Kalman filters have shown to be a very accurate and robust method for SoC estimation. However, their
performance depends heavily on the accuracy of the used battery model and its parameters. These battery
model parameters have shown to vary with the SoH, cell chemistry, temperature and load current.
This paper proposes a data driven battery model parameter estimation technique based on the recursive
least squares method as an alternative to extensively characterizing every cell of interest with time consuming
test procedures, reducing offline characterization time by up to 70%. This allows the parameter
estimation model to be applied, in cooperation with a SoC estimation model, to create a data driven multi
chemistry SoC estimation module.
Original languageEnglish
Title of host publicationElectric Vehicle Symposium 30
Number of pages12
Publication statusPublished - 9 Oct 2017
EventEVS30: Electric Vehicle Symposium - Stuttgart, Germany
Duration: 9 Oct 201711 Oct 2017


Abbreviated titleEVS30
Internet address

ID: 35948784