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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

arXiv:2608.26111v1cs.AI

TL;DR

BPHM needs methods that address limited labeled data, cross-domain generalization, computational constraints, and interpretability across diverse battery applications. This review surveys LM technologies and applications, organizing progress around data scarcity, robustness, domain knowledge, and automation. It finds advantages in data efficiency, transferability, and new orchestration capabilities, while identifying unresolved requirements for validation, trustworthiness, data collaboration, and deployment.

  • Problem

    Conventional BPHM methods face challenges in computational efficiency, cross-domain generalization, dependence on extensive labeled run-to-failure data, and interpretability.

  • Method

    The review surveys Transformer-based, self-supervised, multimodal, and parameter-efficient LM approaches for BPHM across data scarcity, generalization, interpretability, and system-level automation.

  • Results

    LMs demonstrate advantages in data efficiency and transferability, alongside semantic physics-tool orchestration, physics-constrained decoding, and pipeline automation.

  • Takeaways & Limitations

    The proposed roadmap combines collaborative data ecosystems, hierarchical intelligence validation, physics-informed trustworthiness, and efficient cloud-edge-device deployment.

  • Takeaways & Limitations

    LM deployment remains constrained by task-specific adaptation, domain-specific validation, data ownership and harmonization barriers, and resource-limited hardware.

Abstract

from arXiv · show

Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Conventional BPHM approaches, including physics-based models and task-centric deep learning methods, face challenges in computational efficiency and parameterization, cross-domain generalization, dependence on extensive labeled run-to-failure data, and model interpretability. Recent Large Models (LMs), built upon Transformer architectures and self-supervised pre-training, offer a transformative new paradigm to overcome these long-standing bottlenecks. This review provides the first comprehensive survey of LM applications in BPHM, systematically examining how these models address challenges in the field. We begin by elucidating the foundational technologies enabling LMs, including Transformer architectures, self-supervised learning, large-scale multimodal datasets, and PEFT techniques. We then categorize recent progress along four critical dimensions: mitigating data scarcity, enhancing generalization and robustness, integrating domain knowledge for interpretability, and enabling system-level automation. Despite promising results, significant challenges remain across data accessibility, intelligence validation, trustworthiness, and deployment feasibility. To guide future research, we propose a roadmap focused on building collaborative data ecosystems, validating intelligence for industrial applications, enhancing trustworthiness with physics-informed designs, and enabling efficient on-device deployment. This review establishes a systematic approach to understand and advance LM-driven BPHM, providing researchers and practitioners with essential insights for developing next-generation battery management systems capable of safe, reliable, and autonomous operation throughout battery lifecycles.

1. Introduction

BPHM has evolved from interpretable but computationally intensive physics-based models and task-specific supervised learning toward Transformer-based Large Models. This review surveys the enabling technologies, applications, challenges, and roadmap for LM-driven BPHM.

  • Conventional BPHM: Physics-based models offer interpretability but require solving coupled partial differential equations, limiting real-time BMS deployment.Their parameters correspond to physical properties, supporting extrapolation to unseen operating conditions.
  • Conventional BPHM: Conventional deep learning autonomously extracts battery time-series features through architectures including CNNs and Transformers.Transformers use self-attention to model long-range degradation dependencies more effectively than recurrent approaches.
  • Motivation for Large Models: Task-centric BPHM trains separate supervised models for individual objectives, relying heavily on expensive run-to-failure labels and limited datasets.This constrained framework motivates a shift toward reusable, adaptable large models.
  • Large Models: Large Models use massive parameterization, Transformer architectures, and self-supervised pre-training across language, vision, multimodal, and time-series domains.These model types share a common Transformer-based foundation and can learn general representations from large-scale corpora.
  • Review Scope: This paper presents the first systematic review dedicated to LM applications in BPHM and proposes a roadmap for future research.The review covers enabling technologies, application pathways, critical challenges, and potential advancement opportunities.

2. Key Enablers of Large Models for Battery Intelligence

LM-based battery intelligence is enabled by long-range Transformer attention, self-supervised learning from unlabeled data, multimodal scaling, prompting, and parameter-efficient adaptation. These mechanisms target battery aging’s temporal, multimodal, data-scarce, and heterogeneous nature.

  • Transformer and Self-Attention: Self-attention weighs historical battery measurements to capture degradation patterns across long operational lifetimes.Inputs such as voltage, current, and temperature are transformed into Query, Key, and Value representations.
  • Transformer and Self-Attention: Standard self-attention has O(n^2) complexity, motivating sparse, linear, and hardware-aware mechanisms for long battery sequences.Linear attention targets O(n) complexity, while FlashAttention improves memory throughput.
  • Self-Supervised Learning: Self-supervised learning uses abundant unlabeled BMS data as supervision through pretext tasks such as masked reconstruction, autoregressive prediction, and contrastive learning.These tasks support sensor recovery, future trajectory forecasting, and representation learning from battery signals.
  • Multimodal Data and Scaling: Multimodal LM designs integrate time-series, spectral, visual, and microstructural data to address the heterogeneous scales of battery degradation.Large repositories spanning chemistries and operating profiles can support broader electrochemical representations and zero-shot generalization.
  • Effective Adaptation: Prompting and PEFT adapt pretrained models across battery settings without retraining all parameters.Prompting freezes the model, while LoRA trains lightweight rank-decomposition modules for specific vehicles or manufacturers.

3. Progress in Large-Model-driven BPHM

LM-driven BPHM reframes battery state estimation around universal representations, semantic reasoning, and systematic coverage of four application dimensions. The review organizes progress by data scarcity, generalization and robustness, domain knowledge and interpretability, and system-level automation.

  • Paradigm Shift: LM-driven BPHM shifts from task-specific architectures trained from scratch toward few-shot adaptation, cross-modal transfer, and generative reasoning.The approach grounds general-purpose capabilities in the battery domain.
  • Application Dimensions: Recent contributions are categorized into data scarcity, generalization and robustness, domain knowledge and interpretability, and system-level automation.This four-dimensional structure provides the review’s framework for analyzing progress.

3.1. Addressing Domain Data Scarcity

LMs address BPHM data scarcity by reusing pretrained representations, reducing downstream data needs, and structuring information from battery literature. Public datasets and benchmarks broaden resources but remain fragmented and dominated by controlled laboratory data.

  • Leveraging Pre-trained Knowledge: LMs reduce labeled-data requirements by adapting representations learned during large-scale pretraining to battery prognostics tasks.The review describes distillation, foundation-model fine-tuning, and few-shot classification as complementary strategies.
  • Leveraging Pre-trained Knowledge: 0.72% of the target dataset was used for fine-tuning in the Internet of Batteries framework while achieving state-of-the-art prediction accuracy.
  • Leveraging Pre-trained Knowledge: BatteryGPT used few-shot classification to generalize to unseen defect types without requiring extensive fault-example libraries.
  • Automated Data Extraction and Generation: An LLM-based semantic parser structured specifications and digitized cycling curves for over 8,000 cells from raw scientific PDFs.
  • Automated Data Extraction and Generation: Converting unstructured literature into structured databases can self-enrich BPHM training environments and reduce reliance on physical experimentation.
  • Landscape of Open-Source Battery Datasets and Benchmarks: Public battery datasets vary in scale, chemistry, and cycling protocols, while real-world pack-level field data remain severely underrepresented.
  • Landscape of Open-Source Battery Datasets and Benchmarks: BatteryML unifies preprocessing, feature extraction, and training across seven datasets, while BatteryLife integrates 16 source datasets covering 990 batteries.

3.2. Enhancing Generalization and Robustness

LMs improve BPHM generalization by separating degradation reasoning from specific signal formats and adapting representations across chemistries and conditions. Their plasticity mechanisms also support adaptation to evolving battery data distributions, while federated LM learning remains unexplored.

