State of charge Kalman filter estimator for automotive batteries

By O. Barbarisi, F. Vasca, Luigi Glielmo. 2004

144 citations recorded by the source corpus.

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Control oriented 1D electrochemical model of lithium ion battery (2007, background): “[8] neglect the electrolyte phase and negative electrode solid state transport, assume uniform reaction current and discretize the positive electrode solid state diffusion PDE to a 32nd order system of ODEs.”
Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries (2010, unknown): “By spatially discretizing the PDEs, distributed parameter-type estimation algorithms have been developed for the lead-acid battery [9] and the nickel-metal hydride battery [10], though with high order (30–100 states) in comparison to the more commonly used equivalent circuit model-based algorithms (”
The State of Charge Estimating Methods for Battery: A Review (2013, methodology): “[34] presented an extended Kalman filter (EKF) to estimate the concentrations of the main chemical species which are averaged on the thickness of the active material in order to obtain the SOC of the battery, by using the terminal current and voltage measurements.”
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF (2010, background): “Some researchers have used this filter to estimate the open-circuit voltage or other parameters of batteries that have a direct relationship with the SOC [29]–[32].”