Lithium-Ion Battery Aging Analysis of an Electric Vehicle Fleet Using a Tailored Neural Network Structure

Within the presented research study we want to estimate the State of Health (SOH) of a fleet of electric vehicles solely using field data. This information may not only help operators during mission planning, but it can reveal causes of accelerated aging. For this purpose, we use a customized neural network that is able to process the data of all fleet vehicles simultaneously. Thus, information between batteries of the different vehicles is transferred and the extrapolation properties are enhanced. We firstly show results with data gathered from a fleet of 25 electric buses. A prediction accuracy of below 5 mV could be obtained for most validation sections. Furthermore, a proof-of-concept experiment illustrates the advantages of the fleet learning approach.