also explore different simulation scenarios to benchmark how well each method works, in comparison with the others. The scenarios are representative of hybrid-electric-vehicle (HEV) and battery-electric-vehicle (BEV) applications, but the principles learned can be extrapolated to other similar application domains.
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6 vidéos6 lectures6 devoirs5 laboratoires non notés
In the third course of the specialization, you learned how to use extended Kalman filters (EKFs) and sigma-point Kalman filters (SPKFs) to estimate the state of a battery cell. In this honors week, you will learn how to extend those concepts to apply EKF and SPKF to estimating the parameters of a battery-cell model if the state is known, and also how to simultaneously estimate both the state and parameters of a cell model.
Inclus
6 vidéos6 lectures4 devoirs2 laboratoires non notés
You have learned several different total-capacity estimation methods. Some of these methods work better than others in general, but any method is only as good as the data you give it. In this project, you will explore a different way to determine the "x" and "y" data you use as input to the total-capacity estimation methods.
Inclus
1 devoir de programmation1 laboratoire non noté
Instructeur
Recommandé si vous êtes intéressé(e) par Electrical Engineering
Icahn School of Medicine at Mount Sinai
University of Minnesota
University of Colorado System
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