Learn about the main aspects of the neural network design process for battery state of charge estimation. This video includes an overview of the training evaluation testing process, the neural network model structure, data preparation, an approach to improve robustness of the model, and SOC estimation results at multiple temperature, including -10 degrees Celsius.
Watch the four-part series "Estimate Battery SOC With Deep Learning": [ Ссылка ]
- 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: [ Ссылка ]
- Battery Management Systems (BMS) Resources: [ Ссылка ]
- Deep Learning and Traditional Machine Learning: Choosing the Right Approach: [ Ссылка ]
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