Work

Effect of variable temperatures on machine learning battery SoH estimation for auxiliary aircraft batteries

Year: 2025

Type: article

Abstract: In aircraft applications Nickel-based batteries are increasingly being replaced by Lithium-based technologies, particularly for their auxiliary unit, which requires a detailed study of their aging for management purposes. This research examines the influence of temperature variations on the identification and extraction of battery features, assessing their suitability as indicators for estimating the battery State-of-Health (SoH). The effect of temperature variations is also studied for machine learning models, both benchmark ones (kernel-based and neural network) and a proposed TCN - Neural ODE one. Optimized kernel-based models through Bayesian Search Cross-Validation and Grid Search Cross-Validation show improved results. Two neural network models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are tested, but show a worse performance, probably due to the smaller dataset. The hybrid model presented, a TCN - Neural ODE improves upon the neural-network model, showing a better performance with smaller datasets and short and long-term dynamics. The effectiveness of the algorithms is determined using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and computing time.

Source: Journal of Power Sources

DOI: 10.1016/j.jpowsour.2025.238451

Authors: Shivanshu Shekhar, Lucia Gauchia, Hortensia Amaris, Álvaro Pérez-Borondo, Carlos Hernández

Institutions: Universidad Carlos III de Madrid, Airbus (Spain)

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,230