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

TL;DR

This paper develops and compares machine learning models, ARIMA and bagging with decision trees, for predicting the state-of-health of Li-ion batteries in electric trucks, aiming to improve maintenance and operational efficiency.

Contribution

It introduces a novel application of ARIMA and supervised learning models for battery SoH forecasting in electric trucks with limited prior data.

Findings

ARIMA effectively analyzes multiple batteries' data.

Bagging with decision trees outperforms ARIMA with high data variability.

Models enhance battery management and operational planning.

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.