Physics-based battery model parametrisation from impedance data (2412.10896v4)

Abstract: Non-invasive parametrisation of physics-based battery models can be performed by fitting the model to electrochemical impedance spectroscopy (EIS) data containing features related to the different physical processes. However, this requires an impedance model to be derived, which may be complex to obtain analytically. We have developed the open-source software PyBaMM-EIS that provides a fast method to compute the impedance of any PyBaMM model at any operating point using automatic differentiation. Using PyBaMM-EIS, we investigate the impedance of the single particle model, single particle model with electrolyte (SPMe), and Doyle-Fuller-Newman model, and identify the SPMe as a parsimonious option that shows the typical features of measured lithium-ion cell impedance data. We provide a grouped parameter SPMe and analyse the features in the impedance related to each parameter. Using the open-source software PyBOP, we estimate 18 grouped parameters both from simulated impedance data and from measured impedance data from a LG M50LT lithium-ion battery. The parameters that directly affect the response of the SPMe can be accurately determined and assigned to the correct electrode. Crucially, parameter fitting must be done simultaneously to data across a wide range of states-of-charge. Overall, this work presents a practical way to find the parameters of physics-based models.

• The paper introduces a numerical impedance computation using PyBaMM-EIS and automatic differentiation to accelerate model simulations.
• The study demonstrates that the SPMe effectively balances complexity and accuracy by capturing key diffusion dynamics seen in experimental EIS data.
• The research outlines a parameter grouping strategy that improves parameter identifiability, enhancing battery model design and management.

Physics-Based Battery Model Parameterization from Impedance Data

The paper "Physics-based battery model parametrisation from impedance data" describes a methodological framework for parameterizing physics-based battery models using electrochemical impedance spectroscopy (EIS) data. This research leverages the PyBaMM-EIS open-source software, which the authors developed to facilitate rapid computation of impedance for various battery models at any operating point by employing automatic differentiation. The paper thoroughly evaluates the impedance behavior of the single particle model, single particle model with electrolyte (SPMe), and Doyle-Fuller-Newman (DFN) model, ultimately emphasizing the SPMe as a parsimonious, effective model that captures the typical features of impedance data measured from lithium-ion cells.

This work primarily concentrates on the SPMe due to its balance between model complexity and accuracy, as indicated by its ability to replicate characteristic features in experimental EIS data, such as the "diffusion bump" resulting from electrolyte dynamics. An extensive analysis is presented, grouping model parameters to reduce complexity. The authors focus on estimating parameters that significantly impact impedance response, comparing EIS and time-domain voltage data to evaluate parameter estimation efficacy.

Key Contributions and Findings

1. Numerical Frequency Domain Methodology: The authors introduce a numerical approach for computing model impedance, utilizing PyBaMM for spatial discretization and leveraging automatic differentiation for computing exact Jacobians. This method efficiently computes impedance responses across a frequency range, offering considerable speed improvements over traditional brute-force simulations.
2. Impedance Analysis and Model Selection: By examining the impedance of various models, the paper highlights the necessity of considering electrolyte dynamics within the models, as evidenced by the presence of the "diffusion bump" in both simulated and experimental impedance spectra. The SPMe is underscored as optimal for capturing the salient features of lithium-ion battery impedance while keeping computational costs manageable.
3. Parameter Grouping and Sensitivity: Parameters within the SPMe are grouped to reduce dimensionality and improve identifiability. The authors detail how different processes within the battery, occurring at distinct timescales, influence impedance, aiding in understanding parameter impacts on model behavior.
4. Comparative Parameter Estimation: Comparisons between parameter estimation using EIS and time-domain data indicate that while both sources can yield accurate parameters for long-timescale processes, EIS data provides superior insights for short-timescale phenomena.
5. Practical Application and Model Refinement: The practical applicability of the proposed parameter identification techniques is demonstrated through the analysis of a commercial LG M50LT cell. While presenting robust results at higher frequencies, the fit at lower frequencies is noted to be less satisfactory due to potential OCP data dependency and model limitations.

Implications and Future Work

The implications of this research are substantial for both theoretical and practical advancements in battery modeling. By efficiently parameterizing physics-based models using EIS data, battery management systems can significantly enhance state and health estimation accuracy. The developed methodologies pave the way for the integration of more intricate models that account for additional phenomena such as non-ideal diffusion or particle size distribution effects, promising further improvements in capturing real-world battery behaviors.

As battery technologies continue to advance, future research could explore integrating the proposed methods with other data sources or extending models to capture more complex behavior under varying environmental and operational conditions. The PyBaMM-EIS tool offers a robust platform for the ongoing investigation and development of comprehensive battery models, essential for optimizing the design and control of next-generation energy storage systems.