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!

Watch the four-part series "Estimate Battery SOC With Deep Learning": https://youtube.com/playlist?list=PLn8PRpmsu08qEaoBNHa16bPASDDKNBQI0

- 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.

Related Resources:
- Read Li-ion battery dataset: https://bit.ly/35TVStD
- Battery Management Systems (BMS) Resources: https://bit.ly/3ZnPqWi
- Deep Learning and Traditional Machine Learning: Choosing the Right Approach: https://bit.ly/3xL5jHV

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