5 papers
Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte
Gift Modekwe, Qiugang Lu
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models. They…
Equivalent-Circuit Thermal Model for Batteries with One-Shot Parameter Identification
Myisha A. Chowdhury, Qiugang Lu
Accurate state of temperature (SOT) estimation for batteries is crucial for regulating their temperature within a desired range to ensure safe operation and optimal performance. Th…
Lithium-ion Battery Capacity Prediction via Conditional Recurrent Generative Adversarial Network-based Time-Series Regeneration
Myisha A. Chowdhury, Gift Modekwe, Qiugang Lu
Accurate capacity prediction is essential for the safe and reliable operation of batteries by anticipating potential failures beforehand. The performance of state-of-the-art capaci…
Transformer-based Capacity Prediction for Lithium-ion Batteries with Data Augmentation
Gift Modekwe, Saif Al-Wahaibi, Qiugang Lu
Lithium-ion batteries are pivotal to technological advancements in transportation, electronics, and clean energy storage. The optimal operation and safety of these batteries requir…
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols
Myisha A. Chowdhury, Saif S. S. Al-Wahaibi, Qiugang Lu
Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement…