HybridoNet-Adapt: A domain-adapted framework for accurate lithium-ion battery RUL prediction.

ABSTRACT: Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is critical for safe, reliable Battery Health Management in diverse operating conditions. Existing RUL models often fail to generalize when test data diverge from the training distribution. To address this, we introduce HybridoNet-Adapt, a domain-adaptive RUL prediction framework that explicitly bridges the gap between labeled source and unlabeled target domains. During training, we minimize the Maximum Mean Discrepancy (MMD) between feature distributions to learn domain-invariant representations. Simultaneously, we employ two parallel predictors-one tailored to the source domain and one to the target domain-and balance their outputs via two learnable trade-off parameters, enabling the model to dynamically weig

SUBMITTER: Tran K 

PROVIDER: S-EPMC12578170 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature