Degradation-Aware Design Framework

• Degradation-aware design frameworks are explicit approaches that integrate degradation modeling into optimization and control systems to maintain robust performance amid evolving degradation mechanisms.
• They combine physics-informed models, data-driven predictors, and multi-objective optimization to balance operational goals with degradation costs and uncertainties.
• Applications span battery management, power grid scheduling, image restoration, sensor fusion, and circuit design, demonstrating measurable improvements in efficiency and system longevity.

A degradation-aware design framework is an explicit architectural and algorithmic approach that integrates degradation modeling, perception, or guidance into the algorithms, optimizations, and control logics of complex technical systems. The central goal is to maintain optimal or robust system performance under dynamically evolving or heterogeneous degradation mechanisms, rather than assuming high-quality, unchanging, or idealized conditions. These frameworks are operationalized across domains from battery management and power grid optimization to image restoration, sensor fusion, and electronic circuit design. They frequently combine domain-specific physical models, data-driven predictors, multi-objective optimization, and dynamic or modular control schemes, explicitly balancing end-goal metrics with degradation progression costs or uncertainties.

1. Mathematical Formalism and Core Principles

At the heart of degradation-aware frameworks lies explicit integration of degradation variables or models into an optimization or learning loop. For example, in battery grid scheduling, decision variables encompass operational setpoints $p_{t,b}^{\mathrm{BESS}^+}$, $p_{t,b}^{\mathrm{BESS}^-}$, accompanied by a battery-wear cost $C^D$ parameterized by a degradation model that links the cumulative depth-of-discharge (DoD) to cycle life and thus to an amortized investment cost (Pamshetti et al., 9 Jul 2024). The general schema is:

• Multi-objective optimization: $\min\ F_1$(cost/degradation) subject to $F_2$(performance/deviation) $\le \epsilon$ and all constraints (AC power flow, SoC).
• Degradation models: Empirically or physically motivated cost functions of operational history (e.g., SEI growth, DoD stress, usage frequency) directly enter the objective or constraints.
• Decision-theoretic trade-off: Solutions are evaluated along their Pareto front between operational cost and rate or risk of system degradation.

In imaging, degradation-aware frameworks combine explicit degradation embedding or classification (contrastive learned latent space, scalar regression, or prompt extraction) with modular or conditional restoration pathways (e.g., mixture-of-expert blocks, prompt-driven gate modulation). These enable the system to adaptively allocate representation capacity and restoration effort as a function of the assessed degradation (Liu et al., 24 Apr 2025, Zamfir et al., 24 May 2024).

In control and operation of dynamic systems (e.g., BSS MPC), real-time receding-horizon optimization integrates a high-fidelity state-space or surrogate degradation model, permitting explicit co-optimization of profit, service, and cumulative degradation, with switching logic and constraints reflecting discrete event logistics (Li et al., 9 Oct 2025).

2. Degradation Modeling Strategies

Modeling degradation is domain-dependent but universally central to the framework:

• Physics-informed models: For batteries, electrochemical kinetic models track SEI growth, lithium plating, and loss of active material or electrolyte. These are reduced into ODE or PDE surrogates for tractable optimization or inference (Nazeeruddin et al., 17 Dec 2025, Li et al., 9 Oct 2025, Zhang et al., 24 Jan 2025).
• Empirical/statistical surrogates: Data-driven estimation using regression, histogram aggregation (2D current–voltage bins in BMS diagnosis), or Kriging/meta-models in MPC.
• Feature learning and embedding: In imaging, learned degradation embeddings (via contrastive pretraining or prompt-based representations in vision-LLMs) encode the severity and type of distortion or weather phenomenon (Bi et al., 31 Mar 2024, Liu et al., 24 Apr 2025, Li et al., 16 Nov 2025).
• Degradation-aware routing: Modular neural architectures use the degradation embedding or classifier to selectively activate restoration experts or adapt processing trajectories (e.g., Mixture-of-Experts, low-rank subspaces, prompt-state-space models) for computational and representational efficiency (Liu et al., 24 Apr 2025, Zamfir et al., 24 May 2024, Li et al., 16 Nov 2025).

