Version 1
: Received: 28 March 2024 / Approved: 28 March 2024 / Online: 29 March 2024 (07:28:21 CET)
How to cite:
Schmitz, M.; Kowal, J. A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves. Preprints2024, 2024031793. https://doi.org/10.20944/preprints202403.1793.v1
Schmitz, M.; Kowal, J. A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves. Preprints 2024, 2024031793. https://doi.org/10.20944/preprints202403.1793.v1
Schmitz, M.; Kowal, J. A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves. Preprints2024, 2024031793. https://doi.org/10.20944/preprints202403.1793.v1
APA Style
Schmitz, M., & Kowal, J. (2024). A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves. Preprints. https://doi.org/10.20944/preprints202403.1793.v1
Chicago/Turabian Style
Schmitz, M. and Julia Kowal. 2024 "A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves" Preprints. https://doi.org/10.20944/preprints202403.1793.v1
Abstract
The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage and performance optimised deep learning approach to predict the capacity based SOH of LIBs using raw sensor data from partial charging curves under constant current condition. The proposed model is based on a combination of a 1-dimensional convolutional and long-short term memory neural network and processes time, voltage and incremental capacity of partial charging curves as time series. The model is cross-validated on different aging scenarios reaching an overall MAE = 0.418% and RMSE = 0.531%, promising an accurate SOH estimation of LIBs under varying usage and environmental conditions in a real world application.
Keywords
lithium-ion battery; state of health (SOH); deep learning; neural network; charging curve; ageing scenarios
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