  • Cross-Domain and Cross-Chemistry Generalization: Traditional data-driven BPHM models often degrade on unseen chemistries or operating conditions, motivating more generalizable LM foundations.
  • Cross-Domain and Cross-Chemistry Generalization: Prompt-learning strategies reformat numerical battery data into textual or tokenized prompts for SOC and SOH estimation across LFP, NMC, and temperature ranges.
  • Cross-Domain and Cross-Chemistry Generalization: Semantic-space representations decouple degradation logic from cell chemistry, supporting chemistry-agnostic adaptation through minimal prompt engineering or PEFT.
  • Lifelong and Adaptive Learning: Conventional train-then-deploy methods produce static models despite continuous battery aging and changing operational contexts.
  • Lifelong and Adaptive Learning: Online student-model updates can learn from new unlabeled data streams as battery health and data distributions evolve.
  • Lifelong and Adaptive Learning: In-context learning and efficient fine-tuning allow LM systems to co-evolve with battery degradation and address long-term distribution shifts.
  • Lifelong and Adaptive Learning: Federated pre-training or fine-tuning of foundation models across heterogeneous battery sources remains unexplored.

3.3. Integrating Domain Knowledge and Interpretability

LM-based BPHM integrates electrochemical knowledge and structured external evidence to constrain predictions, verify reasoning, and improve interpretability. These approaches shift diagnosis toward physically grounded and evidence-based reasoning.

  • Physics-Informed Reasoning: Conventional deep learning can produce statistically plausible predictions that are electrochemically inconsistent, motivating physics-integrated LM designs.
  • Physics-Informed Reasoning: LLMs can translate natural-language queries into simulation parameters and use electrochemical models as tools for reasoning verification.
  • Physics-Informed Reasoning: Li et al.'s framework closes a plan-execute-validate loop by invoking external electrochemical simulations to verify predictions.
  • Knowledge-Enhanced and Multi-Modal Reasoning: Knowledge graphs and multimodal alignment guide diagnosis through causal fault chains and visual evidence rather than unsupported statistical correlations.
  • Knowledge-Enhanced and Multi-Modal Reasoning: Retrieving structured knowledge and grounding explanations in visual or spectral evidence mitigates hallucination risks and enhances industrial trustworthiness.

3.4. Enabling System-Level Automation and Control

LMs extend BPHM from prediction toward autonomous decision-making and research-pipeline automation. They connect diagnostics with human constraints, multi-objective control, and technical workflow orchestration.

  • Autonomous Control: Conventional methods typically output numerical indicators such as SOH, whereas LMs combine diagnostics with reasoning and planning for intelligent control.
  • Autonomous Control: LLM-BAS interprets diagnostic states alongside electricity prices and user preferences to formulate charging schedules.
  • Autonomous Control: Processing natural-language constraints and multi-objective goals can turn the BMS into a semantic decision-maker for personalized energy management.
  • Research-Pipeline Automation: LMs automate BPHM research tasks including data preprocessing, feature engineering, model selection, and hyperparameter optimization.
  • Research-Pipeline Automation: An LLM-orchestrated SOH pipeline guides preprocessing, feature ranking, and model tuning through structured prompts.
  • Research-Pipeline Automation: Zero-shot time-series model selection and autonomous Wavelet Packet Transformation optimization demonstrate automation beyond battery-specific pipelines.
  • Research-Pipeline Automation: Automating modeling intricacies may let researchers focus on hypothesis generation and accelerate discovery of diagnostic biomarkers and management algorithms.

3.5. Comparative Summary of Representative Works

Table 2 compares representative LM-based BPHM works across model, application, data, supervision, computation, deployment, and reported advantage. Current studies show data-efficiency and transferability benefits, but limited deployment evidence and absent controlled same-budget specialist comparisons.

