Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap
This is the first comprehensive survey examining Large Model (LM) applications in Battery Prognostics and Health Management (BPHM), addressing long-standing challenges in computational efficiency, cross-domain generalization, data dependency, and interpretability. Foundational technologies enabling LMs in BPHM include Transformer architectures, self-supervised learning, large-scale multimodal datasets, and Parameter-Efficient Fine-Tuning (PEFT) techniques. Recent progress is categorized along fo
Analysis
TL;DR
- This is the first comprehensive survey examining Large Model (LM) applications in Battery Prognostics and Health Management (BPHM), addressing long-standing challenges in computational efficiency, cross-domain generalization, data dependency, and interpretability.
- Foundational technologies enabling LMs in BPHM include Transformer architectures, self-supervised learning, large-scale multimodal datasets, and Parameter-Efficient Fine-Tuning (PEFT) techniques.
- Recent progress is categorized along four dimensions: mitigating data scarcity, enhancing generalization and robustness, integrating domain knowledge for interpretability, and enabling system-level automation.
- Significant remaining challenges span data accessibility, intelligence validation, trustworthiness, and deployment feasibility in real-world industrial settings.
- A future research roadmap is proposed focusing on collaborative data ecosystems, industrial intelligence validation, physics-informed trustworthiness, and efficient on-device deployment.
Why It Matters
This review establishes a critical bridge between the rapidly advancing field of Large Models and the domain-specific challenge of battery health management, which is essential for electric vehicles, grid storage, and consumer electronics. For AI practitioners, it provides a systematic framework for understanding how Transformer-based architectures and self-supervised pre-training can overcome traditional limitations in prognostics and health management. The proposed roadmap offers actionable guidance for researchers aiming to develop next-generation, autonomous battery management systems.
Technical Details
- Transformer-based architectures form the backbone of LM applications in BPHM, leveraging self-attention mechanisms to capture complex temporal and spatial dependencies in battery degradation patterns across diverse operating conditions.
- Self-supervised learning enables models to pre-train on large-scale unlabeled multimodal battery data (voltage, current, temperature, impedance spectra), reducing dependence on expensive labeled run-to-failure datasets.
- Parameter-Efficient Fine-Tuning (PEFT) techniques such as LoRA and adapter modules allow domain adaptation of pre-trained LMs to specific battery chemistries and formats without full model retraining, addressing computational and parameterization bottlenecks.
- Multimodal data integration combines electrochemical, thermal, and operational data streams into unified representations, improving cross-domain generalization and robustness across electric vehicles, grid storage, and consumer electronics applications.
- Physics-informed design integrates domain knowledge from battery electrochemistry and degradation physics into LM architectures, enhancing interpretability and trustworthiness of predictions for safety-critical applications.
Industry Insight
- The convergence of Large Models with BPHM represents a paradigm shift from task-centric, narrowly specialized models toward unified, generalizable frameworks—organizations investing in LM-driven battery management will gain significant competitive advantages in predictive accuracy and deployment flexibility across diverse battery types and operating environments.
- Data accessibility remains a critical bottleneck; building collaborative, cross-industry data ecosystems with standardized protocols will be essential to unlock the full potential of self-supervised and foundation model approaches in battery health management.
- Physics-informed Large Models that combine data-driven learning with domain constraints offer the most viable path toward trustworthy, deployable systems—prioritizing hybrid architectures over purely data-driven approaches will accelerate industrial adoption while maintaining the safety standards required for energy storage and electric vehicle applications.
Disclaimer: The above content is generated by AI and is for reference only.