International Journal of Electrical Power & Energy Systems, Volume 62, pp 783-791, November 2014

State of Charge Estimation for Li-Ion Batteries Using Neural Network Modelling and Unscented Kalman Filter-based Error Cancellation

Datong Liu, Yue Luo, Jie Liu, Yu Peng, Limeng Guo and Michael Pecht

Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA


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.

Complete article available to CALCE Consortium Members.

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