Introduction to Battery State of Charge Estimation | Estimate Battery SOC With Deep Learning

Get an introduction to battery state of charge (SOC) estimation, its challenges, and motivations for new ways to perform this task. See a review of the state-of-the-art estimation technique and explore the concept of neural networks.

To say that lithium-ion batteries are important in our lives would be an understatement. They are everywhere—from our mobile phones, laptops, and wearable electronics to electric vehicles and smart grids—so knowing how long their charge will last is important, too!

- An Introduction to Battery State of Charge Estimation
- The Experiment Using Neural Networks
- Neural Networks for SOC Estimation
- Training and Prediction in MATLAB and Simulink Implementation

The focus of this video series is the application of neural networks to battery state of charge estimation. State of charge estimation is the task of the battery management system, or BMS. An accurate determination of the State of Charge (SOC) in a battery indicates to the user how long they can continue to use the battery-powered device before a recharge is needed. In a car, for example, an accurate knowledge of the time to recharge reduces anxiety and allows for appropriate trip planning.

The materials presented in this video series are the result of the work done by Carlos Vidal and - Phil Kollmeyer, both researchers at McMaster University in Hamilton, Ontario. The work was done in collaboration with engineers from FCA and published last year as an SAE paper.

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[siddhantsarraf]

Kindly upload next part sir.
And thankyou so much🙏🏼🙏🏼

[hamzanadir]

Thanks a lot, we need more details about it, especially the Kalman filter application:) thank you our great expert

[DrJALAGHARI]

Thank you so much for sharing this valuable information through this amazing video.

[abdullahmohamed]

thanks a lot, i need to create a SOC lookup table from the coulomb counting method, can you tell me how I can do that in simulink?