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

## TL;DR

This paper introduces HybridoNet-Adapt, a new framework that improves the accuracy of predicting lithium-ion battery life under different conditions.

## Contribution

The novel use of Maximum Mean Discrepancy and hybrid prediction with domain-specific predictors enhances RUL prediction under domain shifts.

## Key findings

- HybridoNet-Adapt reduces RMSE by up to 152 cycles on battery datasets compared to non-adaptive models.
- The framework outperforms baselines like XGBoost and Elastic Net in domain-shifted scenarios.
- Combining LSTM, attention, and Neural ODE blocks with domain adaptation improves robustness.

## 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 weight domain-specific insights. Our architecture couples this adaptation strategy with LSTM, multi-head attention, and Neural ODE blocks for deep temporal feature extraction, but its core novelty lies in the MMD-based alignment and hybrid prediction mechanism. On two large, publicly available battery datasets, HybridoNet-Adapt consistently outperforms non-adaptive baselines (Structural Pruning, Multi-Time Scale Feature Extraction Hybrid model, XGBoost, Elastic Net), archiving an RMSE reduction of up to 152 cycles under domain shifts. These results demonstrate that incorporating domain adaptation into RUL modeling substantially enhances robustness and real-world applicability.