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

## TL;DR

This paper introduces a new AI-based battery management system that combines physics and neural networks to predict battery behavior and mission feasibility.

## Contribution

The paper introduces the 'state of mission' (SOM), a novel diagnostic metric for assessing battery mission feasibility.

## Key findings

- The hybrid AI model accurately predicts electrochemical-thermal dynamics and mission outcomes.
- The framework was validated using NASA PCoE and Oxford datasets, showing robust performance.
- SOM enables real-time, mission-aware battery state estimation for safer and adaptive systems.

## Abstract

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.

•Neural ODE framework models electrochemical, thermal, and aging dynamics•Physics-informed learning ensures physically consistent battery state predictions•SOM enables mission-aware battery state estimation for real-time decision-making•Hybrid AI model supports predictive, safe, and adaptive battery management systems

Neural ODE framework models electrochemical, thermal, and aging dynamics

Physics-informed learning ensures physically consistent battery state predictions

SOM enables mission-aware battery state estimation for real-time decision-making

Hybrid AI model supports predictive, safe, and adaptive battery management systems

Applied sciences; Energy management; Energy Systems