Estimate Battery SOC With Deep Learning

 To say that lithium-ion batteries are important in our lives would be an understatement. They are absolutely everywhere from our mobile phones, laptops, and wearable electronics, all the way up to electric vehicles and smart grids. The main contribution of this work was the application of neural networks to battery state of charge estimation. State of charge estimation is the task of great importance trusted to the Battery Management System, or a BMS. An accurate determination of the State of Charge (SOC) in a battery indicates to the user the extent to which 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 range anxiety and allows for appropriate trip planning. 

This video series will provide an the following 

- An introduction to Battery State of Charge Estimation

- Experiment using Neural Networks

- The theory and implementation of Neural Networks for SOC Estimation

- Training and prediction in MATLAB & Simulink Implementation

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

Part 1: Introduction to Battery State of Charge Estimation | Estimate Battery SOC With Deep Learning, Part 1

Part 2: The Experiment Using Neural Networks | Estimate Battery SOC With Deep Learning, Part 2

Part 3:  Neural Networks for State of Charge Estimation | Estimate Battery SOC With Deep Learning, Part 3