Machine learning models have become integral to battery management systems, enabling advanced state estimation, fault detection, and lifespan prediction. However, the black-box nature of complex algorithms like deep neural networks raises concerns about trust and reliability. Explainable AI (XAI) techniques bridge this gap by providing insights into model decisions, revealing degradation mechanisms, and ensuring safety in real-world deployment. Three prominent methods—SHAP, LIME, and saliency maps—are particularly valuable for interpreting battery ML models.

SHAP (SHapley Additive exPlanations) quantifies the contribution of each input feature to a model’s prediction by leveraging game theory. For battery applications, SHAP values can identify which operational parameters—such as voltage hysteresis, temperature, or charge rate—most influence degradation predictions. For instance, a high SHAP value for temperature during fast charging may indicate lithium plating as a dominant degradation mode. Similarly, a strong contribution from capacity fade over cycles could point to solid-electrolyte interphase (SEI) growth. By analyzing SHAP values across diverse cycling conditions, researchers have uncovered nonlinear interactions between stressors, such as how elevated temperatures exacerbate mechanical strain in silicon anodes.

LIME (Local Interpretable Model-Agnostic Explanations) approximates black-box models with simpler, interpretable models (e.g., linear regression) for specific predictions. In battery health estimation, LIME can highlight localized decision boundaries—for example, why a model flags a sudden voltage drop as an early warning of dendrite formation. By perturbing input features around a specific cycle and observing changes in the output, LIME reveals which variables the model treats as critical for that scenario. This is particularly useful for identifying edge cases, such as a BMS misclassifying a harmless sensor drift as a thermal runaway precursor due to over-reliance on a single temperature node.

Saliency maps, commonly used in convolutional neural networks, visualize input sensitivity by computing gradients of the output with respect to the input features. In image-based battery diagnostics, such as X-ray or SEM analysis, saliency maps pinpoint regions where microstructural defects (cracks, pore formation) correlate with model predictions of failure. For time-series data, saliency maps can show which segments of a charging curve are most influential in predicting remaining useful life. For example, a model may focus on the relaxation voltage plateau to infer lithium inventory loss, aligning with known electrochemical mechanisms.

Feature importance analysis extends these techniques by ranking inputs based on their predictive power. In one study, a random forest model trained on cycling data from lithium-ion cells identified charge C-rate and end-of-discharge voltage as top features for SOH estimation. Further analysis revealed these features were proxies for SEI growth and active material loss, validated by post-mortem electrode characterization. Another case showed how a gradient-boosted model’s reliance on internal resistance trends uncovered unexpected copper current collector corrosion in high-voltage cells, a failure mode previously attributed solely to cathode instability.

However, interpretability often trades off against accuracy. Simplified models like decision trees are transparent but may miss complex nonlinearities captured by deep learning. For example, a linear approximation might overlook the synergistic effect of temperature and SOC on lithium plating, leading to underestimation of risks. Hybrid approaches address this by using black-box models for prediction and XAI for post-hoc analysis. In one BMS deployment, a neural network achieved 2% higher state-of-charge accuracy than a physics-based model, while SHAP analysis confirmed its adherence to known voltage-SOC relationships, ensuring trustworthiness.

XAI has also exposed unexpected failure modes. In a grid storage application, a model trained to predict capacity fade flagged cells with abnormal self-discharge rates as healthy. LIME revealed the model was overly reliant on cycle count and ignored subtle voltage noise indicative of micro-shorts. Retraining with adversarial examples corrected this bias. Similarly, saliency maps applied to a fast-charging optimization algorithm showed undue emphasis on initial voltage spikes, leading to revised current protocols that avoided lithium plating.

In cell design, XAI has uncovered counterintuitive insights. A generative model for electrolyte formulations initially prioritized high conductivity additives. SHAP analysis, however, showed that low-conductivity additives with better SEI-stabilizing properties yielded longer cycle life—a finding later confirmed experimentally. Another case involved a deep learning model predicting cell swelling: saliency maps revealed that the model keyed in on minute pressure fluctuations during rest periods, a previously overlooked indicator of gas evolution from electrolyte decomposition.

Deploying interpretable models in BMS involves balancing computational constraints and safety. SHAP and LIME are resource-intensive, making them better suited for offline analysis, while lightweight techniques like feature ablation (systematically removing inputs to test robustness) can be implemented on-edge. For example, an electric vehicle BMS might use a simplified decision tree for real-time fault detection, with periodic cloud-based SHAP audits to ensure consistency.

Regulatory and ethical considerations further drive the need for interpretability. A BMS denying a warranty claim based on ML predictions must justify its reasoning—a task feasible with SHAP but opaque in pure black-box systems. In one incident, XAI analysis of a recalled battery batch showed the BMS had incorrectly learned to associate a harmless manufacturing artifact with failure, leading to unnecessary replacements.

The future of battery XAI lies in integrating these techniques with physical models. Hybrid approaches that combine ML with mechanistic equations (e.g., coupling SHAP with Newman-type degradation models) can provide both accuracy and interpretability. As batteries grow more complex—with solid-state electrolytes, lithium-metal anodes, and multi-scale degradation—XAI will be critical in ensuring safe, reliable, and transparent management systems. The insights gained not only validate models but also feed back into better designs, creating a virtuous cycle of improvement.