Fault detection and diagnostics in battery systems are critical for ensuring safety, reliability, and longevity. Traditional approaches rely either on physics-based models or purely data-driven methods, but hybrid techniques that combine both are increasingly proving superior. By integrating the interpretability of physics-based models with the adaptability of machine learning, hybrid methods enhance diagnostic accuracy, especially in complex, real-world scenarios where uncertainty and noise are prevalent.

Physics-based models use electrochemical and thermal principles to simulate battery behavior. These models, such as equivalent circuit models (ECMs) or pseudo-two-dimensional (P2D) models, provide a structured understanding of internal states like state of charge (SOC) and state of health (SOH). However, they often struggle with unmodeled dynamics or parameter variations over time. Data-driven techniques, particularly neural networks, excel at identifying patterns in large datasets but lack interpretability and may fail under conditions not represented in training data.

A hybrid approach leverages the strengths of both paradigms. One effective method involves using Kalman filters to generate residuals, which are then classified by neural networks. Kalman filters, including extended (EKF) or unscented (UKF) variants, estimate battery states by fusing sensor data with model predictions. The difference between measured and estimated values, known as residuals, serves as an indicator of anomalies. Under normal operation, residuals follow a predictable noise distribution, but faults cause statistically significant deviations.

For instance, a lithium-ion battery undergoing a soft internal short circuit may exhibit subtle voltage deviations. A Kalman filter tuned to normal operation would produce residuals that deviate when the fault occurs. These residuals are then fed into a neural network classifier trained on labeled fault data. The classifier distinguishes between different fault types, such as internal shorts, sensor biases, or thermal runaway precursors. The hybrid system thus benefits from the Kalman filter’s real-time state estimation while using the neural network’s ability to learn complex fault signatures.

Co-simulation frameworks further enhance hybrid diagnostics by enabling parallel execution of physics-based models and data-driven algorithms. Tools like Functional Mock-up Interface (FMI) allow battery models developed in MATLAB/Simulink to interact with Python-based machine learning classifiers. This modularity facilitates iterative refinement—physics models can be updated without retraining neural networks, and vice versa. Co-simulation also supports hardware-in-the-loop (HIL) testing, where real battery management systems (BMS) interact with virtual models to validate fault detection strategies under diverse scenarios.

Uncertainty quantification is another critical aspect. Physics-based models have parametric uncertainties due to manufacturing variability or aging, while data-driven methods suffer from epistemic uncertainties (lack of training data) and aleatoric uncertainties (sensor noise). Hybrid approaches mitigate these issues by propagating uncertainty through both components. For example, Bayesian neural networks can quantify prediction confidence, while Monte Carlo simulations assess how parameter variances affect Kalman filter residuals. This dual uncertainty analysis improves fault detection robustness, reducing false positives and missed alarms.

Purely data-driven methods often require extensive labeled datasets, which are costly to obtain for rare faults. Purely model-based techniques may fail to capture nonlinear degradation modes. Hybrid methods address these gaps. A physics model narrows the hypothesis space for the neural network, reducing the data needed for training. Conversely, the neural network compensates for model inaccuracies by learning correction terms from real-world data.

Practical implementations show measurable benefits. In electric vehicle BMS, hybrid fault diagnosis has achieved fault detection rates above 95% with fewer than 2% false alarms, outperforming standalone methods. The Kalman filter residuals enable early detection of subtle anomalies, while the neural network classifies faults with high precision. Co-simulation accelerates development cycles, and uncertainty quantification ensures reliability across operating conditions.

The hybrid approach is not without challenges. Synchronizing physics models with data-driven components demands careful calibration. Computational overhead increases compared to simpler methods, though advances in edge computing are mitigating this. Despite these hurdles, the fusion of physics and machine learning represents a promising direction for battery fault diagnosis, balancing interpretability with adaptability to deliver safer and more reliable energy storage systems.

Future advancements may involve embedding physical constraints directly into neural network architectures or using reinforcement learning to adapt models in real time. As battery systems grow more complex, hybrid approaches will remain indispensable for robust fault diagnostics.