Modelling Acoustic Wave Propagation in Batteries: From Physics to Usable Tooling

Modelling Acoustic Wave Propagation in Batteries: From Physics to Usable Tooling

Acoustic methods are an increasingly attractive way to probe battery state without tearing cells apart. Changes in temperature, state of charge, and mechanical condition all affect how acoustic waves travel through a cell. The challenge is not generating acoustic signals — it’s interpreting them.

During a consultancy project with Sention, we built a modelling framework designed to bridge that gap: a fast, configurable acoustic wave model of a layered battery structure that could be rerun by non-modelling specialists to match experimental data and extract effective material properties.

This post outlines the design decisions behind that framework, from the physical model through to the software architecture, and why simplicity and efficiency mattered more than dimensional fidelity.

Design goals

From the outset, the project had a few clear constraints:

• Usable by non-modellers
  Experimentalists needed to rerun simulations without touching PDE solvers or numerical settings.

• Fast and configurable
  The primary task was parameter variation: acoustic velocity, density, impedance, attenuation — across electrodes, separators, and current collectors.

• Scalable for dataset generation
  The long-term goal was not just individual fits, but the creation of a large synthetic dataset for future machine learning models.

• Physically grounded but minimal
  A model that captured the dominant physics of wave propagation, without unnecessary complexity.

This led directly to a 1D layered acoustic model, prioritising speed, robustness, and interpretability.

Physical model: 1D layered acoustics

The battery was represented as a 1D stack of material layers, corresponding to:

• Positive electrode

• Separator

• Negative electrode

• Current collectors

Each layer was assigned its own acoustic properties (e.g. wave speed, density, impedance), allowing discontinuities at interfaces and reflections to emerge naturally from the physics.

While clearly an approximation, the 1D assumption offered two key advantages:

1. It captures the dominant through-thickness wave behaviour relevant to many experimental setups.

2. It allows extremely fast simulation and dense parameter sweeps.

The goal was never to perfectly reproduce every experimental detail, but to build a useful inverse tool.

Numerical engine: why Clawpack?

For the underlying physics engine, we used Clawpack, a finite-volume framework designed for hyperbolic PDEs and wave propagation problems.

Clawpack was a good fit because it provides:

• Robust handling of wave propagation and reflections

• Clear separation between physics and numerics

• Excellent performance for 1D problems

• Mature, well-tested solvers

Crucially, it allowed the physics to remain explicit and inspectable, rather than hidden behind a black-box solver.

A domain-specific API for battery acoustics

To make the model usable, we built a small domain-specific API on top of Clawpack.

Instead of defining grids and coefficients manually, users could specify:

• Layer ordering (electrode / separator / current collector)

• Thicknesses

• Acoustic properties per layer

• Source and sensor locations

This abstraction meant that changing a material property or layer configuration became a matter of editing a few parameters — not rewriting solver code.

End-to-end architecture

To make the system easy to deploy and rerun, the full workflow was containerised and split into three main components:

Backend

• Dockerised Clawpack-based simulation server

• Exposed via an API for parameterised runs with FastAPI

Frontend

• Built in Streamlit

• Interactive control of layer properties

• Immediate visualisation of wave propagation and received signals

Workflow

1. User selects layer properties and experimental conditions

2. Backend runs the acoustic simulation

3. Results are returned and visualised

4. Parameters are adjusted to match experimental data

This setup allowed experimentalists to explore “what-if” scenarios without needing to understand the underlying PDEs.

Matching experimental data

A typical use case was:

• Fix the known geometry

• Vary acoustic properties with temperature and state of charge

• Compare simulated and measured signals

• Identify effective material parameters that best explain the data

Because simulations were fast and reproducible, it was practical to run large parameter sweeps and build intuition about sensitivity and identifiability.

Towards data-driven interpretation

While the immediate goal was parameter fitting, the longer-term vision was to generate large synthetic datasets spanning:

• Material property variations

• Temperature

• State of charge

These datasets could then be used to train machine learning models capable of rapidly interpreting acoustic signals — effectively learning the inverse mapping that is expensive to compute directly.

The modelling framework was designed with this future step in mind from day one.

Closing thoughts

This project reinforced a recurring lesson in applied modelling: the most useful model is rarely the most detailed one.

By focusing on:

• the dominant physics,

• a clean abstraction layer,

• and strong tooling around the solver,

it was possible to build a framework that served both immediate experimental needs and future data-driven ambitions.

If you’re interested in acoustic diagnostics, inverse problems, or building modelling tools that non-specialists can actually use, this kind of approach scales surprisingly well.