  • Table 2 compares representative works by model type, target application, data scale, supervision level, computational cost, deployment setting, and reported advantage.
  • Most surveyed works remain at laboratory scale and use GPU-server deployment.
  • Pre-trained LMs show advantages in data efficiency and transferability, particularly under low-data regimes.
  • LMs provide semantic orchestration of physics-based tools, physics-constrained decoding, and pipeline automation beyond conventional deep learning capabilities.
  • Controlled comparisons against well-tuned specialists using the same data budget are absent, while evidence for deployment-ready edge inference remains scarce.

4. Current Challenges in Building LMs for BPHM

LM-driven BPHM faces challenges spanning data availability, scientific validity, trustworthiness, and deployment feasibility. Although pre-training offers advantages, current systems still require task-specific adaptation and substantial validation before industrial use.

  • Paradigm-level limitations: Pre-training reduces labeled-data requirements but does not remove task-specific labels, protocols, failure definitions, or deployment constraints.These requirements include measured capacity, verified fault modes, manufacturer-specific reference tests, application-specific end-of-life thresholds, latency, interpretability, and safety certification.
  • Data challenges: Large-scale, diverse battery corpora remain scarce because operational data are proprietary, fragmented, and difficult to harmonize across manufacturers.Differences in sensor noise, missing values, formats, sampling rates, and logging protocols introduce reliability risks and make data preparation resource-intensive.
  • Intelligence validation: LM intelligence observed in open-domain tasks has not been systematically validated under controlled, physics-constrained industrial BPHM conditions.Battery applications require causal reasoning, physical consistency, and reliable reasoning, which larger parameter counts do not guarantee.
  • Generalization and robustness: Generalization does not ensure superiority over carefully engineered specialists on individual battery tasks.Physics-informed neural networks or LSTMs may still outperform a generalist LM for a single chemistry or well-characterized degradation dataset.
  • Trustworthiness: LMs create trustworthiness risks through opaque representations, absent inherent electrochemical understanding, and physically unsupported hallucinated outputs.These issues complicate the explanation and certification of predictions in safety-critical research and industrial diagnostics.
  • Deployment challenges: Resource-constrained BMS hardware conflicts with LM memory, power, computation, and latency requirements.Dynamic events such as fast charging, thermal runaway, or sudden faults may require millisecond-level decisions, while cloud inference adds latency and bandwidth demands.

5. Solutions and Future Roadmap

The roadmap organizes advancement of LM-driven BPHM around data accessibility, model intelligence, trustworthiness, and deployment, with collaborative data ecosystems and rigorous validation as key directions. It also proposes privacy-preserving collaboration, synthetic data augmentation, hybrid model architectures, and standardized industrial benchmarks.

  • The roadmap targets four challenge areas: data accessibility, model intelligence, trustworthiness, and deployment.
  • Data accessibility: Pre-competitive industrial consortia and shared datasets are proposed to aggregate diverse battery data beyond fragmented proprietary laboratory collections.The roadmap points to field telemetry, shared databases, and open datasets as precedents for collaborative data ecosystems.
  • Data accessibility: Federated Learning enables organizations to jointly train global battery models without sharing raw sensitive data.Differential privacy, secure aggregation, preprocessing harmonization, and contribution-weighted aggregation address privacy, compatibility, and governance concerns.
  • Data accessibility: Generative augmentation and battery digital twins could synthesize realistic data for rare faults and underrepresented operating conditions.These approaches are intended to fill gaps in real-world datasets and improve model generalization and robustness.
  • Model intelligence: LM validation should move beyond empirical performance toward systematic cognitive-effectiveness assessment using interpretable metrics and standardized industrial benchmarks.The proposed benchmark spans battery chemistries, degradation pathways, and noise levels with unified tasks, prompts, and metrics.
  • Model intelligence: Small Model Augmentation assigns high-level memory and orchestration to LMs while lightweight or physics-based models perform fine-grained numerical reasoning.

5.3. Enhancing Trustworthiness: Physics-Guided, Secure, Safe, and Adaptive LMs

The trustworthiness roadmap combines physics-guided modeling, uncertainty quantification, security defenses, and adaptive learning. Its objective is to constrain LM behavior across the data, training, model, and deployment pipeline.

