We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves.
- Authors
Schmitz, Mano; Kowal, Julia
- 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 estimate 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 one-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 ageing 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.
- Subjects
DEEP learning; LITHIUM-ion batteries; ONLINE education; TIME series analysis; DEIONIZATION of water
- Publication
Batteries, 2024, Vol 10, Issue 6, p206
- ISSN
2313-0105
- Publication type
Article
- DOI
10.3390/batteries10060206