State of mission: Battery management with neural networks and electrochemical AI

This work introduces a hybrid modeling framework for advanced battery management that combines neural ordinary differential equations (Neural ODEs) with physics-informed neural networks (PINNs) to achieve physically consistent, data-driven predictions of battery behavior. Sequential learning models, including long short-term memory (LSTMs) and Transformers, are integrated to capture temporal dependencies and provide continuous-time, high-fidelity estimation. A central contribution is the introduction of the state of mission (SOM), a mission-aware diagnostic metric that quantifies whether a battery can successfully complete a specific operational task. Unlike conventional measures such as state of charge (SOC) or state of health (SOH), SOM integrates internal state evolution, mission profiles, and safety constraints to forecast mission feasibility. The framework was validated through simulations and experimental data from the NASA PCoE and Oxford datasets. Results demonstrate robust prediction of coupled electrochemical-thermal dynamics and mission outcomes, offering a forward-looking tool for next-generation battery management systems in electric vehicles, aerial systems, and grid storage.