  • Trustworthy BPHM LMs require a multi-layered, domain-specific defense strategy rather than generic safeguards.
  • Physics-guided interpretability: Physics-informed architectures embed electrochemical principles into model training to shift interpretability toward physically grounded diagnostics.Physics-informed neural networks incorporate electrochemical partial differential equations into the loss function.
  • Uncertainty and safety: Probabilistic outputs, uncertainty quantification, retrieval grounding, and constrained generation are proposed to mitigate hallucination and physically implausible predictions.
  • Security: Security should use defense in depth, including telemetry validation and anomaly detection before data enter the training pipeline.
  • Security: Federated training requires differential privacy and robust aggregation to limit information leakage and reduce vulnerability to malicious or compromised updates.
  • Safety: BPHM-specific adversarial training and physics-constrained output guardrails address realistic sensor perturbations and electrochemically invalid predictions.Relevant perturbations include sensor drift, quantization error, temperature-dependent bias, and impedance spectroscopy artifacts.
  • Adaptation: Test-Time Training enables continual adaptation to battery aging, while on-device PEFT is intended to make online learning feasible on constrained BMS hardware.

5.4. Enabling Industrial Deployment: Efficient and Scalable Model Integration

Industrial deployment requires compact models and a hierarchical cloud-edge-device architecture that matches functions to latency, interpretability, and maturity constraints. The roadmap supports real-time estimation and optimization at the edge while reserving safety-critical control for future or hybrid approaches.

  • Industrial BPHM deployment is constrained by LM computational overhead, latency, interpretability, and safety requirements.
  • Model compression: Hierarchical distillation, quantization, and pruning transfer cloud-model capabilities into lightweight students for real-time edge inference.Quantization lowers parameter precision, while pruning removes redundant weights or neurons to reduce computation and memory.
  • Distributed architecture: A cloud-edge-device hierarchy assigns each function to the tier matching its latency tolerance, interpretability requirements, and technological maturity.
  • Tier 1: GPU-server analytics can support fleet degradation modeling, second-life what-if simulation, long-term lifetime prediction, and offline root-cause analysis.These functions tolerate seconds-to-minutes latency and are described as short-term deployment opportunities.
  • Tier 2: Edge-distilled inference targets millisecond-level SOH and SOC estimation, adaptive charging optimization, and online anomaly detection.This tier depends on successful knowledge distillation and model compression and is characterized as a medium-term target.
  • Tier 3: Safety-critical functions such as thermal-runaway prevention and sub-millisecond fault response are not yet feasible for direct LM deployment.The stated barriers are insufficient real-time guarantees, opaque decisions, and absent formal verification; hybrid systems are proposed as a transition.
  • Distributed architecture: Modular distribution places global inference and long-term learning on GPU servers while latency-sensitive predictions run at edge nodes.
  • Future hardware: Future Tier 3 deployment depends on hardware-algorithm co-design, including energy-efficient architectures such as neuromorphic and photonic computing.

6. Conclusions

The review synthesizes LM applications in BPHM across enabling technologies and four application dimensions, finding the clearest advantages in low-data and cross-domain settings. It proposes collaborative data, validation, physics-informed trustworthiness, and hierarchical deployment while emphasizing that domain adaptation and safety validation remain necessary.

  • The review is presented as the first systematic survey of LM applications in BPHM.
  • Its synthesis covers Transformer architectures, self-supervised learning, multimodal data fusion, PEFT, and four dimensions of LM-driven BPHM progress.The four dimensions are data scarcity, generalization, domain-knowledge integration, and system-level automation.
  • LMs show their clearest advantages in low-data and cross-domain settings while supporting physics-constrained reasoning and agentic decision-making.
  • LMs are framed as shared infrastructure rather than universal replacements for task-specific models, requiring domain adaptation, safety validation, and interpretability guarantees.
  • The roadmap calls for federated and consortium-based data ecosystems, hierarchical intelligence validation, physical principles for trustworthiness, and cloud-edge-device deployment.
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