Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap
Jiale Liu, Huan Wang, Weicheng Wang, Rong Zhu, Qiqi Wang, Min Xie
Published in Renewable and Sustainable Energy Reviews | arXiv:2608.26111 [cs.AI]
Abstract
Battery Prognostics and Health Management (BPHM) is essential for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Traditional BPHM methods—ranging from physics-based models to task-specific deep learning—are often limited by computational inefficiency, complex parameterization, poor cross-domain generalization, heavy reliance on extensive labeled run-to-failure data, and a lack of interpretability. Recent advances in large models (LMs), built on Transformer architectures and self-supervised pretraining, present a transformative opportunity to overcome these long-standing challenges. This review offers the first comprehensive survey of LM applications in BPHM, systematically examining how these models address key issues in the field. We first introduce the foundational technologies enabling LMs, including Transformer architectures, self-supervised learning, large-scale multimodal datasets, and parameter-efficient fine-tuning (PEFT). We then categorize current progress across four critical dimensions: mitigating data scarcity, enhancing generalization and robustness, embedding domain knowledge for interpretability, and enabling system-level automation. Despite promising outcomes, significant challenges remain in data accessibility, intelligence validation, trustworthiness, and deployment feasibility. To guide future work, we propose a roadmap centered on building collaborative data ecosystems, validating intelligence for industrial applications, improving trustworthiness through physics-informed designs, and enabling efficient on-device deployment. This review establishes a systematic framework for understanding and advancing LM-driven BPHM, providing researchers and practitioners with essential insights for developing next-generation battery management systems that support safe, reliable, and autonomous operation throughout battery lifecycles.
1. Introduction
The rapid electrification of transportation and the expansion of renewable energy storage have placed battery systems at the heart of modern energy infrastructure. Ensuring their safe, reliable, and cost-effective operation is paramount. Battery Prognostics and Health Management (BPHM) addresses this need by monitoring, predicting, and managing battery health and remaining useful life (RUL). However, conventional BPHM approaches—whether physics-based or purely data-driven—face persistent limitations: high computational costs, difficulty in generalizing across different cell chemistries and operating conditions, heavy dependence on labeled run-to-failure data, and limited interpretability for safety-critical decisions.
In response, the field has witnessed a paradigm shift toward large models (LMs)—AI systems built on Transformer architectures and pretrained via self-supervised learning on massive datasets. These models, originally developed for natural language processing and computer vision, have demonstrated remarkable capabilities in representation learning, few-shot adaptation, and cross-domain transfer. By 2026, LMs have become a driving force in scientific and engineering applications, including battery management, where they promise to overcome the bottlenecks that have hindered traditional approaches.
This review provides the first comprehensive and systematic survey of LM applications in BPHM. We examine how LMs address core challenges, highlight recent advances, and propose a roadmap for future research and deployment.
2. Background and Motivation
2.1 Challenges in Conventional BPHM
Traditional BPHM methods can be broadly categorized into physics-based models and task-centric deep learning. Physics-based models, such as electrochemical and equivalent-circuit models, offer mechanistic insights but are computationally intensive and require accurate parameterization for each specific battery system. They also struggle to adapt to new chemistries or dynamic operating conditions.
On the other hand, deep learning methods—including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants—have shown strong predictive performance on specific tasks like state-of-charge (SOC) estimation or RUL prediction. However, they typically require large amounts of labeled run-to-failure data, which is scarce and expensive to obtain. Moreover, these models often fail to generalize across different battery types, degradation modes, or environmental conditions, and their "black-box" nature hinders trust in safety-critical applications.
2.2 The Emergence of Large Models
Large models, characterized by their scale (often billions of parameters) and architecture (primarily Transformers), have revolutionized machine learning. Key enablers include self-supervised pretraining on vast unlabeled datasets, which allows the model to learn general representations without explicit labels; attention mechanisms that capture long-range dependencies; and parameter-efficient fine-tuning (PEFT) techniques, which adapt pretrained models to specific tasks with minimal computational overhead.
In 2026, LMs are increasingly applied to scientific domains, and battery research is no exception. Their ability to handle heterogeneous data (e.g., time-series sensor data, text from manuals, images from X-ray diagnostics) and to generalize from limited labeled samples makes them particularly attractive for BPHM.
3. Foundational Technologies for Large Models in BPHM
To appreciate how LMs can be leveraged for BPHM, it is essential to understand the core technologies that underpin their capabilities:
- Transformer Architectures: The self-attention mechanism enables LMs to process sequential data (like time-series voltage/current profiles) while capturing both short-term fluctuations and long-term dependencies. Variants such as the Vision Transformer (ViT) and Time-Series Transformer have been adapted for battery data.
- Self-Supervised Learning (SSL): SSL allows LMs to learn rich representations from unlabeled sensor data by solving pretext tasks (e.g., masked signal modeling, contrastive learning). This significantly reduces the need for expensive labeled run-to-failure datasets.
