Battery State of Charge Estimation Using Deep Learning

This example shows how to perform an end-to-end workflow for battery state of charge estimation. This workflow involves these steps: define requirements, prepare data, train model, test model, integrate into Simulink, SIL simulation, and verify requirements. This figure shows the steps that this example covers.

Battery state of charge (SOC) is the level of charge of an electric battery relative to its capacity, measured as a percentage. SOC is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behavior of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge of the battery composition as well as its physical response. In contrast, using neural networks is a data-driven approach that requires minimal knowledge of the battery or its nonlinear behavior.

This example shows how to perform these steps:

Related Topics

• Use Requirements to Develop and Verify MATLAB Functions (Requirements Toolbox)
• Deep Learning in MATLAB