3. Solution Algorithms and Decision Methodologies

Degradation-aware frameworks are characterized by hybrid and multi-objective algorithmic pipelines:

• Pareto Front Exploration: In power grid battery scheduling, the $\epsilon$-constraint method generates a family of non-inferior solutions by sweeping network-performance bounds and optimizing monetary+degradation cost, followed by fuzzy-logic membership for final selection (Pamshetti et al., 9 Jul 2024).
• Modular and Iterative Enhancement: For underwater imaging, an iterative pipeline first classifies the dominant degradation type in each step, applies the specialist network, and repeates until a "clean" state is reached or maximum iteration (Singh et al., 26 Jun 2024).
• Guided fusion/joint domain processing: Dual-domain (frequency/spatial), multi-modal (visible/infrared), or decomposition-based network designs inject degradation cues (prompt, embedding, or VLM-provided prior) at multiple hierarchical levels, enabling both global semantic alignment and local artifact correction (Zhang et al., 5 Sep 2025, Li et al., 16 Nov 2025).
• Adaptive control/allocation: Dynamic capacity allocation via learned routing (DaLe, DPMambaIR) or expert selection, with auxiliary classification/regression loss to ground the routing in degradation labels or severity (Zamfir et al., 24 May 2024, Liu et al., 24 Apr 2025).
• Hybrid loss objectives: Composite loss functions balance structure, color, perceptual, and distributional fidelity with explicit terms weighting for degradation-aware objectives (Huang et al., 30 Jul 2025, Xiong et al., 7 Apr 2025).

4. Applications and Case Studies Across Domains

The degradation-aware framework paradigm is multi-disciplinary; key application domains and instantiations include:

Domain	Degradation Modeled	Framework/Algorithm	Quantitative Outcome
Grid BESS	Battery cycle fade, DoD	Bach: bi-objective NLP+fuzzy logic	Reduces losses by 16.1%, degradation by 56.3% (Pamshetti et al., 9 Jul 2024)
Battery BMS	LLI, LAM_NE, LAM_PE	DeepHPM+XGBoost, digital twin	High phase detection, online prognosis (Zhang et al., 24 Jan 2025)
BSS operations	Capacity fade, SEI growth	BSS-MPC: Kriging MINLP MPC	24% higher profit, 30% lower fade (Li et al., 9 Oct 2025)
Li-ion Design	Li/porosity/electrolyte	Reservoir-based DFN/Mass-balance	+30% life from 1% electrolyte/5% porosity (Nazeeruddin et al., 17 Dec 2025)
Imaging (Rest.)	Noise, blur, weather	DPMambaIR, DA²Diff, DeeDSR, DACA-Net	SOTA PSNR/SSIM/perceptual scores across datasets
Image Fusion	Haze, rain, snow	MdaIF (VLM-prior, DCAM, DMoE)	+1‒2 dB PSNR vs. baselines on MSRS/FMB (Li et al., 16 Nov 2025)
Medical AI	Drift, concept shift	ReclAIm (multi-agent, LLM-orchestrated)	41.1% recall drop corrected to <1.5% deviation (Tzanis et al., 19 Oct 2025)
Lidar SLAM	Occlusion, sparsity	Sensor-aware, phenomenological pipeline	Robust error benchmarking, integration with ROS (Felix et al., 9 Dec 2025)
Circuit Design	Transistor aging/drift	Genetic approx. netlist optimization	100% guardband removal, 1208× lower error (Balaskas et al., 2022)

These frameworks are evaluated on established testbeds (IEEE 33-bus, SOTS/Rain100/ImageNet512), with benchmarking against prior SOTA and domain-accepted error, reliability, or economic indices.

