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

by   Matti Huotari, et al.

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.



page 7


Battery Cloud with Advanced Algorithms

A Battery Cloud or cloud battery management system leverages the cloud c...

A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling

The widespread adoption of Electric Vehicles (EVs) is limited by their r...

Data Driven Prediction of Battery Cycle Life Before Capacity Degradation

Ubiquitous use of lithium-ion batteries across multiple industries prese...

Vulnerabilities of Electric Vehicle Battery Packs to Cyberattacks on Auxiliary Components

Modern automobiles are entirely controlled by electronic circuits and pr...

Space-Filling Subset Selection for an Electric Battery Model

Dynamic models of the battery performance are an essential tool througho...

Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning

A state of charge estimator is an essential component of battery managem...

Cloud-Based Dynamic Programming for an Electric City Bus Energy Management Considering Real-Time Passenger Load Prediction

Electric city bus gains popularity in recent years for its low greenhous...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.