Transfer learning has emerged as a powerful approach to address the challenge of limited experimental data in battery research. By leveraging knowledge from pre-trained models or related domains, researchers can improve the accuracy and generalizability of predictions for battery performance, degradation, and material properties. This is particularly valuable given the high cost and time-intensive nature of battery experiments. The following sections explore key transfer learning strategies, including the use of pre-trained models, domain adaptation techniques, and cross-chemistry validation.

Pre-trained models serve as a foundational tool for transfer learning in battery research. These models are initially trained on large datasets from related fields, such as materials science, electrochemistry, or even non-battery-specific datasets like image or text corpora, before being fine-tuned for battery applications. For example, a neural network pre-trained on general electrochemical data, such as cyclic voltammetry or impedance spectra from various materials, can be adapted to predict the cycling behavior of a new battery chemistry with minimal additional data. The key advantage lies in the model’s ability to extract high-level features—such as reaction kinetics or diffusion characteristics—that are transferable across different systems. Fine-tuning involves retraining only the final layers of the model on a smaller battery-specific dataset, preserving the learned representations from the broader domain. This approach has been successfully applied to predict state-of-health (SOH) in lithium-ion batteries, where models pre-trained on aging data from one cell type achieved high accuracy when adapted to another with limited calibration.

Domain adaptation techniques are critical when source and target datasets exhibit distributional shifts, such as differences in battery chemistries, operating conditions, or measurement protocols. These methods align the feature spaces of disparate datasets to enable knowledge transfer. One common strategy is feature-based adaptation, where the model learns to map data from both domains into a shared representation space. For instance, a model trained on lithium-ion battery degradation data can be adapted to sodium-ion systems by minimizing the discrepancy between their feature distributions. Techniques like Maximum Mean Discrepancy (MMD) or adversarial training are often employed to achieve this alignment. Another approach is instance-based adaptation, where weights are assigned to source data points based on their relevance to the target domain. This is particularly useful when transferring knowledge between batteries with different electrode materials but similar failure mechanisms. Domain adaptation has shown promise in predicting capacity fade, where models trained on one chemistry successfully generalized to others after alignment.

Cross-chemistry validation is essential to ensure the robustness of transfer learning approaches. Battery chemistries vary significantly in their underlying mechanisms, making it challenging to apply models trained on one system to another without validation. A systematic approach involves evaluating model performance across multiple chemistries, such as transitioning from lithium cobalt oxide (LCO) to lithium iron phosphate (LFP) or solid-state batteries. For example, a model predicting voltage profiles under varying currents might be validated by testing its accuracy on both organic liquid and solid-state electrolytes. Key metrics include the mean absolute error (MAE) for regression tasks or classification accuracy for failure prediction. Cross-validation helps identify which features are truly transferable and which are chemistry-specific. Research has demonstrated that certain degradation patterns, like lithium plating or SEI growth, exhibit universal characteristics that can be leveraged across chemistries, while others require domain-specific adjustments.

The integration of physics-based models with transfer learning further enhances generalizability. Hybrid approaches combine mechanistic equations—such as those from pseudo-two-dimensional (P2D) models or equivalent circuit models—with data-driven techniques. For instance, a neural network can be pre-trained on synthetic data generated by physics-based simulations, then fine-tuned on experimental data. This leverages the robustness of physical principles while adapting to real-world variability. Such methods have been applied to predict temperature-dependent performance, where simulations provide a broad baseline and experimental data refine the predictions for specific cell designs. The inclusion of physical constraints also mitigates the risk of unphysical predictions when transferring across domains.

Challenges remain in selecting appropriate source domains and avoiding negative transfer, where irrelevant knowledge degrades performance. Not all pre-trained models or source datasets are equally useful; the choice depends on the similarity of underlying mechanisms. For example, transferring knowledge between lithium-sulfur and lithium-ion batteries may require careful feature selection due to differences in polysulfide shuttling versus intercalation. Techniques like transferability metrics or task similarity assessments can guide the selection process. Additionally, the quality and diversity of the source data are critical—models trained on narrow or noisy datasets may propagate biases to the target domain.

Practical applications of transfer learning in battery research span multiple areas. In material discovery, models trained on existing databases of electrode properties can accelerate the screening of novel compositions with limited experimental data. For manufacturing, knowledge transferred from one production line can optimize processes for another, reducing the need for extensive trial-and-error. In operational management, predictive models for one battery pack configuration can be adapted to others, improving state estimation and lifespan prediction. The scalability of these methods makes them particularly valuable for emerging technologies like solid-state or sodium-ion batteries, where experimental datasets are sparse.

Future directions include the development of standardized benchmarks for evaluating transfer learning performance across battery applications. Open datasets and collaborative efforts will be crucial to establish robust protocols. Advances in self-supervised learning may also play a role, enabling models to pre-train on unlabeled data from diverse battery systems before fine-tuning on smaller labeled datasets. The combination of transfer learning with other AI techniques, such as reinforcement learning for adaptive testing or generative models for synthetic data augmentation, holds additional promise.

In summary, transfer learning offers a viable path to overcome data limitations in battery research by strategically reusing knowledge from related domains. Pre-trained models, domain adaptation, and rigorous cross-validation enable accurate predictions even with sparse experimental data. As battery technologies continue to diversify, these approaches will become increasingly vital for accelerating innovation and reducing reliance on costly trial-and-error experimentation.