5. Quantitative and Architectural Impact

Degradation-aware frameworks consistently outperform non-aware and classical approaches in both robustness and efficiency:

• In grid BESS scheduling, degradation modeling yields Pareto-optimal solutions that achieve near-minimal energy/loss cost with greatly reduced battery wear. The Bach algorithm demonstrates that, compared to cost-only optimization, joint degradation-awareness can reduce energy losses by ≈16% and voltage deviation, while incurring <1% additional cost (Pamshetti et al., 9 Jul 2024).
• All-in-one image restorers employing dynamic routing (DaAIR) demonstrate superior performance (32.51 dB/0.91 SSIM on 3 tasks, 30.24 dB/0.88 SSIM on 5) at a fraction of the parameter and compute overhead of prompt-ensemble models (Zamfir et al., 24 May 2024).
• Weather-aware diffusion models (DA²Diff) using CLIP-learned prompts and dynamic mixtures-of-experts deliver state-of-the-art results: PSNR gains of +1 dB and superior perceptual metrics across all-weather benchmarks (Xiong et al., 7 Apr 2025).
• Medical imaging maintenance frameworks (ReclAIm) control for catastrophic generalization drop, autonomously restoring degraded archetectures to within 1.5% of baseline precision/recall post drift-finetuning (Tzanis et al., 19 Oct 2025).
• Reservoir-based battery cell design demonstrates the quantitative leverage of jointly tuning lithium, porosity, and electrolyte: +25‒30% lifetime with minimal cost to density, and clear guidance on compound failure modes from poorly coordinated microstructural tuning (Nazeeruddin et al., 17 Dec 2025).

6. Modularity, Customization, and Extensibility

Degradation-aware frameworks, by virtue of modular architectures and explicit model parameterization, provide a platform for the rapid integration of new degradation phenomena, decision criteria, or constraints:

• Machinery can readily adapt to additional cost terms (e.g., calendar aging, tariffs), constraints (dynamic line ratings), or decision logic (receding horizon in grid scheduling) (Pamshetti et al., 9 Jul 2024).
• Modular NLP/MILP optimizers and multi-expert neural architectures enable seamless specialization for novel image degradations or multi-spectral modalities (Liu et al., 24 Apr 2025, Li et al., 16 Nov 2025).
• Data-driven transferability protocols (fine-tuning DeepHPM, dynamic prompt/adaptor learning) support robust deployment across new operational environments, battery chemistries, or image weather types (Zhang et al., 24 Jan 2025, Xiong et al., 7 Apr 2025).
• Hierarchical pipeline design allows for lightweight, conditional selection between specialist and generic pathways (iterative restoration, expert fusion), facilitating both efficiency and reliability (Singh et al., 26 Jun 2024, Li et al., 16 Nov 2025).

7. Design Guidelines and Lessons

Empirical evidence across domains suggests a universal set of design principles:

• Always model degradation explicitly when it interacts nontrivially with primary optimization objectives.
• Quantify and balance tradeoffs using Pareto or multi-objective methods.
• Use physics-informed or tightly validated empirical models for degradation evolution when possible; otherwise, rigorously benchmark learned surrogates.
• Leverage modularity and routing for task adaptation but maintain shared priors for cross-degradation generalization.
• Monitor and dynamically adapt via online diagnosis, expert gating, or regularization to avoid degradation- or scenario-specific overfitting.
• Design for extensibility: insert new degradations or constraints via modular interface layers and fine-tuning protocols.

In sum, the degradation-aware design framework paradigm provides a robust, extensible, and quantitatively justified methodology for the optimization and control of complex systems under realistic, temporally or spatially variable degradation—offering superior performance, reliability, and operational insight versus classical, degradation-agnostic methods (Pamshetti et al., 9 Jul 2024, Nazeeruddin et al., 17 Dec 2025, Zhang et al., 24 Jan 2025, Li et al., 9 Oct 2025, Liu et al., 24 Apr 2025, Zamfir et al., 24 May 2024, Li et al., 16 Nov 2025).