State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation

• An optimal neural network is used to model SOC as a function of system measurements.
• This modeling approach eliminates the need to determine an OCV–SOC relationship.
• UKF is employed to improve the accuracy of the neural network-based SOC estimation.
• Driving cycle case studies are presented to validate the developed method.

Lithium-ion batteries have been widely used as the energy storage systems in personal portable electronics (e.g. cell phones, laptop computers), telecommunication systems, electric vehicles and in various aerospace applications. To prevent the sudden loss of power of battery-powered systems, there are various approaches to estimate and manage the battery's state of charge (SOC). In this paper, an artificial neural network–based battery model is developed to estimate the SOC, based on the measured current and voltage. An unscented Kalman filter is used to reduce the errors in the neural network-based SOC estimation. The method is validated using LiFePO4 battery data collected from the Federal Driving Schedule and dynamical stress testing.