Neural network predicts battery wear from charging signals
Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation
Machine Learning
Summary
Knowing when a lithium-ion battery is wearing out helps keep devices safe and long-lasting. The authors found a way to guess how worn out a battery is by looking just at charging data instead of slow discharge tests. They trained a computer model with lots of battery data so it can spot signs of damage quickly and while the battery is charging fast. This method could help devices like electric cars better manage their batteries in real time without slowing down.
What this means in practice
- •For electric vehicle engineers: Improve real-time battery health tracking during fast charging to enhance safety and lifespan.
- •For energy storage operators: Monitor battery degradation quickly without interrupting service by using charging data instead of slow discharge tests.
Authors
Amir Madmolilvand, Farzaneh Abdollahi
Abstract
Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applications such as electric vehicles and energy storage systems. Traditional incremental capacity (IC) analysis methods require low-current cycling for discharge measurements, limiting their practical use in real-time battery management. This paper proposes a novel neural network-based framework that predicts discharge IC features directly from charging signals, eliminating the need for low-current discharge. Trained on a comprehensive dataset of 53 battery cells cycled under diverse fast-charging protocols, the model demonstrates robust generalization ability, effectively estimating degradation indicators on unseen battery data. Among several architectures evaluated, the LSTM model provides the best balance of prediction accuracy and computational efficiency. The proposed approach enables real-time integration into Battery Management Systems (BMS), enhancing degradation monitoring without disrupting normal battery operation. This paper presents a study to bridge IC analysis with practical, fast-charging scenarios, marking a significant step towards intelligent and scalable battery health monitoring.