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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

4 Sitaatiot (Scopus)
134 Lataukset (Pure)


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.
JulkaisupaikkaPortland, USA
KustantajaAmerican Society of Mechanical Engineers
ISBN (elektroninen)978-0-7918-8456-0
DOI - pysyväislinkit
TilaJulkaistu - 16 helmik. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaASME International Mechanical Engineering Congress and Exposition - Virtual, Online
Kesto: 16 marrask. 202019 marrask. 2020


NimiASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)


ConferenceASME International Mechanical Engineering Congress and Exposition
KaupunkiVirtual, Online


Sukella tutkimusaiheisiin 'A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä