How to Estimate Battery State of Charge Using Deep Learning
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!
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.
This video series has four parts:
- 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 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.
An Introduction to Battery State of Charge Estimation Get an introduction of battery state of charge (SOC) estimation, including a review of using neural networks.
The Experiment Using Neural Networks Discover the experimental process involved in training and testing the neural network.
Neural Networks for SOC Estimation Explore the theory and implementation of the deep neural network used in this study; motivation and tradeoffs for the utilization of certain network architectures; and training, testing, validation, and analysis of the network performance.
Training and Prediction in MATLAB and Simulink Implementation See the neural network training process and the Simulink implementation of the method.