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Physics-Based Battery Insight
ARTIPhy is developing next-generation modelling technology for lithium-ion batteries.
Our work combines advances in electrochemical modelling and scientific machine learning to create ultra-fast neural surrogates of battery physics.
These models enable new capabilities across battery management, design and analytics by making high-fidelity physics models accessible in real-time applications.
World-class expertise in electrochemical modelling and scientific machine learning
University of Portsmouth
Professor of Industrial & Applied Mathematics
Research focuses on electrochemical battery modelling and scientific computing.
University of Portsmouth
Professor of Probability & Statistical Physics
Expertise in applied mathematics, complex systems and data-driven modelling.
University of Portsmouth
Research Fellow
Research spans machine learning, statistical physics and scientific modelling.
University of Portsmouth
PhD Researcher
Working on surrogate modelling techniques for electrochemical battery systems.
ARTIPhy may be able to help you if you need to:
Electrochemical battery models provide deep insight into the internal behaviour of lithium-ion cells. They describe ion transport, reaction kinetics and electrode dynamics using systems of coupled differential equations. These models are powerful but computationally demanding, which limits their use in real-time applications. ARTIPhy addresses this limitation by constructing neural surrogates of electrochemical battery models. These surrogates replicate the behaviour of the underlying physics model while running dramatically faster, making high-fidelity modelling accessible in applications where traditional solvers are impractical.
ARTIPhy models are neural surrogates designed to reproduce the behaviour of electrochemical battery models with extremely high computational efficiency. Once constructed, these surrogate models can evaluate battery behaviour rapidly without solving the underlying system of equations. This enables physics-based prediction and analysis to be performed orders of magnitude faster than with classical modelling approaches. The resulting models combine the interpretability of physics-based modelling with the speed and efficiency required for real-time applications.
ARTIPhy models retain access to physically meaningful internal variables such as lithium concentration and reaction rates.
Model sizes are typically under 100 KB, enabling deployment on embedded processors and battery management hardware.
Models accept arbitrary current profiles and operating conditions.
Fast evaluation enables advanced statistical techniques such as Bayesian parameter inference and uncertainty quantification.
FIXME: Intro sentence
Physics-based battery models provide insight into internal battery states that cannot be measured directly. However, traditional models are too computationally expensive to run within real-time control systems. ARTIPhy enables electrochemical battery models to run directly within battery management systems, providing improved state estimation and predictive capability.
Cell performance depends strongly on design parameters such as electrode thickness, particle size and porosity. ARTIPhy models allow rapid exploration of this design space by replacing slow numerical simulations with ultra-fast surrogate evaluations, enabling accelerated design optimisation and sensitivity analysis.
Understanding battery degradation across large fleets requires analysing large volumes of operational data. ARTIPhy models enable scalable physics-based analysis across many cells and vehicles, supporting improved lifetime prediction and asset management.