Artificial Intelligence-Enhanced Digital Twin System for a New Generation of Intelligent Battery Management

Fatemeh ShakeriHosseinabad, Hamidreza Zareipour, Behrouz Far
Proceedings of the The 39th Canadian Conference on Artificial Intelligence, PMLR 318:163-174, 2026.

Abstract

Battery management systems are essential to estimate internal battery states and regulate current safely under nonlinear dynamics, noisy sensing, and changing operating regimes. We propose a closed-loop digital-twin–AI framework that couples (i) a physics-informed neural network (PINN) observer for real-time state estimation, (ii) a high-fidelity single-particle-model digital twin, and (iii) a reinforcement learning (RL) controller that optimizes discharge current under different C-rates. The digital twin provides physically consistent trajectories and synthetic supervision, while the PINN provides a low-latency state of charge (SOC) and state of health (SOH), from noisy measurements, allowing the RL agent to act with realistic partial observability. We evaluated the framework at different C rates, for both a single cell and a pack of cells. In addition, we include a particle swarm optimization capacity-identification module as an independent SOH benchmark. The results demonstrate stable AI-driven SOC regulation across C-rates and scalable extension from cell-to pack-level monitoring. Furthermore, this approach demonstrates a clear pathway to enhance capacity-based SOH estimation accuracy through richer physics integration and expanded training coverage.

Cite this Paper

BibTeX
@InProceedings{pmlr-v318-shakerihosseinabad26a, title = {Artificial Intelligence-Enhanced Digital Twin System for a New Generation of Intelligent Battery Management}, author = {ShakeriHosseinabad, Fatemeh and Zareipour, Hamidreza and Far, Behrouz}, booktitle = {Proceedings of the The 39th Canadian Conference on Artificial Intelligence}, pages = {163--174}, year = {2026}, editor = {Bouzar-Benlabiod, Lydia and Leung, Carson}, volume = {318}, series = {Proceedings of Machine Learning Research}, month = {25--29 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v318/main/assets/shakerihosseinabad26a/shakerihosseinabad26a.pdf}, url = {https://proceedings.mlr.press/v318/shakerihosseinabad26a.html}, abstract = {Battery management systems are essential to estimate internal battery states and regulate current safely under nonlinear dynamics, noisy sensing, and changing operating regimes. We propose a closed-loop digital-twin–AI framework that couples (i) a physics-informed neural network (PINN) observer for real-time state estimation, (ii) a high-fidelity single-particle-model digital twin, and (iii) a reinforcement learning (RL) controller that optimizes discharge current under different C-rates. The digital twin provides physically consistent trajectories and synthetic supervision, while the PINN provides a low-latency state of charge (SOC) and state of health (SOH), from noisy measurements, allowing the RL agent to act with realistic partial observability. We evaluated the framework at different C rates, for both a single cell and a pack of cells. In addition, we include a particle swarm optimization capacity-identification module as an independent SOH benchmark. The results demonstrate stable AI-driven SOC regulation across C-rates and scalable extension from cell-to pack-level monitoring. Furthermore, this approach demonstrates a clear pathway to enhance capacity-based SOH estimation accuracy through richer physics integration and expanded training coverage.} }
Endnote
%0 Conference Paper %T Artificial Intelligence-Enhanced Digital Twin System for a New Generation of Intelligent Battery Management %A Fatemeh ShakeriHosseinabad %A Hamidreza Zareipour %A Behrouz Far %B Proceedings of the The 39th Canadian Conference on Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Lydia Bouzar-Benlabiod %E Carson Leung %F pmlr-v318-shakerihosseinabad26a %I PMLR %P 163--174 %U https://proceedings.mlr.press/v318/shakerihosseinabad26a.html %V 318 %X Battery management systems are essential to estimate internal battery states and regulate current safely under nonlinear dynamics, noisy sensing, and changing operating regimes. We propose a closed-loop digital-twin–AI framework that couples (i) a physics-informed neural network (PINN) observer for real-time state estimation, (ii) a high-fidelity single-particle-model digital twin, and (iii) a reinforcement learning (RL) controller that optimizes discharge current under different C-rates. The digital twin provides physically consistent trajectories and synthetic supervision, while the PINN provides a low-latency state of charge (SOC) and state of health (SOH), from noisy measurements, allowing the RL agent to act with realistic partial observability. We evaluated the framework at different C rates, for both a single cell and a pack of cells. In addition, we include a particle swarm optimization capacity-identification module as an independent SOH benchmark. The results demonstrate stable AI-driven SOC regulation across C-rates and scalable extension from cell-to pack-level monitoring. Furthermore, this approach demonstrates a clear pathway to enhance capacity-based SOH estimation accuracy through richer physics integration and expanded training coverage.
APA
ShakeriHosseinabad, F., Zareipour, H. & Far, B.. (2026). Artificial Intelligence-Enhanced Digital Twin System for a New Generation of Intelligent Battery Management. Proceedings of the The 39th Canadian Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 318:163-174 Available from https://proceedings.mlr.press/v318/shakerihosseinabad26a.html.

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