A hybrid lithium‐ion battery model for system‐level analyses. (8th August 2016) - Lindgren, Juuso; Asghar, Imran; Lund, Peter D.
Summary: We describe an advanced lithium‐ion battery model for system‐level analyses such as electric vehicle fleet simulation or distributed energy storage applications. The model combines an empirical multi‐parameter model and an artificial neural network with particular emphasis on thermal effects such as battery internal heating. The model is fast and can accurately describe constant current charging and discharging of a battery cell at a variety of ambient temperatures. Comparison to a commonly used linear kilowatt‐hour counter battery model indicates that a linear model overestimates the usable capacity of a battery at low temperatures. We highlight the importance of including internal heating in a battery model at low temperatures, as more capacity is available when internal heating is taken into account. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : An empirical‐artificial neural network hybrid battery model for lithium manganese dioxide chemistry is presented and validated for temperature range –10 °C to +50 °C and for C‐rates up to 1.9. The model pays special attention to internal heating during battery operation and is completely self‐sufficient with regard to battery temperature and state‐of‐charge. Comparison against the commonly encountered simple linear kilowatt‐hour counter battery model indicates that the use of the simple model at low temperature may lead to overestimation of usable battery capacity.