In-Operando Virtual Look inside the Battery: The Role of Physics-Based Models in Advancing State-of-X Diagnostics of Batteries with Phase-Separating Materials
Authors: Katrašnik Tomaž , Zelič Klemen , Mele Igor 
Published: December 2024

Lithium iron phosphate (LFP)-based batteries, as well as their mixed analogues, such as lithium iron manganese phosphate batteries, are gaining importance owing to their high safety, high power density, acceptable energy density, durability, lack of resource-limiting critical metals, and especially their excellent price-performance ratio. However, these phase-separating active materials are characterized by several intriguing phenomena, as for example inherent voltage hysteresis, when subjected to finite (nearly) constant currents [1], newly reported entering into the voltage hysteresis during transient battery operation [2], memory effect [3], electrochemical oscillations [4] and chemical inductor properties [5]. Combining these effects with relatively flat open circuit potential of these materials imposes a significant challenge on detailed State-of-X diagnostics of batteries with phase separating materials, which is a challenge for advanced health and safety aware battery management.

This paper tackles this challenge by applying innovative physics-based models to boost advanced State-of-X diagnostics of batteries with phase-separating active materials, hence elucidating an emerging area of model-based monitoring, diagnostics and management of such batteries. Innovative State-of-X diagnostics functionalities are made possible by the use of multi-scale simulation framework for batteries, which is scalable in terms of model complexity and thus its computational expenses.

The multi-scale simulation framework is based on the state-of-the-art submodels that incorporate key aspects of electrochemical phenomena in phase-separating materials, while featuring spatial and temporal resolution [6, 7]. Starting from this advanced basis, this paper highlights coupling of the multi-scale simulation framework with parameter identification techniques and methods for assessing uniqueness of parameter identification into an innovative State-of-X diagnostics tool, which can be executed in the cloud. This innovative State-of-X diagnostics tool, thus, makes possible advanced virtual sensing of intra-cell states and determination of model parameters, which are interlinked with performance and degradation descriptors of the cells. These innovative functionalities make possible obtaining unprecedented model-based insight into intra-cell phenomena.

One of the key merits of the proposed multi-scale simulation framework is also its scalability and computational efficiency, which opens perspectives to run computationally optimized version of the models on the vehicle, e.g. in the Battery Management System (BMS) or edge device, hence, making possible unprecedentedly accurate monitoring and management of batteries. In addition, connectivity with the State-of-X diagnostics tool in the cloud makes possible automated online updates of model parameters, control limiters and State-of-X descriptors between the State-of-X diagnostics tool in the cloud and the computationally optimized BMS/edge version of the model, complying with the vision of the Software Defined Vehicle (SDV).

Presented results demonstrate capability of the newly proposed model-based framework to perform advanced State-of-X diagnostics focusing on three key aspects: 1. demonstrating capability of the multi-scale simulation framework to model specifics of phase-separating material, being enabler for adequate model-based State-of-X diagnostics; 2. providing methodological guidelines to efficiently execute State-of-X diagnostics; and 3. presenting SDV compatible methodology to distribute functionalities between the computationally optimized BMS/edge version of the model and the detailed State-of-X diagnostics tool in the cloud. Specific results, hence, demonstrate, how applied advanced physics-based models enable: 1. more accurate determination of Li-throughput in phase-separating materials, 2. more accurate determination of active particle fraction and potentials of the electrodes, 3. more accurate determination of intra- and inter-particle phase separated states [2], which determine chemical potential and electrode potential as well as its EIS response [5], and 4. duration of presence of phase boundaries in active particles, which are associated with material degradation.

It will be summarized how such innovative chemistry-informed State-of-X diagnostics tool opens new perspectives in: 1. future digital twins for in-operando assessment of State-of-X diagnostics of battery with phase-separating active materials, 2. virtual exploration of BMS protocols, 3. innovative model-based DoE methodologies to maximize the amount of information obtained from a minimum amount of experimental data to identify State-of-X descriptors as uniquely as possible, and 4. future functionalities such as orchestration of preemptive self-healing processes.

Presented methodology and results, therefore, indicate that proposed State-of-X diagnostics based on the multi-scale simulation framework pushes boundaries of State-of-X diagnostics and opens emerging area of advanced monitoring, diagnostics, and management of batteries with phase-separating materials.

References

[1] W. Dreyer et al. Nature materials, 9, 448–453, 2010

[2] T. Katrašnik et al. Advanced materials, 35, 2210937, 2023

[3] T. Sasaki et al. Nature materials, 12, 569–575, 2013

[4] Y. Sun et al. Joule, 2, 1265–1277, 2018

[5] K. Zelič et al. Energy Storage Materials, 56, 489–494, 2023

[6] I. Mele et al. Journal of The Electrochemical Society, 167, 060531, 2020.

[7] T. Katrašnik et al. Energy Conversion and Management, 236, 114036, 2021