State of health estimation in battery systems requires robust methodologies to combine multiple degradation indicators into a reliable assessment framework. Single-indicator approaches often fail to capture the complex interplay of aging mechanisms, leading to inaccurate predictions. Data fusion architectures address this challenge by integrating diverse measurements such as capacity fade, impedance rise, thermal behavior, and voltage response into a unified health assessment.

Bayesian belief networks provide a probabilistic framework for combining state of health indicators. These networks model conditional dependencies between variables, allowing the system to update belief probabilities as new evidence becomes available. A typical implementation might include nodes for capacity measurements, internal resistance, charge acceptance, and temperature characteristics. The network weights each indicator based on its measured reliability and updates the state of health probability distribution through Bayesian inference. For example, if capacity measurements show 20% fade while impedance data suggests 15% degradation, the network evaluates the conditional probability of each scenario given historical data on correlation patterns between these parameters.

Dempster-Shafer theory offers an alternative approach that handles uncertainty more explicitly than Bayesian methods. This framework operates with belief functions rather than probabilities, accommodating situations where evidence might be conflicting or incomplete. In battery health assessment, Dempster-Shafer can represent the confidence interval around each indicator measurement and combine them using Dempster's rule of combination. When impedance and capacity measurements disagree, the theory calculates a belief interval that reflects the consensus between sources while quantifying remaining uncertainty. Field deployments show this method reduces false alarm rates by 30-40% compared to threshold-based single-indicator systems.

Deep learning architectures have emerged as powerful tools for fusing heterogeneous health indicators. Neural networks can automatically learn weighting schemes for different input signals through training on large datasets. A typical architecture might include convolutional layers for processing time-series data like charge/discharge curves, recurrent layers for capturing temporal dependencies in aging patterns, and fusion layers that combine features from multiple pathways. The network outputs a health estimate that reflects the learned relationships between all input indicators. Experimental results demonstrate that such systems achieve 92-95% accuracy in predicting remaining useful life, compared to 75-85% for single-feature models.

Weighting conflicting signals requires careful consideration of each indicator's reliability and relevance under different operating conditions. Impedance measurements typically show earlier response to certain degradation modes like lithium plating, while capacity measurements better reflect active material loss. Advanced fusion systems implement dynamic weighting schemes where each indicator's influence varies based on operating context. For example, impedance data might receive higher weight during fast charging conditions where plating risk exists, while capacity measurements dominate during normal cycling. Field data from electric vehicle fleets shows this contextual approach reduces prediction errors by 50% compared to static weighting.

Missing data scenarios present significant challenges for health assessment systems. Practical implementations must handle cases where certain measurements become unavailable due to sensor failures or operational constraints. Robust fusion architectures employ several strategies: probabilistic imputation using historical correlation patterns, confidence-based weighting that reduces reliance on missing signals, and fallback modes that prioritize available indicators. In grid storage applications, systems using these techniques maintain 85% prediction accuracy even with 30% of input channels missing, compared to complete failure of simpler models.

Case studies from various industries demonstrate the advantages of multi-indicator fusion approaches. In electric vehicle batteries, combining impedance spectroscopy with incremental capacity analysis and thermal profiling reduced end-of-life prediction errors from ±15% to ±5% across 2000 charge cycles. Grid storage systems using fused health models achieved 99.9% uptime by detecting early-stage degradation that single-indicator systems missed. Aerospace applications report 40% improvement in remaining useful life estimates when supplementing capacity measurements with pressure sensor data and gas evolution analysis.

Implementation challenges include computational complexity, calibration requirements, and the need for comprehensive training datasets. Bayesian networks demand careful construction of conditional probability tables based on extensive experimental data. Dempster-Shafer implementations must define appropriate mass functions that reflect real-world uncertainty patterns. Deep learning systems require large labeled datasets covering diverse aging scenarios and operating conditions. Successful deployments typically involve phased implementation, starting with laboratory validation before field deployment.

Future developments point toward hybrid architectures that combine the strengths of different fusion approaches. Some systems now use deep learning for feature extraction followed by probabilistic fusion for final health estimation. Others implement hierarchical models where local neural networks process raw sensor data and higher-level Bayesian networks combine the extracted features. These advanced architectures show promise for handling increasingly complex battery systems with multiple degradation pathways and operating modes.

The transition from single-indicator approaches to sophisticated fusion architectures represents a significant advancement in battery health assessment. By properly combining multiple degradation signals, these systems provide more accurate, reliable, and robust state of health estimates across diverse applications. Continued refinement of fusion methodologies will further improve prediction accuracy while addressing practical implementation challenges in real-world battery systems.