Battery State of Charge Estimation Using Neural Networks | Estimate Battery SOC With Deep Learning

Learn about the experimental setup and procedure for designing and performing the measurements for training and testing a neural network to estimate battery state of charge. Two battery cells were tested including Panasonic 18650PF, NCA Chemistry cell, and an LG HG2 NMC Chemistry cell. The battery cells were tested at a range of temperatures, with drive cycle power profiles calculated for electric vehicle applications. For the Panasonic cells, prototype Ford F150 electric truck was modeled with a 35 kilowatt-hour pack, consisting of 3,360 of the cells. For the LG HG2 cells, a Fiat 500e electric vehicle was modeled with a small 7.3 kilowatt-hour pack, consisting of 672 cells. Each battery was tested with many different drive cycles. A portion of these cycles were used for training the SOC estimation algorithm, and the remainder were used for testing the accuracy of the algorithm. Watch the four-part series "Estimate Battery SOC With Deep Learning":    • How to Estimate Battery State of Charge us...   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 -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2021 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.