A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study

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Abstract

It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are
used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to
maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the
presence of large variation in data for one battery.
Original languageEnglish
Title of host publicationEnergy
Place of PublicationPortland, USA
PublisherAmerican Society of Mechanical Engineers
Number of pages10
ISBN (Electronic)9780791884560
DOIs
Publication statusPublished - 16 Feb 2021
MoE publication typeA4 Article in a conference publication
EventASME International Mechanical Engineering Congress and Exposition - Virtual, Online
Duration: 16 Nov 202019 Nov 2020

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
PublisherASME
Volume8

Conference

ConferenceASME International Mechanical Engineering Congress and Exposition
Abbreviated titleIMECE
CityVirtual, Online
Period16/11/202019/11/2020

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