Li-ion Battery Prognostics with Statistical Model and RNN Trained with EIS-Based Features

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The mass adoption of battery electric vehicles introduces new challenges in the automotive industry, such as designing high-performance Li-ion batteries, finding the optimal operating condition for the vehicle, and proposing a sustainable recycling solution for retired Li-ion batteries. This paper addresses the longevity and performance aspects of Li-ion batteries by proposing a method to calculate the Li-ion battery cells' remaining useful life (RUL) and state of health (SOH). The methodology of the prognostics algorithm is based on the cycling history of the first life of the battery and on assuming a certain load profile in the second life of the battery. RUL forecasting is done using an AutoRegressive Integrated Moving Average (ARIMA) statistical analysis model combined with a Recurrent Neural Network (RNN) with long short-term memory (LSTM) layers. The RNN prognostics model confirms the expectation that the batteries with the highest C-rate and highest depth of discharge (ΔDOD) loose the most capacity over a certain number of cycles. The prognostics model furthermore shows that high DOD's lead to significantly faster degradation than high C-rates. This finding can assist EV users in choosing the optimal driving profile aimed at minimising battery degradation.

Original languageEnglish
Title of host publication2023 IEEE Energy Conversion Congress and Exposition (ECCE)
PublisherIEEE
Pages1699-1705
Number of pages7
ISBN (Electronic)979-8-3503-1644-5, 979-8-3503-1643-8
ISBN (Print)979-8-3503-1645-2
DOIs
Publication statusPublished - 2023
Event2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 - Nashville, United States
Duration: 29 Oct 20232 Nov 2023

Publication series

NameIEEE Energy Conversion Congress and Exposition
PublisherIEEE
ISSN (Print)2329-3721
ISSN (Electronic)2329-3748

Conference

Conference2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
Abbreviated titleECCE 2023
Country/TerritoryUnited States
CityNashville
Period29/10/232/11/23

Keywords

  • 2024 OA procedure
  • Electric Vehicles
  • Electrochemical impedance spectroscopy
  • Equivalent circuit model
  • Long-short term memory
  • Recurrent neural network
  • Remaining Useful life (RUL)
  • Second Life Batteries
  • State of Health (SOH)
  • Battery (Li-ion)

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