- Large-Scale Multimodal Datasets: LMs can integrate diverse data types—electrical measurements, thermal images, chemical composition data, and textual maintenance logs—to build more holistic models of battery health.
- Parameter-Efficient Fine-Tuning (PEFT): Techniques like LoRA (Low-Rank Adaptation) and adapters allow efficient fine-tuning of large pretrained models on downstream BPHM tasks, reducing computational costs and avoiding catastrophic forgetting.
4. Applications of Large Models in BPHM
We categorize recent advances along four critical dimensions that address the primary pain points of conventional approaches:
4.1 Mitigating Data Scarcity
One of the most significant advantages of LMs is their ability to perform well with limited labeled data, thanks to pretraining and few-shot learning. For BPHM, this means:
- Transfer Learning Across Battery Cells: Pretrained LMs on a large corpus of battery data from various chemistries can be fine-tuned for a new cell type with minimal samples.
- Data Augmentation via Generative Models: LMs, particularly those with generative capabilities, can synthesize realistic degradation trajectories, enriching sparse run-to-failure datasets.
- Zero-Shot Prediction: With sufficient pretraining, LMs can make reasonable RUL predictions for batteries they have never seen, based on textual descriptions or minimal measurements.
4.2 Enhancing Generalization and Robustness
LMs exhibit superior generalization to unseen conditions and robustness to noisy inputs, which is critical for real-world battery operation:
- Cross-Domain Adaptation: LMs can adapt to changes in operating temperature, load profiles, and even electrode materials without complete retraining.
- Anomaly Detection: By learning the normal operating envelope, LMs can flag early anomalies (e.g., internal short circuits) more reliably than task-specific models.
- Robustness to Sensor Noise: The attention mechanism inherently tolerates missing or corrupted data points, making LMs resilient to sensor failures.
4.3 Integrating Domain Knowledge for Interpretability
Safety-critical applications demand interpretability. LMs facilitate this by:
- Physics-Informed Pretraining: Incorporating governing equations or physical constraints into the training objective ensures predictions respect known battery behavior.
- Attention-Based Diagnostics: Attention weights can highlight which input features (e.g., voltage dips at specific cycles) drove a prediction, offering intuitive insights for engineers.
- Natural Language Explanations: Some LMs can generate textual explanations of their predictions, bridging the gap between AI and human operators.
4.4 Enabling System-Level Automation
Beyond component-level health monitoring, LMs enable higher-level automation:
- Fleet Management: LMs can analyze data from thousands of electric vehicles to optimize charging strategies and predict maintenance needs across an entire fleet.
- Battery Passport Integration: LMs can align with emerging regulatory frameworks (e.g., the EU Battery Passport) by ingesting lifecycle data in multimodal formats, ensuring compliance and transparency.
- Autonomous Decision-Making: By integrating BPHM with battery management systems (BMS), LMs can trigger protective actions (e.g., derating) or recommend service interventions without human intervention.
5. Remaining Challenges and Open Issues
Despite the potential, LM-driven BPHM faces several hurdles:
- Data Accessibility and Privacy: Battery data is often proprietary and sensitive. Building shared datasets while preserving privacy (e.g., via federated learning) remains a challenge.
- Intelligence Validation: Proving the reliability of LMs in safety-critical industrial contexts requires rigorous validation frameworks, which are still under development.
- Trustworthiness and Uncertainty Quantification: LMs often lack well-calibrated uncertainty estimates, which is problematic for risk-averse applications.
- Deployment Feasibility: Large models are computationally heavy; deploying them on embedded systems within vehicles or grid controllers is challenging due to memory and power constraints.
6. Future Roadmap
To realize the full potential of LMs in BPHM, we propose the following research directions:
- Building Collaborative Data Ecosystems: Establish shared, anonymized battery datasets with standardized formats and benchmarks. Leverage federated learning to enable collaborative model training without compromising proprietary data.
- Validating Intelligence for Industrial Applications: Develop robust evaluation protocols that include edge-case testing, adversarial scenarios, and real-world pilot deployments. Certification standards for AI-based BPHM should be studied.
- Enhancing Trustworthiness with Physics-Informed Designs: Integrate physics-based constraints into LM architectures and training to improve physical consistency and interpretability, and to provide uncertainty bounds.
- Enabling Efficient On-Device Deployment: Explore model compression techniques (quantization, pruning, knowledge distillation) and novel hardware accelerators to make LMs runnable on edge devices in real time.
7. Conclusion
Large models present a paradigm shift for battery prognostics and health management, addressing the limitations of conventional approaches in data efficiency, generalization, interpretability, and automation. Our review provides a systematic overview of the current landscape, highlighting both promise and challenges. By following the proposed roadmap, researchers and practitioners can develop next-generation battery management systems that are safer, more reliable, and more autonomous, ultimately accelerating the transition to a sustainable energy future.
via ArXiv AI